Integrating Personality, Psychological Skills and Psychophysiological Performance Factors in Athlete Profiling: Evidence from Team and Individual Sports

Integrating Personality, Psychological Skills and Psychophysiological Performance Factors in Athlete Profiling: Evidence from Team and Individual Sports | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Personality, Psychological Skills and Psychophysiological Performance Factors in Athlete Profiling: Evidence from Team and Individual Sports Katrina Volgemute, Gundega Ulme, Agita Abele, Zermena Vazne, Kristers Ansons, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8224965/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Sports performance is shaped by the interaction of various factors, including the athlete's personality, psychological skills and psychophysiological performance. The aim of this study was to develop a multi-factorial profile of athletes, combining personality traits, psychological skills and psychophysiological performance indicators. An additional aim was to investigate the differences between team and individual sports athletes and at different levels of achievements. A total of 304 (female and male) athletes completed standardized assessments of personality traits, psychological skills and psychophysiological performance, including reaction time, stress tolerance, impulsivity, decisiveness and performance consistency. Multivariate analysis revealed significant differences between athletes taking into account the type of sport (individual vs team), as well as the level of sport (elite, pre-elite, amateur). Cluster analysis identified four distinct athlete multi-factorial profiles. Qualitative validation by an expert panel confirmed that these profiles reflect recognizable athlete types in real training and competition contexts, providing recommendations for the implementation of practical interventions. Integrating personality, psychological skills, and psychophysiological performance indicators provides a comprehensive understanding of how athletes perform under different performance demands. These four multi-factorial profiles offer a practical framework for individual training, psychological preparation, and identification of potential risks. Health sciences/Health care Biological sciences/Physiology Biological sciences/Psychology Social science/Psychology athlete profiling personality psychological skills psychophysiological performance Figures Figure 1 Figure 2 Figure 3 1. Introduction 1.1. The Role of Personality and Psychological Skills in Athletic Performance Personality traits and psychological skills are equally important factors in supporting successful athletic performance. It is important to note that the optimal personality profile may vary across sport disciplines [ 1 ]. Personality characteristics such as emotional stability, decisiveness, and self-confidence shape athletes’ attitudes toward competition, while acquired psychological skills such as visualization, positive self-talk, mental toughness, imagery, and motivational self-statements help athletes maintain focus, manage anxiety, and perform under elevated pressure [ 2 ]. Understanding the interaction of these factors allows athletes and coaches to develop training and competition strategies that support both performance consistency and individual development. Xu and Hao [ 2 ] demonstrated that higher conscientiousness and extraversion consistently predict superior performance. The authors identified three mechanisms through which personality influences performance: (1) direct neurobiological pathways, including dopaminergic sensitivity and prefrontal regulatory control; (2) indirect psychological mediation, particularly through motivation and self-regulation; and (3) contextual moderation, such as sport type, competitive level, and cultural environment. Research indicates that elite athletes typically exhibit greater emotional stability, which supports cognitive clarity and consistent decision-making under pressure. For example, Fabbricatore et al. [ 3 ] found that top-level swimmers scored significantly lower in neuroticism than non-elite athletes. These psychological characteristics operate within physiological and situational conditions, meaning that performance depends not only on what athletes think and feel, but also on how effectively they regulate their psychophysiological responses in real time. 1.2. Psychophysiological Adaptation in Sport The competitive sports environment requires athletes not only to have high psychological readiness, but also to be able to effectively regulate cognitive and physiological responses under pressure. Psychophysiological indicators, such as reaction speed, inhibitory control, and stress tolerance, reflect how effectively athletes process information, maintain attention, and make decisions during competition, where demands change rapidly [ 4 , 5 ]. These abilities are closely related to performance outcomes, especially in time-constrained or unpredictable environments [ 6 ]. It is well known that stress affects athlete performance and results. Exposure to stress simultaneously activates autonomic, cognitive, and emotional systems during performance, requiring athletes to regulate both physiological arousal and cognitive processing [ 7 , 8 ]. Reaction time and inhibitory control serve as practical indicators of this regulation capacity, reflecting an athlete’s ability to suppress impulsive responses and maintain decision accuracy under load [ 6 , 9 ]. Sports competitions involve dynamic interactions that affect an athlete’s readiness to achieve high results. Psychophysiological performance measurements provide essential insights into how to plan and implement the process of psychological preparation of athletes and beyond. Therefore, to understand performance in sports, psychophysiological functioning must be examined alongside personality traits and psychological skills, not in isolation. 1.3. Integrative Approaches to Athlete Profiling Performance is multidimensional and dynamic where psychological, technical–tactical, and physiological variables interact with each other and fluctuate over time. Personality traits, psychological skills, and physiological capacities each explain different mechanisms of adaptation to physical and psychological load or environmental requirements. Therefore, a single-dimension metric often transfers misleading conclusions when used across contexts. Recent authors argue that a multidisciplinary and integrative perspective is required to understand how multiple interacting factors shape athletes’ performance [ 10 ]. Accordingly, Glazier [ 11 ] proposed a Grand Unified Theory, which supports the notion that sports performance is governed by complex interactions across fields. For example, psychology, physiology, and biomechanics. An extensive research analysis conducted by Zentgraf and Raab [ 12 ] indicates that individual factors associated with expertise are well discussed. However, most interactions between these key factors have not been investigated together, and it would be premature to draw conclusions about how their combined effects influence expertise development and sport performance. As noted by Neumann et al. [ 10 ], sports performance is an emergent process governed by individual-specific, interacting biopsychosocial factors. This means that measurements are needed in several domains, and data from different areas must be synthesized to create an integrative view, allowing the athlete’s profile to be seen “in one picture.” When developing an individual profile, the specifics of the sport type, the level of athletic performance, and gender differences must be considered, as each of these may create distinct performance demands and require different psychological skills and regulation strategies. The more variables are considered when differentiating sport competence, the more researchers conclude that general models require adaptation to specific sport contexts in order to meaningfully guide practice [ 13 ]. This leads to the conclusion that a model tailored to sport type, gender, age, and competitive level functions more effectively. In this context, integrating personality traits (stable dispositions), psychological skills (trainable self-regulation strategies), and psychophysiological performance indicators (such as stress-related response efficiency) enables a more ecologically valid understanding of how athletes’ function under real performance conditions. An integrated athlete profile is needed because performance arises from interactions between variables, not from any single factor alone. By linking psychological and psychophysiological indicators to performance-relevant outcomes, athlete profiling becomes directly applicable to multidisciplinary sports support teams, including coaches and spot specialists such as physicians, physiotherapists and sport psychologists. 1.4. Current Gaps and Limitations in Athlete Typologies Substantial empirical evidence suggests that athletes from different sport types display distinct personality trait profiles. For example, Shuai et al. [ 1 ] showed that team sport athletes tend to score higher conscientiousness and extraversion, whereas findings for traits such as agreeableness and openness vary across specific sports and competitive contexts. Although some studies have observed differences between individual- and team-sport athletes, the overall evidence remains inconsistent and strongly dependent on methodological and contextual factors. Several conceptual perspectives have been proposed to explain how personality traits and sport participation may be related. One of the fundamental foundations of the personality perspective in sport is Eysenck's [ 14 ] theoretical concept, which states that individuals are naturally drawn to environments, including sport settings, that matches their pre-existing personality traits. Consequently, personality influences sport selection, while participation within a particular sport environment may, over time, further reinforce or shape certain psychological characteristics. Conzelmann et al [ 15 ] suggested that long-term participation in sport may promote gradual personality development over time through socialization processes and learning experiences. Other approaches to the concept of personality emphasize that personality and sport contexts mutually influence each other, which is consistent with broader trait-situation interaction frameworks in personality psychology [ 16 ]. In addition to personality traits, psychological and psychophysiological factors play a critical role in athletic performance, injury susceptibility, and recovery. Research shows that heightened stress, cognitive interference, and maladaptive coping patterns increase injury risk [ 17 ], while psychological readiness strongly influences return-to-sport outcomes after injury [ 18 ]. Integrating these factors into athlete profiling may therefore enhance early risk detection and support more individualised prevention and rehabilitation strategies. Despite advances in the field, several key limitations still remain. Many existing typologies rely primarily on self-report personality measures, which may oversimplify the psychological foundations of performance and overlook the trainability of psychological skills. There is also a lack of longitudinal research, limiting understanding of how athlete profiles evolve across developmental stages. Additionally, cultural variation, gender differences, and sport-specific demands are often underrepresented, reducing the generalizability of existing models. A further limitation is the frequent absence of objective performance and psychophysiological indicators in athlete profiling. Without integrating behavioral or stress-response data, typologies have limited practical utility for performance planning or intervention design. Therefore, there is a need for empirically grounded, multi-factorial athlete profiles, validated through both quantitative data and qualitative expert evaluation, to ensure relevance and application in real training and competition settings. The present study addresses these gaps by developing an integrated profiling approach that combines personality traits, psychological skills, and psychophysiological performance indicators. 1.5. Study Aim and Hypothesis The present study aimed to develop a multi-factorial profile of Latvian athletes by integrating measures of personality traits, psychological skills, and psychophysiological performance variables. Specifically, the study sought to (1) identify distinct athlete profiles using multivariate statistical techniques, (2) examine differences across sport types (team vs. individual) and competitive levels (elite, pre-elite, amateur) and explore how personality and psychological skills are associated with psychophysiological performance indicators. Hypotheses: (1) There are significant multivariate differences in personality, psychological, and psychophysiological performance characteristics between team and individual sport athletes as well as between athletes of different competitive levels. (2) Distinct athlete profiles (clusters) can be identified based on the integration of personality traits, psychological skills, and psychophysiological performance indicators, reflecting different styles of emotional regulation, motivation, and performance efficiency. 2. Materials and Methods 2.1. Participants In this cross-sectional study, data were collected from a purposive sample of 304 active competitive Latvian athletes both males (n = 183, 60.2%) and females (n = 121, 39.8%), recruited through sport federations, sport schools, and national teams across multiple regions. All assessments were conducted individually under controlled laboratory conditions. The mean age of the sample was 19.63 years (SD = 3.64), with an average training load of 6.06 hours per week (SD = 2.89) and 9.22 years (SD = 4.18) of sport-specific experience. A total of 177 athletes (58.2%) represented team sports (most commonly: basketball (n = 86); football (n = 26); hockey (n = 18); handball (n = 16) etc) and 123 athletes (40.5%) represented individual sports (most commonly: luge (n = 14); athletics (n = 10); skeleton (n = 8); cycling (n = 7) etc). Based on competitive achievement level, athletes were classified as: (1) Elite (n = 54, 17.8%; Mage = 19.27, SD = 0.42; training load = 8.46 h/week, SD = 0.40; experience = 9.00 years, SD = 0.52); (2) Pre-elite (n = 160, 53.6%; Mage = 18.93, SD = 0.27; training load = 6.24 h/week, SD = 0.19; experience = 9.84 years, SD = 0.32) and (3) Amateur (n = 87, 28.6%; Mage = 20.61, SD = 0.50; training load = 4.24 h/week, SD = 0.27; experience = 8.26 years, SD = 0.49). At the time of data collection, 54 athletes (17.8%) held an active professional sport contract. To qualitatively validate the athlete profiles obtained from the cluster analysis, a panel of nine applied sport psychology experts was convened. The panel included practicing sport psychologists (n = 6) who were actively providing psychological support to athletes and were registered with the national Sport Psychology Association, as well as senior coaches specializing in psychological preparation (n = 3). Their professional experience in working directly with competitive athletes ranged from 3 to 30 years (M = 15.5, SD = 8.44), providing a diverse and well-informed expert perspective for the validation process. 2.2. Measures 2.2.1. Personality To assess athletes’ personality traits, the Latvian Personality Inventory (LPI-v3) [ 19 ] was used, specifically the sports-validated version (LPI-v3s). The original LPI-v3 is a widely used in Latvia, which is also clinically validated multidimensional personality assessment instrument, applied in both clinical practice and psychological research, ensuring strong psychometric reliability and construct validity. The LPI-v3s is an adaptation developed for performance and sport contexts, maintaining the same theoretical factor structure while refining item interpretation and normative ranges for athletic populations. The inventory assesses five personality domains: (1) Neuroticism, (2) Conscientiousness, (3) Extraversion, (4) Agreeableness, and (5) Openness to Experience and includes an additional response validity scale (Lie scale) to detect inconsistent or socially desirable responding. It consists of 57 items rated on a 5-point Likert scale (1 = does not match; 5 = matches). Internal consistency values in the present sample ranged from α = 0.58 to 0.79, consistent with prior validation studies. 2.2.2. Psychological Skills To assess athletes’ psychological skills, the Psychological Skills Inventory for Sport (PSIS-R5) [ 20 ] was used in its Latvian language adaptation (PSIS-R5-L) [ 21 ]. The PSIS-R5 is one of the most widely used standardized instruments for evaluating psychological performance attributes in competitive sport contexts and has been validated across multiple athletic populations. The Latvian adaptation preserves the theoretical factor structure and scoring system of the original scale while ensuring linguistic and cultural equivalence for local sport settings. The PSIS-R5-L consists of four subscales: (1) Self-Confidence (belief in one’s ability to perform successfully), (2) Motivation (persistence, striving, and task engagement), (3) Team Emphasis (cooperation and interpersonal functioning in team contexts), and (4) Visualization (use of imagery and mental rehearsal strategies). The inventory contains 17 items, each rated on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree), with higher scores reflecting stronger psychological skill development. Internal consistency reliability coefficients in present athletic samples range from α = 0.43 to 0.75, and in the current sample coefficients fell within this expected range. 2.2.3. Psychophysiological Performance Measures In this study, psychophysiological performance measures were obtained using the Vienna Test System (VTS) [ 22 ], a computerized and standardized assessment platform widely used in performance psychology and neurocognitive evaluation. All testing was conducted individually under controlled laboratory conditions to ensure consistency and to avoid external environmental interference. The test battery included three VTS modules relevant to cognitive–motor performance in sport: the Determination Test (DT), the Reaction Test (RT), and the Attitude towards Work (AHA). The DT test assesses the ability to respond rapidly and accurately to multiple simultaneously presented visual and auditory stimuli, requiring fast and flexible choice reactions. Within the framework of the Cattell–Horn–Carroll (CHC) model of cognitive abilities [ 23 ], this ability is associated with the broad factor of processing speed and the more specific component of reaction and decision speed. The test also indexes reactive stress tolerance, which reflects the capacity to maintain performance under time pressure and increasing task demands. In the present context, reactive stress tolerance is operationalized as the stability, precision, and persistence of responses during high-speed stimulus discrimination and motor execution. This construct is particularly relevant in competitive sport, where athletes must sustain decision-making accuracy under rapidly changing and physiologically arousing conditions. To complete the DT test, it takes approximately 8 minutes. The study applied the AHA test battery to assess performance-related behavioural tendencies. The AHA provides indicators that describe how individuals plan, sustain, and regulate task performance under varying cognitive demands. The constructions assessed are closely related to performance consistency in sport, particularly in situations requiring precision, decision-making, and the management of errors. The AHA battery includes the following variables: (1) Exactitude, reflecting precision and accuracy in task execution; (2) Decisiveness, indicating the ability to make timely decisions even under uncertainty; (3) Impulsiveness vs. Reflexivity, distinguishing between rapid, instinctive responding and more deliberate, controlled responding; (4) Performance Level, assessing sustained concentration and productivity; (5) Aspiration Level, reflecting the realism of self-set performance goals; (6) Frustration Tolerance, denoting the ability to maintain performance following errors or negative feedback; and (7) Target Discrepancy, indicating the consistency between predicted and actual performance outcomes. Together, these indices provide a multi-factorial profile of performance regulation processes that are directly relevant to athletic training and competitive settings. AHA test takes approximately 15 minutes to complete. In the AHA impulsivity vs reflectivity index, higher scores indicate greater inhibitory control and reflective responding, whereas lower scores indicate faster, more impulsive and less controlled responding. The Reaction (RT) test from the VTS was used to assess psychomotor response speed. Two test forms were administered: S1, which measures simple reaction time, and S5, which measures choice reaction time. Both tests record latent (perceptual) reaction time, reflecting how quickly the athlete detects and processes a stimulus, as well as motor reaction time, indicating the speed of the physical response. The combination of S1 and S5 therefore provides an index of both fundamental sensorimotor responsiveness and rapid decision-making efficiency under choice conditions. The total testing time for both forms was approximately 6 minutes. All tests were completed individually in consecutive order, following standardized administration procedures. Before each test, athletes received on-screen instructions and completed a trial phase to ensure full task comprehension. Only after successful completion of the practice phase did the formal testing begin. All tasks were non-verbal, minimizing language-based performance effects. Administration strictly adhered to standardized procedures to maintain administrative reliability, ensuring that all athletes received identical instructions, timing, and testing conditions. For the purposes of statistical analysis and cluster modelling, the following performance indices were used from the VTS battery: stress tolerance score (mean accuracy under high time pressure), exactitude, decisiveness, impulsiveness vs. reflexivity, frustration tolerance, and performance level, latent and motor reaction time, and latent and motor choice reaction time. These variables were selected as theoretically relevant indicators of cognitive–motor efficiency and performance regulation in sport. All VTS test results are presented in standardized scoring protocols, which include raw scores, T-scores (M = 50, SD = 10) and percentile ranks. For the purposes of this study, statistical analyses and interpretation were conducted using T-scores, as they allow comparability across tests and individuals irrespective of age or sport type. All variables listed above were included in the multivariate analyses and subsequent cluster modelling. 2.2.4. Qualitative Validation of Athlete Multi-Factorial Profiles (Expert Panel) To evaluate the ecological validity of the athlete profiles derived from the cluster analysis, a panel of nine applied sport psychology experts was engaged. Experts participated in a structured open-ended questionnaire designed to evaluate: (1) the perceived real-world relevance and recognizability of each athlete profile (Q1), (2) typical behavioural patterns and stress responses in training and competition (Q2), and (3) appropriate psychological intervention and skill-development priorities for each profile (Q3). Each expert reviewed short narrative descriptions of the four profiles (summarising personality, psychological skills, and psychophysiological performance characteristics) and answered three open-ended questions: (1) Do these profiles reflect real athlete types you encounter in practice? (2) What typical behaviours or situational reactions correspond to each profile in training and competition? (3) What psychological interventions or skill-development directions would you recommend for each type? Responses were analyzed using a hybrid inductive–deductive thematic content approach. Coding first followed predefined conceptual categories aligned with the profile structure, and additional emergent themes were identified inductively. Convergence and divergence across expert perspectives were then synthesized into profile-level summaries. The thematic results are presented in narrative form (see Appendix A). 2.3. Design and Data Collection Procedure This study employed a mixed-methods sequential explanatory design, in which quantitative athlete profiling was followed by qualitative expert validation to evaluate the ecological applicability of the identified profiles. The quantitative phase consisted of standardized laboratory-based assessments of: (1) personality traits, (2) psychological skills, and (3) psychophysiological performance indices. Data collection took place over approximately 18 months, between May 2024 and November 2025. Athletes were recruited through sport federations, sport schools, national teams, and professional coaches, using purposive selection to ensure inclusion of active competitive athletes across sport types and performance levels. Each participant attended the research laboratory individually, where all assessments were conducted under controlled, quiet, distraction-free conditions. The full testing session lasted approximately 1.5–2 hours per athlete (see Fig. 1 ). Prior to participation, each athlete received a standardized explanation of the study purpose, procedures, data use, and confidentiality safeguards. Written informed consent was obtained from all participants. Athletes then completed the assessments in a uniform standardized order. Participants first completed the LPI-v3s and PSIS-R5-L inventories, along with a demographic and sport-related background form (sport type, years of experience, training load, competition level, highest achievements, and whether a valid professional contract was held). After the questionnaires, athletes completed the VTS batteries in the following fixed sequence: (1) DT, (2) AHA) and (3) RT: forms S1 and S5. This order was selected to maintain progression from higher cognitive-attentional load tasks (DT, AHA) toward more automated sensorimotor response tasks (RT), minimizing fatigue-related confounding. At the end of testing, participants could receive individual feedback on their results, provided upon request in a standardized interpretation protocol. Following quantitative data analysis, a k-means cluster analysis was performed, resulting in four distinct athletes’ multi-factorial profiles, representing patterns across personality, psychological skills, and psychophysiological performance variables. These profile descriptions (without raw data or scoring details) were provided only to the expert validation panel. In the qualitative phase, sport psychology experts (registered psychologists who practice in field of sport and senior psychological preparation coaches) evaluated the real-world relevance of each profile. Experts responded to three structured open-ended questions. Their responses were analyzed using inductive–deductive thematic coding, allowing both data-driven interpretation and alignment with the theoretical constructs underlying the profiles. The mixed-methods structure allowed quantitative profile identification and qualitative confirmation that these profiles correspond to recognizable athlete types in applied sport contexts. The study protocol received approval from the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and was conducted in accordance with the Declaration of Helsinki. All data were anonymized, encrypted, and stored on secure institutional servers accessible only to the research team. Data protection procedures followed the registered ARGOS Data Management Plan (OpenAIRE). Participation was voluntary, and athletes retained the right to withdraw at any point without explanation or negative consequences. 2.4. Statistical Analysis All statistical analyses were performed using IBM SPSS Statistics Version 28.0 for Windows. Initially, data were collected from 317 athletes. Following data cleaning procedures, 13 cases were excluded due to incomplete test sessions or invalid response patterns, resulting in a final analytic sample of n = 304 athletes. Descriptive statistics (means, standard deviations, skewness, kurtosis) were computed for all study variables. Data normality was evaluated based on skewness and kurtosis values, with distributions considered acceptable if values fell within ± 1. Internal consistency of the LPI-v3s and PSIS-R5-L subscales was examined using Cronbach’s α. Subscales with α ≥ 0.50 were retained, consistent with prior validation studies of multidimensional constructs in sport psychology. The Agreeableness scale was excluded from further analyses due to insufficient reliability. Pearson’s bivariate correlation analyses were conducted to examine associations between personality, psychological skills and psychophysiological performance variables and demographic factors (age, gender, training load, sport experience). Differences in indicators across sport type (team vs. individual) and competitive level (elite, pre-elite, amateur) were examined using Multivariate Analysis of Covariance (MANCOVA). Sport type and competitive level were entered as fixed factors, while gender, age, sport experience, and training load were entered as covariates. Significant multivariate effects were followed by univariate tests and interpreted using partial eta squared (η²) as an index of effect size. Effect sizes were interpreted via partial eta squared (η²), where values of 0.01, 0.06, and 0.14 indicated small, medium, and large effects, respectively. For comparability across indicators, T-scores (M = 50, SD = 10) provided by the VTS were used in all analyses. Alternative cluster solutions (2–5 clusters) were compared using Elbow and Silhouette criteria, interpretability of cluster centroids, and balance in group sizes. A four-cluster solution was selected based on optimal separation of personality, psychological skills and psychophysiological patterns and theoretical coherence. The solution’s classification stability was evaluated using Discriminant Function Analysis (DFA), which tested how accurately cluster membership could be predicted based on the final model variables. Cross-validated classification accuracy exceeding 80% was considered evidence of strong cluster discrimination. The significance level for all statistical tests was set at p ≤ 0.05. To determine the adequacy of the sample size, a priori power analysis was conducted using G*Power 3.1. For the planned MANCOVA with three achievement-level groups, medium effect size (f = 0.25), α = 0.05, and power (1 − β) = 0.80 indicated a required minimum sample of N = 159. The final sample of 304 therefore exceeded the required threshold, ensuring sufficient power for group comparisons and supporting stable cluster estimation. Following the quantitative phase, the qualitative expert validation was analyzed using an inductive–deductive thematic coding approach, allowing integration of expert-derived themes with predefined conceptual dimensions of the athlete profiles. Coding and theme development were conducted in Microsoft Excel, and themes were aggregated across experts to determine areas of consensus and divergence. 3. Results 3.1. Descriptive Statistics In Appendix B, Table S1 summarises the means, standard deviations, skewness, and kurtosis for 23 variables representing personality, psychological skills, and psychophysiological performance factors among competitive athletes (n = 304). Data reliability was assessed for the self − report scales of personality and psychological skills using Cronbach’s alpha coefficients. The results indicated adequate internal consistency for most scales, with α values ranging from 0.53 to 0.79. The lowest reliability was observed for Agreeableness (α = 0.38) and Visualization (α = 0.43), which is considered marginal but acceptable given the short scale length and exploratory nature of the study. The Agreeableness scale was excluded from further analyses due to its low internal consistency and limited theoretical relevance to performance characteristics in the current sample. In contrast, the Visualization scale, while showing a lower alpha (α = 0.43), was retained. This decision was theoretically justified, as visualization is a key psychological skill closely linked to mental rehearsal, concentration, and performance optimization in athletes. Furthermore, its reliability falls within the moderate range (0.50–0.80) suggested by Salvucci et al. [ 24 ], and the relatively small number of items (four) likely attenuated the alpha value, as Cronbach’s alpha is sensitive to item count [ 25 ]. Therefore, the Visualization scale was retained to preserve the conceptual completeness of the multi-factorial profile. The lowest mean scores in the athlete sample were on Neuroticism (M = 28.12, SD = 4.43) and Openness (M = 29.47, SD = 5.49), suggesting relatively greater emotional stability and a lower tendency toward anxiety or mood fluctuations but also reduced openness to novel experiences. In contrast, Conscientiousness (M = 33.39, SD = 6.48), Extraversion (M = 33.28, SD = 6.49), and Adventurism (M = 34.48, SD = 8.37) were moderately high, reflecting discipline, sociability, and curiosity or a preference for challenge. Among psychological skills, the highest mean scores were found for Self − confidence (M = 19.12, SD = 6.32) and Motivation (M = 14.91, SD = 3.63), suggesting a well − developed psychological readiness for performance. The lowest mean score was observed for Team emphasis (M = 12.99, SD = 2.22), which may reflect the inclusion of both team and individual sport athletes in the sample. In the psychophysiological performance domain, athletes showed high performance indicators, particularly in Choice reaction speed (M = 67.02, SD = 9.78), Choice motor speed (M = 65.65, SD = 8.41), and Motor speed (M = 62.38, SD = 8.69), suggesting above − average psychomotor efficiency and response accuracy. Stress tolerance (M = 54.32, SD = 7.61) and Aspiration (M = 56.58, SD = 7.62) were also above normative averages, indicating strong coping resources and achievement orientation. The skewness and kurtosis values fell within acceptable limits (− 1 < Sk < 1; −1 < Ku < 1), confirming that the data distributions did not significantly deviate from normality and were suitable for further multivariate analyses. 3.2. Pearson Correlation Analysis The Pearson correlation results are presented in Appendix C, Table S1 . The correlation result analysis revealed several significant associations among demographic, personality, psychological and psychophysiological performance variables. Pearson’s correlation coefficients were interpreted following guidelines established for the social sciences (Cohen, 1988), where correlations of r ≥ 0.10 are considered weak, r ≥ 0.30 medium, and r ≥ 0.50 strong. Sport type was positively associated with personality variables Openness (r = 0.16, p < 0.01) and Adventurism (r = 0.20, p < 0.01), indicating greater openness to experience and novelty-seeking among athletes competing in individual sports. Conversely, negative correlations were found with psychophysiological performance and psychological skills variables such as Aspiration (r = − 0.12, p < 0.05), Team emphasis (r = − 0.23, p < 0.01), and Motivation (r = − 0.25, p < 0.01), suggesting that team sport athletes were more cooperative, and motivated. All correlation is evaluated as small to moderate. Sport level correlated positively with personality and psychological skills variables Neuroticism (r = 0.14, p < 0.05) and Confidence (r = 0.13, p < 0.05), while showing negative associations with Motivation (r = − 0.27, p < 0.01), Exactitude (r = − 0.11, p < 0.05), Motor speed (r = − 0.22, p < 0.01), Choice reaction speed (r = − 0.14, p < 0.05), and Choice motor speed (r = − 0.22, p < 0.01). This indicates that athletes at higher competitive levels (elite and pre-elite) exhibited greater emotional reactivity and self-confidence, while their psychomotor performance tended to stabilize rather than increase further. Gender correlated negatively with personality and psychophysiological performance variables Neuroticism (r = − 0.32, p < 0.01) and positively with Conscientiousness (r = 0.17, p < 0.01), Reaction speed (r = 0.11, p < 0.05), Motor speed (r = 0.32, p < 0.01), Choice reaction speed (r = 0.14, p < 0.05), and Choice motor speed (r = 0.37, p < 0.01), indicating lower emotional instability and faster psychomotor performance among men compared to women. Age and experience both were positively related to Confidence (r = 0.15, p < 0.01), while negatively related to Motivation (r = − 0.35, p < 0.01) and Team emphasis (r = − 0.20, p < 0.01). By which it can be concluded that older and more experienced athletes demonstrated greater self-confidence but slightly reduced motivational drive and team-oriented attitudes. Regarding interrelations among personality and psychological variables, Confidence was strongly negatively correlated with Neuroticism (r = − 0.65, p < 0.01), and Motivation was negatively associated with both Neuroticism (r = − 0.11, p < 0.05) and Modesty (r = − 0.22, p < 0.01). Conscientiousness correlated positively with Team emphasis (r = 0.14, p < 0.05), Motivation (r = 0.12, p < 0.05), and Visualization (r = 0.25, p < 0.01). Among psychophysiological performance indicators, strong positive associations were observed between Motor speed and Choice reaction speed (r = 0.58, p < 0.01), Motor speed and Choice motor speed (r = 0.75, p < 0.01), as well as Reaction speed and Choice motor speed (r = 0.39, p < 0.01), confirming high internal coherence among psychomotor performance measures. Results suggest systematic patterns linking demographic and psychological characteristics to psychophysiological performance. Team sport athletes tended to be more emotionally stable and socially oriented, while individual sport athletes were more open and novelty-seeking. Male and older athletes showed higher psychomotor efficiency, and personality traits such as conscientiousness, confidence, and low neuroticism were associated with better control and performance consistency. 3.3 Multivariate Analysis of Covariance (MANCOVA) Two MANCOVAs were conducted to examine differences in athletes’ personality traits, psychological skills, and psychophysiological performance factors based on (1) sport type (team vs. individual) and (2) performance level (elite, pre − elite, amateur). Table 1 presents the results of the MANCOVA for sport type. The multivariate test using Pillai’s Trace revealed a statistically significant overall effect of sport type on the combined dependent variables, V = 0.168, F (23, 272) = 2.382, p < 0.001, η² = 0.168. This indicates that athletes participating in team and individual sports differ significantly across the set of measured psychological and psychophysiological performance characteristics. The sample consisted of 58.16% (n = 177) team sport athletes and 40.46% (n = 123) individual sport athletes. Follow − up univariate analyses (Table 1 ) identified significant group differences across several variables. Team sport athletes scored lower on Neuroticism (F(1, 294) = 6.85, p < 0.05, η² = 0.104), Adventurism (F(1, 294) = 4.70, p < 0.05, η² = 0.074), and Modesty (F(1, 294) = 3.53, p < 0.05, η² = 0.057), but higher on Openness (F(1, 294) = 2.38, p < 0.05, η² = 0.039). In terms of psychological skills, individual sport athletes showed significantly higher Confidence (F(1, 294) = 8.12, p < 0.05, η² = 0.121), while team sport athletes reported higher Motivation (F(1, 294) = 17.84, p < 0.001, η² = 0.233) and Team emphasis (F(1, 294) = 8.99, p < 0.05, η² = 0.133). Among psychophysiological performance variables, team athletes outperformed individual athletes on Motor speed (F(1, 294) = 8.7, p < 0.01, η² = 0.129), Choice motor speed (F(1, 294) = 11.16, p < 0.01, η² = 0.16), and Aspiration (F(1, 294) = 4.41, p < 0.05, η² = 0.07). These results suggest that team sport athletes tend to exhibit faster psychomotor responses and greater achievement orientation, whereas individual sport athletes show higher confidence and openness. Gender, training load, experience, and age were included as covariates in the model. Table 1 MANCOVA Results Comparing Personality, Psychological Skills, and Psychophysiological Performance Factors Between Team and Individual Sport Athletes Factor Team (n = 177) Individual (n = 123) F η² Personality factors F1: Neuroticism 27.82 (0.33) 28.58 (0.4) 6.85* 0.104 F2: Conscientiousness 33.87 (0.45) 32.8 (0.64) 0.77 0.013 F3: Extraversion 33.47 (0.48) 32.87 (0.59) 0.56 0.009 F4: Adventurism 33.11 (0.62) 36.56 (0.74) 4.70* 0.074 F5: Openness 28.71 (0.38) 30.46 (0.54) 2.38* 0.039 F6: Modesty 30.55 (0.63) 31.14 (0.86) 3.53* 0.057 Psychological skills factors F7: Confidence 18.52 (6.24) 20.00 (6.35) 8.12* 0.121 F8: Motivation 15.67 (3.45) 13.85 (3.65) 17.84* 0.233 F9: Team emphasis 13.41 (2.08) 12.39 (2.26) 8.99* 0.133 F10: Visualization 9.72 (2.47) 10.15 (2.42) 2.04 0.034 Psychophysiological performance factors F11: Stress tolerance 53.86 (7.60) 54.94 (7.65) 2.38* 0.039 F12: Exactitude 49.11 (7.21) 50.24 (6.87) 2.05 0.034 F13: Decisiveness 61.97 (8.94) 62.28 (7.35) 2.67* 0.043 F14: Impulsivity 41.31 (9.85) 42.48 (9.41) 1.77 0.029 F15: Performance 61.55 (6.03) 61.53 (6.10) 0.61 0.01 F16: Aspiration 57.37 (7.50) 55.49 (7.75) 4.41* 0.07 F17: Frustration tolerance 45.15 (5.93) 44.53 (6.13) 2.08 0.034 F18: Target discrepancy 49.89 (0.66) 49.45 (0.82) 1.03 0.017 F19: Reaction speed 61.78 (9.16) 60.64 (8.65) 2.61* 0.042 F20: Motor speed 62.81 (8.46) 61.78 (8.85) 8.7* 0.129 F21: Choice reaction speed 68.20 (9.63) 67.09 (9.72) 3.88* 0.062 F22: Choice motor speed 66.48 (8.07) 64.63 (8.07) 11.16* 0.016 Pillai’s Trace = 0.168, F(23, 272) = 2.382, p < 0.001, η² = 0.168. p < 0.05. Athletes’ gender, training load, experience and age are included as covariates; *Significant at the 0.05 level (two − tailed). A second MANCOVA was conducted to examine differences in personality, psychological skills, and psychophysiological performance factors across athletes’ performance levels (elite, pre − elite, and amateur). Gender, training load, experience, and age were included as covariates in the model. Athletes were classified into three competitive levels based on objective performance criteria. The classification was determined by combining training load, competition level, and years of experience, ensuring that the grouping reflected actual performance demands rather than self-reported status. Elite athletes were those engaged in a professional or semi-professional training regime, participating in ≥ 8 training sessions or ≥ 12 hours per week, with at least five years of structured sport experience. In addition, they were required to have achieved high-level competitive results, such as being finalists or medallists in National Championships (adult higher leagues) or having participated in international competitions, including European Championships, World Cups, World Championships (junior or senior), or the Olympic Games. Pre-elite athletes trained at ≥ 5 sessions or ≥ 7.5 hours per week and were active in national championship competition (junior or adult divisions) or regional and university-level leagues. These athletes were typically situated in high-performance development environments (such as national youth teams, sport academy or sport school programs) but had not yet consistently competed at senior international level. Amateur athletes participated in ≥ 2 training sessions or ≥ 4 hours per week and competed primarily in regional, local, or lower-division national competitions, without national team selection or professional status. This tiered system aligns with contemporary athlete development models distinguishing elite, developmental, and participation-level performers, and enables meaningful comparison of psychological preparedness across competitive standards. The multivariate test using Pillai’s Trace indicated that the combined dependent variables differed significantly across performance levels, V = 0.239, F(46, 542) = 1.60, p < 0.05, η² = 0.18. Although the overall multivariate effect was statistically significant but small in magnitude, several univariate effects reached statistical significance, suggesting level − specific variations in select psychological and psychophysiological performance indicators (Table 2 ). The sample consisted of 17.8% (n = 54) elite level athletes, 53.6% (n = 163) pre − elite level athletes and 28.6% (n = 87) amateur level athletes. Specifically, significant differences emerged for Confidence (F(2, 294) = 4.43, p < 0.05, η² = 0.031) and Motivation (F(2, 294) = 3.89, p < 0.05, η² = 0.028), indicating that elite athletes reported higher self − confidence and intrinsic motivation compared to pre − elite and amateur athletes. In the psychophysiological performance domain, Decisiveness (F(2, 294) = 4.21, p < 0.05, η² = 0.03) and Aspiration (F(2, 294) = 3.14, p < 0.05, η² = 0.022) also differed significantly, with elite athletes again showing higher mean scores than other groups. These findings suggest that while personality traits and general psychophysiological capacities were relatively stable across levels, psychological readiness and decision − making efficiency distinguish higher − performing athletes from their less experienced counterparts. The combination of elevated confidence, motivation, and aspiration reflects a more advanced performance mindset, aligning with theoretical models of elite psychological functioning. Table 2 MANCOVA Results for Personality, Psychological Skills, and Psychophysiological Performance Factors Across Competitive Levels (Elite, Pre − elite, Amateur) Factor Elite (n = 54) Pre − elite (n = 163) Amateur (n = 87) F η² Personality factors F1: Neuroticism 27.07 (0.53) 28.12 (0.35) 28.78 (0.49) 1.23 0.09 F2: Conscientiousness 32.57 (1) 33.53 (0.5) 33.77 (0.69) 1.36 0.01 F3: Extraversion 33.14 (0.91) 33.59 (0.52) 32.6 (0.69) 0.97 0.007 F4: Adventurism 37.45 (1.12) 34.69 (0.63) 32.47 (0.94) 1.18 0.009 F5: Openness 28.75 (0.77) 29.29 (0.44) 30.08 (0.57) 1.01 0.007 F6: Modesty 30.44 (1.11) 30.62 (0.73) 31.31 (0.93) 0.92 0.007 Psychological skill factors F7: Confidence 18.5 (0.87) 18.61 (0.52) 20.46 (0.6) 4.43* 0.031 F8: Motivation 16.58 (0.4) 15.1 (0.29) 13.61 (0.37) 3.89* 0.028 F9: Team emphasis 12.90 (0.31) 13.33 (0.15) 12.44 (0.28) 1.09 0.008 F10: Visualization 10.65 (0.39) 9.84 (0.18) 9.55 (0.26) 1.12 0.008 Psychophysiological performance factors F11: Stress tolerance 54.29 (0.6) 54.22 (0.78) 50.37 (0.9) 1.02 0.007 F12: Exactitude 50.06 (0.56) 48.21 (0.78) 63 (1) 1.09 0.008 F13: Decisiveness 62.39 (0.65) 62.39 (0.65) 61.03 (1.03) 4.21* 0.03 F14: Impulsivity 41.9 (1.14) 42.14 (0.73) 41.08 (1.12) 0.97 0.006 F15: Performance 61.96 (0.8) 61.9 (0.49) 60.6 (0.64) 1.42 0.01 F16: Aspiration 55.83 (1.07) 56.8 (0.59) 56.68 (0.85) 3.14* 0.022 F17: Frustration tolerance 43.27 (0.87) 44.78 (0.46) 46.13 (0.64) 1.09 0.008 F18: Target discrepancy 49.9 (1.06) 50.03 (0.72) 49 (0.98) 1.12 0.08 F19: Reaction speed 63 (1.13) 61.59 (0.7) 59.8 (0.93) 0.84 0.006 F20: Motor speed 65.9 (1.24) 62.42 (0.66) 60.23 (0.88) 1.01 0.007 F21: Choice reaction speed 67.1 (1.38) 68.52 (0.79) 64.15 (0.93) 0.76 0.005 F22: Choice motor speed 69.75 (1.13) 65.4 (0.66) 63.92 (0.83) 0.82 0.006 Pillai’s Trace = 0.239, F(46, 542) = 1.60, p < 0.001, η² = 0.18. p < 0.05. Athletes’ gender, training load, experience and age are included as covariates; *Significant at the 0.05 level (two − tailed). 3.4. Cluster Analysis: Identification of Multi-factorial Athlete Multi-Factorial Profiles A k − means cluster analysis was further performed to classify athletes based on their personality traits, psychological skills, and psychophysiological performance factors, considering sport type and performance level. A four − cluster solution was identified as the best fit, considering both the sample size (n = 304) and the number of included variables. Alternative cluster solutions (two−, three−, and five − cluster models) did not demonstrate meaningful or statistically distinct group differentiation and were therefore rejected. All variable scores were standardized into z − scores prior to analysis to ensure comparability across different measurement scales. The final cluster solution converged successfully after 9 iterations, confirming model stability. Table S2 in Appendix B presents the descriptive statistics and standardized z − scores for each of the four clusters, while Fig. 2 visualizes the cluster profiles across the 22 measured variables. The results revealed clear differentiation among clusters in psychophysiological and psychological performance indicators, with significant between − group differences confirmed by ANOVA (Tukey’s HSD, p < 0.05). A one − way ANOVA revealed significant differences across the four clusters in all psychophysiological performance indicators (p < 0.001), indicating that the groups were clearly differentiated based on performance − related capacities. Cluster 1 demonstrated the highest stress tolerance, decisiveness, performance, and fast motor and reaction speeds, but also higher impulsivity (lower impulse control), suggesting superior performance efficiency under pressure but a tendency toward rapid, less filtered responding. Cluster 2 showed the highest exactitude but slower response speeds, lower stress tolerance, and lower impulsivity (greater impulse control), suggesting a cautious and controlled performance style that may become strained in demanding conditions. Cluster 3 displayed generally reduced performance, motivation, and decisiveness, combined with slower psychomotor responding, lower stress tolerance, and higher impulsivity, indicating inhibited activation and reduced efficiency in high-pressure situations. Cluster 4 exhibited the fastest reaction and motor speeds and the highest stress tolerance, along with moderate impulsivity and lower decisiveness, reflecting a fast, instinctive, and reactive performance style that prioritizes rapid responding over deliberate decision-making. 3.5. Cluster Interpretation The K − means cluster analysis identified four distinct athlete profiles (see Table 3 and Fig. 2 ), each characterized by specific psychological and psychophysiological performance patterns. Cluster 1 (n = 102; 33.6%) group demonstrated the most balanced and self-regulated performance profile. Athletes in Cluster 1 scored above the total sample mean on stress tolerance (M = 56.56, SD = 6.64), decisiveness (M = 67.22, SD = 7.14), performance (M = 64.26, SD = 6.09), aspiration (M = 55.72, SD = 7.83), and reaction speed (M = 64.45, SD = 6.97). Impulsivity was comparatively low (M = 36.25, SD = 5.97), indicating faster, more spontaneous/impulsive response style. Personality traits were near the sample average, showing emotional stability and composure (Neuroticism M = 28.03, SD = 4.55). Cluster 1 primarily included team-sport athletes (n = 64; 21.1% of total sample; ≈ 62.7% within cluster), predominantly at the pre-elite (19.1%) and elite (7.6%) levels. These athletes represent emotionally stable, achievement-oriented performers capable of maintaining consistent output under pressure. Cluster 2 (n = 75; 24.7%) athletes in this cluster exhibited a controlled and disciplined approach, characterized by high exactitude (M = 55.35, SD = 6.14) and conscientiousness (M = 33.07, SD = 6.20). They showed moderate stress tolerance (M = 52.07, SD = 7.02) and higher impulsivity (M = 51.67, SD = 7.14), reflecting strong emotional control and strong inhibitory control and deliberate responding. Performance (M = 59.80, SD = 6.20) and decisiveness (M = 57.63, SD = 7.14) were slightly above average, consistent with an analytical and strategic style rather than fast, intuitive responding. Motivation (M = 14.81, SD = 3.66) and confidence (M = 19.88, SD = 5.97) were moderate, aligning with a more reserved personality pattern. Cluster 2 consisted mainly of pre-elite (13.8%) and amateur (7.6%) athletes, with team-sport athletes representing 25.3% of the total sample (≈ 60% within cluster). Cluster 3 (n = 85; 28%) athletes showed lower stress tolerance (M = 51.71, SD = 8.27), low impulsivity (indicate reactive responding; M = 36.52, SD = 5.92), and slightly below-average performance (M = 58.35, SD = 8.53). They also scored lower on aspiration (M = 50.89, SD = 8.91) and frustration tolerance (M = 46.63, SD = 9.35), indicating difficulty maintaining composure and persistence under pressure. Reaction speed was relatively slow (M = 55.02, SD = 8.10), suggesting reduced psychophysiological performance efficiency. Personality profiles revealed slightly elevated neuroticism (M = 28.58, SD = 4.36) and average conscientiousness (M = 33.21, SD = 7.08), consistent with emotional reactivity and weaker self-regulation. This group included a higher proportion of individual-sport athletes (n = 41; 13.5% of total sample; ≈ 49% within cluster) and amateurs (12.2%). Cluster 4 (n = 42; 13.8%) athletes in this cluster displayed the most distinct psychophysiological performance pattern, characterized by very high stress tolerance (M = 58.19, SD = 7.02) and exceptional reaction and motor speeds (Reaction speed M = 72.45, SD = 7.23; Motor speed M = 67.86, SD = 7.85). They also demonstrated high performance (M = 65.30, SD = 8.53) and aspiration (M = 60.90, SD = 8.53). Impulsivity was average (M = 48.40, SD = 5.86) between cluster, showing that although these athletes are reactive, they maintain good control over emotional responses. Personality traits included low neuroticism (M = 27.47, SD = 4.38) and moderately high extraversion (M = 32.30, SD = 5.30), reflecting emotional resilience and assertiveness. This cluster included both team-sport athletes (n = 25; 8.2% of total sample; ≈ 58% within cluster) and individual-sport athletes (n = 17; 5.6% of total sample; ≈ 40% within cluster), with the highest proportion of elite competitors (11; 3.6%). Table 3 Distribution of Sport Type and Competitive Level Within the Four Athlete Clusters Cluster 1 Cluster 2 Cluster 3 Cluster 4 Total n % n % n % n % n Sport type Team sports 64 21.1 45 14.8 44 14.5 25 8.2 178 Individual sports 38 12.5 30 9.9 41 13.5 17 5.6 126 Level Elite 23 7.6 10 3.3 10 3.3 11 3.6 54 Pre − elite 58 19.1 42 13.8 38 12.5 25 8.2 163 Amateur 21 6.9 23 7.6 37 12.2 6 2 87 Total 102 33.6 75 24.7 84 28 43 13.8 304 3.6. Discriminant Function Analysis: Validation of Cluster Solution A discriminant function analysis was conducted to determine whether the clusters identified through k − means clustering could be accurately differentiated based on the psychological and psychophysiological performance variables. The analysis yielded three discriminant functions, of which the first two explained 92.4% of the total between − group variance. Function 1 accounted for 51.5% (canonical correlation = 0.83), and Function 2 accounted for 40.9% (canonical correlation = 0.80). Wilks’ Lambda indicated that all three functions were statistically significant (Λ₁₋₃ = 0.086, χ²(69) = 711.41, p < 0.001), supporting the adequacy of the model in distinguishing among the clusters. The classification results (Table 4 ) showed that 80.6% of the cases were correctly classified, indicating good internal consistency and stability of the cluster solution. Cluster − specific classification accuracy was high, ranged from 95.1% to 97.6%, with the highest accuracy observed for Profile 1 and the lowest for Profile 3 (97.6%) and similar high accuracy for the other clusters. Table 4 Classification Accuracy of Discriminant Function Analysis for the Four Athlete Profiles Cluster Predicted group membership n % 1 2 3 4 Profile 1 97 2 2 1 102 95.1 Profile 2 1 72 1 0 75 96 Profile 3 0 1 83 2 85 97.6 Profile 4 1 0 1 40 42 95.2 Total 304 80.6 The spatial representation of the discriminant functions is shown in Fig. 2 . The figure illustrates the separation of the four clusters along the first two discriminant dimensions. Cluster centroids were located at (Function 1, Function 2): Cluster 1 = (1.20, 1.16), Cluster 2 = (− 0.91, − 1.76), Cluster 3 = (− 1.72, 0.88), and Cluster 4 = (2.18, − 1.46). The first function primarily differentiated clusters based on psychophysiological performance efficiency, whereas the second function reflected differences in impulse control and emotional self-regulation. The first two discriminant functions explained 92.4% of the between − group variance and were therefore used for visualization (Fig. 3 ). The third function explained only 7.6% and was not plotted due to its limited discriminative contribution. Based on the results obtained, four distinct athlete profiles were identified, reflecting differences associated with sport type (team vs. individual) and competitive level (elite, pre-elite, amateur). The profiles were derived from the integrated analysis of personality traits, psychological skills, and psychophysiological performance variables, which together represent the core determinants of athletic functioning. 3.7. Qualitative Validation of Athlete Multi-Factorial Profiles Table 5 summarises the qualitative analysis of the three open-ended questions addressed to experts, who were either psychologists who practice in sport or psychological preparation coaches with a minimum of six years of professional experience working with athletes. The experts generally confirmed the ecological validity of the four athlete profiles. A total of nine experts participated in the qualitative validation of developed profiles of athletes. Agreement rates were high for Profile 1 (8/9 experts), Profile 2 (8/9), and Profile 4 (8/9), and moderate for Profile 3 (7/9). One expert (E8) expressed general scepticism, emphasising that athlete typologies are often situationally fluid rather than trait based. Across responses, common descriptors included stability, precision, emotional lability, and speed/impulsivity, indicating substantial convergence between expert perceptions and the empirically derived athlete profiles. Table 5 Thematic Mapping of Validated Athlete Profiles (Q1–Q3) Profile Theme Code Example raw data quotes Profile 1 Profile validity (Q1) leadership_stability “Profile 1 matches high-level players and team leaders; emotionally balanced, self-regulated, focused under pressure.” (E1) “Experienced, elite athletes in team and individual sports.” (E2) “Future elite talents.” (E9) Behaviour under stress (Q2) leadership_support focus_under_pressure “Confident, inspire younger teammates; stable performance.” (E2) “Takes over late-game responsibility; composed; analyses failures for next time.” (E4) “After errors they analyse and adapt quickly; keep calm and task-focused under stress.” (E1) “Maintain focus in intense situations; planful training; stabilising presence for team.” (E7) Psychological intervention (Q3) self_reflection mental_flexibility “Maintain and improve psychological flexibility between load and recovery; update goal programs; mindfulness, breathing, mental training, imagery, self-talk.” (E3) “Maintain optimal motivation; avoid stagnation; develop creativity, flexibility, openness; goal refinement; leadership; adaptability.” (E7) Profile 2 Profile validity (Q1) precision_overcontrol “Disciplined, conscientious, well-trainable; strong responsibility; sometimes struggle to adapt to unexpected situations.” (E1) “Experienced amateurs / national level.” (E2) Behaviour under stress (Q2) cautious_decisions routine_precision “Diligent, compliant; cautious/avoidant; less effective under stress.” (E2) “More introverted; prefer individual training; public aspect hinders performance.” (E6) “Repeat drills until fully precise; control holds if plan works; unexpected events can unsettle.” (E1) “Checks equipment and routines; inner tension with errors, rarely outward.” (E3) Psychological intervention (Q3) reduce_overcontrol relaxation_breathing “Reduce overcontrol, trust the body, allow flow; relaxation and breathing; cognitive acceptance of mistakes; biofeedback.” (E3) “Emotional freedom, spontaneity and self-reliance; reduce anxiety and overthinking; relaxation; cognitive reframing; switch to automatic execution; strengthen self-efficacy.” (E7) “Train stress tolerance using breathing and concentration; use positive self-talk; simulate unexpected situations.” (E1) Profile 3 Profile validity (Q1) emotional_reactivity “Common among younger athletes; emotional fluctuations and quick frustration after errors.” (E1) “Youth or early adult transition.” (E2) Behaviour under stress (Q2) emotional_volatility motivation_swings “May love training but dislike competing.” (E5) “Mood-dependent engagement; emotional reactions; need structured self-regulation.” (E7) Psychological intervention (Q3) emotional_regulation self_regulation_package “Develop emotional self-regulation and mindfulness (box breathing, 3-second reset). Reduce fear of mistakes via reflection.” (E1) “Emotion regulation and stress-coping skills.” (E4) “Strengthen self-regulation and intrinsic motivation; emotion awareness and management; self-esteem; focus on progress; ABC model; routines and attentional focus; self-efficacy.” (E3) “Build self-regulation, emotional resilience and concentration; routines, micro-goals, positive self-talk.” (E7) Profile 4 Profile validity (Q1) reactive_speed “Energetic, instinctive; speed is a strength; impulsivity may cause emotional outbursts.” (E1) “Pre-elite/elite in growth stage.” (E2) Behaviour under stress (Q2) quick_decisionmaking high_stress_tolerance “High stress tolerance.” (E2) “Mobilise under stress; resilient until cumulative failures/trauma.” (E6) Psychological intervention (Q3) impulse_control visualization “Impulse control recognises escalation moments and refocus with a brief reset ritual (self-talk, breath, sensory cue).” (E1) “Develop patience, sustained attention, and impulse control; perceptual slowing drills; cognitive control and decision quality under pressure; rhythm and focus training.” (E3) “Imagery, self-regulation, routines, self-talk, problem-focused sessions.” (E5) Note. Q1–Q3 refer to the three open-ended questions provided to experts. Expert responses are coded as E1–E9, representing nine sport psychology professionals (psychologists and psychological preparation coaches) who participated in the qualitative validation survey. According to the results of the qualitative validation, it can be concluded that the experts consistently recognized that the four derived athlete profiles reflect real athlete types observed in professional practice, with exception of one expert, who indicated that he had not encountered athletes with such characteristics in his practice. In particular, the thematic analysis of behavioural examples revealed clear and internally consistent patterns. Experts described athletes in Profile 1 as being able to remain calm, focused, and analytical under pressure (concentration_under_pressure; n = 4), especially in high-stress situations such as decisive moments during competition. These athletes were often identified as elite or high-level performers who demonstrate psychological stability and self-regulation. In their descriptions of Profile 2, experts referred to athletes who are conscientious and precise, with a strong desire to avoid mistakes. This tendency toward accuracy and control, however, frequently leads to delayed decision-making and difficulty adapting to sudden changes (routine_precision, cautious_decisions; n = 5). Experts noted that this profile is often observed among pre-elite or amateur athletes who rely heavily on structure and predictability. Profile 3 was also recognised as common in applied practice and was described as representing emotionally unstable athletes whose performance often declines following mistakes (emotional_instability, motivational_fluctuations; n = 6). Such athletes were characterised as inconsistent and reactive, relying on momentary emotions or impulses, which can manifest in explosive responses during competition. Experts provided highly consistent evaluations of Profile 4, repeatedly noting that these athletes are easily recognisable in practice. They display rapid decision-making and adaptability, yet at times exhibit impulsivity or lapses in sustained attention (quick_decision_making; n = 5). The experts proposed a series of targeted mental-skills interventions tailored to each athlete profile. For Profile 1, the primary focus should be on maintaining long-term motivation and leadership capacity through self-reflection, mindfulness, and empathy training. Profile 2 would benefit from interventions aimed at reducing excessive self-control and promoting flexibility, such as relaxation techniques, cognitive reframing, and spontaneity exercises, to enhance resilience under stress. For Profile 3, experts recommended strengthening emotional regulation, self-efficacy, and structured self-regulation routines to improve performance stability and confidence during competition. Profile 4 should focus on developing impulse control, frustration tolerance, and sustained concentration under prolonged or monotonous pressure. Based on the integration of quantitative cluster characteristics and qualitative expert evaluation, four athlete multi-factorial profiles were identified and labelled as follows: Profile 1: “Stable High-Performance Athletes”, Profile 2: “Controlled Precision Athletes”. Profile 3: “Low-Regulation Reactive Athletes” and Profile 4: “Reactive High-Speed Athletes”. Across all multi-factorial profiles, experts emphasised biofeedback, visualisation, and psychophysiological readiness training as integrative approaches supporting adaptive functioning and consistent performance. All together, these results demonstrate that the four athlete profiles are both statistically distinct and ecologically recognizable, indicating that integrated psychological and psychophysiological performance factors meaningfully differentiate athletic functioning in applied settings. 4. Discussion The aim of the study was to develop a multi-factorial profile of Latvian athletes by integrating measures of personality traits, psychological skills, and psychophysiological performance variables. Based on the results, four athlete multi-factorial profiles were identified and qualitatively validated with the help of experts, demonstrating different models in terms of personality structure, psychological skill development, and psychophysiological functioning. These profiles can be used to plan and implement more targeted and effective interventions, promoting long-term athlete development and improving performance in both training and competition. 4.1. Interpretation of the Identified Athlete Multi-Factorial Profiles Profile 1 (Stable High-Performance Athletes), this profile represents athletes with moderate to high emotional stability, balanced sociability, and sufficient conscientiousness. They demonstrate higher self-confidence, motivation, and focused goal orientation. Their lower impulsivity scale scores indicate a faster, more spontaneous response style, with reduced inhibitory control and less reflective regulation but at the same time they have high decisiveness. These athletes exhibit relatively high stress tolerance (second highest among the profiles) and good reaction and motor speed, enabling them to maintain stable performance under competitive pressure. Profile 1 reflects emotionally stable, self-regulated athletes who maintain performance under pressure, consistent with findings linking psychological resilience to high-level performance [ 26 , 27 ]. Previous studies have often shown that these aspects are specific and reflect elite-level athletes [ 28 , 29 ]. This profile is the most common among team-sport athletes (≈ 63% within the profile) and included a substantial proportion of pre-elite and elite competitors, suggesting that emotional stability, stress tolerance, and consistent decision-making may be particularly advantageous in cooperative and tactically dynamic sport environments. Profile 2 (Controlled Precision Athletes) athletes in this are emotionally stable and tend to present as cooperative and socially constructive in team environments, although their extraversion scores did not differ significantly from other profiles. Their interaction style reflects communication discipline rather than sociability level. This profile also displayed notably lower Openness, reflecting a preference for structure, routine and low novelty in training environments. They are conscientious, organized, and disciplined, preferring structured and predictable performance environments. Their impulsivity scale scores are the highest, indicating strong reflective control and deliberate decision-making. They demonstrate the highest precision (exactitude) and consistent execution, while showing relatively slower reaction and motor speed and lower stress tolerance compared to Profiles 1 and 4. This reflects a methodical and planned performance style, where accuracy is prioritized over speed. Such athletes excel in tasks requiring technical consistency and routine adherence, although they may be less adaptable in rapidly changing competitive contexts. This aligns with evidence linking conscientiousness to persistence, task planning, and technical mastery in sport [ 30 , 31 ]. Profile 2 corresponds to a strategic and deliberate performance style, which is advantageous in technically stable tasks but may require targeted training to improve adaptability under uncertainty. Profile 2 was also more represented in team-sport athletes and occurred primarily among pre-elite and amateur athletes, reflecting a performance style that favours structured training environments, progressive skill development, and stable team roles. Profile 3 (Reactive Low-Regulation Athletes), these athletes show higher neuroticism and the lowest conscientiousness among the profiles, along with greater emotional reactivity and reduced self-esteem. They demonstrate lower motivation, weaker stress-management skills, and lower impulsivity scale scores, which indicates greater spontaneous impulsive responding and inconsistent self-regulation. They exhibit the lowest stress tolerance, slower reaction and motor speed, and reduced performance stability. This pattern corresponds to athletes with underdeveloped emotional regulation strategies, who may experience performance disruption following stress or mistakes [ 32 , 33 ]. This profile corresponds to developmentally earlier stages of psychological skill acquisition, where athletes may possess physical ability but lack regulatory strategies to manage pressure effectively. These are younger athletes. Strengthening emotional resilience, confidence, and coping resources is therefore crucial for athletes in this group. Profile 3 (Low-Regulation Reactive Athletes) included a higher proportion of individual-sport athletes and amateur-level competitors, indicating that limited emotional regulation and lower stress tolerance may be more characteristic of athletes earlier in their developmental trajectory or those in less socially regulated performance contexts. Profile 4 (Reactive High-Speed Athletes) include emotionally stable and moderately extraverted athletes with high motivation and a strong competitive drive. Their impulsivity scale scores are in the mid-range, indicating rapid responding with partial regulatory control. They demonstrate the highest stress tolerance and the fastest reaction and motor speed among all profiles, reflecting a fast, dynamic, reactive performance style. Such athletes are well-suited to intense, rapidly changing competitive environments where quick decision-making and tactical adaptability are critical [ 34 ]. Such athletes thrive in dynamic, rapidly changing environments, where speeded responses are advantageous. However, this performance style relies on instinctive rather than reflective control, meaning sustained performance quality may depend on developing better inhibitory control and decision regulation. Recent sport analytics work supports the importance of integrating perceptual-cognitive training to maintain decision accuracy in high-speed performers [ 35 ]. Profile 4 contained the highest proportion of elite-level athletes, and was represented in both team and individual sports, suggesting that the combination of high stress tolerance and rapid psychomotor response speed supports success in high-intensity, time-pressured performance environments. 4.2. Integration Across Profiles and Broader Theoretical Implications The four profiles illustrate that psychological performance in sport is not defined by a single optimal pattern, but rather by different combinations of emotional stability, motivational orientation, and psychophysiological regulation. The profiles identified here closely reflect contemporary models of athlete long term development, which emphasize that athletes progress along different psychological pathways depending on their training history, competitive environment, and regulatory skill acquisition [ 36 , 37 ]. Athletes’ developmental patterns are consistent with dynamic systems perspectives on athlete growth, self-regulation models of performance under stress [ 38 ], and the theory of challenge and threat states in athletes [ 39 ], all of which emphasize that athletes differ in how they adapt to performance demands. This differentiation is important because it demonstrates that athletes require different types of psychological and training support depending on their regulatory profile rather than a uniform “one-size-fits-all” approach. Profiling allows coaches and sport psychologists to identify whether an athlete benefits more from stability and composure training (as in Profile 3), adaptability and decision-speed challenges (Profile 2), sustained emotional control under pressure (Profile 1), or inhibitory and tactical regulation during fast-paced performance (Profile 4). In this way, multi-factorial profiling can guide individualized intervention planning, inform role assignment within teams, and support long-term athlete development by aligning training demands with each athlete’s current regulatory capacities and developmental strategies. For example, Sanz-Fernandez et al. [ 40 ] demonstrated that athletes display distinct behavioral patterns associated with specific psychological profiles, and that aligning training with these profiles improves performance and intervention effectiveness. Similarly, Shannon et al. [ 41 ] reported that athletes belonging to less optimal psychological profiles may particularly benefit from needs-supportive communication and psychological skills training, highlighting the relevance of profiling for both performance enhancement and mental health support in sport environments. Profiles 1 and 4 align more closely with performance-ready psychological functioning, particularly in environments involving rapid tactical decision-making and competitive pressure. Profiles 2 and 3, meanwhile, reflect earlier or more structurally dependent developmental stages, where performance consistency is supported either by external routine (Profile 2) or may be disrupted by emotional stress (Profile 3). This differentiation supports the practical value of profiling systems for targeted psychological skill intervention and individualized training design. 4.3. Practical Implications The practical contribution of this study can be found in its potential to support and improve the work of sport coaches, sport psychologists and sport specialists whose goal is to strengthen athletes’ psychological preparation. Multi-factorial profiling enables practitioners to identify both strengths and aspect requiring development in athletes, making an opportunity to more targeted and individually tailored intervention planning. Multi-factorial profiling can function as a practical assessment tool in applied sport settings, especially when it is based on a structured evaluation of key psychological and psychophysiological performance components. Profiling can inform the work of sports medicine professionals by identifying athletes who may be more vulnerable to overtraining, burnout, or injury risk, especially those with low stress tolerance or heightened emotional reactivity (like Profile 3). This information can support preventive strategies, guide return to sport decisions after injury, and assist with individualized load management. The psychological preparation of athletes should be considered as a core element of the training process, alongside physical, technical, and tactical preparation. Integrating psychological development into regular training planning contributes not only to improved performance under pressure, but also to the sustainable long-term development of athletes. The focus should be shifted toward this aspect. Systematic assessment of psychological functioning allows to determine which psychological skills require strengthening and to select appropriate training methods or interventions for athletes. Beyond applied use, the findings of this study contribute to sport science by offering a deeper understanding of the psychological characteristics of contemporary athletes. The multi-factorial profiles identified here are based on empirically grounded evidence and provide a framework for interpreting how personality, psychological skills, and psychophysiological readiness shape performance. This knowledge supports the development of evidence-based approaches to psychological preparation and highlights the value of profiling as a tool for individualized athlete development. 4.4. Limitation Despite the practical value of multi-factorial profiling in identifying athletes’ strengths, weaknesses, and individual development needs, several limitations of this study should be acknowledged when interpreting the results. First, the timing within the annual training cycle (periodization phase) was not controlled. Athletes were assessed at different points in their season, and certain psychophysiological performance indicators, such as stress tolerance and reaction speed, may vary depending on whether testing occurs during preparatory, competitive, or transition phases. Personality traits are theoretically more stable and therefore less influenced by seasonal fluctuations, but psychophysiological readiness can vary considerably. It is also important to note that the sample consisted exclusively of Latvian athletes, which limits the generalizability of the findings to athletes from other countries or sporting cultures, where psychological preparation systems, coaching traditions, and sociocultural norms may differ. While similar patterns could be anticipated in comparable regional contexts, replication in other populations would strengthen the external validity of the profiling model. Another limitation is the experience of psychologist experts in characterizing the profiles. Although the profiles were qualitatively validated by sport psychology experts, the composition of the panel may be expanded in future research. Involving a greater number of experts, including performance coaches and psychological preparation coaches who work daily with athletes in training and competition environments, would enrich the ecological validity of the interpretations and strengthen the practical application of the profiles. Future research should employ longitudinal designs to track how multi-factorial profiles evolve across training phases, competitive progression, and career stages. Future research should also examine profile differences within specific sport types, as sport-specifics demands and performance environments may shape psychological and psychophysiological regulation patterns differently. A more fine-grained sport-specific analysis could reveal additional sub profiles or nuances in regulatory functioning that were not captured in the broader categorization used in the present study. The profiles suggest possible links to burnout or injury risk vulnerability, but these outcomes were not directly measured in this study. Future research should examine how different psychological and psychophysiological patterns are related to burnout trajectories, overtraining, and injury incidence. 5. Conclusions This study developed a multi-factorial multi-factorial profile for Latvian athletes, integrating personality traits, psychological skills, and psychophysiological performance indicators. Based on cluster analysis, four athlete profiles were identified. The profiles differed not only in their indicators, but also in different sports (individual vs. team) and competition levels (elite, pre-elite, amateur), indicating that team sports athletes and higher performance athletes were more often represented in profiles 1 and 4, while individual sports and amateur athletes were more often found in profiles 2 and 3. This suggests that the development of athletes is not uniform, and psychological functioning is shaped by both training demands and the competition environment. The results indicate that multi-factorial profiling can serve as a practical value in sports practice. It allows coaches and sport psychologists to identify strengths and development needs, as well as plan more targeted, individualized psychological training strategies that support stable performance under stressful conditions. Declarations Supporting information Appendix A. Expert validation coding framework for athlete profiles. Appendix B. Supplementary tables Appendix C. Pearson correlation matrix of study variables. Ethics approval and consent to participate This study was approved by the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and adhered to the ethical guidelines outlined in the Declaration of Helsinki. Informed consent was obtained from all participants included in the study and participants were fully informed that their data would be used solely within the framework of this research. Confidentiality was strictly maintained, with all data securely stored to protect participant privacy. Participants were also informed of their right to withdraw from study at any time without penalty. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The author(s) declared financial support was received for the research, authorship, and/or publication of this article. This research is funded under the Grant No. RSU/LSPA-PA-2024/1–0010 of the project No. 5.2.1.1.i.0/2/24/I/CFLA/005 “RSU Internal and RSU with LASE External Consolidation” (funded by the European Union Recovery and Resilience Facility and the budget of the Republic of Latvia). Author Contribution K.V. conceived the study, developed the methodology, acquired funding, supervised the project, conducted the investigation, curated the data, performed the formal analyses, validated the findings, prepared the visualizations, and wrote the original draft of the manuscript. G.U., A.A., K.A., and R.L. contributed to data curation and participated in the investigation. Z.V. contributed to data curation, formal analysis, and validation, and assisted in writing and reviewing the manuscript. A.K., Z.V., A.A., G.U., and R.L. contributed to reviewing and editing the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgement We sincerely thank the members of the Latvian Sport Psychology Association and the coaches specializing in psychological preparation for their participation in the expert panel and for providing valuable insights and feedback that contributed to this research. Data Availability The used datasets can be accessed at Riga Stradiņš University Dataverse repository: Volgemute, Katrina. 2025. “Athletes Personality Traits, Psychological Skills, and Psychophysiological Performance.” Rīga Stradiņš University Institutional Repository Dataverse. https://doi.org/doi:10.48510/FK2/0B871H. For long-term access or institutional inquiries, data requests may also be directed to the RSU Research Department at [email protected] . References Shuai, Y., Wang, S., Liu, X., Kueh, Y. C. & Kuan, G. 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Psychol. 43 (1), 71–82. https://doi.org/10.1123/jsep.2019-0295 (2021). Additional Declarations No competing interests reported. Supplementary Files AppendixA.xlsx AppendixB.docx AppendixC.xlsx Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Dec, 2025 Reviews received at journal 19 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor invited by journal 01 Dec, 2025 Editor assigned by journal 28 Nov, 2025 Submission checks completed at journal 28 Nov, 2025 First submitted to journal 27 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:46:12","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195593,"visible":true,"origin":"","legend":"","description":"","filename":"7685dc0d522f40f38afa6057018c408c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/6e302141b8063ec4e82eddf6.xml"},{"id":97900520,"identity":"77280a79-2639-40f2-a90e-b0ae37ebd4d0","added_by":"auto","created_at":"2025-12-10 15:45:36","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":207758,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/38a9e68cca769419bda8e38d.html"},{"id":97890811,"identity":"d11af1b0-0191-4cc5-b695-7d2e7dea09dc","added_by":"auto","created_at":"2025-12-10 14:30:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":364495,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Protocol Overview\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/e9ef8ee20cc8ef13cb07ebe0.png"},{"id":97900620,"identity":"69d80cf5-e2db-4280-9a1b-a35452b24707","added_by":"auto","created_at":"2025-12-10 15:45:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":149414,"visible":true,"origin":"","legend":"\u003cp\u003eProfiles of Athletes Based on Standardized Z−Scores Across Personality, Psychological Skills, and Psychophysiological Performance Variables.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/edc9fe0826b18b977eb9fb9f.png"},{"id":97900608,"identity":"55478dcb-9953-4bb4-934a-01a3c4f071be","added_by":"auto","created_at":"2025-12-10 15:45:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140332,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot of Discriminant Function Scores by Cluster Profile\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Squares represent group centroids (mean scores for each cluster on the discriminant functions).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/408d8e0e0504f605cb0d1c18.png"},{"id":100069150,"identity":"222b8d5e-84e9-47d2-bed0-fb81be932675","added_by":"auto","created_at":"2026-01-12 16:10:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2065235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/89b9e071-50a6-47e6-822d-7811dc70d6e4.pdf"},{"id":97900958,"identity":"ed801d26-d3d3-494f-9c50-938f37df48d3","added_by":"auto","created_at":"2025-12-10 15:46:12","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":24365,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/eb820b7631a6e28491762087.xlsx"},{"id":97900049,"identity":"bb2038f3-b194-46e6-a081-6f23d1f77eb6","added_by":"auto","created_at":"2025-12-10 15:45:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26241,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixB.docx","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/70a28c64e977c9f2391c2b5a.docx"},{"id":97899501,"identity":"6d841f7a-f7ce-4d15-ad81-f07cc2711245","added_by":"auto","created_at":"2025-12-10 15:44:39","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13226,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixC.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8224965/v1/5ebc39d3b021ced0709103ca.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Personality, Psychological Skills and Psychophysiological Performance Factors in Athlete Profiling: Evidence from Team and Individual Sports","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. The Role of Personality and Psychological Skills in Athletic Performance\u003c/h2\u003e\u003cp\u003ePersonality traits and psychological skills are equally important factors in supporting successful athletic performance. It is important to note that the optimal personality profile may vary across sport disciplines [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Personality characteristics such as emotional stability, decisiveness, and self-confidence shape athletes\u0026rsquo; attitudes toward competition, while acquired psychological skills such as visualization, positive self-talk, mental toughness, imagery, and motivational self-statements help athletes maintain focus, manage anxiety, and perform under elevated pressure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Understanding the interaction of these factors allows athletes and coaches to develop training and competition strategies that support both performance consistency and individual development.\u003c/p\u003e\u003cp\u003eXu and Hao [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] demonstrated that higher conscientiousness and extraversion consistently predict superior performance. The authors identified three mechanisms through which personality influences performance: (1) direct neurobiological pathways, including dopaminergic sensitivity and prefrontal regulatory control; (2) indirect psychological mediation, particularly through motivation and self-regulation; and (3) contextual moderation, such as sport type, competitive level, and cultural environment. Research indicates that elite athletes typically exhibit greater emotional stability, which supports cognitive clarity and consistent decision-making under pressure. For example, Fabbricatore et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] found that top-level swimmers scored significantly lower in neuroticism than non-elite athletes.\u003c/p\u003e\u003cp\u003eThese psychological characteristics operate within physiological and situational conditions, meaning that performance depends not only on what athletes think and feel, but also on how effectively they regulate their psychophysiological responses in real time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Psychophysiological Adaptation in Sport\u003c/h2\u003e\u003cp\u003eThe competitive sports environment requires athletes not only to have high psychological readiness, but also to be able to effectively regulate cognitive and physiological responses under pressure. Psychophysiological indicators, such as reaction speed, inhibitory control, and stress tolerance, reflect how effectively athletes process information, maintain attention, and make decisions during competition, where demands change rapidly [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These abilities are closely related to performance outcomes, especially in time-constrained or unpredictable environments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is well known that stress affects athlete performance and results. Exposure to stress simultaneously activates autonomic, cognitive, and emotional systems during performance, requiring athletes to regulate both physiological arousal and cognitive processing [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Reaction time and inhibitory control serve as practical indicators of this regulation capacity, reflecting an athlete\u0026rsquo;s ability to suppress impulsive responses and maintain decision accuracy under load [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSports competitions involve dynamic interactions that affect an athlete\u0026rsquo;s readiness to achieve high results. Psychophysiological performance measurements provide essential insights into how to plan and implement the process of psychological preparation of athletes and beyond. Therefore, to understand performance in sports, psychophysiological functioning must be examined alongside personality traits and psychological skills, not in isolation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3. Integrative Approaches to Athlete Profiling\u003c/h2\u003e\u003cp\u003ePerformance is multidimensional and dynamic where psychological, technical\u0026ndash;tactical, and physiological variables interact with each other and fluctuate over time. Personality traits, psychological skills, and physiological capacities each explain different mechanisms of adaptation to physical and psychological load or environmental requirements. Therefore, a single-dimension metric often transfers misleading conclusions when used across contexts. Recent authors argue that a multidisciplinary and integrative perspective is required to understand how multiple interacting factors shape athletes\u0026rsquo; performance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Accordingly, Glazier [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] proposed a Grand Unified Theory, which supports the notion that sports performance is governed by complex interactions across fields. For example, psychology, physiology, and biomechanics. An extensive research analysis conducted by Zentgraf and Raab [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] indicates that individual factors associated with expertise are well discussed. However, most interactions between these key factors have not been investigated together, and it would be premature to draw conclusions about how their combined effects influence expertise development and sport performance.\u003c/p\u003e\u003cp\u003eAs noted by Neumann et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], sports performance is an emergent process governed by individual-specific, interacting biopsychosocial factors. This means that measurements are needed in several domains, and data from different areas must be synthesized to create an integrative view, allowing the athlete\u0026rsquo;s profile to be seen \u0026ldquo;in one picture.\u0026rdquo; When developing an individual profile, the specifics of the sport type, the level of athletic performance, and gender differences must be considered, as each of these may create distinct performance demands and require different psychological skills and regulation strategies. The more variables are considered when differentiating sport competence, the more researchers conclude that general models require adaptation to specific sport contexts in order to meaningfully guide practice [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This leads to the conclusion that a model tailored to sport type, gender, age, and competitive level functions more effectively. In this context, integrating personality traits (stable dispositions), psychological skills (trainable self-regulation strategies), and psychophysiological performance indicators (such as stress-related response efficiency) enables a more ecologically valid understanding of how athletes\u0026rsquo; function under real performance conditions. An integrated athlete profile is needed because performance arises from interactions between variables, not from any single factor alone. By linking psychological and psychophysiological indicators to performance-relevant outcomes, athlete profiling becomes directly applicable to multidisciplinary sports support teams, including coaches and spot specialists such as physicians, physiotherapists and sport psychologists.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e1.4. Current Gaps and Limitations in Athlete Typologies\u003c/h2\u003e\u003cp\u003eSubstantial empirical evidence suggests that athletes from different sport types display distinct personality trait profiles. For example, Shuai et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] showed that team sport athletes tend to score higher conscientiousness and extraversion, whereas findings for traits such as agreeableness and openness vary across specific sports and competitive contexts. Although some studies have observed differences between individual- and team-sport athletes, the overall evidence remains inconsistent and strongly dependent on methodological and contextual factors.\u003c/p\u003e\u003cp\u003eSeveral conceptual perspectives have been proposed to explain how personality traits and sport participation may be related. One of the fundamental foundations of the personality perspective in sport is Eysenck's [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] theoretical concept, which states that individuals are naturally drawn to environments, including sport settings, that matches their pre-existing personality traits. Consequently, personality influences sport selection, while participation within a particular sport environment may, over time, further reinforce or shape certain psychological characteristics. Conzelmann et al [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] suggested that long-term participation in sport may promote gradual personality development over time through socialization processes and learning experiences. Other approaches to the concept of personality emphasize that personality and sport contexts mutually influence each other, which is consistent with broader trait-situation interaction frameworks in personality psychology [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to personality traits, psychological and psychophysiological factors play a critical role in athletic performance, injury susceptibility, and recovery. Research shows that heightened stress, cognitive interference, and maladaptive coping patterns increase injury risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], while psychological readiness strongly influences return-to-sport outcomes after injury [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Integrating these factors into athlete profiling may therefore enhance early risk detection and support more individualised prevention and rehabilitation strategies.\u003c/p\u003e\u003cp\u003eDespite advances in the field, several key limitations still remain. Many existing typologies rely primarily on self-report personality measures, which may oversimplify the psychological foundations of performance and overlook the trainability of psychological skills. There is also a lack of longitudinal research, limiting understanding of how athlete profiles evolve across developmental stages. Additionally, cultural variation, gender differences, and sport-specific demands are often underrepresented, reducing the generalizability of existing models. A further limitation is the frequent absence of objective performance and psychophysiological indicators in athlete profiling. Without integrating behavioral or stress-response data, typologies have limited practical utility for performance planning or intervention design. Therefore, there is a need for empirically grounded, multi-factorial athlete profiles, validated through both quantitative data and qualitative expert evaluation, to ensure relevance and application in real training and competition settings. The present study addresses these gaps by developing an integrated profiling approach that combines personality traits, psychological skills, and psychophysiological performance indicators.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e1.5. Study Aim and Hypothesis\u003c/h2\u003e\u003cp\u003eThe present study aimed to develop a multi-factorial profile of Latvian athletes by integrating measures of personality traits, psychological skills, and psychophysiological performance variables. Specifically, the study sought to (1) identify distinct athlete profiles using multivariate statistical techniques, (2) examine differences across sport types (team vs. individual) and competitive levels (elite, pre-elite, amateur) and explore how personality and psychological skills are associated with psychophysiological performance indicators.\u003c/p\u003e\u003cp\u003eHypotheses: (1) There are significant multivariate differences in personality, psychological, and psychophysiological performance characteristics between team and individual sport athletes as well as between athletes of different competitive levels. (2) Distinct athlete profiles (clusters) can be identified based on the integration of personality traits, psychological skills, and psychophysiological performance indicators, reflecting different styles of emotional regulation, motivation, and performance efficiency.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants\u003c/h2\u003e\u003cp\u003eIn this cross-sectional study, data were collected from a purposive sample of 304 active competitive Latvian athletes both males (n\u0026thinsp;=\u0026thinsp;183, 60.2%) and females (n\u0026thinsp;=\u0026thinsp;121, 39.8%), recruited through sport federations, sport schools, and national teams across multiple regions. All assessments were conducted individually under controlled laboratory conditions. The mean age of the sample was 19.63 years (SD\u0026thinsp;=\u0026thinsp;3.64), with an average training load of 6.06 hours per week (SD\u0026thinsp;=\u0026thinsp;2.89) and 9.22 years (SD\u0026thinsp;=\u0026thinsp;4.18) of sport-specific experience. A total of 177 athletes (58.2%) represented team sports (most commonly: basketball (n\u0026thinsp;=\u0026thinsp;86); football (n\u0026thinsp;=\u0026thinsp;26); hockey (n\u0026thinsp;=\u0026thinsp;18); handball (n\u0026thinsp;=\u0026thinsp;16) etc) and 123 athletes (40.5%) represented individual sports (most commonly: luge (n\u0026thinsp;=\u0026thinsp;14); athletics (n\u0026thinsp;=\u0026thinsp;10); skeleton (n\u0026thinsp;=\u0026thinsp;8); cycling (n\u0026thinsp;=\u0026thinsp;7) etc).\u003c/p\u003e\u003cp\u003eBased on competitive achievement level, athletes were classified as: (1) Elite (n\u0026thinsp;=\u0026thinsp;54, 17.8%; Mage\u0026thinsp;=\u0026thinsp;19.27, SD\u0026thinsp;=\u0026thinsp;0.42; training load\u0026thinsp;=\u0026thinsp;8.46 h/week, SD\u0026thinsp;=\u0026thinsp;0.40; experience\u0026thinsp;=\u0026thinsp;9.00 years, SD\u0026thinsp;=\u0026thinsp;0.52); (2) Pre-elite (n\u0026thinsp;=\u0026thinsp;160, 53.6%; Mage\u0026thinsp;=\u0026thinsp;18.93, SD\u0026thinsp;=\u0026thinsp;0.27; training load\u0026thinsp;=\u0026thinsp;6.24 h/week, SD\u0026thinsp;=\u0026thinsp;0.19; experience\u0026thinsp;=\u0026thinsp;9.84 years, SD\u0026thinsp;=\u0026thinsp;0.32) and (3) Amateur (n\u0026thinsp;=\u0026thinsp;87, 28.6%; Mage\u0026thinsp;=\u0026thinsp;20.61, SD\u0026thinsp;=\u0026thinsp;0.50; training load\u0026thinsp;=\u0026thinsp;4.24 h/week, SD\u0026thinsp;=\u0026thinsp;0.27; experience\u0026thinsp;=\u0026thinsp;8.26 years, SD\u0026thinsp;=\u0026thinsp;0.49). At the time of data collection, 54 athletes (17.8%) held an active professional sport contract.\u003c/p\u003e\u003cp\u003eTo qualitatively validate the athlete profiles obtained from the cluster analysis, a panel of nine applied sport psychology experts was convened. The panel included practicing sport psychologists (n\u0026thinsp;=\u0026thinsp;6) who were actively providing psychological support to athletes and were registered with the national Sport Psychology Association, as well as senior coaches specializing in psychological preparation (n\u0026thinsp;=\u0026thinsp;3). Their professional experience in working directly with competitive athletes ranged from 3 to 30 years (M\u0026thinsp;=\u0026thinsp;15.5, SD\u0026thinsp;=\u0026thinsp;8.44), providing a diverse and well-informed expert perspective for the validation process.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Measures\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1. Personality\u003c/h2\u003e\u003cp\u003eTo assess athletes\u0026rsquo; personality traits, the Latvian Personality Inventory (LPI-v3) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was used, specifically the sports-validated version (LPI-v3s). The original LPI-v3 is a widely used in Latvia, which is also clinically validated multidimensional personality assessment instrument, applied in both clinical practice and psychological research, ensuring strong psychometric reliability and construct validity. The LPI-v3s is an adaptation developed for performance and sport contexts, maintaining the same theoretical factor structure while refining item interpretation and normative ranges for athletic populations. The inventory assesses five personality domains: (1) Neuroticism, (2) Conscientiousness, (3) Extraversion, (4) Agreeableness, and (5) Openness to Experience and includes an additional response validity scale (Lie scale) to detect inconsistent or socially desirable responding. It consists of 57 items rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;does not match; 5\u0026thinsp;=\u0026thinsp;matches). Internal consistency values in the present sample ranged from α\u0026thinsp;=\u0026thinsp;0.58 to 0.79, consistent with prior validation studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2. Psychological Skills\u003c/h2\u003e\u003cp\u003eTo assess athletes\u0026rsquo; psychological skills, the Psychological Skills Inventory for Sport (PSIS-R5) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was used in its Latvian language adaptation (PSIS-R5-L) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The PSIS-R5 is one of the most widely used standardized instruments for evaluating psychological performance attributes in competitive sport contexts and has been validated across multiple athletic populations. The Latvian adaptation preserves the theoretical factor structure and scoring system of the original scale while ensuring linguistic and cultural equivalence for local sport settings. The PSIS-R5-L consists of four subscales: (1) Self-Confidence (belief in one\u0026rsquo;s ability to perform successfully), (2) Motivation (persistence, striving, and task engagement), (3) Team Emphasis (cooperation and interpersonal functioning in team contexts), and (4) Visualization (use of imagery and mental rehearsal strategies). The inventory contains 17 items, each rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree), with higher scores reflecting stronger psychological skill development. Internal consistency reliability coefficients in present athletic samples range from α\u0026thinsp;=\u0026thinsp;0.43 to 0.75, and in the current sample coefficients fell within this expected range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3. Psychophysiological Performance Measures\u003c/h2\u003e\u003cp\u003eIn this study, psychophysiological performance measures were obtained using the Vienna Test System (VTS) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], a computerized and standardized assessment platform widely used in performance psychology and neurocognitive evaluation. All testing was conducted individually under controlled laboratory conditions to ensure consistency and to avoid external environmental interference. The test battery included three VTS modules relevant to cognitive\u0026ndash;motor performance in sport: the Determination Test (DT), the Reaction Test (RT), and the Attitude towards Work (AHA).\u003c/p\u003e\u003cp\u003eThe DT test assesses the ability to respond rapidly and accurately to multiple simultaneously presented visual and auditory stimuli, requiring fast and flexible choice reactions. Within the framework of the Cattell\u0026ndash;Horn\u0026ndash;Carroll (CHC) model of cognitive abilities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], this ability is associated with the broad factor of processing speed and the more specific component of reaction and decision speed. The test also indexes reactive stress tolerance, which reflects the capacity to maintain performance under time pressure and increasing task demands. In the present context, reactive stress tolerance is operationalized as the stability, precision, and persistence of responses during high-speed stimulus discrimination and motor execution. This construct is particularly relevant in competitive sport, where athletes must sustain decision-making accuracy under rapidly changing and physiologically arousing conditions. To complete the DT test, it takes approximately 8 minutes.\u003c/p\u003e\u003cp\u003eThe study applied the AHA test battery to assess performance-related behavioural tendencies. The AHA provides indicators that describe how individuals plan, sustain, and regulate task performance under varying cognitive demands. The constructions assessed are closely related to performance consistency in sport, particularly in situations requiring precision, decision-making, and the management of errors. The AHA battery includes the following variables: (1) Exactitude, reflecting precision and accuracy in task execution; (2) Decisiveness, indicating the ability to make timely decisions even under uncertainty; (3) Impulsiveness vs. Reflexivity, distinguishing between rapid, instinctive responding and more deliberate, controlled responding; (4) Performance Level, assessing sustained concentration and productivity; (5) Aspiration Level, reflecting the realism of self-set performance goals; (6) Frustration Tolerance, denoting the ability to maintain performance following errors or negative feedback; and (7) Target Discrepancy, indicating the consistency between predicted and actual performance outcomes. Together, these indices provide a multi-factorial profile of performance regulation processes that are directly relevant to athletic training and competitive settings. AHA test takes approximately 15 minutes to complete. In the AHA impulsivity vs reflectivity index, higher scores indicate greater inhibitory control and reflective responding, whereas lower scores indicate faster, more impulsive and less controlled responding.\u003c/p\u003e\u003cp\u003eThe Reaction (RT) test from the VTS was used to assess psychomotor response speed. Two test forms were administered: S1, which measures simple reaction time, and S5, which measures choice reaction time. Both tests record latent (perceptual) reaction time, reflecting how quickly the athlete detects and processes a stimulus, as well as motor reaction time, indicating the speed of the physical response. The combination of S1 and S5 therefore provides an index of both fundamental sensorimotor responsiveness and rapid decision-making efficiency under choice conditions. The total testing time for both forms was approximately 6 minutes.\u003c/p\u003e\u003cp\u003eAll tests were completed individually in consecutive order, following standardized administration procedures. Before each test, athletes received on-screen instructions and completed a trial phase to ensure full task comprehension. Only after successful completion of the practice phase did the formal testing begin. All tasks were non-verbal, minimizing language-based performance effects. Administration strictly adhered to standardized procedures to maintain administrative reliability, ensuring that all athletes received identical instructions, timing, and testing conditions. For the purposes of statistical analysis and cluster modelling, the following performance indices were used from the VTS battery: stress tolerance score (mean accuracy under high time pressure), exactitude, decisiveness, impulsiveness vs. reflexivity, frustration tolerance, and performance level, latent and motor reaction time, and latent and motor choice reaction time. These variables were selected as theoretically relevant indicators of cognitive\u0026ndash;motor efficiency and performance regulation in sport. All VTS test results are presented in standardized scoring protocols, which include raw scores, T-scores (M\u0026thinsp;=\u0026thinsp;50, SD\u0026thinsp;=\u0026thinsp;10) and percentile ranks. For the purposes of this study, statistical analyses and interpretation were conducted using T-scores, as they allow comparability across tests and individuals irrespective of age or sport type. All variables listed above were included in the multivariate analyses and subsequent cluster modelling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.2.4. Qualitative Validation of Athlete Multi-Factorial Profiles (Expert Panel)\u003c/h2\u003e\u003cp\u003eTo evaluate the ecological validity of the athlete profiles derived from the cluster analysis, a panel of nine applied sport psychology experts was engaged. Experts participated in a structured open-ended questionnaire designed to evaluate: (1) the perceived real-world relevance and recognizability of each athlete profile (Q1), (2) typical behavioural patterns and stress responses in training and competition (Q2), and (3) appropriate psychological intervention and skill-development priorities for each profile (Q3). Each expert reviewed short narrative descriptions of the four profiles (summarising personality, psychological skills, and psychophysiological performance characteristics) and answered three open-ended questions: (1) Do these profiles reflect real athlete types you encounter in practice? (2) What typical behaviours or situational reactions correspond to each profile in training and competition? (3) What psychological interventions or skill-development directions would you recommend for each type?\u003c/p\u003e\u003cp\u003eResponses were analyzed using a hybrid inductive\u0026ndash;deductive thematic content approach. Coding first followed predefined conceptual categories aligned with the profile structure, and additional emergent themes were identified inductively. Convergence and divergence across expert perspectives were then synthesized into profile-level summaries. The thematic results are presented in narrative form (see Appendix A).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Design and Data Collection Procedure\u003c/h2\u003e\u003cp\u003eThis study employed a mixed-methods sequential explanatory design, in which quantitative athlete profiling was followed by qualitative expert validation to evaluate the ecological applicability of the identified profiles. The quantitative phase consisted of standardized laboratory-based assessments of: (1) personality traits, (2) psychological skills, and (3) psychophysiological performance indices. Data collection took place over approximately 18 months, between May 2024 and November 2025. Athletes were recruited through sport federations, sport schools, national teams, and professional coaches, using purposive selection to ensure inclusion of active competitive athletes across sport types and performance levels. Each participant attended the research laboratory individually, where all assessments were conducted under controlled, quiet, distraction-free conditions. The full testing session lasted approximately 1.5\u0026ndash;2 hours per athlete (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePrior to participation, each athlete received a standardized explanation of the study purpose, procedures, data use, and confidentiality safeguards. Written informed consent was obtained from all participants. Athletes then completed the assessments in a uniform standardized order. Participants first completed the LPI-v3s and PSIS-R5-L inventories, along with a demographic and sport-related background form (sport type, years of experience, training load, competition level, highest achievements, and whether a valid professional contract was held). After the questionnaires, athletes completed the VTS batteries in the following fixed sequence: (1) DT, (2) AHA) and (3) RT: forms S1 and S5. This order was selected to maintain progression from higher cognitive-attentional load tasks (DT, AHA) toward more automated sensorimotor response tasks (RT), minimizing fatigue-related confounding. At the end of testing, participants could receive individual feedback on their results, provided upon request in a standardized interpretation protocol.\u003c/p\u003e\u003cp\u003eFollowing quantitative data analysis, a k-means cluster analysis was performed, resulting in four distinct athletes\u0026rsquo; multi-factorial profiles, representing patterns across personality, psychological skills, and psychophysiological performance variables. These profile descriptions (without raw data or scoring details) were provided only to the expert validation panel.\u003c/p\u003e\u003cp\u003eIn the qualitative phase, sport psychology experts (registered psychologists who practice in field of sport and senior psychological preparation coaches) evaluated the real-world relevance of each profile. Experts responded to three structured open-ended questions. Their responses were analyzed using inductive\u0026ndash;deductive thematic coding, allowing both data-driven interpretation and alignment with the theoretical constructs underlying the profiles. The mixed-methods structure allowed quantitative profile identification and qualitative confirmation that these profiles correspond to recognizable athlete types in applied sport contexts.\u003c/p\u003e\u003cp\u003eThe study protocol received approval from the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and was conducted in accordance with the Declaration of Helsinki. All data were anonymized, encrypted, and stored on secure institutional servers accessible only to the research team. Data protection procedures followed the registered ARGOS Data Management Plan (OpenAIRE). Participation was voluntary, and athletes retained the right to withdraw at any point without explanation or negative consequences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics Version 28.0 for Windows. Initially, data were collected from 317 athletes. Following data cleaning procedures, 13 cases were excluded due to incomplete test sessions or invalid response patterns, resulting in a final analytic sample of n\u0026thinsp;=\u0026thinsp;304 athletes.\u003c/p\u003e\u003cp\u003eDescriptive statistics (means, standard deviations, skewness, kurtosis) were computed for all study variables. Data normality was evaluated based on skewness and kurtosis values, with distributions considered acceptable if values fell within \u0026plusmn;\u0026thinsp;1. Internal consistency of the LPI-v3s and PSIS-R5-L subscales was examined using Cronbach\u0026rsquo;s α. Subscales with α\u0026thinsp;\u0026ge;\u0026thinsp;0.50 were retained, consistent with prior validation studies of multidimensional constructs in sport psychology. The Agreeableness scale was excluded from further analyses due to insufficient reliability.\u003c/p\u003e\u003cp\u003ePearson\u0026rsquo;s bivariate correlation analyses were conducted to examine associations between personality, psychological skills and psychophysiological performance variables and demographic factors (age, gender, training load, sport experience). Differences in indicators across sport type (team vs. individual) and competitive level (elite, pre-elite, amateur) were examined using Multivariate Analysis of Covariance (MANCOVA). Sport type and competitive level were entered as fixed factors, while gender, age, sport experience, and training load were entered as covariates. Significant multivariate effects were followed by univariate tests and interpreted using partial eta squared (η\u0026sup2;) as an index of effect size. Effect sizes were interpreted via partial eta squared (η\u0026sup2;), where values of 0.01, 0.06, and 0.14 indicated small, medium, and large effects, respectively.\u003c/p\u003e\u003cp\u003eFor comparability across indicators, T-scores (M\u0026thinsp;=\u0026thinsp;50, SD\u0026thinsp;=\u0026thinsp;10) provided by the VTS were used in all analyses. Alternative cluster solutions (2\u0026ndash;5 clusters) were compared using Elbow and Silhouette criteria, interpretability of cluster centroids, and balance in group sizes. A four-cluster solution was selected based on optimal separation of personality, psychological skills and psychophysiological patterns and theoretical coherence. The solution\u0026rsquo;s classification stability was evaluated using Discriminant Function Analysis (DFA), which tested how accurately cluster membership could be predicted based on the final model variables. Cross-validated classification accuracy exceeding 80% was considered evidence of strong cluster discrimination.\u003c/p\u003e\u003cp\u003eThe significance level for all statistical tests was set at p\u0026thinsp;\u0026le;\u0026thinsp;0.05. To determine the adequacy of the sample size, a priori power analysis was conducted using G*Power 3.1. For the planned MANCOVA with three achievement-level groups, medium effect size (f\u0026thinsp;=\u0026thinsp;0.25), α\u0026thinsp;=\u0026thinsp;0.05, and power (1\u0026thinsp;\u0026minus;\u0026thinsp;β)\u0026thinsp;=\u0026thinsp;0.80 indicated a required minimum sample of N\u0026thinsp;=\u0026thinsp;159. The final sample of 304 therefore exceeded the required threshold, ensuring sufficient power for group comparisons and supporting stable cluster estimation.\u003c/p\u003e\u003cp\u003eFollowing the quantitative phase, the qualitative expert validation was analyzed using an inductive\u0026ndash;deductive thematic coding approach, allowing integration of expert-derived themes with predefined conceptual dimensions of the athlete profiles. Coding and theme development were conducted in Microsoft Excel, and themes were aggregated across experts to determine areas of consensus and divergence.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Descriptive Statistics\u003c/h2\u003e\u003cp\u003eIn Appendix B, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e summarises the means, standard deviations, skewness, and kurtosis for 23 variables representing personality, psychological skills, and psychophysiological performance factors among competitive athletes (n\u0026thinsp;=\u0026thinsp;304). Data reliability was assessed for the self\u0026thinsp;\u0026minus;\u0026thinsp;report scales of personality and psychological skills using Cronbach\u0026rsquo;s alpha coefficients. The results indicated adequate internal consistency for most scales, with α values ranging from 0.53 to 0.79. The lowest reliability was observed for Agreeableness (α\u0026thinsp;=\u0026thinsp;0.38) and Visualization (α\u0026thinsp;=\u0026thinsp;0.43), which is considered marginal but acceptable given the short scale length and exploratory nature of the study.\u003c/p\u003e\u003cp\u003eThe Agreeableness scale was excluded from further analyses due to its low internal consistency and limited theoretical relevance to performance characteristics in the current sample. In contrast, the Visualization scale, while showing a lower alpha (α\u0026thinsp;=\u0026thinsp;0.43), was retained. This decision was theoretically justified, as visualization is a key psychological skill closely linked to mental rehearsal, concentration, and performance optimization in athletes. Furthermore, its reliability falls within the moderate range (0.50\u0026ndash;0.80) suggested by Salvucci et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the relatively small number of items (four) likely attenuated the alpha value, as Cronbach\u0026rsquo;s alpha is sensitive to item count [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, the Visualization scale was retained to preserve the conceptual completeness of the multi-factorial profile.\u003c/p\u003e\u003cp\u003eThe lowest mean scores in the athlete sample were on Neuroticism (M\u0026thinsp;=\u0026thinsp;28.12, SD\u0026thinsp;=\u0026thinsp;4.43) and Openness (M\u0026thinsp;=\u0026thinsp;29.47, SD\u0026thinsp;=\u0026thinsp;5.49), suggesting relatively greater emotional stability and a lower tendency toward anxiety or mood fluctuations but also reduced openness to novel experiences. In contrast, Conscientiousness (M\u0026thinsp;=\u0026thinsp;33.39, SD\u0026thinsp;=\u0026thinsp;6.48), Extraversion (M\u0026thinsp;=\u0026thinsp;33.28, SD\u0026thinsp;=\u0026thinsp;6.49), and Adventurism (M\u0026thinsp;=\u0026thinsp;34.48, SD\u0026thinsp;=\u0026thinsp;8.37) were moderately high, reflecting discipline, sociability, and curiosity or a preference for challenge.\u003c/p\u003e\u003cp\u003eAmong psychological skills, the highest mean scores were found for Self\u0026thinsp;\u0026minus;\u0026thinsp;confidence (M\u0026thinsp;=\u0026thinsp;19.12, SD\u0026thinsp;=\u0026thinsp;6.32) and Motivation (M\u0026thinsp;=\u0026thinsp;14.91, SD\u0026thinsp;=\u0026thinsp;3.63), suggesting a well\u0026thinsp;\u0026minus;\u0026thinsp;developed psychological readiness for performance. The lowest mean score was observed for Team emphasis (M\u0026thinsp;=\u0026thinsp;12.99, SD\u0026thinsp;=\u0026thinsp;2.22), which may reflect the inclusion of both team and individual sport athletes in the sample.\u003c/p\u003e\u003cp\u003eIn the psychophysiological performance domain, athletes showed high performance indicators, particularly in Choice reaction speed (M\u0026thinsp;=\u0026thinsp;67.02, SD\u0026thinsp;=\u0026thinsp;9.78), Choice motor speed (M\u0026thinsp;=\u0026thinsp;65.65, SD\u0026thinsp;=\u0026thinsp;8.41), and Motor speed (M\u0026thinsp;=\u0026thinsp;62.38, SD\u0026thinsp;=\u0026thinsp;8.69), suggesting above \u0026minus;\u0026thinsp;average psychomotor efficiency and response accuracy. Stress tolerance (M\u0026thinsp;=\u0026thinsp;54.32, SD\u0026thinsp;=\u0026thinsp;7.61) and Aspiration (M\u0026thinsp;=\u0026thinsp;56.58, SD\u0026thinsp;=\u0026thinsp;7.62) were also above normative averages, indicating strong coping resources and achievement orientation.\u003c/p\u003e\u003cp\u003eThe skewness and kurtosis values fell within acceptable limits (\u0026minus;\u0026thinsp;1\u0026thinsp;\u0026lt;\u0026thinsp;Sk\u0026thinsp;\u0026lt;\u0026thinsp;1; \u0026minus;1\u0026thinsp;\u0026lt;\u0026thinsp;Ku\u0026thinsp;\u0026lt;\u0026thinsp;1), confirming that the data distributions did not significantly deviate from normality and were suitable for further multivariate analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Pearson Correlation Analysis\u003c/h2\u003e\u003cp\u003eThe Pearson correlation results are presented in Appendix C, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The correlation result analysis revealed several significant associations among demographic, personality, psychological and psychophysiological performance variables. Pearson\u0026rsquo;s correlation coefficients were interpreted following guidelines established for the social sciences (Cohen, 1988), where correlations of r\u0026thinsp;\u0026ge;\u0026thinsp;0.10 are considered weak, r\u0026thinsp;\u0026ge;\u0026thinsp;0.30 medium, and r\u0026thinsp;\u0026ge;\u0026thinsp;0.50 strong.\u003c/p\u003e\u003cp\u003eSport type was positively associated with personality variables Openness (r\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Adventurism (r\u0026thinsp;=\u0026thinsp;0.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating greater openness to experience and novelty-seeking among athletes competing in individual sports. Conversely, negative correlations were found with psychophysiological performance and psychological skills variables such as Aspiration (r = \u0026minus;\u0026thinsp;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Team emphasis (r = \u0026minus;\u0026thinsp;0.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Motivation (r = \u0026minus;\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that team sport athletes were more cooperative, and motivated. All correlation is evaluated as small to moderate. Sport level correlated positively with personality and psychological skills variables Neuroticism (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Confidence (r\u0026thinsp;=\u0026thinsp;0.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while showing negative associations with Motivation (r = \u0026minus;\u0026thinsp;0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Exactitude (r = \u0026minus;\u0026thinsp;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Motor speed (r = \u0026minus;\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Choice reaction speed (r = \u0026minus;\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Choice motor speed (r = \u0026minus;\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This indicates that athletes at higher competitive levels (elite and pre-elite) exhibited greater emotional reactivity and self-confidence, while their psychomotor performance tended to stabilize rather than increase further.\u003c/p\u003e\u003cp\u003eGender correlated negatively with personality and psychophysiological performance variables Neuroticism (r = \u0026minus;\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and positively with Conscientiousness (r\u0026thinsp;=\u0026thinsp;0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Reaction speed (r\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Motor speed (r\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Choice reaction speed (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Choice motor speed (r\u0026thinsp;=\u0026thinsp;0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating lower emotional instability and faster psychomotor performance among men compared to women.\u003c/p\u003e\u003cp\u003eAge and experience both were positively related to Confidence (r\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while negatively related to Motivation (r = \u0026minus;\u0026thinsp;0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and Team emphasis (r = \u0026minus;\u0026thinsp;0.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). By which it can be concluded that older and more experienced athletes demonstrated greater self-confidence but slightly reduced motivational drive and team-oriented attitudes.\u003c/p\u003e\u003cp\u003eRegarding interrelations among personality and psychological variables, Confidence was strongly negatively correlated with Neuroticism (r = \u0026minus;\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Motivation was negatively associated with both Neuroticism (r = \u0026minus;\u0026thinsp;0.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Modesty (r = \u0026minus;\u0026thinsp;0.22, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Conscientiousness correlated positively with Team emphasis (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Motivation (r\u0026thinsp;=\u0026thinsp;0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and Visualization (r\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eAmong psychophysiological performance indicators, strong positive associations were observed between Motor speed and Choice reaction speed (r\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Motor speed and Choice motor speed (r\u0026thinsp;=\u0026thinsp;0.75, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as well as Reaction speed and Choice motor speed (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming high internal coherence among psychomotor performance measures.\u003c/p\u003e\u003cp\u003eResults suggest systematic patterns linking demographic and psychological characteristics to psychophysiological performance. Team sport athletes tended to be more emotionally stable and socially oriented, while individual sport athletes were more open and novelty-seeking. Male and older athletes showed higher psychomotor efficiency, and personality traits such as conscientiousness, confidence, and low neuroticism were associated with better control and performance consistency.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Multivariate Analysis of Covariance (MANCOVA)\u003c/h2\u003e\u003cp\u003eTwo MANCOVAs were conducted to examine differences in athletes\u0026rsquo; personality traits, psychological skills, and psychophysiological performance factors based on (1) sport type (team vs. individual) and (2) performance level (elite, pre\u0026thinsp;\u0026minus;\u0026thinsp;elite, amateur). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the MANCOVA for sport type. The multivariate test using Pillai\u0026rsquo;s Trace revealed a statistically significant overall effect of sport type on the combined dependent variables, V\u0026thinsp;=\u0026thinsp;0.168, F (23, 272)\u0026thinsp;=\u0026thinsp;2.382, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u0026sup2; = 0.168. This indicates that athletes participating in team and individual sports differ significantly across the set of measured psychological and psychophysiological performance characteristics. The sample consisted of 58.16% (n\u0026thinsp;=\u0026thinsp;177) team sport athletes and 40.46% (n\u0026thinsp;=\u0026thinsp;123) individual sport athletes.\u003c/p\u003e\u003cp\u003eFollow\u0026thinsp;\u0026minus;\u0026thinsp;up univariate analyses (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) identified significant group differences across several variables. Team sport athletes scored lower on Neuroticism (F(1, 294)\u0026thinsp;=\u0026thinsp;6.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.104), Adventurism (F(1, 294)\u0026thinsp;=\u0026thinsp;4.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.074), and Modesty (F(1, 294)\u0026thinsp;=\u0026thinsp;3.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.057), but higher on Openness (F(1, 294)\u0026thinsp;=\u0026thinsp;2.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.039).\u003c/p\u003e\u003cp\u003eIn terms of psychological skills, individual sport athletes showed significantly higher Confidence (F(1, 294)\u0026thinsp;=\u0026thinsp;8.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.121), while team sport athletes reported higher Motivation (F(1, 294)\u0026thinsp;=\u0026thinsp;17.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u0026sup2; = 0.233) and Team emphasis (F(1, 294)\u0026thinsp;=\u0026thinsp;8.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.133).\u003c/p\u003e\u003cp\u003eAmong psychophysiological performance variables, team athletes outperformed individual athletes on Motor speed (F(1, 294)\u0026thinsp;=\u0026thinsp;8.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, η\u0026sup2; = 0.129), Choice motor speed (F(1, 294)\u0026thinsp;=\u0026thinsp;11.16, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, η\u0026sup2; = 0.16), and Aspiration (F(1, 294)\u0026thinsp;=\u0026thinsp;4.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.07). These results suggest that team sport athletes tend to exhibit faster psychomotor responses and greater achievement orientation, whereas individual sport athletes show higher confidence and openness. Gender, training load, experience, and age were included as covariates in the model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMANCOVA Results Comparing Personality, Psychological Skills, and Psychophysiological Performance Factors Between Team and Individual Sport Athletes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeam\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;177)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eη\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePersonality factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1: Neuroticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.82 (0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.58 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.85*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2: Conscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.87 (0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.8 (0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF3: Extraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.47 (0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.87 (0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF4: Adventurism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.11 (0.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.56 (0.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.70*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF5: Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.71 (0.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.46 (0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.38*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF6: Modesty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.55 (0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.14 (0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.53*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsychological skills factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF7: Confidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.52 (6.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.00 (6.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF8: Motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.67 (3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.85 (3.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.84*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF9: Team emphasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.41 (2.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.39 (2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.99*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF10: Visualization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.72 (2.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.15 (2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsychophysiological performance factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF11: Stress tolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.86 (7.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.94 (7.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.38*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF12: Exactitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.11 (7.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.24 (6.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF13: Decisiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.97 (8.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.28 (7.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.67*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF14: Impulsivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.31 (9.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.48 (9.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF15: Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.55 (6.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.53 (6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF16: Aspiration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.37 (7.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55.49 (7.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.41*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF17: Frustration tolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.15 (5.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.53 (6.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF18: Target discrepancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.89 (0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.45 (0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF19: Reaction speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.78 (9.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60.64 (8.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.61*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF20: Motor speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.81 (8.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.78 (8.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.7*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF21: Choice reaction speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68.20 (9.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.09 (9.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF22: Choice motor speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.48 (8.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.63 (8.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.16*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePillai\u0026rsquo;s Trace\u0026thinsp;=\u0026thinsp;0.168, F(23, 272)\u0026thinsp;=\u0026thinsp;2.382, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u0026sup2; = 0.168. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Athletes\u0026rsquo; gender, training load, experience and age are included as covariates; *Significant at the 0.05 level (two\u0026thinsp;\u0026minus;\u0026thinsp;tailed).\u003c/p\u003e\u003cp\u003eA second MANCOVA was conducted to examine differences in personality, psychological skills, and psychophysiological performance factors across athletes\u0026rsquo; performance levels (elite, pre\u0026thinsp;\u0026minus;\u0026thinsp;elite, and amateur). Gender, training load, experience, and age were included as covariates in the model.\u003c/p\u003e\u003cp\u003eAthletes were classified into three competitive levels based on objective performance criteria. The classification was determined by combining training load, competition level, and years of experience, ensuring that the grouping reflected actual performance demands rather than self-reported status. Elite athletes were those engaged in a professional or semi-professional training regime, participating in \u0026ge;\u0026thinsp;8 training sessions or \u0026ge;\u0026thinsp;12 hours per week, with at least five years of structured sport experience. In addition, they were required to have achieved high-level competitive results, such as being finalists or medallists in National Championships (adult higher leagues) or having participated in international competitions, including European Championships, World Cups, World Championships (junior or senior), or the Olympic Games.\u003c/p\u003e\u003cp\u003ePre-elite athletes trained at \u0026ge;\u0026thinsp;5 sessions or \u0026ge;\u0026thinsp;7.5 hours per week and were active in national championship competition (junior or adult divisions) or regional and university-level leagues. These athletes were typically situated in high-performance development environments (such as national youth teams, sport academy or sport school programs) but had not yet consistently competed at senior international level.\u003c/p\u003e\u003cp\u003eAmateur athletes participated in \u0026ge;\u0026thinsp;2 training sessions or \u0026ge;\u0026thinsp;4 hours per week and competed primarily in regional, local, or lower-division national competitions, without national team selection or professional status. This tiered system aligns with contemporary athlete development models distinguishing elite, developmental, and participation-level performers, and enables meaningful comparison of psychological preparedness across competitive standards.\u003c/p\u003e\u003cp\u003eThe multivariate test using Pillai\u0026rsquo;s Trace indicated that the combined dependent variables differed significantly across performance levels, V\u0026thinsp;=\u0026thinsp;0.239, F(46, 542)\u0026thinsp;=\u0026thinsp;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.18. Although the overall multivariate effect was statistically significant but small in magnitude, several univariate effects reached statistical significance, suggesting level\u0026thinsp;\u0026minus;\u0026thinsp;specific variations in select psychological and psychophysiological performance indicators (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The sample consisted of 17.8% (n\u0026thinsp;=\u0026thinsp;54) elite level athletes, 53.6% (n\u0026thinsp;=\u0026thinsp;163) pre\u0026thinsp;\u0026minus;\u0026thinsp;elite level athletes and 28.6% (n\u0026thinsp;=\u0026thinsp;87) amateur level athletes.\u003c/p\u003e\u003cp\u003eSpecifically, significant differences emerged for Confidence (F(2, 294)\u0026thinsp;=\u0026thinsp;4.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.031) and Motivation (F(2, 294)\u0026thinsp;=\u0026thinsp;3.89, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.028), indicating that elite athletes reported higher self\u0026thinsp;\u0026minus;\u0026thinsp;confidence and intrinsic motivation compared to pre\u0026thinsp;\u0026minus;\u0026thinsp;elite and amateur athletes. In the psychophysiological performance domain, Decisiveness (F(2, 294)\u0026thinsp;=\u0026thinsp;4.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.03) and Aspiration (F(2, 294)\u0026thinsp;=\u0026thinsp;3.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, η\u0026sup2; = 0.022) also differed significantly, with elite athletes again showing higher mean scores than other groups. These findings suggest that while personality traits and general psychophysiological capacities were relatively stable across levels, psychological readiness and decision\u0026thinsp;\u0026minus;\u0026thinsp;making efficiency distinguish higher\u0026thinsp;\u0026minus;\u0026thinsp;performing athletes from their less experienced counterparts. The combination of elevated confidence, motivation, and aspiration reflects a more advanced performance mindset, aligning with theoretical models of elite psychological functioning.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMANCOVA Results for Personality, Psychological Skills, and Psychophysiological Performance Factors Across Competitive Levels (Elite, Pre\u0026thinsp;\u0026minus;\u0026thinsp;elite, Amateur)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eElite\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre\u0026thinsp;\u0026minus;\u0026thinsp;elite (n\u0026thinsp;=\u0026thinsp;163)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAmateur (n\u0026thinsp;=\u0026thinsp;87)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eη\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePersonality factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF1: Neuroticism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.07 (0.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.12 (0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.78 (0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF2: Conscientiousness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.57 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.53 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.77 (0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF3: Extraversion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.14 (0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.59 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.6 (0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF4: Adventurism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.45 (1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.69 (0.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.47 (0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF5: Openness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28.75 (0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.29 (0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.08 (0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF6: Modesty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.44 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.62 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.31 (0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsychological skill factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF7: Confidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.5 (0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.61 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.46 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.43*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF8: Motivation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.58 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.1 (0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.61 (0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.89*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF9: Team emphasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.90 (0.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.33 (0.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.44 (0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF10: Visualization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.65 (0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.84 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.55 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePsychophysiological performance factors\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF11: Stress tolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54.29 (0.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.22 (0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.37 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF12: Exactitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.06 (0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.21 (0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63 (1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF13: Decisiveness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.39 (0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.39 (0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.03 (1.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.21*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF14: Impulsivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.9 (1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.14 (0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.08 (1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF15: Performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.96 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.9 (0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.6 (0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF16: Aspiration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55.83 (1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.8 (0.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.68 (0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.14*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF17: Frustration tolerance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.27 (0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.78 (0.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.13 (0.64)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF18: Target discrepancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.9 (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.03 (0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF19: Reaction speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.59 (0.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.8 (0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF20: Motor speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.9 (1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.42 (0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.23 (0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF21: Choice reaction speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67.1 (1.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.52 (0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.15 (0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF22: Choice motor speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.75 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.4 (0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.92 (0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePillai\u0026rsquo;s Trace\u0026thinsp;=\u0026thinsp;0.239, F(46, 542)\u0026thinsp;=\u0026thinsp;1.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, η\u0026sup2; = 0.18. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Athletes\u0026rsquo; gender, training load, experience and age are included as covariates; *Significant at the 0.05 level (two\u0026thinsp;\u0026minus;\u0026thinsp;tailed).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e\u003cem\u003e3.4. Cluster Analysis: Identification of Multi-factorial Athlete Multi-Factorial Profiles\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eA k\u0026thinsp;\u0026minus;\u0026thinsp;means cluster analysis was further performed to classify athletes based on their personality traits, psychological skills, and psychophysiological performance factors, considering sport type and performance level. A four\u0026thinsp;\u0026minus;\u0026thinsp;cluster solution was identified as the best fit, considering both the sample size (n\u0026thinsp;=\u0026thinsp;304) and the number of included variables. Alternative cluster solutions (two\u0026minus;, three\u0026minus;, and five\u0026thinsp;\u0026minus;\u0026thinsp;cluster models) did not demonstrate meaningful or statistically distinct group differentiation and were therefore rejected.\u003c/p\u003e\u003cp\u003eAll variable scores were standardized into z\u0026thinsp;\u0026minus;\u0026thinsp;scores prior to analysis to ensure comparability across different measurement scales. The final cluster solution converged successfully after 9 iterations, confirming model stability. Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e in Appendix B presents the descriptive statistics and standardized z\u0026thinsp;\u0026minus;\u0026thinsp;scores for each of the four clusters, while Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes the cluster profiles across the 22 measured variables. The results revealed clear differentiation among clusters in psychophysiological and psychological performance indicators, with significant between \u0026minus;\u0026thinsp;group differences confirmed by ANOVA (Tukey\u0026rsquo;s HSD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eA one\u0026thinsp;\u0026minus;\u0026thinsp;way ANOVA revealed significant differences across the four clusters in all psychophysiological performance indicators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the groups were clearly differentiated based on performance\u0026thinsp;\u0026minus;\u0026thinsp;related capacities. Cluster 1 demonstrated the highest stress tolerance, decisiveness, performance, and fast motor and reaction speeds, but also higher impulsivity (lower impulse control), suggesting superior performance efficiency under pressure but a tendency toward rapid, less filtered responding. Cluster 2 showed the highest exactitude but slower response speeds, lower stress tolerance, and lower impulsivity (greater impulse control), suggesting a cautious and controlled performance style that may become strained in demanding conditions. Cluster 3 displayed generally reduced performance, motivation, and decisiveness, combined with slower psychomotor responding, lower stress tolerance, and higher impulsivity, indicating inhibited activation and reduced efficiency in high-pressure situations. Cluster 4 exhibited the fastest reaction and motor speeds and the highest stress tolerance, along with moderate impulsivity and lower decisiveness, reflecting a fast, instinctive, and reactive performance style that prioritizes rapid responding over deliberate decision-making.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Cluster Interpretation\u003c/h2\u003e\u003cp\u003eThe K\u0026thinsp;\u0026minus;\u0026thinsp;means cluster analysis identified four distinct athlete profiles (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), each characterized by specific psychological and psychophysiological performance patterns.\u003c/p\u003e\u003cp\u003eCluster 1 (n\u0026thinsp;=\u0026thinsp;102; 33.6%) group demonstrated the most balanced and self-regulated performance profile. Athletes in Cluster 1 scored above the total sample mean on stress tolerance (M\u0026thinsp;=\u0026thinsp;56.56, SD\u0026thinsp;=\u0026thinsp;6.64), decisiveness (M\u0026thinsp;=\u0026thinsp;67.22, SD\u0026thinsp;=\u0026thinsp;7.14), performance (M\u0026thinsp;=\u0026thinsp;64.26, SD\u0026thinsp;=\u0026thinsp;6.09), aspiration (M\u0026thinsp;=\u0026thinsp;55.72, SD\u0026thinsp;=\u0026thinsp;7.83), and reaction speed (M\u0026thinsp;=\u0026thinsp;64.45, SD\u0026thinsp;=\u0026thinsp;6.97). Impulsivity was comparatively low (M\u0026thinsp;=\u0026thinsp;36.25, SD\u0026thinsp;=\u0026thinsp;5.97), indicating faster, more spontaneous/impulsive response style. Personality traits were near the sample average, showing emotional stability and composure (Neuroticism M\u0026thinsp;=\u0026thinsp;28.03, SD\u0026thinsp;=\u0026thinsp;4.55). Cluster 1 primarily included team-sport athletes (n\u0026thinsp;=\u0026thinsp;64; 21.1% of total sample; \u0026asymp; 62.7% within cluster), predominantly at the pre-elite (19.1%) and elite (7.6%) levels. These athletes represent emotionally stable, achievement-oriented performers capable of maintaining consistent output under pressure.\u003c/p\u003e\u003cp\u003eCluster 2 (n\u0026thinsp;=\u0026thinsp;75; 24.7%) athletes in this cluster exhibited a controlled and disciplined approach, characterized by high exactitude (M\u0026thinsp;=\u0026thinsp;55.35, SD\u0026thinsp;=\u0026thinsp;6.14) and conscientiousness (M\u0026thinsp;=\u0026thinsp;33.07, SD\u0026thinsp;=\u0026thinsp;6.20). They showed moderate stress tolerance (M\u0026thinsp;=\u0026thinsp;52.07, SD\u0026thinsp;=\u0026thinsp;7.02) and higher impulsivity (M\u0026thinsp;=\u0026thinsp;51.67, SD\u0026thinsp;=\u0026thinsp;7.14), reflecting strong emotional control and strong inhibitory control and deliberate responding. Performance (M\u0026thinsp;=\u0026thinsp;59.80, SD\u0026thinsp;=\u0026thinsp;6.20) and decisiveness (M\u0026thinsp;=\u0026thinsp;57.63, SD\u0026thinsp;=\u0026thinsp;7.14) were slightly above average, consistent with an analytical and strategic style rather than fast, intuitive responding. Motivation (M\u0026thinsp;=\u0026thinsp;14.81, SD\u0026thinsp;=\u0026thinsp;3.66) and confidence (M\u0026thinsp;=\u0026thinsp;19.88, SD\u0026thinsp;=\u0026thinsp;5.97) were moderate, aligning with a more reserved personality pattern. Cluster 2 consisted mainly of pre-elite (13.8%) and amateur (7.6%) athletes, with team-sport athletes representing 25.3% of the total sample (\u0026asymp;\u0026thinsp;60% within cluster).\u003c/p\u003e\u003cp\u003eCluster 3 (n\u0026thinsp;=\u0026thinsp;85; 28%) athletes showed lower stress tolerance (M\u0026thinsp;=\u0026thinsp;51.71, SD\u0026thinsp;=\u0026thinsp;8.27), low impulsivity (indicate reactive responding; M\u0026thinsp;=\u0026thinsp;36.52, SD\u0026thinsp;=\u0026thinsp;5.92), and slightly below-average performance (M\u0026thinsp;=\u0026thinsp;58.35, SD\u0026thinsp;=\u0026thinsp;8.53). They also scored lower on aspiration (M\u0026thinsp;=\u0026thinsp;50.89, SD\u0026thinsp;=\u0026thinsp;8.91) and frustration tolerance (M\u0026thinsp;=\u0026thinsp;46.63, SD\u0026thinsp;=\u0026thinsp;9.35), indicating difficulty maintaining composure and persistence under pressure. Reaction speed was relatively slow (M\u0026thinsp;=\u0026thinsp;55.02, SD\u0026thinsp;=\u0026thinsp;8.10), suggesting reduced psychophysiological performance efficiency. Personality profiles revealed slightly elevated neuroticism (M\u0026thinsp;=\u0026thinsp;28.58, SD\u0026thinsp;=\u0026thinsp;4.36) and average conscientiousness (M\u0026thinsp;=\u0026thinsp;33.21, SD\u0026thinsp;=\u0026thinsp;7.08), consistent with emotional reactivity and weaker self-regulation. This group included a higher proportion of individual-sport athletes (n\u0026thinsp;=\u0026thinsp;41; 13.5% of total sample; \u0026asymp; 49% within cluster) and amateurs (12.2%).\u003c/p\u003e\u003cp\u003eCluster 4 (n\u0026thinsp;=\u0026thinsp;42; 13.8%) athletes in this cluster displayed the most distinct psychophysiological performance pattern, characterized by very high stress tolerance (M\u0026thinsp;=\u0026thinsp;58.19, SD\u0026thinsp;=\u0026thinsp;7.02) and exceptional reaction and motor speeds (Reaction speed M\u0026thinsp;=\u0026thinsp;72.45, SD\u0026thinsp;=\u0026thinsp;7.23; Motor speed M\u0026thinsp;=\u0026thinsp;67.86, SD\u0026thinsp;=\u0026thinsp;7.85). They also demonstrated high performance (M\u0026thinsp;=\u0026thinsp;65.30, SD\u0026thinsp;=\u0026thinsp;8.53) and aspiration (M\u0026thinsp;=\u0026thinsp;60.90, SD\u0026thinsp;=\u0026thinsp;8.53). Impulsivity was average (M\u0026thinsp;=\u0026thinsp;48.40, SD\u0026thinsp;=\u0026thinsp;5.86) between cluster, showing that although these athletes are reactive, they maintain good control over emotional responses. Personality traits included low neuroticism (M\u0026thinsp;=\u0026thinsp;27.47, SD\u0026thinsp;=\u0026thinsp;4.38) and moderately high extraversion (M\u0026thinsp;=\u0026thinsp;32.30, SD\u0026thinsp;=\u0026thinsp;5.30), reflecting emotional resilience and assertiveness. This cluster included both team-sport athletes (n\u0026thinsp;=\u0026thinsp;25; 8.2% of total sample; \u0026asymp; 58% within cluster) and individual-sport athletes (n\u0026thinsp;=\u0026thinsp;17; 5.6% of total sample; \u0026asymp; 40% within cluster), with the highest proportion of elite competitors (11; 3.6%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Sport Type and Competitive Level Within the Four Athlete Clusters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eCluster 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSport type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeam sports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual sports\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLevel\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre\u0026thinsp;\u0026minus;\u0026thinsp;elite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmateur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Discriminant Function Analysis: Validation of Cluster Solution\u003c/h2\u003e\u003cp\u003eA discriminant function analysis was conducted to determine whether the clusters identified through k\u0026thinsp;\u0026minus;\u0026thinsp;means clustering could be accurately differentiated based on the psychological and psychophysiological performance variables.\u003c/p\u003e\u003cp\u003eThe analysis yielded three discriminant functions, of which the first two explained 92.4% of the total between \u0026minus;\u0026thinsp;group variance. Function 1 accounted for 51.5% (canonical correlation\u0026thinsp;=\u0026thinsp;0.83), and Function 2 accounted for 40.9% (canonical correlation\u0026thinsp;=\u0026thinsp;0.80).\u003c/p\u003e\u003cp\u003eWilks\u0026rsquo; Lambda indicated that all three functions were statistically significant (Λ₁₋₃ = 0.086, χ\u0026sup2;(69)\u0026thinsp;=\u0026thinsp;711.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting the adequacy of the model in distinguishing among the clusters.\u003c/p\u003e\u003cp\u003eThe classification results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that 80.6% of the cases were correctly classified, indicating good internal consistency and stability of the cluster solution.\u003c/p\u003e\u003cp\u003eCluster\u0026thinsp;\u0026minus;\u0026thinsp;specific classification accuracy was high, ranged from 95.1% to 97.6%, with the highest accuracy observed for Profile 1 and the lowest for Profile 3 (97.6%) and similar high accuracy for the other clusters.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClassification Accuracy of Discriminant Function Analysis for the Four Athlete Profiles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003ePredicted group membership\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e304\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e80.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe spatial representation of the discriminant functions is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eThe figure illustrates the separation of the four clusters along the first two discriminant dimensions. Cluster centroids were located at (Function 1, Function 2): Cluster 1\u0026thinsp;=\u0026thinsp;(1.20, 1.16), Cluster 2 = (\u0026minus;\u0026thinsp;0.91, \u0026minus;\u0026thinsp;1.76), Cluster 3 = (\u0026minus;\u0026thinsp;1.72, 0.88), and Cluster 4\u0026thinsp;=\u0026thinsp;(2.18, \u0026minus;\u0026thinsp;1.46). The first function primarily differentiated clusters based on psychophysiological performance efficiency, whereas the second function reflected differences in impulse control and emotional self-regulation. The first two discriminant functions explained 92.4% of the between \u0026minus;\u0026thinsp;group variance and were therefore used for visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The third function explained only 7.6% and was not plotted due to its limited discriminative contribution.\u003c/p\u003e\u003cp\u003eBased on the results obtained, four distinct athlete profiles were identified, reflecting differences associated with sport type (team vs. individual) and competitive level (elite, pre-elite, amateur). The profiles were derived from the integrated analysis of personality traits, psychological skills, and psychophysiological performance variables, which together represent the core determinants of athletic functioning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.7. \u003cem\u003eQualitative Validation of Athlete Multi-Factorial Profiles\u003c/em\u003e\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarises the qualitative analysis of the three open-ended questions addressed to experts, who were either psychologists who practice in sport or psychological preparation coaches with a minimum of six years of professional experience working with athletes. The experts generally confirmed the ecological validity of the four athlete profiles. A total of nine experts participated in the qualitative validation of developed profiles of athletes. Agreement rates were high for Profile 1 (8/9 experts), Profile 2 (8/9), and Profile 4 (8/9), and moderate for Profile 3 (7/9). One expert (E8) expressed general scepticism, emphasising that athlete typologies are often situationally fluid rather than trait based.\u003c/p\u003e\u003cp\u003eAcross responses, common descriptors included stability, precision, emotional lability, and speed/impulsivity, indicating substantial convergence between expert perceptions and the empirically derived athlete profiles.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThematic Mapping of Validated Athlete Profiles (Q1\u0026ndash;Q3)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTheme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExample raw data quotes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eProfile 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfile validity (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eleadership_stability\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Profile 1 matches high-level players and team leaders; emotionally balanced, self-regulated, focused under pressure.\u0026rdquo; (E1) \u0026ldquo;Experienced, elite athletes in team and individual sports.\u0026rdquo; (E2) \u0026ldquo;Future elite talents.\u0026rdquo; (E9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour under stress (Q2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eleadership_support\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003efocus_under_pressure\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Confident, inspire younger teammates; stable performance.\u0026rdquo; (E2) \u0026ldquo;Takes over late-game responsibility; composed; analyses failures for next time.\u0026rdquo; (E4) \u0026ldquo;After errors they analyse and adapt quickly; keep calm and task-focused under stress.\u0026rdquo; (E1) \u0026ldquo;Maintain focus in intense situations; planful training; stabilising presence for team.\u0026rdquo; (E7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsychological intervention (Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eself_reflection\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003emental_flexibility\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Maintain and improve psychological flexibility between load and recovery; update goal programs; mindfulness, breathing, mental training, imagery, self-talk.\u0026rdquo; (E3) \u0026ldquo;Maintain optimal motivation; avoid stagnation; develop creativity, flexibility, openness; goal refinement; leadership; adaptability.\u0026rdquo; (E7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eProfile 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfile validity (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eprecision_overcontrol\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Disciplined, conscientious, well-trainable; strong responsibility; sometimes struggle to adapt to unexpected situations.\u0026rdquo; (E1) \u0026ldquo;Experienced amateurs / national level.\u0026rdquo; (E2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour under stress (Q2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ecautious_decisions\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eroutine_precision\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Diligent, compliant; cautious/avoidant; less effective under stress.\u0026rdquo; (E2) \u0026ldquo;More introverted; prefer individual training; public aspect hinders performance.\u0026rdquo; (E6) \u0026ldquo;Repeat drills until fully precise; control holds if plan works; unexpected events can unsettle.\u0026rdquo; (E1) \u0026ldquo;Checks equipment and routines; inner tension with errors, rarely outward.\u0026rdquo; (E3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsychological intervention (Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ereduce_overcontrol\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003erelaxation_breathing\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Reduce overcontrol, trust the body, allow flow; relaxation and breathing; cognitive acceptance of mistakes; biofeedback.\u0026rdquo; (E3) \u0026ldquo;Emotional freedom, spontaneity and self-reliance; reduce anxiety and overthinking; relaxation; cognitive reframing; switch to automatic execution; strengthen self-efficacy.\u0026rdquo; (E7) \u0026ldquo;Train stress tolerance using breathing and concentration; use positive self-talk; simulate unexpected situations.\u0026rdquo; (E1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eProfile 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfile validity (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eemotional_reactivity\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Common among younger athletes; emotional fluctuations and quick frustration after errors.\u0026rdquo; (E1) \u0026ldquo;Youth or early adult transition.\u0026rdquo; (E2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour under stress (Q2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eemotional_volatility\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003emotivation_swings\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;May love training but dislike competing.\u0026rdquo; (E5) \u0026ldquo;Mood-dependent engagement; emotional reactions; need structured self-regulation.\u0026rdquo; (E7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsychological intervention (Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eemotional_regulation\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eself_regulation_package\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Develop emotional self-regulation and mindfulness (box breathing, 3-second reset). Reduce fear of mistakes via reflection.\u0026rdquo; (E1) \u0026ldquo;Emotion regulation and stress-coping skills.\u0026rdquo; (E4) \u0026ldquo;Strengthen self-regulation and intrinsic motivation; emotion awareness and management; self-esteem; focus on progress; ABC model; routines and attentional focus; self-efficacy.\u0026rdquo; (E3) \u0026ldquo;Build self-regulation, emotional resilience and concentration; routines, micro-goals, positive self-talk.\u0026rdquo; (E7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eProfile 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eProfile validity (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ereactive_speed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Energetic, instinctive; speed is a strength; impulsivity may cause emotional outbursts.\u0026rdquo; (E1) \u0026ldquo;Pre-elite/elite in growth stage.\u0026rdquo; (E2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBehaviour under stress (Q2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003equick_decisionmaking\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ehigh_stress_tolerance\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;High stress tolerance.\u0026rdquo; (E2) \u0026ldquo;Mobilise under stress; resilient until cumulative failures/trauma.\u0026rdquo; (E6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePsychological intervention (Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eimpulse_control\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003evisualization\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ldquo;Impulse control recognises escalation moments and refocus with a brief reset ritual (self-talk, breath, sensory cue).\u0026rdquo; (E1) \u0026ldquo;Develop patience, sustained attention, and impulse control; perceptual slowing drills; cognitive control and decision quality under pressure; rhythm and focus training.\u0026rdquo; (E3) \u0026ldquo;Imagery, self-regulation, routines, self-talk, problem-focused sessions.\u0026rdquo; (E5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Q1\u0026ndash;Q3 refer to the three open-ended questions provided to experts. Expert responses are coded as E1\u0026ndash;E9, representing nine sport psychology professionals (psychologists and psychological preparation coaches) who participated in the qualitative validation survey.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to the results of the qualitative validation, it can be concluded that the experts consistently recognized that the four derived athlete profiles reflect real athlete types observed in professional practice, with exception of one expert, who indicated that he had not encountered athletes with such characteristics in his practice. In particular, the thematic analysis of behavioural examples revealed clear and internally consistent patterns. Experts described athletes in Profile 1 as being able to remain calm, focused, and analytical under pressure (concentration_under_pressure; n\u0026thinsp;=\u0026thinsp;4), especially in high-stress situations such as decisive moments during competition. These athletes were often identified as elite or high-level performers who demonstrate psychological stability and self-regulation.\u003c/p\u003e\u003cp\u003eIn their descriptions of Profile 2, experts referred to athletes who are conscientious and precise, with a strong desire to avoid mistakes. This tendency toward accuracy and control, however, frequently leads to delayed decision-making and difficulty adapting to sudden changes (routine_precision, cautious_decisions; n\u0026thinsp;=\u0026thinsp;5). Experts noted that this profile is often observed among pre-elite or amateur athletes who rely heavily on structure and predictability.\u003c/p\u003e\u003cp\u003eProfile 3 was also recognised as common in applied practice and was described as representing emotionally unstable athletes whose performance often declines following mistakes (emotional_instability, motivational_fluctuations; n\u0026thinsp;=\u0026thinsp;6). Such athletes were characterised as inconsistent and reactive, relying on momentary emotions or impulses, which can manifest in explosive responses during competition.\u003c/p\u003e\u003cp\u003eExperts provided highly consistent evaluations of Profile 4, repeatedly noting that these athletes are easily recognisable in practice. They display rapid decision-making and adaptability, yet at times exhibit impulsivity or lapses in sustained attention (quick_decision_making; n\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e\u003cp\u003eThe experts proposed a series of targeted mental-skills interventions tailored to each athlete profile. For Profile 1, the primary focus should be on maintaining long-term motivation and leadership capacity through self-reflection, mindfulness, and empathy training. Profile 2 would benefit from interventions aimed at reducing excessive self-control and promoting flexibility, such as relaxation techniques, cognitive reframing, and spontaneity exercises, to enhance resilience under stress. For Profile 3, experts recommended strengthening emotional regulation, self-efficacy, and structured self-regulation routines to improve performance stability and confidence during competition. Profile 4 should focus on developing impulse control, frustration tolerance, and sustained concentration under prolonged or monotonous pressure. Based on the integration of quantitative cluster characteristics and qualitative expert evaluation, four athlete multi-factorial profiles were identified and labelled as follows: Profile 1: \u0026ldquo;Stable High-Performance Athletes\u0026rdquo;, Profile 2: \u0026ldquo;Controlled Precision Athletes\u0026rdquo;. Profile 3: \u0026ldquo;Low-Regulation Reactive Athletes\u0026rdquo; and Profile 4: \u0026ldquo;Reactive High-Speed Athletes\u0026rdquo;.\u003c/p\u003e\u003cp\u003eAcross all multi-factorial profiles, experts emphasised biofeedback, visualisation, and psychophysiological readiness training as integrative approaches supporting adaptive functioning and consistent performance. All together, these results demonstrate that the four athlete profiles are both statistically distinct and ecologically recognizable, indicating that integrated psychological and psychophysiological performance factors meaningfully differentiate athletic functioning in applied settings.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe aim of the study was to develop a multi-factorial profile of Latvian athletes by integrating measures of personality traits, psychological skills, and psychophysiological performance variables. Based on the results, four athlete multi-factorial profiles were identified and qualitatively validated with the help of experts, demonstrating different models in terms of personality structure, psychological skill development, and psychophysiological functioning. These profiles can be used to plan and implement more targeted and effective interventions, promoting long-term athlete development and improving performance in both training and competition.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Interpretation of the Identified Athlete Multi-Factorial Profiles\u003c/h2\u003e\u003cp\u003eProfile 1 (Stable High-Performance Athletes), this profile represents athletes with moderate to high emotional stability, balanced sociability, and sufficient conscientiousness. They demonstrate higher self-confidence, motivation, and focused goal orientation. Their lower impulsivity scale scores indicate a faster, more spontaneous response style, with reduced inhibitory control and less reflective regulation but at the same time they have high decisiveness. These athletes exhibit relatively high stress tolerance (second highest among the profiles) and good reaction and motor speed, enabling them to maintain stable performance under competitive pressure. Profile 1 reflects emotionally stable, self-regulated athletes who maintain performance under pressure, consistent with findings linking psychological resilience to high-level performance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have often shown that these aspects are specific and reflect elite-level athletes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This profile is the most common among team-sport athletes (\u0026asymp;\u0026thinsp;63% within the profile) and included a substantial proportion of pre-elite and elite competitors, suggesting that emotional stability, stress tolerance, and consistent decision-making may be particularly advantageous in cooperative and tactically dynamic sport environments.\u003c/p\u003e\u003cp\u003eProfile 2 (Controlled Precision Athletes) athletes in this are emotionally stable and tend to present as cooperative and socially constructive in team environments, although their extraversion scores did not differ significantly from other profiles. Their interaction style reflects communication discipline rather than sociability level. This profile also displayed notably lower Openness, reflecting a preference for structure, routine and low novelty in training environments. They are conscientious, organized, and disciplined, preferring structured and predictable performance environments. Their impulsivity scale scores are the highest, indicating strong reflective control and deliberate decision-making. They demonstrate the highest precision (exactitude) and consistent execution, while showing relatively slower reaction and motor speed and lower stress tolerance compared to Profiles 1 and 4. This reflects a methodical and planned performance style, where accuracy is prioritized over speed. Such athletes excel in tasks requiring technical consistency and routine adherence, although they may be less adaptable in rapidly changing competitive contexts. This aligns with evidence linking conscientiousness to persistence, task planning, and technical mastery in sport [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Profile 2 corresponds to a strategic and deliberate performance style, which is advantageous in technically stable tasks but may require targeted training to improve adaptability under uncertainty. Profile 2 was also more represented in team-sport athletes and occurred primarily among pre-elite and amateur athletes, reflecting a performance style that favours structured training environments, progressive skill development, and stable team roles.\u003c/p\u003e\u003cp\u003eProfile 3 (Reactive Low-Regulation Athletes), these athletes show higher neuroticism and the lowest conscientiousness among the profiles, along with greater emotional reactivity and reduced self-esteem. They demonstrate lower motivation, weaker stress-management skills, and lower impulsivity scale scores, which indicates greater spontaneous impulsive responding and inconsistent self-regulation. They exhibit the lowest stress tolerance, slower reaction and motor speed, and reduced performance stability. This pattern corresponds to athletes with underdeveloped emotional regulation strategies, who may experience performance disruption following stress or mistakes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This profile corresponds to developmentally earlier stages of psychological skill acquisition, where athletes may possess physical ability but lack regulatory strategies to manage pressure effectively. These are younger athletes. Strengthening emotional resilience, confidence, and coping resources is therefore crucial for athletes in this group. Profile 3 (Low-Regulation Reactive Athletes) included a higher proportion of individual-sport athletes and amateur-level competitors, indicating that limited emotional regulation and lower stress tolerance may be more characteristic of athletes earlier in their developmental trajectory or those in less socially regulated performance contexts.\u003c/p\u003e\u003cp\u003eProfile 4 (Reactive High-Speed Athletes) include emotionally stable and moderately extraverted athletes with high motivation and a strong competitive drive. Their impulsivity scale scores are in the mid-range, indicating rapid responding with partial regulatory control. They demonstrate the highest stress tolerance and the fastest reaction and motor speed among all profiles, reflecting a fast, dynamic, reactive performance style. Such athletes are well-suited to intense, rapidly changing competitive environments where quick decision-making and tactical adaptability are critical [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Such athletes thrive in dynamic, rapidly changing environments, where speeded responses are advantageous. However, this performance style relies on instinctive rather than reflective control, meaning sustained performance quality may depend on developing better inhibitory control and decision regulation. Recent sport analytics work supports the importance of integrating perceptual-cognitive training to maintain decision accuracy in high-speed performers [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Profile 4 contained the highest proportion of elite-level athletes, and was represented in both team and individual sports, suggesting that the combination of high stress tolerance and rapid psychomotor response speed supports success in high-intensity, time-pressured performance environments.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Integration Across Profiles and Broader Theoretical Implications\u003c/h2\u003e\u003cp\u003eThe four profiles illustrate that psychological performance in sport is not defined by a single optimal pattern, but rather by different combinations of emotional stability, motivational orientation, and psychophysiological regulation. The profiles identified here closely reflect contemporary models of athlete long term development, which emphasize that athletes progress along different psychological pathways depending on their training history, competitive environment, and regulatory skill acquisition [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Athletes\u0026rsquo; developmental patterns are consistent with dynamic systems perspectives on athlete growth, self-regulation models of performance under stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and the theory of challenge and threat states in athletes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], all of which emphasize that athletes differ in how they adapt to performance demands.\u003c/p\u003e\u003cp\u003eThis differentiation is important because it demonstrates that athletes require different types of psychological and training support depending on their regulatory profile rather than a uniform \u0026ldquo;one-size-fits-all\u0026rdquo; approach. Profiling allows coaches and sport psychologists to identify whether an athlete benefits more from stability and composure training (as in Profile 3), adaptability and decision-speed challenges (Profile 2), sustained emotional control under pressure (Profile 1), or inhibitory and tactical regulation during fast-paced performance (Profile 4). In this way, multi-factorial profiling can guide individualized intervention planning, inform role assignment within teams, and support long-term athlete development by aligning training demands with each athlete\u0026rsquo;s current regulatory capacities and developmental strategies. For example, Sanz-Fernandez et al. [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] demonstrated that athletes display distinct behavioral patterns associated with specific psychological profiles, and that aligning training with these profiles improves performance and intervention effectiveness. Similarly, Shannon et al. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] reported that athletes belonging to less optimal psychological profiles may particularly benefit from needs-supportive communication and psychological skills training, highlighting the relevance of profiling for both performance enhancement and mental health support in sport environments.\u003c/p\u003e\u003cp\u003eProfiles 1 and 4 align more closely with performance-ready psychological functioning, particularly in environments involving rapid tactical decision-making and competitive pressure. Profiles 2 and 3, meanwhile, reflect earlier or more structurally dependent developmental stages, where performance consistency is supported either by external routine (Profile 2) or may be disrupted by emotional stress (Profile 3). This differentiation supports the practical value of profiling systems for targeted psychological skill intervention and individualized training design.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Practical Implications\u003c/h2\u003e\u003cp\u003eThe practical contribution of this study can be found in its potential to support and improve the work of sport coaches, sport psychologists and sport specialists whose goal is to strengthen athletes\u0026rsquo; psychological preparation. Multi-factorial profiling enables practitioners to identify both strengths and aspect requiring development in athletes, making an opportunity to more targeted and individually tailored intervention planning. Multi-factorial profiling can function as a practical assessment tool in applied sport settings, especially when it is based on a structured evaluation of key psychological and psychophysiological performance components. Profiling can inform the work of sports medicine professionals by identifying athletes who may be more vulnerable to overtraining, burnout, or injury risk, especially those with low stress tolerance or heightened emotional reactivity (like Profile 3). This information can support preventive strategies, guide return to sport decisions after injury, and assist with individualized load management.\u003c/p\u003e\u003cp\u003eThe psychological preparation of athletes should be considered as a core element of the training process, alongside physical, technical, and tactical preparation. Integrating psychological development into regular training planning contributes not only to improved performance under pressure, but also to the sustainable long-term development of athletes. The focus should be shifted toward this aspect. Systematic assessment of psychological functioning allows to determine which psychological skills require strengthening and to select appropriate training methods or interventions for athletes.\u003c/p\u003e\u003cp\u003eBeyond applied use, the findings of this study contribute to sport science by offering a deeper understanding of the psychological characteristics of contemporary athletes. The multi-factorial profiles identified here are based on empirically grounded evidence and provide a framework for interpreting how personality, psychological skills, and psychophysiological readiness shape performance. This knowledge supports the development of evidence-based approaches to psychological preparation and highlights the value of profiling as a tool for individualized athlete development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Limitation\u003c/h2\u003e\u003cp\u003eDespite the practical value of multi-factorial profiling in identifying athletes\u0026rsquo; strengths, weaknesses, and individual development needs, several limitations of this study should be acknowledged when interpreting the results. First, the timing within the annual training cycle (periodization phase) was not controlled. Athletes were assessed at different points in their season, and certain psychophysiological performance indicators, such as stress tolerance and reaction speed, may vary depending on whether testing occurs during preparatory, competitive, or transition phases. Personality traits are theoretically more stable and therefore less influenced by seasonal fluctuations, but psychophysiological readiness can vary considerably.\u003c/p\u003e\u003cp\u003eIt is also important to note that the sample consisted exclusively of Latvian athletes, which limits the generalizability of the findings to athletes from other countries or sporting cultures, where psychological preparation systems, coaching traditions, and sociocultural norms may differ. While similar patterns could be anticipated in comparable regional contexts, replication in other populations would strengthen the external validity of the profiling model.\u003c/p\u003e\u003cp\u003eAnother limitation is the experience of psychologist experts in characterizing the profiles. Although the profiles were qualitatively validated by sport psychology experts, the composition of the panel may be expanded in future research. Involving a greater number of experts, including performance coaches and psychological preparation coaches who work daily with athletes in training and competition environments, would enrich the ecological validity of the interpretations and strengthen the practical application of the profiles.\u003c/p\u003e\u003cp\u003eFuture research should employ longitudinal designs to track how multi-factorial profiles evolve across training phases, competitive progression, and career stages. Future research should also examine profile differences within specific sport types, as sport-specifics demands and performance environments may shape psychological and psychophysiological regulation patterns differently. A more fine-grained sport-specific analysis could reveal additional sub profiles or nuances in regulatory functioning that were not captured in the broader categorization used in the present study. The profiles suggest possible links to burnout or injury risk vulnerability, but these outcomes were not directly measured in this study. Future research should examine how different psychological and psychophysiological patterns are related to burnout trajectories, overtraining, and injury incidence.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study developed a multi-factorial multi-factorial profile for Latvian athletes, integrating personality traits, psychological skills, and psychophysiological performance indicators. Based on cluster analysis, four athlete profiles were identified. The profiles differed not only in their indicators, but also in different sports (individual vs. team) and competition levels (elite, pre-elite, amateur), indicating that team sports athletes and higher performance athletes were more often represented in profiles 1 and 4, while individual sports and amateur athletes were more often found in profiles 2 and 3. This suggests that the development of athletes is not uniform, and psychological functioning is shaped by both training demands and the competition environment.\u003c/p\u003e\u003cp\u003eThe results indicate that multi-factorial profiling can serve as a practical value in sports practice. It allows coaches and sport psychologists to identify strengths and development needs, as well as plan more targeted, individualized psychological training strategies that support stable performance under stressful conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eSupporting information\u003c/h2\u003e\u003cp\u003eAppendix A. Expert validation coding framework for athlete profiles.\u003c/p\u003e\u003cp\u003eAppendix B. Supplementary tables\u003c/p\u003e\u003cp\u003eAppendix C. Pearson correlation matrix of study variables.\u003c/p\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eThis study was approved by the Ethics Committee of the Latvian Academy of Sport Education (Protocol No. 8, Statement No. 1, April 19, 2024) and adhered to the ethical guidelines outlined in the Declaration of Helsinki. Informed consent was obtained from all participants included in the study and participants were fully informed that their data would be used solely within the framework of this research. Confidentiality was strictly maintained, with all data securely stored to protect participant privacy. Participants were also informed of their right to withdraw from study at any time without penalty.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe author(s) declared financial support was received for the research, authorship, and/or publication of this article. This research is funded under the Grant No. RSU/LSPA-PA-2024/1\u0026ndash;0010 of the project No. 5.2.1.1.i.0/2/24/I/CFLA/005 \u0026ldquo;RSU Internal and RSU with LASE External Consolidation\u0026rdquo; (funded by the European Union Recovery and Resilience Facility and the budget of the Republic of Latvia).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.V. conceived the study, developed the methodology, acquired funding, supervised the project, conducted the investigation, curated the data, performed the formal analyses, validated the findings, prepared the visualizations, and wrote the original draft of the manuscript. G.U., A.A., K.A., and R.L. contributed to data curation and participated in the investigation. Z.V. contributed to data curation, formal analysis, and validation, and assisted in writing and reviewing the manuscript. A.K., Z.V., A.A., G.U., and R.L. contributed to reviewing and editing the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe sincerely thank the members of the Latvian Sport Psychology Association and the coaches specializing in psychological preparation for their participation in the expert panel and for providing valuable insights and feedback that contributed to this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe used datasets can be accessed at Riga Stradiņš University Dataverse repository: Volgemute, Katrina. 2025. \u0026ldquo;Athletes Personality Traits, Psychological Skills, and Psychophysiological Performance.\u0026rdquo; Rīga Stradiņš University Institutional Repository Dataverse. https://doi.org/doi:10.48510/FK2/0B871H. For long-term access or institutional inquiries, data requests may also be directed to the RSU Research Department at [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShuai, Y., Wang, S., Liu, X., Kueh, Y. C. \u0026amp; Kuan, G. The influence of the five-factor model of personality on performance in competitive sports: a review. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1284378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2023.1284378\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2023.1284378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, J. \u0026amp; Hao, F. The role of personality traits in athlete selection: a systematic review. \u003cem\u003eActa Psychol. 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Psychol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (1), 71\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1123/jsep.2019-0295\u003c/span\u003e\u003cspan address=\"10.1123/jsep.2019-0295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"athlete profiling, personality, psychological skills, psychophysiological performance","lastPublishedDoi":"10.21203/rs.3.rs-8224965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8224965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSports performance is shaped by the interaction of various factors, including the athlete's personality, psychological skills and psychophysiological performance. The aim of this study was to develop a multi-factorial profile of athletes, combining personality traits, psychological skills and psychophysiological performance indicators. An additional aim was to investigate the differences between team and individual sports athletes and at different levels of achievements. A total of 304 (female and male) athletes completed standardized assessments of personality traits, psychological skills and psychophysiological performance, including reaction time, stress tolerance, impulsivity, decisiveness and performance consistency. Multivariate analysis revealed significant differences between athletes taking into account the type of sport (individual vs team), as well as the level of sport (elite, pre-elite, amateur). Cluster analysis identified four distinct athlete multi-factorial profiles. Qualitative validation by an expert panel confirmed that these profiles reflect recognizable athlete types in real training and competition contexts, providing recommendations for the implementation of practical interventions. Integrating personality, psychological skills, and psychophysiological performance indicators provides a comprehensive understanding of how athletes perform under different performance demands. These four multi-factorial profiles offer a practical framework for individual training, psychological preparation, and identification of potential risks.\u003c/p\u003e","manuscriptTitle":"Integrating Personality, Psychological Skills and Psychophysiological Performance Factors in Athlete Profiling: Evidence from Team and Individual Sports","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-10 14:30:18","doi":"10.21203/rs.3.rs-8224965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T03:43:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T16:39:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-12T14:03:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98582283807851144369274782862986793033","date":"2025-12-09T08:57:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3889210039080282151150822410892282752","date":"2025-12-08T11:21:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-08T09:21:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-01T17:27:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-28T06:27:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-28T06:26:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-27T20:39:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dfb887e0-b79f-4241-8066-12548f3922bd","owner":[],"postedDate":"December 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":59333757,"name":"Health sciences/Health care"},{"id":59333758,"name":"Biological sciences/Physiology"},{"id":59333759,"name":"Biological sciences/Psychology"},{"id":59333760,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-12T16:01:33+00:00","versionOfRecord":{"articleIdentity":"rs-8224965","link":"https://doi.org/10.1038/s41598-026-35809-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-09 15:57:31","publishedOnDateReadable":"January 9th, 2026"},"versionCreatedAt":"2025-12-10 14:30:18","video":"","vorDoi":"10.1038/s41598-026-35809-7","vorDoiUrl":"https://doi.org/10.1038/s41598-026-35809-7","workflowStages":[]},"version":"v1","identity":"rs-8224965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8224965","identity":"rs-8224965","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}