{"paper_id":"2ff15c29-edcc-4e3d-a7b7-e975004e712d","body_text":"A Case Study using Physiological and Wellness Indicators for Performance Optimization in Basketball | 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 Case Report A Case Study using Physiological and Wellness Indicators for Performance Optimization in Basketball Giulia Corazza, Shehneela Jamil, Renis Dema, Sonia Zorba, Luca Bonetta, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8815351/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose This study investigated relationships between recovery-related biomarkers and game performance in professional basketball players to identify actionable indicators for optimizing athletic performance. Methods Thirteen professional male basketball players were monitored longitudinally over 15 weeks during games at 3-4 day intervals. Twelve independent variables—four salivary biomarkers (testosterone, cortisol, testosterone-to-cortisol ratio, salivary nitrates) and eight self-reported measures (anger, calmness, stress, energy, muscle soreness, tiredness, sleep duration, sleep quality)—were examined against 26 game performance metrics. Stratification analysis on four key outcomes (Plus/Minus, Efficiency, Player Impact Rating per minute, minutes played) identified non-linear relationships and predictor importance. Results Among the 69 significant correlations among recovery variables and game performance metrics, Tiredness and muscle soreness showed the strongest positive relationships, accounting for 39.1% of significant correlations. Sleep duration was the most consistent negative predictor (13 inverse correlations). Stratification revealed 20 significant associations with predominantly non-linear patterns. Sleep duration best predicted minutes played (H=18.47, p=0.0001, ε²=0.131); muscle soreness best predicted PIR/min (H=10.97, p=0.0042, ε²=0.081). Psychological variables primarily influenced playing time and Plus/Minus. Salivary biomarkers showed small but significant effects. Subjective recovery metrics demonstrated superior predictive value (43.8%) versus biomarkers (18.8%). Conclusions Recovery-performance relationships in elite basketball are complex, non-linear, and context-dependent. Subjective markers provide more informative readiness assessment than isolated biomarkers. Inverse sleep duration-performance associations suggest compensatory mechanisms, emphasizing individualized interpretation. Practical monitoring should prioritize daily self-reports, player-specific reference ranges, and integrate recovery data with training load contextual factors. Sports Medicine and Kinesiology athletic performance optimization physiological biomarkers TCR salivary nitrates stratification analysis machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 1 | INTRODUCTION Athletic performance in professional sports represents a complex interplay of physiological capacity, psychological state, technical skill, and biomechanical execution ( 1 ). While traditional performance analysis has focused primarily on observable game statistics, emerging evidence suggests that underlying physiological and psychological factors may be powerful predictors of on-court success and the optimization of athletic performance ( 2 , 3 ). The integration of biomarker assessment, subjective wellness monitoring, and advanced statistical modeling offers unprecedented opportunities to understand and optimize athlete performance. Recent advances in wearable technology, laboratory analysis capabilities, and computational methods have enabled unprecedented collection of multi-modal performance-related data ( 4 ). Current non-invasive biomarker assessments have enabled real-time monitoring of hormonal, metabolic, and immunological status through salivary analysis ( 5 , 6 ). Salivary biomarkers including cortisol, testosterone, testosterone-to-cortisol ratio (TCR), and nitrates provide valuable insights into stress response, anabolic-catabolic balance, and cardiovascular function, respectively ( 7 – 9 ). Simultaneously, self-reported wellness metrics, including mood states, perceived fatigue, and sleep quality, offer complementary information about performance readiness of an athlete ( 2 , 10 ). Hormonal status profoundly influences athletic performance through multiple mechanisms. Testosterone, the primary androgenic hormone, enhances muscle protein synthesis, competitive motivation, spatial cognition, and risk-taking behavior, which are all relevant to athletic performance ( 5 , 11 ). Cortisol, while often characterized as a “stress hormone,” plays essential roles in energy mobilization, cardiovascular regulation, and cognitive arousal at moderate levels ( 6 ). TCR has been proposed as an index of anabolic-catabolic balance, though its relationship with performance appears complex and context-dependent ( 12 , 13 ). Salivary biomarkers offer non-invasive assessment of physiological status. Salivary nitrates, derived from dietary sources and oral bacterial reduction, serve as precursors to nitric oxide, which enhances cardiovascular function and exercise efficiency ( 14 ). Psychological state significantly influences athletic performance through effects on attention, decision-making, motor control, and effort allocation ( 15 ). Negative emotional states, particularly anger, can impair cognitive flexibility, increase risk-taking, and disrupt fine motor control ( 16 ). Conversely, optimal arousal states characterized by calmness and focus enhance precision-based skills and decision-making quality ( 17 ). The relationship between arousal and performance follows the Yerkes-Dodson principle, with optimal performance occurring at intermediate arousal levels, though the optimal point varies by task complexity and individual characteristics ( 18 ). Perceived stress negatively impacts performance through multiple pathways including attentional narrowing, increased muscle tension, and altered decision-making patterns ( 19 , 20 ). Understanding the specific performance domains most vulnerable to psychological perturbations can inform targeted intervention strategies. Recovery status, reflected in subjective markers such as muscle soreness and tiredness, influences both performance capacity and playing style ( 21 ). While incomplete recovery typically impairs performance, athletes may unconsciously or strategically alter their approach in response to recovery status ( 22 ). The relationship between recovery markers and performance may therefore be more complex than simple impairment models suggest. Sleep represents a critical recovery modality, influencing hormonal status, cognitive function, motor skill consolidation, and immune function ( 23 ). However, the relationship between sleep parameters and athletic performance appears more nuanced than simple dose-response relationships, with both insufficient and excessive sleep potentially detrimental, and timing and quality potentially more important than duration alone ( 24 ). Despite growing interest in these monitoring approaches, several critical gaps remain in the literature. First, most studies examine isolated biomarkers or psychological variables rather than comprehensive, multivariate models. Second, the directional relationships between specific biomarkers and discrete performance outcomes (e.g., shooting accuracy, rebounding, assists) remain poorly characterized. Third, traditional linear statistical approaches may fail to capture the complex, non-linear interactions that characterize real-world athletic performance. Therefore, the integration and interpretation of these diverse data streams present significant analytical challenges. Population statistic approaches capable of identifying intricate patterns within multi-dimensional data offer several advantages over traditional statistical methods for sports science applications ( 25 – 26 ). These analyses explore not only the weighted contribution of psychological and physiological factors, such as acute fluctuations in testosterone in the context of competitive motivation and offensive behavior, but also their functional relationships with performance outcomes. In this study, we employed correlation to comprehensively examine the associations of 12 key independent variables on 26 field performance metrics in professional basketball players. Finally, stratification analysis allowed to assess which of the 12 variables are the strongest contributor the aggregate performance rating, Plus/Minus, EFF, PIR/min and minutes played. We hypothesized that: ( 1 ) biological and physiological self-reported metrics can provide a more complete profile of each athlete than one metric alone; ( 2 ) hormonal markers (testosterone, cortisol, TCR) may reflect HPA axis dysregulation in times of intense load; ( 3 ) psychological (anger, calmness, stress) and physiological indicators (muscle soreness, tiredness), and sleep variable (quality and time) would show interplays influencing performance in the field. The present investigation aims to provide accurate, interpretable, and actionable insights crucial for optimizing performance, managing risk, and furthering the fundamental understanding of athlete adaptation. 2 | MATERIALS AND METHODS 2.1 | Study Design This observational cohort study employed a cross-sectional design to analyze the relationships among hormonal and physiological biomarkers, psychological well-being indicators, and field performance metrics in competitive athletes. Data were collected over a complete competitive season, from January 13 to May 7, 2025, from 13 professional male basketball players, on days between matches and on the day of the match, for 15 consecutive weeks. A minimum of 129 complete observations per metric were used for descriptive and stratification statistics. All participants provided informed consent, and the study was approved by the institutional ethics review board in alignment with the WMA Declaration of Helsinki - Ethical Principles for Medical Research Involving Human Participants. Figure 1 gives a schematic representation of the study design. 2.2 | Participants This study focused on 13 competitive male basketball players of Caucasian, African American, and Hispanic ethnicity from an Italian league (Pallacanestro Trieste team, Trieste, Italy). The cohort included diverse playing positions and experience levels representative of professional basketball populations. Players demonstrated mean ± SD weight of 93.2 ± 8.8 kg (range: 80–109 kg) and height of 198.7 ± 9.5 cm (range: 183–213 cm), representative of professional basketball populations. 2.3 | Data collection The dataset contains 12 assessment metrics in an inter-game day and 23 Field metrics which were collected from each player for 15 games. The following sections elaborate further the assessment and performance metrics as independent and dependent variables, respectively. 2.3.1 | Independent Variables 2.3.1.1 | Salivary Biomarkers (n = 4) Salivary samples were collected in between matches (2–3 days after the last match, Recovery day) under standardized conditions (morning collection, fasted state, no oral intake 30 minutes prior) using the passive drool technique. Samples were immediately stored at -20°C until batch analysis. Table 1 provides the data obtained for salivary biomarkers, quantified using paper-strip dry-chemistry (Nitrates; MyFit Strip, USA) or validated commercial ELISA kits (Cortisol and Testosterone; Diametra, Italy). Table 1 Data values obtained for 12 recovery biometrics Biometric Mean Std Dev Range Salivary Nitrates (level) 1.73 0.75 1.00–3.00 Cortisol (ng/ml) 6.45 3.25 0.00–21.70 Testosterone (pg/ml) 245.88 100.16 0.00–575.57 TCR (T/C Ratio) 0.06 0.04 0.00–0.28 Tiredness 4.16 1.88 1–10 Sleep Quality 6.33 1.51 1–10 Sleep Duration (hrs) 7.52 1.20 5–10 Energy Level 6.10 1.68 1–10 Stress Level 3.05 1.75 1–10 Calmness 6.76 2.28 1–10 Anger 2.71 1.78 1–10 Muscle Soreness 3.95 1.86 1–10 2.3.3 | Self-Reported Psychological and Wellness Metrics (n = 8) Athletes completed wellness questionnaires on the day of sampling using validated 10-point Likert scales assessing Self-reported psychological states including Anger, Calmness, Stress level, Energy Level, Muscle Soreness, Tiredness, Sleep Quality, and Sleep Time (Table 2 ). Sleep Time Duration was categorically and assessed based on the previous night's sleep broken down in 3 time scales: i) 7–9 hours, ii) 5–7 hours, and iii) less than 5 hours. Table 2 Data values obtained for field goal and free throw in percentage (%). Game Metric Distribution 2-Point Field Goal % (2 PT%) 61.0% (Std: 24.2%) 3-Point Field Goal % (3 PT %) 44.7% (Std: 20.4%) Free Throw % (FT %) 77.3% (Std: 22.7%) 2.4 | Dependent Variables (Game Metrics) Field performance metrics (n = 26) extracted from official game statistics from LBA Italy are as follows: Playing Time and Status (Minutes and Starters), Fouls (Fouls Committed, Fouls Suffered) Two-Point Shooting (2PT Made, 2PT Attempted, 2PT %), Three-Point Shooting (3PT Made, 3PT Attempted, 3PT %), Free Throws (FT Made, FT Attempted, FT %), Rebounds (Off Rebounds, Def Rebounds and Total Rebounds), Ball handling (Scoring Play, Turnovers, Steals, Assists), blocks (Blocks Given, Blocks Received) and composite Metrics [Player efficiency rating (EFF), Performance Index Rating/minutes played (PIR/min) and Plus/Minus]. 2.5 | Statistical Analysis 2.5.1 | Differentiation Analysis Descriptive statistics was conducted on each variable to assess the distribution of each metric in the sampled population. Variables are represented as mean and SD from the mean and/or distribution of the values expressed in % points. Multiple variables were differentiated using 1-way ANOVA. Kruskal-Wallis H tests were conducted to identify individual variables with statistically significant differences across performance strata, as the assumption of normality was not met for all variables. Statistical significance was set at α = 0.05 2.5.2 | Correlation Analysis Prior to conducting regression analyses, Pearson or Spearman correlations were conducted to assess the strength of correlations between the 12 independent variables. Collinearity diagnostics was performed to mitigate overfitting in subsequent regression analyses. Collinearity diagnostics was performed to mitigate overfitting in subsequent regression analyses. Also, to examine the relationships/correlations between independent variables and game performance metrics, we assessed multicollinearity among the 12 predictor variables (4 biomarkers and 8 self-reported measures). Pearson or Spearman correlations, Variance Inflation Factor (VIF), condition number analysis, and eigenvalue decomposition were employed to comprehensively evaluate the collinearity structure among the independent variables. 2.5.3| Classification Analysis 2.5.3.1 | Player Stratification Players were stratified into three performance groups (Low, Medium, High) based on Plus/Minus (+/-), Player efficiency rating (EFF), Performance Index Rating/minutes played (PIR/min) and minutes played (min) using tertile-based classification. Differences in the 12 recovery variables between groups were assessed using Kruskal-Wallis test. 2.5.5 | Data processing and analysis Missing data were handled using complete-case analysis. Variables were standardized prior to model fitting. Model performance was evaluated using out-of-bag error estimates inherent to SVM methodology. All analyses were conducted using Python 3.12 with the following packages: scikit-learn (v1.3.0) implementation and cross-validation, and SciPy (Virtanen et al., 2020) for non-parametric statistical tests. Pandas (v2.0.0) for data manipulation, and Matplotlib (v3.7.0) and Seaborn (v0.12.0) for visualization. 3 | RESULTS 3.1 | Team Performance Overview In the 2025 season (January 13 - May 7th) had a win rate of 56.3%, maintaining its league rating (Rank = 6) among the best 8 teams in Italy. 3.2 | Biological and Psychophysical Status Salivary Nitrites were analyzed from whole saliva using paper strips on a graded scale (1 = low, 2 = medium, 3 = high) (Table 1 ). Testosterone and Cortisol were analyzed by ELISA and computed as TCR (Table 1 ), representing anabolic-catabolic balance ( 12 ). Athletes completed a wellness questionnaire corresponding to a Mini-P OMS using validated 10-point Likert scales assessing Self-reported psychological states including: Tiredness, Sleep Quality, Energy Level, Stress Level, Calmness, Anger, Muscle Soreness (Table 1 ). Sleep Time Duration was categorically and assessed based on previous night's sleep broken down in three time scales: i) 7–9 hours (49.2%), 5–7 hours (43.0%) and less than 5 hours (5.5%) (Table 1 ). 3.3 | Match-day Metrics The Field metrics on Match Day are collected per athlete and summarized in Tables 2 and 3 capturing the average field metrics for the season. The team showed a balanced distribution of Game metrics expressed in % points (2-Point Field Goals, 3-Point Field Goals and Free Throws) (Table 2 ) or absolute numbers (Table 3 ) aligned with their league rating. Table 3 Data values obtained for other game metrics in absolute numbers. Game Metric Mean Std Dev Range Starters 0.41 0.49 0.00–1.00 Fouls Committed 1.42 1.34 0.00–5.00 Fouls Suffered 1.62 1.84 0.00–9.00 2PT Made 1.38 1.57 0.00–7.00 2PT Attempted 2.68 2.77 0.00–13.00 Scoring Plays 0.15 0.43 0.00–2.00 3PT Made 1.19 1.45 0.00–6.00 3PT Attempted 3.05 3.12 0.00–11.00 FT Made 1.17 1.82 0.00–10.00 FT Attempted 1.62 2.23 0.00–11.00 Off Rebounds 0.91 1.39 0.00–7.00 Def Rebounds 2.16 2.36 0.00–10.00 Total Rebounds 3.07 3.16 0.00–13.00 Blocks Given 0.14 0.40 0.00–2.00 Blocks Received 0.17 0.41 0.00–2.00 Turnovers 1.14 1.46 0.00–6.00 Steals 0.38 0.68 0.00–3.00 Assists 1.52 2.14 0.00–11.00 League Rating 7.87 7.86 -3.00–29.00 OER Rating 0.77 0.57 0.00–2.40 Plus/Minus -1.32 13.01 -35.00–27.00 3.4 | Associations among independent variables Prior to conducting regression analyses to examine the relationships between recovery variables and game performance metrics, we assessed multicollinearity among the 12 predictor variables (4 biomarkers and 8 self-reported measures). Pearson correlations, Variance Inflation Factor (VIF), condition number analysis, and eigenvalue decomposition were employed to comprehensively evaluate the collinearity structure among the recovery variables. 3.4.1 | Correlations among bio-psycho-physical variables Figure 2A shows the correlation matrix for the 12 predictor variables (4 biomarkers and 8 self-reported measures), computed using Pearson correlations. The multicollinearity assessment indicated a moderate overall risk, which was deemed manageable for subsequent regression analyses. Of the 66 possible variable pairs, 35 (53%) showed statistically significant correlations (p < 0.05) (Fig. 2A). Among these, seven pairs exhibited strong correlations (|r| ≥ 0.5), while six pairs demonstrated moderate correlations (0.3 ≤ |r| < 0.5) (Fig. 2A). The observed correlations among recovery variables were largely expected and conceptually meaningful. The strongest correlation was found between Stress level and Anger (r = + 0.69), reflecting the psychological overlap between these emotional states. Similarly, Stress level showed a strong negative correlation with Calmness (r = -0.64), as these represent opposite ends of the arousal-affect spectrum. Sleep quality and Sleep Time Score were positively correlated (r = + 0.63), which is expected given both variables capture aspects of sleep recovery. The correlation between Tiredness and Muscle Soreness (r = + 0.59) suggests a physical fatigue cluster where athletes experiencing muscular discomfort also report higher subjective tiredness. The negative correlation between Cortisol and TCR (r = -0.56) is mathematically expected since TCR is calculated using Cortisol in the denominator. 3.4.1 | Collinearity among bio-psycho-physical variables Multicollinearity occurs when predictor variables are highly correlated, which can inflate standard errors, reduce statistical power, and render coefficient estimates unstable and difficult to interpret. We employed multiple diagnostic approaches Variance Inflation Factor (VIF), condition number analysis, and eigenvalue decomposition to comprehensively evaluate the collinearity structure among recovery variables as shown in Fig. 2B. All 12 variables had VIF values below the commonly accepted threshold of 5, with the highest VIF observed for Stress level (VIF = 2.66) and the lowest for Salivary Nitrates (VIF = 1.08) (Fig. 2B). The VIF analysis confirmed that multicollinearity was within acceptable limits for all predictor variables. VIF quantifies how much the variance of a regression coefficient is inflated due to linear dependence with other predictors. A VIF value of 1 indicates no correlation with other variables, while values exceeding 5 or 10 are typically considered problematic. In the present study, all VIF values ranged from 1.08 to 2.66, indicating that no single predictor was excessively collinear with the remaining variables. Consistent with this, all tolerance values (1/VIF) exceeded 0.2, further supporting the absence of severe multicollinearity. Additional diagnostic measures corroborated these findings. The condition number of the predictor correlation matrix was 3.96, well below the threshold of 30 that would indicate problematic multicollinearity. Eigenvalue analysis revealed no values below 0.1, which would have suggested near-linear dependencies among predictors. These results collectively indicate that although the recovery variables share some common variance, the degree of multicollinearity does not threaten the validity of regression analyses. 3.5 | Associations among independent variables and Field Metrics Spearman Correlation analysis was used to assess associations among self-reported psycho-physical measures, recovery fluid biomarkers, and 23 game metrics to investigate the multifaceted influence on basketball field outcomes (Fig. 3A and 3B). Composite metrics such as EFF, Plus/Minus and PIR/min were excluded from the correlation analysis to avoid inflation. 3.5.1 | Recovery & Overload markers Muscle soreness exhibited the highest number of significant correlations (n = 14), all positive in direction, indicating that greater reported fatigue was consistently associated with enhanced game performance metrics (Table 4 and Fig. 3A). The strongest positive correlations were observed were observed with contact-related metrics: fouls suffered (r = + 0.43, p < 0.001) and FT attempted (r = + 0.42, p < 0.001), followed by FT made (r = + 0.37, p < 0.001). Rebounding metrics showed consistent positive associations, including total rebounds (r = + 0.36, p < 0.001), defensive rebounds (r = + 0.32, p = 0.001), and offensive rebounds (r = + 0.29, p = 0.003). Shooting variables demonstrated moderate correlations with 2PT attempted (r = + 0.30, p = 0.002) and 2PT made (r = + 0.27, p = 0.006). Performance indicators including, scoring plays (r = + 0.26, p = 0.007), and points scored (r = + 0.26, p = 0.007) were also positively associated. Weaker but significant correlations were found with FT percentage (r = + 0.21, p = 0.030), blocks given (r = + 0.21, p = 0.031), and fouls committed (r = + 0.20, p = 0.037). Table 4 Data values obtained for Top 10 Significant Correlations found in Pearson and Spearman correlation analysis. Recovery Variable Field Metric r p-value Muscle_Soreness Fouls_Suffered + 0.43 < 0.001 Muscle_Soreness FT_Attempted + 0.42 < 0.001 Anger Turnovers −0.41 < 0.001 Anger 3PT_Made −0.39 < 0.001 Anger 3PT_Attempted −0.39 < 0.001 Tiredness Total_Rebounds + 0.37 < 0.001 Muscle_Soreness FT_Made + 0.37 < 0.001 Muscle_Soreness Total_Rebounds + 0.36 < 0.001 Tiredness FT_Attempted + 0.36 < 0.001 Tiredness 2PT_Attempted + 0.35 < 0.001 Tiredness exhibited the second highest number of significant correlations (n = 13) all positive in direction, indicating that greater reported fatigue was consistently associated with enhanced game performance metrics. (Table 4 and Fig. 3). The strongest associations were observed with rebounding variables: total rebounds (r = + 0.37, p < 0.001), offensive rebounds (r = + 0.35, p < 0.001), and defensive rebounds (r = + 0.31, p = 0.001). Free throw metrics also demonstrated robust positive correlations, including FT attempted (r = + 0.36, p < 0.001) and FT made (r = + 0.27, p = 0.005). Shooting involvement showed moderate positive associations, with 2PT attempted (r = + 0.35, p < 0.001) and 2PT made (r = + 0.28, p = 0.004). Physical contact metrics were positively correlated, including fouls suffered (r = + 0.34, p < 0.001) and blocks received (r = + 0.28, p = 0.005). Additional significant positive correlations were found with points scored (r = + 0.25, p = 0.009), starter status (r = + 0.25, p = 0.011), and blocks given (r = + 0.21, p = 0.033). Sleep Time Score exhibited 13 significant correlations, all negative in direction, representing a paradoxical finding wherein longer sleep duration was associated with reduced game performance metrics (Fig. 3). The strongest negative correlation was observed with shooting variables: 2PT made (r = − 0.33, p < 0.001) and 2PT attempted (r = − 0.32, p < 0.001). Physical engagement metrics demonstrated consistent negative associations, including fouls suffered (r = − 0.32, p < 0.001), starter status (r = − 0.31, p = 0.001), and total rebounds (r = − 0.30, p = 0.002). Scoring metrics were negatively correlated, including points (r = − 0.30, p = 0.002) and FT attempted (r = − 0.29, p = 0.003). Additional negative correlations were found with offensive rebounds (r = − 0.29, p = 0.002), FT made (r = − 0.26, p = 0.008), defensive rebounds (r = − 0.25, p = 0.009), blocks received (r = − 0.22, p = 0.021), and minutes played (r = − 0.21, p = 0.032). Sleep Quality demonstrated only one significant correlation: a negative association with blocks received (r = − 0.20, p = 0.037; Fig. 3). The limited associations suggest that subjective sleep quality may be less relevant to game performance than sleep duration on Recovery days. 3.5.2 | Psychophysical Metrics Anger emerged as the most consistent negative predictor across multiple game metrics (Table 4 and Fig. 3). The most robust negative association was observed with turnovers (r = − 0.41, p < 0.001), indicating that lower anger levels were associated with increased ball-handling activity. Three-point shooting metrics showed consistent strong negative correlations: 3PT made (r = − 0.39, p < 0.001), 3PT attempted (r = − 0.39, p < 0.001), and 3PT percentage (r = − 0.34, p < 0.001). Playing involvement metrics demonstrated moderate negative associations, including minutes played (r = − 0.31, p = 0.001), assists (r = − 0.31, p = 0.001), and fouls committed (r = − 0.31, p = 0.001). Additional negative correlations were found with points scored (r = − 0.28, p = 0.004) and starter status (r = − 0.25, p = 0.011). These findings suggest that elevated anger levels are consistently associated with reduced offensive engagement and perimeter shooting performance. Calmness exhibited eight significant correlations, predominantly positive (n = 7), representing an inverse pattern to Anger (Fig. 3). The strongest associations were with three-point shooting metrics: 3PT percentage (r = + 0.33, p < 0.001), 3PT made (r = + 0.32, p < 0.001), and 3PT attempted (r = + 0.29, p = 0.002). Playmaking and game involvement showed positive associations with assists (r = + 0.25, p = 0.009), fouls committed (r = + 0.25, p = 0.011), turnovers (r = + 0.22, p = 0.025), and minutes played (r = + 0.20, p = 0.040). Conversely, calmness was negatively associated with blocks given (r = − 0.23, p = 0.017), suggesting that heightened composure may reduce defensive shot-blocking aggression. Stress level demonstrated four significant correlations, all negative in direction (Fig. 3). The strongest negative association was observed with fouls committed (r = − 0.24, p = 0.015), followed by 3PT percentage (r = − 0.22, p = 0.027), turnovers (r = − 0.20, p = 0.044), and assists (r = − 0.19, p = 0.047). This pattern suggests that elevated stress levels may impair both perimeter shooting accuracy and playmaking involvement. Energy Level showed only one significant correlation: a negative association with blocks given (r = − 0.21, p = 0.028) (Fig. 3). The paucity of significant associations indicates that self-reported energy levels on Recovery days may not directly translate to game performance metrics. 3.5.3 | Salivary Steroidal Hormones and Markers Testosterone demonstrated three significant correlations with mixed directionality (Fig. 3). A positive association was observed with fouls committed (r = + 0.22, p = 0.025), consistent with testosterone's role in promoting aggressive behavior. Conversely, negative correlations were found with blocks given (r = − 0.25, p = 0.008) and FT percentage (r = − 0.23, p = 0.019), suggesting potential trade-offs between hormonal arousal and fine motor precision. Cortisol exhibited two significant positive correlations: blocks received (r = + 0.28, p = 0.004) and steals (r = + 0.23, p = 0.017) (Fig. 3). These associations suggest that elevated cortisol levels may enhance alertness and reactive defensive behaviors, potentially reflecting an adaptive stress response that promotes active engagement in physical play. TCR, commonly interpreted as an indicator of anabolic-catabolic balance, demonstrated no significant correlations with any field performance metric on Recovery days (Fig. 3). This null finding suggests that the hormonal balance measured during recovery periods may not directly predict subsequent game performance, or that the relationship between TCR and performance is mediated by other factors not captured in the current analysis. Salivary Nitrates demonstrated one significant positive correlation with steals (r = + 0.23, p = 0.017) (Fig. 3). Nitric oxide, derived from dietary nitrates, enhances blood flow and oxygen delivery to working muscles. This association suggests that adequate nitrate availability may support the sustained high-intensity efforts required for successful defensive plays. 3.6 | Stratification analysis A stratification analysis was performed to examine the associations between recovery metrics, biomarkers, and four key efficiency outcomes in basketball: Plus/Minus (+/-), Efficiency (EFF), Player Impact Rating per minute (PIR/min), and minutes played (MIN). The dataset comprised 145 recovery observations with available performance data (n = 139 for EFF and PIR/min due to missing values). Each outcome was stratified into tertiles (Low, Medium, High) to identify non-linear relationships between predictor variables and performance levels. 3.6.1 | Distribution of Outcome Metrics Tertile stratification evidenced the distribution of the strata per metric outcome (Fig. 4 A). The Plus/Minus metric showed a near-normal distribution centered slightly below zero (tertile thresholds: Low < -2.048, Medium − 2.048 to 7.000, High ≥ 7.000), reflecting balanced positive and negative on-court contributions. Efficiency (EFF) displayed a right-skewed distribution (tertile thresholds: Low < 5.000, Medium 5.000 to 12.000, High ≥ 12.000), with most observations concentrated in the lower efficiency range. PIR/min exhibited a relatively uniform distribution across tertiles (Low < 0.281, Medium 0.281 to 0.539, High ≥ 0.539), suggesting consistent variability in player impact. Minutes played (MIN) showed a bimodal pattern (tertile thresholds: Low < 14.952, Medium 14.952 to 25.000, High ≥ 25.000), distinguishing between limited and substantial playing time. 3.6.2 | Significant Predictors of Performance Outcomes Non-parametric tests were conducted to identify variables with statistically significant differences across strata considering the 8 recovery self-reported metrics [Muscle Soreness, Anger, Calmness, Stress Level, Energy Level, Tiredness, Sleep Quality, and Sleep Time Score] (Fig. 4 B) and 4 biological variables: Nitrates, Cortisol (nmol/L), Testosterone (nmol/L), and TCR, psychological metrics (Fig. 4 C). Plus/Minus performance showed significant associations with psychological recovery dimensions (Table 5 ). Anger emerged as the strongest predictor (H = 10.66, p = 0.0049, ε² = 0.076, medium effect), with players in the low Plus/Minus tertile reporting lower anger levels (M = 2.10) compared to those in the medium tertile (M = 3.26). Stress also demonstrated a significant association (H = 7.73, p = 0.021, ε² = 0.055, small effect), following a similar pattern with lowest stress in the low Plus/Minus group (M = 3.00) and highest in the medium tertile (M = 3.65). These findings suggest that affective state, particularly anger and stress, may influence net on-court contribution, though the relationships were non-linear across performance strata. Table 5 Associations between recovery-related variables and Plus/Minus Variable H p-value ε² Effect size Low Medium High Δ High–Low Anger 10.66 0.0049 0.076 Medium 2.10 3.26 2.47 + 0.36 Stress 7.73 0.021 0.055 Small 3.00 3.65 2.81 −0.19 Tiredness 5.19 0.075 0.037 Small 3.79 3.66 4.40 + 0.61 Efficiency performance was significantly associated with sleep-related recovery and muscle condition (Table 6 ). Sleep time showed the strongest association with EFF (H = 11.57, p = 0.0031, ε² = 0.086, medium effect), with an inverse relationship where the low EFF tertile reported the highest sleep duration (M = 0.97 hrs above baseline) compared to the high EFF tertile (M = 0.89 hrs above baseline). Muscle soreness demonstrated a positive association (H = 6.09, p = 0.048, ε² = 0.045, small effect), with higher EFF tertiles reporting greater muscle soreness (Low: M = 3.43; High: M = 4.29). Table 6 Associations between recovery-related variables and Efficiency (EFF) Variable H p-value ε² Effect size Low Medium High Δ High–Low Sleep time 11.57 0.0031 0.086 Medium 0.97 0.90 0.89 −0.08 Muscle soreness 6.09 0.048 0.045 Small 3.43 3.95 4.29 + 0.86 PIR/min emerged as the outcome metric most sensitive to multiple recovery dimensions, showing seven significant associations spanning subjective recovery measures and physiological biomarkers (Table 7 ). Muscle soreness was the strongest predictor (H = 10.97, p = 0.0042, ε² = 0.081, medium effect), with the high PIR/min tertile reporting substantially greater soreness (M = 4.60) compared to the low tertile (M = 3.53). Stress demonstrated a significant but non-linear association (H = 8.65, p = 0.013, ε² = 0.064, medium effect), with the medium tertile showing lowest stress levels (M = 2.59). Tiredness showed a positive gradient across tertiles (H = 7.74, p = 0.021, ε² = 0.057, small effect), with higher PIR/min associated with greater tiredness (Low: M = 3.36; High: M = 4.26). Calmness was also significantly associated (H = 7.01, p = 0.030, ε² = 0.052, small effect). Among physiological biomarkers (Table 7 ), salivary nitrates (H = 6.84, p = 0.033, ε² = 0.050, small effect) and cortisol (H = 6.17, p = 0.046, ε² = 0.045, small effect) showed significant associations with PIR/min, though both displayed non-monotonic patterns across tertiles. Sleep time again showed an inverse association (H = 6.12, p = 0.047, ε² = 0.045, small effect). These findings suggest that PIR/min integrates neuromuscular, psychological, and endocrine recovery dimensions, potentially serving as a comprehensive indicator of physiological readiness. Table 7 Associations between recovery-related variables and Player Impact Rating per minute (PIR/min) Variable H p-value ε² Effect size Low Medium High Δ High–Low Muscle soreness 10.97 0.0042 0.081 Medium 3.53 3.59 4.60 + 1.06 Stress 8.65 0.013 0.064 Medium 3.38 2.59 3.50 + 0.12 Tiredness 7.74 0.0209 0.057 Small 3.36 3.98 4.26 + 0.91 Nitrates 6.84 0.033 0.050 Small 1.70 2.04 1.62 −0.08 Cortisol 6.17 0.046 0.045 Small 16.03 19.22 15.47 −0.56 Sleep time 6.12 0.047 0.045 Small 0.95 0.91 0.89 −0.06 Minutes played showed seven significant associations, with the strongest effects observed for sleep and psychological variables (Table 8 ). Sleep time was the most robust predictor across all outcomes (H = 18.47, p = 0.0001, ε² = 0.131, medium effect), with an inverse relationship where players with fewer minutes reported higher sleep duration (Low: M = 0.97 hrs above baseline) compared to those with more minutes (High: M = 0.88 hrs above baseline). Stress (H = 12.85, p = 0.002, ε² = 0.091, medium effect) and anger (H = 10.94, p = 0.004, ε² = 0.078, medium effect) both showed strong negative associations with playing time, with the high minutes tertile reporting substantially lower stress (M = 2.78 vs. 3.80 in low tertile) and anger (M = 2.14 vs. 3.15 in low tertile). Calmness demonstrated a positive association (H = 7.46, p = 0.024, ε² = 0.053, small effect), increasing across tertiles (Low: M = 6.02; High: M = 7.16). Tiredness showed a positive gradient (H = 6.42, p = 0.040, ε² = 0.046, small effect) and calmness was also significant (H = 7.46, p = 0.023, ε² = 0.054, small effect). Among salivary biomarkers, testosterone was significantly associated with minutes played (H = 8.00, p = 0.018, ε² = 0.056, small effect), with the medium and high tertiles showing elevated testosterone levels (Medium: M = 0.89; High: M = 0.85) compared to the low tertile (M = 0.71). These findings indicate that minutes played is sensitive to both psychological state and endocrine function, suggesting that coaches’ playing time decisions may implicitly integrate multiple dimensions of athlete readiness. Table 8 Associations between recovery-related variables and minutes played Variable H p-value ε² Effect size Low Medium High Δ High–Low Sleep Time Score 18.47 0.000 0.131 Medium 0.97 0.88 0.88 -0.09 Stress level 12.85 0.002 0.091 Medium 3.80 2.89 2.78 -1.02 Anger 10.94 0.004 0.078 Medium 3.15 2.54 2.14 -1.01 Testosterone nmol/L 8.00 0.018 0.056 Small 0.71 0.89 0.85 + 0.13 Calmness 7.46 0.024 0.053 Small 6.02 6.65 7.16 + 1.14 Tiredness 6.42 0.040 0.046 Small 3.51 3.93 4.39 + 0.88 4. DISCUSSION 4.1 Principal Findings The purpose of this study was to examine associations between biological, psychological, and physical recovery markers and game performance in professional basketball across a competitive season. Within a high-performance context (top-8 league standing; 56.3% win rate), several key findings emerged. First, subjective indicators of tiredness and muscle soreness were positively associated with performance metrics reflecting competitive engagement. Second, sleep-related variables showed inverse associations with several performance indicators, contrary to prevailing assumptions in the literature. Third, psychological states demonstrated clear domain-specific relationships with performance, particularly for shooting-related outcomes. Fourth, hormonal biomarkers were selectively associated with offensive and defensive behaviors. Finally, a multivariate model incorporating recovery-related variables provided modest but meaningful discrimination between performance strata. Collectively, these findings highlight the complex, non-linear, and context-dependent nature of recovery–performance relationships in elite basketball. 4.2 Physical Recovery Markers as as Proxies for Competitive Load Self-reported tiredness and muscle soreness were positively associated with minutes played, free throw activity, and interior play actions. Within this cohort, these subjective markers appear to reflect exposure to competitive demands rather than impaired readiness. This interpretation is consistent with frameworks suggesting that moderate physiological and perceptual strain may occur alongside maintained performance in elite athletes operating within their adaptive capacity ( 27 , 28 ). Muscle soreness likely reflects the eccentric loading and physical contact inherent to rebounding and interior play ( 29 ). These associations should not be interpreted as evidence that fatigue or soreness enhances performance. Rather, they indicate that subjective recovery markers may co-vary with playing role and match exposure, underscoring the importance of contextualized and longitudinal interpretation in applied monitoring. 4.3 Sleep Metrics and the Recovery–Performance Paradox Sleep duration and perceived sleep quality were inversely associated with several performance indicators. These findings contrast with controlled experimental evidence demonstrating performance benefits of sleep extension ( 23 , 30 ), suggesting that contextual factors inherent to elite competition may influence observed associations in applied settings. Several explanations remain plausible, including reverse causality related to playing time, the distinction between sleep duration and sleep quality or timing ( 31 ), and potential non-linear relationships between sleep duration and performance ( 32 ). Given the observational design, these explanations cannot be disentangled. Accordingly, these results indicate that sleep duration alone may be an insufficient marker of readiness in professional basketball, and should be interpreted alongside playing exposure, individual baselines, and longitudinal trends. 4.4 Psychological States and Task-Specific Performance Demands Psychological states demonstrated task-specific associations with performance outcomes. Anger was negatively associated with shooting efficiency and playing time, consistent with evidence linking elevated negative affect to impaired attentional control and decision-making ( 33 , 34 ). Calmness was positively associated with three-point shooting and assists, aligning with optimal arousal models for precision-based tasks ( 35 , 36 ). Stress showed differentiated associations, negatively related to perimeter shooting but positively associated with interior scoring and defensive actions. This pattern is consistent with prior evidence that elevated arousal may differentially affect precision and reactive performance demands( 37 – 39 ). These findings support the interpretation that psychological readiness is domain-specific, rather than uniformly beneficial or detrimental across performance outcomes. 4.5 Hormonal Biomarkers and Behavioral Tendencies Hormonal biomarkers were selectively associated with behavioral performance indicators. Testosterone was associated with markers of engagement and risk-taking, consistent with prior work linking testosterone to dominance-related behaviors ( 40 , 41 ). However, its negative association with plus/minus suggests that increased individual activity does not necessarily translate to improved team outcomes. Cortisol was positively associated with defensive actions and interior scoring, consistent with evidence that moderate cortisol elevations may coincide with heightened vigilance and reactivity ( 39 , 42 ). The TCR showed limited associations, suggesting restricted utility as an acute performance marker in this context. Salivary nitrates were modestly associated with assists and steals, in line with literature linking nitric oxide bioavailability to vascular and cognitive-motor processes ( 43 ). Given the observational design, these associations should be interpreted cautiously. 4.6 Multivariate weighted stratification and Practical Considerations The stratification analysis extended the univariate findings by examining how recovery-related variables discriminate across tertiles of performance outcomes. This approach revealed several patterns with practical implications for athlete monitoring and performance prediction in elite basketball. The four performance outcomes demonstrated distinct sensitivity to recovery-related variables. Minutes played and PIR/min each showed seven significant predictors (35% of tests), indicating that these metrics integrate multiple dimensions of athlete readiness. In contrast, Plus/Minus demonstrated only two significant predictors (10% of tests), suggesting that this team-oriented metric may be less sensitive to individual recovery states or more strongly influenced by contextual factors such as opponent quality, lineup composition, and game situation. The predominance of sleep time as a predictor across multiple outcomes (H = 18.47 for MIN, H = 11.57 for EFF, H = 6.12 for PIR/min) underscores its cross-cutting relevance, though the inverse associations observed suggest compensatory rather than causal relationships. Similarly, psychological variables (anger, stress, calmness, tiredness) collectively accounted for the majority of significant associations, particularly for MIN and Plus/Minus, reinforcing the interpretation that affective state is a critical component of performance readiness in basketball. The finding that muscle soreness was the strongest predictor of PIR/min (H = 10.97, p = 0.0042, ε² = 0.081) but showed weaker associations with other outcomes suggests that this variable may be particularly informative for metrics reflecting physical engagement and competitive intensity. This pattern aligns with the interpretation in section 4.2 that muscle soreness serves as a proxy for competitive load exposure rather than impairment. The stratification approach revealed predominantly non-monotonic patterns across tertiles, including U-shaped, inverted U-shaped, and threshold effects. For instance, stress showed lowest levels in the medium PIR/min tertile (M = 2.59) compared to both low (M = 3.38) and high (M = 3.50) tertiles, suggesting an optimal arousal zone for performance impact. Similarly, anger demonstrated non-linear associations with Plus/Minus, with the medium tertile showing highest anger levels (M = 3.26) despite intermediate performance. These non-linear patterns underscore a critical limitation of linear correlation approaches and highlight the value of stratification methods for identifying threshold effects and optimal ranges. From an applied perspective, these findings suggest that athlete monitoring systems should incorporate individualized reference ranges and consider within-person changes relative to baseline, rather than relying on universal cut-points or group norms. Across all outcomes, subjective recovery metrics (tiredness, muscle soreness, stress, anger, calmness, sleep quality) accounted for 43.8% of significant findings (14 of 32 tests), substantially outperforming biomarkers (18.8%; 3 of 16 tests). This pattern is consistent with prior evidence suggesting that self-report measures capture integrated information about physiological, psychological, and contextual factors that may not be reflected in isolated biomarkers (44,45). However, the significant associations observed for testosterone, cortisol, and nitrates (particularly for PIR/min and MIN) indicate that physiological markers provide complementary information beyond subjective perceptions. The modest effect sizes observed for biomarkers (ε² = 0.045–0.056) suggest that these variables contribute incremental predictive value when combined with subjective measures, rather than serving as standalone indicators. 4.7 Limitation and Directions Several methodological limitations should be acknowledged when interpreting the findings of this study, while also pointing toward clear directions for future research. First, the observational and correlational design precludes causal inference. Although meaningful associations were identified between recovery-related variables and performance outcomes, these relationships may reflect reverse causality, bidirectional effects, or the influence of unmeasured confounders. Experimental approaches, including randomized controlled trials manipulating recovery strategies (e.g., sleep optimization, psychological regulation techniques, or nutritional interventions), are necessary to establish causal mechanisms and determine the efficacy of targeted interventions. Second, the study was conducted within a single professional team, which limits the generalizability of the findings. Recovery–performance relationships may differ across competition levels, tactical systems, cultural environments, and training loads. Future studies should include multi-team and multi-league cohorts to assess the robustness and external validity of the observed patterns. Third, measurement timing and temporal resolution represent important constraints. Recovery variables were assessed at single daily time points, typically on the morning of game day, which may not adequately capture the dynamic and non-linear nature of recovery processes. Longitudinal designs incorporating repeated daily measurements across training, competition, and recovery cycles would allow for a more nuanced understanding of within-athlete fluctuations, delayed effects, and cumulative load responses. Fourth, several key variables—particularly psychological states and wellness indicators—relied on self-report measures, which are inherently subject to response bias, social desirability effects, and individual differences in perception. While self-reported data remain valuable in applied sport settings, future research would benefit from integrating more objective assessment tools, such as actigraphy for sleep, ecological momentary assessment for mood, and sensor-based measures of physical load. Fifth, performance metrics were not adjusted for contextual factors such as opponent strength, game tempo, tactical role, or situational demands (e.g., score margin, home vs. away games). These contextual variables can substantially influence performance statistics and may moderate recovery–performance relationships. Multilevel or hierarchical modeling approaches that account for player-, game-, and team-level variance would provide more precise and ecologically valid estimates. Sixth, while salivary biomarkers offer a practical and non-invasive monitoring solution, they present inherent biological limitations. Single-sample assessments do not capture diurnal rhythms, acute fluctuations, or tissue-level hormonal activity, potentially attenuating observed associations. Future studies should incorporate repeated sampling protocols or complementary physiological measures to better characterize endocrine dynamics. Looking forward, several avenues for future research emerge. Within-athlete longitudinal monitoring across multiple seasons would help identify individualized recovery–performance profiles and optimal readiness thresholds. Mechanistic studies integrating physiological, psychological, and neurocognitive assessments could clarify how recovery variables differentially influence specific performance domains. Position-specific analyses are also warranted, given the distinct physical and cognitive demands placed on guards, forwards, and centers. Additionally, examining team-level dynamics, including how individual recovery status aggregates to influence collective performance, would support a more systems-oriented understanding of elite sport. Finally, advanced modeling approaches, including machine learning and non-linear predictive frameworks, may better capture complex interactions among recovery variables than traditional linear models. Such approaches hold promise for improving predictive accuracy and supporting more personalized, data-driven athlete management strategies. 5. CONCLUSIONS This comprehensive investigation of recovery-performance relationships in professional basketball revealed a complex, multifaceted picture that challenges several conventional assumptions. Contrary to expectations, physical recovery markers (tiredness, muscle soreness) showed positive associations with performance indicators, likely reflecting competitive engagement rather than performance impairment. Sleep metrics demonstrated unexpected inverse relationships with performance, highlighting the complexity of sleep-performance dynamics and the importance of considering sleep quality, timing, and contextual factors beyond absolute duration. Psychological states demonstrated domain-specific effects, with calmness enhancing precision tasks (three-point shooting) and anger impairing multiple performance domains. Hormonal biomarkers showed selective associations with specific behavioral patterns, with testosterone relating to aggressive engagement, cortisol enhancing defensive vigilance, and nitrates potentially supporting cognitive-motor performance. Stratification analysis incorporating 12 recovery and biomarker variables achieved modest but meaningful discrimination between performance strata with sleep variables contributing most substantially (30.5%), followed by psychological indicators (22.9%) and hormonal biomarkers (17.7%). The modest predictive effect highlights that while recovery monitoring provides valuable information, athletic performance is multifactorially determined by numerous factors beyond physiological readiness. These findings have important practical implications for athlete monitoring and performance optimization. Recovery data should be interpreted contextually, considering individual baselines, performance demands, and the distinction between markers of competitive engagement versus performance impairment. Psychological preparation should target emotional states appropriate for specific performance demands, and sleep monitoring should emphasize quality and circadian alignment rather than duration alone. Future research employing experimental designs, longitudinal monitoring, and mechanistic investigations will further clarify the complex relationships between recovery and performance in basketball, ultimately informing more effective, individualized approaches to athlete management and performance optimization. Declarations CONFLICT OF INTEREST The authors declare no conflicts of interest. ORCID [Author ORCID identifiers to be specified] FUNDING The study is supported by the Booster Grant for Life Sciences of the Regione Autonoma Friuli Venezia Giulia (CUP: G97H24001640002). AUTHOR CONTRIBUTIONS Conceptualization (LA, LB, FC), Data curation (LB, LA, SZ), Formal analysis (LA, GC, SJ, RD), Funding acquisition (LA, DC), Investigation (GC, LB, LA, RD, SZ), Methodology (GC, RD, LB, SZ), Project administration (LA, LB), Resources (DC, LB, LA, FC), Software (SZ, SJ), Supervision (LA, LB, DC), Validation (FC), Visualization (LA, GC), Writing – original draft (LA, GC, SJ), Writing – review & editing (LA, GC, SJ, FC, LB, RD, SZ) ACKNOWLEDGMENTS All authors acknowledge that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation, and state that results of the present study do not constitute endorsement by ACSM. We thank Giulia Cordaro at CRI for her technical support and Paolo Soave for his administrative assistance at CRI. DATA AVAILABILITY STATEMENT Data can be released on request. References Kellmann M, Bertollo M, Bosquet L, Brink M, Coutts AJ, Duffield R et al (2018) Recovery and performance in sport: Consensus statement. Int J Sports Physiol Perform 13(2):240–245 Halson SL (2014) Monitoring training load to understand fatigue in athletes. Sports Med 44(Suppl 2S2):S139–S147 Claudino JG, Capanema D, de O TV, Serrão JC, Machado Pereira AC, Nassis GP (2019) Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review. Sports Med Open 5(1):28 Seshadri DR, Li RT, Voos JE, Rowbottom JR, Alfes CM, Zorman CA et al (2019) Wearable sensors for monitoring the physiological and biochemical profile of the athlete. NPJ Digit Med 2(1):72 Crewther BT, Cook C, Cardinale M, Weatherby RP, Lowe T (2011) Two emerging concepts for elite athletes: the short-term effects of testosterone and cortisol on the neuromuscular system and the dose-response training role of these endogenous hormones. Sports Med 41(2):103–123 Duclos M, Tabarin A (2016) Exercise and the hypothalamo-pituitary-adrenal axis. Front Horm Res 47:12–26 Finn KJ, Ransone J, Martinez M (2019) Salivary biomarkers in college female basketball players during the late competition season. Med Sci Sports Exerc 51(6S):27–28 Tomescu V, Bellar D (2019) Seasonal changes in salivary biomarkers and psychomotor function among elite fencers. Med Sci Sports Exerc 51(6S):324–324 Deminice R, Sicchieri T, Payão PO, Jordão AA (2010) Blood and salivary oxidative stress biomarkers following an acute session of resistance exercise in humans. Int J Sports Med 31(9):599–603 Lochbaum M, Zanatta T, Kirschling D, May E (2021) The profile of moods states and athletic performance: A meta-analysis of published studies. Eur J Investig Health Psychol Educ 11(1):50–70 Carré JM, Olmstead NA (2015) Social neuroendocrinology of human aggression: examining the role of competition-induced testosterone dynamics. Neuroscience 286:171–186 Papacosta E, Gleeson M, Nassis GP (2013) Salivary hormones, IgA, and performance during intense training and tapering in judo athletes. J Strength Cond Res 27(9):2569–2580 Obmiński Z, Stupnicki R (1997) Comparison of the testosterone-to-cortisol ratio values obtained from hormonal assays in saliva and serum. J Sports Med Phys Fit 37(1):50–55 Townsend JR, Hart TL, Haynes JT 4th, Woods CA, Toy AM, Pihera BC et al (2022) Influence of dietary nitrate supplementation on physical performance and body composition following offseason training in Division I athletes. J Diet Suppl 19(4):534–549 Beedie CJ, Terry PC, Lane AM (2000) The profile of mood states and athletic performance: Two meta-analyses. J Appl Sport Psychol 12(1):49–68 Robazza C, Bortoli L (2007) Perceived impact of anger and anxiety on sporting performance in rugby players. Psychol Sport Exerc 8(6):875–896 Hanin YL (2012) Emotions in Sport: Current Issues and Perspectives. Handbook of Sport Psychology. John Wiley & Sons, Inc., Hoboken, NJ, USA, pp 31–58 Teigen KH, Yerkes-Dodson (1994) A law for all seasons. Theory Psychol 4(4):525–547 Liang WM, Xiao J, Ren FF, Chen ZS, Li CR, Bai ZM et al (2023) Acute effect of breathing exercises on muscle tension and executive function under psychological stress. Front Psychol 14:1155134 FeldmanHall O, Raio CM, Kubota JT, Seiler MG, Phelps EA (2015) The effects of social context and acute stress on decision making under uncertainty. Psychol Sci 26(12):1918–1926 Thorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B, Gregson W (2017) The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players. Int J Sports Physiol Perform 12(Suppl 2):S2107–S2113 Saw AE, Main LC, Gastin PB (2016) Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med 50(5):281–291 Fullagar HHK, Skorski S, Duffield R, Hammes D, Coutts AJ, Meyer T (2015) Sleep and athletic performance: the effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Med 45(2):161–186 Walsh NP, Halson SL, Sargent C, Roach GD, Nédélec M, Gupta L et al (2020) Sleep and the athlete: narrative review and 2021 expert consensus recommendations. Br J Sports Med 55(7):356–368 Epidemiology and biostatistics: An introduction to clinical research, 2nd edition. Med Sci Sports Exerc (2020) ;52(2):523 Croux C, Filzmoser P, Joossens K (2005) Robust linear discriminant analysis for multiple groups: Influence and classification efficiencies. SSRN Electron J [Internet]. ; Available from: http://dx.doi.org/10.2139/ssrn.876896 McEwen BS, Wingfield JC (2003) The concept of allostasis in biology and biomedicine. Horm Behav 43(1):2–15 Nieman DC (2010) Mental fatigue impairs physical performance in humans. Year B Sports Med 2010:145–146 Cheung K, Hume PA, Maxwell L (2003) Delayed onset muscle soreness. Sports Med 33(2):145–164 Mah CD, Mah KE, Kezirian EJ, Dement WC (2011) The effects of sleep extension on the athletic performance of collegiate basketball players. Sleep 34(7):943–950 Vitale KC, Owens R, Hopkins SR, Malhotra A (2019) Sleep hygiene for optimizing recovery in athletes: Review and recommendations. Int J Sports Med 40(8):535–543 Chaput JP, Dutil C, Sampasa-Kanyinga H (2018) Sleeping hours: what is the ideal number and how does age impact this? Nat Sci Sleep 10:421–430 Easterbrook JA (1959) The effect of emotion on cue utilization and the organization of behavior. Psychol Rev 66(3):183–201 Lane AM, Beedie CJ, Devonport TJ, Stanley DM (2011) Instrumental emotion regulation in sport: relationships between beliefs about emotion and emotion regulation strategies used by athletes. Scand J Med Sci Sports 21(6):e445–e451 Eldadi O, Tenenbaum G (2025) Team cognition (TC) in sport: Foundations, development, and performance implications. Psychol Sport Exerc 80(102927):102927 Concepcion RY (2004) Foundations of sport and exercise psychology, 3rd edition. Med Sci Sports Exerc. ;1449 Derakshan N, Eysenck MW (2009) Anxiety, processing efficiency, and cognitive performance. Eur Psychol 14(2):168–176 Eysenck MW, Calvo MG (1992) Anxiety and performance: The processing efficiency theory. Cogn Emot 6(6):409–434 Rohleder N, Beulen SE, Chen E, Wolf JM, Kirschbaum C (2007) Stress on the dance floor: the cortisol stress response to social-evaluative threat in competitive ballroom dancers. Pers Soc Psychol Bull 33(1):69–84 Wingfield JC, Hegner RE, Dufty AM Jr, Ball GF (1990) The challenge hypothesis: Theoretical implications for patterns of testosterone secretion, mating systems, and breeding strategies. Am Nat 136(6):829–846 Carré JM, Geniole SN, Ortiz TL, Bird BM, Videto A, Bonin PL (2017) Exogenous testosterone rapidly increases aggressive behavior in dominant and impulsive men. Biol Psychiatry 82(4):249–256 Salvador A, Costa R (2009) Coping with competition: neuroendocrine responses and cognitive variables. Neurosci Biobehav Rev 33(2):160–170 Jones AM, Thompson C, Wylie LJ, Vanhatalo A (2018) Dietary nitrate and physical performance. Annu Rev Nutr 38(1):303–328 Impellizzeri FM, Marcora SM, Coutts AJ (2019) Internal and external training load: 15 years on. Int J Sports Physiol Perform 14(2):270–273 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8815351\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Case Report\",\"associatedPublications\":[],\"authors\":[{\"id\":587436087,\"identity\":\"277978ec-faee-4fd0-b269-a7ed3a90b703\",\"order_by\":0,\"name\":\"Giulia Corazza\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"VitalizeDx Eu-Personalized Care\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Giulia\",\"middleName\":\"\",\"lastName\":\"Corazza\",\"suffix\":\"\"},{\"id\":587436125,\"identity\":\"c521ea5f-e85f-4988-b395-ce0f11d8f828\",\"order_by\":1,\"name\":\"Shehneela Jamil\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"VitalizeDx Eu-Personalized Care\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shehneela\",\"middleName\":\"\",\"lastName\":\"Jamil\",\"suffix\":\"\"},{\"id\":587436372,\"identity\":\"8743ec39-4dc0-42b9-88c2-5f7e533df912\",\"order_by\":2,\"name\":\"Renis Dema\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Croce Rossa Italiana\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Renis\",\"middleName\":\"\",\"lastName\":\"Dema\",\"suffix\":\"\"},{\"id\":587436373,\"identity\":\"7c8cc778-0a06-4f2d-9dd7-5f364735f742\",\"order_by\":3,\"name\":\"Sonia Zorba\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"VitalizeDx Eu-Personalized Care\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sonia\",\"middleName\":\"\",\"lastName\":\"Zorba\",\"suffix\":\"\"},{\"id\":587436374,\"identity\":\"ba32da80-82e9-45a4-b275-af3cecad66a8\",\"order_by\":4,\"name\":\"Luca Bonetta\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Pallacanestro Trieste\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Luca\",\"middleName\":\"\",\"lastName\":\"Bonetta\",\"suffix\":\"\"},{\"id\":587436375,\"identity\":\"9ab608c5-2eb3-45ab-969a-796325caf0a7\",\"order_by\":5,\"name\":\"Daniele Cavalliero\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Pallacanestro Trieste\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Daniele\",\"middleName\":\"\",\"lastName\":\"Cavalliero\",\"suffix\":\"\"},{\"id\":587436376,\"identity\":\"447d669b-0bec-478e-9542-e6ad9b1dc3e2\",\"order_by\":6,\"name\":\"Francesco Cuzzolin\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Umana Reyer\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Francesco\",\"middleName\":\"\",\"lastName\":\"Cuzzolin\",\"suffix\":\"\"},{\"id\":587436051,\"identity\":\"9f447594-40b9-4f01-b150-c5376f547420\",\"order_by\":7,\"name\":\"Lavinia Alberi Auber\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACPiA+wGDAwMDPwNjAwFAgAeQn4NfCBtMi2cDY2MBgQKQWMDA4ALLGgIEILexnDA8XFNjJG99Ibn/ww8DCnp89x4DhR8U23Fp4cgwOzzBINtx2I7GxscdAglmy51kCY8+Z23gclpZwmMeAmXHbmYONDTwGEmwGN5IPMDO24dHC/wykpd5+c8/BxsY/BhI89jcSG/BrkUg+ANRyOHEDe2NjM9AWCQMJQrZIPAZpOZ4843hj42wZA6COM88SDuLzCz9/YvNnnj/Vtv3N7A8+vqmos+dvzzF88KMCtxbs4ACJ6kfBKBgFo2AUoAEAtKZT1/k0hBYAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0000-0002-0446-5337\",\"institution\":\"VitalizeDx Eu-Personalized Care\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Lavinia\",\"middleName\":\"Alberi\",\"lastName\":\"Auber\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-07 12:16:25\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":true,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":true,\"humanSubjectConsent\":true,\"humanSubjectClinicalTrial\":true,\"humanSubjectCaseReport\":true,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-8815351/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8815351/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":102309846,\"identity\":\"2ef515f2-ebba-44b8-9ec0-8afd7275cf4b\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 11:51:52\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":462267,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic of study design. Flowchart of data collection design: 12 biometrics in an inter-game day and 26 field metrics/athlete are collected from each player for 12 games.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.tif.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8815351/v1/f6e118e70e14dd84e2f0dd53.jpg\"},{\"id\":102310470,\"identity\":\"074f620e-dfe6-4a92-90f7-de6ba3a81c07\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 11:54:06\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":477859,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCorrelation analysis of independent metrics.\\u003cstrong\\u003eA) \\u003c/strong\\u003eCorrelation matrix showing the association among the independent variables. \\u003cstrong\\u003eB)\\u003c/strong\\u003e Bar graph indicating a low collinearity among independent variables, based on the variance inflation factor (VIF). TCR= Testosterone-to-Cortisol ratio, *= p\\u0026lt;0.05\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.tif.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8815351/v1/333224fdd45a5aec13d648b0.jpg\"},{\"id\":102310174,\"identity\":\"f86ab1bf-bb5f-4f18-864a-e03432c83853\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 11:52:53\",\"extension\":\"jpg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":550360,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eAssociations between 12 recovery biometrics and 23 field metrics. Correlation matrix showing the associations and their significance according to Spearman among \\u003cstrong\\u003eA\\u003c/strong\\u003e) the 8 self-reported variables sampled and \\u003cstrong\\u003eB\\u003c/strong\\u003e) salivary biomarkers with the game field metrics. Pts=Points, Min=minutes, SF=starters, FC=Fouls committed, FD=Fouls drawn, 2PM=2-points made, 2PA=2-points attempted, 2P%=2-points in %, 3PM=3-points made, 3PA=3-points attempted, 3P%=3-points in %, FTM=free throws made, FTA= free throws attempted, FT%=free throws in %, ORB=off rebounds, DRB=Defensive rebounds, TRB=total rebounds, SP=scoring play, BLK=blocks made. BLKr=blocks received, TO=turnover, STL=steals, AST=assists, TCR= Testosterone-to-Cortisol ratio, *= p\\u0026lt;0.05\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8815351/v1/8a8b94102e7f708727304a10.jpg\"},{\"id\":102310304,\"identity\":\"98ffb332-4306-4544-8fc0-5c100c2b036a\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 11:53:13\",\"extension\":\"jpg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":532148,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTertile Stratification of Plus/minus, EFF, PIR/min and min and weighted predictors of strata. \\u003cstrong\\u003eA) \\u003c/strong\\u003eDensity plots showing the strata distributions by High, Medium, Low Plus/minus, EFF, PIR/min and min game metrics. Heat maps showing the effect size (e\\u003csup\\u003e2\\u003c/sup\\u003e) of \\u003cstrong\\u003eB\\u003c/strong\\u003e) recovery self-reported metrics and \\u003cstrong\\u003eC\\u003c/strong\\u003e) salivary biomarkers on the stratification of the game outcome metrics. +/-=Plus/Minus; EFF= Efficiency rating; PIR/min=Performance index rating/minutes played; min=minutes played.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8815351/v1/b9d6813701aa1c3e3a8f2146.jpg\"},{\"id\":102312096,\"identity\":\"b81a46c4-06f4-4a18-b76f-81cab080400c\",\"added_by\":\"auto\",\"created_at\":\"2026-02-10 12:00:04\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4380238,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8815351/v1/92efcb39-5b0d-4a2a-95d0-306b22be5527.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003eA Case Study using Physiological and Wellness Indicators for Performance Optimization in Basketball\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1 | INTRODUCTION\",\"content\":\"\\u003cp\\u003eAthletic performance in professional sports represents a complex interplay of physiological capacity, psychological state, technical skill, and biomechanical execution (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). While traditional performance analysis has focused primarily on observable game statistics, emerging evidence suggests that underlying physiological and psychological factors may be powerful predictors of on-court success and the optimization of athletic performance (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). The integration of biomarker assessment, subjective wellness monitoring, and advanced statistical modeling offers unprecedented opportunities to understand and optimize athlete performance.\\u003c/p\\u003e \\u003cp\\u003eRecent advances in wearable technology, laboratory analysis capabilities, and computational methods have enabled unprecedented collection of multi-modal performance-related data (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Current non-invasive biomarker assessments have enabled real-time monitoring of hormonal, metabolic, and immunological status through salivary analysis (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Salivary biomarkers including cortisol, testosterone, testosterone-to-cortisol ratio (TCR), and nitrates provide valuable insights into stress response, anabolic-catabolic balance, and cardiovascular function, respectively (\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). Simultaneously, self-reported wellness metrics, including mood states, perceived fatigue, and sleep quality, offer complementary information about performance readiness of an athlete (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHormonal status profoundly influences athletic performance through multiple mechanisms. Testosterone, the primary androgenic hormone, enhances muscle protein synthesis, competitive motivation, spatial cognition, and risk-taking behavior, which are all relevant to athletic performance (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). Cortisol, while often characterized as a \\u0026ldquo;stress hormone,\\u0026rdquo; plays essential roles in energy mobilization, cardiovascular regulation, and cognitive arousal at moderate levels (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). TCR has been proposed as an index of anabolic-catabolic balance, though its relationship with performance appears complex and context-dependent (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). Salivary biomarkers offer non-invasive assessment of physiological status. Salivary nitrates, derived from dietary sources and oral bacterial reduction, serve as precursors to nitric oxide, which enhances cardiovascular function and exercise efficiency (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePsychological state significantly influences athletic performance through effects on attention, decision-making, motor control, and effort allocation (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Negative emotional states, particularly anger, can impair cognitive flexibility, increase risk-taking, and disrupt fine motor control (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Conversely, optimal arousal states characterized by calmness and focus enhance precision-based skills and decision-making quality (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e). The relationship between arousal and performance follows the Yerkes-Dodson principle, with optimal performance occurring at intermediate arousal levels, though the optimal point varies by task complexity and individual characteristics (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Perceived stress negatively impacts performance through multiple pathways including attentional narrowing, increased muscle tension, and altered decision-making patterns (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). Understanding the specific performance domains most vulnerable to psychological perturbations can inform targeted intervention strategies.\\u003c/p\\u003e \\u003cp\\u003eRecovery status, reflected in subjective markers such as muscle soreness and tiredness, influences both performance capacity and playing style (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). While incomplete recovery typically impairs performance, athletes may unconsciously or strategically alter their approach in response to recovery status (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e). The relationship between recovery markers and performance may therefore be more complex than simple impairment models suggest. Sleep represents a critical recovery modality, influencing hormonal status, cognitive function, motor skill consolidation, and immune function (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). However, the relationship between sleep parameters and athletic performance appears more nuanced than simple dose-response relationships, with both insufficient and excessive sleep potentially detrimental, and timing and quality potentially more important than duration alone (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDespite growing interest in these monitoring approaches, several critical gaps remain in the literature. First, most studies examine isolated biomarkers or psychological variables rather than comprehensive, multivariate models. Second, the directional relationships between specific biomarkers and discrete performance outcomes (e.g., shooting accuracy, rebounding, assists) remain poorly characterized. Third, traditional linear statistical approaches may fail to capture the complex, non-linear interactions that characterize real-world athletic performance. Therefore, the integration and interpretation of these diverse data streams present significant analytical challenges.\\u003c/p\\u003e \\u003cp\\u003ePopulation statistic approaches capable of identifying intricate patterns within multi-dimensional data offer several advantages over traditional statistical methods for sports science applications (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). These analyses explore not only the weighted contribution of psychological and physiological factors, such as acute fluctuations in testosterone in the context of competitive motivation and offensive behavior, but also their functional relationships with performance outcomes.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we employed correlation to comprehensively examine the associations of 12 key independent variables on 26 field performance metrics in professional basketball players. Finally, stratification analysis allowed to assess which of the 12 variables are the strongest contributor the aggregate performance rating, Plus/Minus, EFF, PIR/min and minutes played. We hypothesized that: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) biological and physiological self-reported metrics can provide a more complete profile of each athlete than one metric alone; (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) hormonal markers (testosterone, cortisol, TCR) may reflect HPA axis dysregulation in times of intense load; (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) psychological (anger, calmness, stress) and physiological indicators (muscle soreness, tiredness), and sleep variable (quality and time) would show interplays influencing performance in the field.\\u003c/p\\u003e \\u003cp\\u003eThe present investigation aims to provide accurate, interpretable, and actionable insights crucial for optimizing performance, managing risk, and furthering the fundamental understanding of athlete adaptation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"2 | MATERIALS AND METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 | Study Design\\u003c/h2\\u003e \\u003cp\\u003eThis observational cohort study employed a cross-sectional design to analyze the relationships among hormonal and physiological biomarkers, psychological well-being indicators, and field performance metrics in competitive athletes. Data were collected over a complete competitive season, from January 13 to May 7, 2025, from 13 professional male basketball players, on days between matches and on the day of the match, for 15 consecutive weeks. A minimum of 129 complete observations per metric were used for descriptive and stratification statistics. All participants provided informed consent, and the study was approved by the institutional ethics review board in alignment with the WMA Declaration of Helsinki - Ethical Principles for Medical Research Involving Human Participants. Figure\\u0026nbsp;1 gives a schematic representation of the study design.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 | Participants\\u003c/h2\\u003e \\u003cp\\u003eThis study focused on 13 competitive male basketball players of Caucasian, African American, and Hispanic ethnicity from an Italian league (Pallacanestro Trieste team, Trieste, Italy). The cohort included diverse playing positions and experience levels representative of professional basketball populations. Players demonstrated mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD weight of 93.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.8 kg (range: 80\\u0026ndash;109 kg) and height of 198.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5 cm (range: 183\\u0026ndash;213 cm), representative of professional basketball populations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 | Data collection\\u003c/h2\\u003e \\u003cp\\u003eThe dataset contains 12 assessment metrics in an inter-game day and 23 Field metrics which were collected from each player for 15 games. The following sections elaborate further the assessment and performance metrics as independent and dependent variables, respectively.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.1 | Independent Variables\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section4\\\"\\u003e \\u003ch2\\u003e2.3.1.1 | Salivary Biomarkers (n\\u0026thinsp;=\\u0026thinsp;4)\\u003c/h2\\u003e \\u003cp\\u003eSalivary samples were collected in between matches (2\\u0026ndash;3 days after the last match, Recovery day) under standardized conditions (morning collection, fasted state, no oral intake 30 minutes prior) using the passive drool technique. Samples were immediately stored at -20\\u0026deg;C until batch analysis. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e provides the data obtained for salivary biomarkers, quantified using paper-strip dry-chemistry (Nitrates; MyFit Strip, USA) or validated commercial ELISA kits (Cortisol and Testosterone; Diametra, Italy).\\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\\u003eData values obtained for 12 recovery biometrics\\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=\\\"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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e Biometric\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStd Dev\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRange\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSalivary Nitrates (level)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.00\\u0026ndash;3.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCortisol (ng/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;21.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTestosterone (pg/ml)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e245.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e100.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;575.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTCR (T/C Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;0.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTiredness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSleep Quality\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSleep Duration (hrs)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEnergy Level\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStress Level\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCalmness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnger\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMuscle Soreness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1\\u0026ndash;10\\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 \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.3 | Self-Reported Psychological and Wellness Metrics (n\\u0026thinsp;=\\u0026thinsp;8)\\u003c/h2\\u003e \\u003cp\\u003eAthletes completed wellness questionnaires on the day of sampling using validated 10-point Likert scales assessing Self-reported psychological states including Anger, Calmness, Stress level, Energy Level, Muscle Soreness, Tiredness, Sleep Quality, and Sleep Time (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Sleep Time Duration was categorically and assessed based on the previous night's sleep broken down in 3 time scales: i) 7\\u0026ndash;9 hours, ii) 5\\u0026ndash;7 hours, and iii) less than 5 hours.\\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\\u003eData values obtained for field goal and free throw in percentage (%).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGame Metric\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDistribution\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2-Point Field Goal % (2 PT%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e61.0% (Std: 24.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3-Point Field Goal % (3 PT %)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44.7% (Std: 20.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFree Throw % (FT %)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e77.3% (Std: 22.7%)\\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 \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 | Dependent Variables (Game Metrics)\\u003c/h2\\u003e \\u003cp\\u003eField performance metrics (n\\u0026thinsp;=\\u0026thinsp;26) extracted from official game statistics from LBA Italy are as follows: Playing Time and Status (Minutes and Starters), Fouls (Fouls Committed, Fouls Suffered) Two-Point Shooting (2PT Made, 2PT Attempted, 2PT %), Three-Point Shooting (3PT Made, 3PT Attempted, 3PT %), Free Throws (FT Made, FT Attempted, FT %), Rebounds (Off Rebounds, Def Rebounds and Total Rebounds), Ball handling (Scoring Play, Turnovers, Steals, Assists), blocks (Blocks Given, Blocks Received) and composite Metrics [Player efficiency rating (EFF), Performance Index Rating/minutes played (PIR/min) and Plus/Minus].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 | Statistical Analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.5.1 | Differentiation Analysis\\u003c/h2\\u003e \\u003cp\\u003eDescriptive statistics was conducted on each variable to assess the distribution of each metric in the sampled population. Variables are represented as mean and SD from the mean and/or distribution of the values expressed in % points. Multiple variables were differentiated using 1-way ANOVA. Kruskal-Wallis H tests were conducted to identify individual variables with statistically significant differences across performance strata, as the assumption of normality was not met for all variables. Statistical significance was set at α\\u0026thinsp;=\\u0026thinsp;0.05\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.5.2 | Correlation Analysis\\u003c/h2\\u003e \\u003cp\\u003ePrior to conducting regression analyses, Pearson or Spearman correlations were conducted to assess the strength of correlations between the 12 independent variables. Collinearity diagnostics was performed to mitigate overfitting in subsequent regression analyses. Collinearity diagnostics was performed to mitigate overfitting in subsequent regression analyses.\\u003c/p\\u003e \\u003cp\\u003eAlso, to examine the relationships/correlations between independent variables and game performance metrics, we assessed multicollinearity among the 12 predictor variables (4 biomarkers and 8 self-reported measures). Pearson or Spearman correlations, Variance Inflation Factor (VIF), condition number analysis, and eigenvalue decomposition were employed to comprehensively evaluate the collinearity structure among the independent variables.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.5.3| Classification Analysis\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section4\\\"\\u003e \\u003ch2\\u003e2.5.3.1 | Player Stratification\\u003c/h2\\u003e \\u003cp\\u003ePlayers were stratified into three performance groups (Low, Medium, High) based on Plus/Minus (+/-), Player efficiency rating (EFF), Performance Index Rating/minutes played (PIR/min) and minutes played (min) using tertile-based classification. Differences in the 12 recovery variables between groups were assessed using Kruskal-Wallis test.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.5.5 | Data processing and analysis\\u003c/h2\\u003e \\u003cp\\u003eMissing data were handled using complete-case analysis. Variables were standardized prior to model fitting. Model performance was evaluated using out-of-bag error estimates inherent to SVM methodology. All analyses were conducted using Python 3.12 with the following packages: scikit-learn (v1.3.0) implementation and cross-validation, and SciPy (Virtanen et al., 2020) for non-parametric statistical tests. Pandas (v2.0.0) for data manipulation, and Matplotlib (v3.7.0) and Seaborn (v0.12.0) for visualization.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"3 | RESULTS\",\"content\":\"\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 | Team Performance Overview\\u003c/h2\\u003e \\u003cp\\u003eIn the 2025 season (January 13 - May 7th) had a win rate of 56.3%, maintaining its league rating (Rank\\u0026thinsp;=\\u0026thinsp;6) among the best 8 teams in Italy.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 | Biological and Psychophysical Status\\u003c/h2\\u003e \\u003cp\\u003eSalivary Nitrites were analyzed from whole saliva using paper strips on a graded scale (1\\u0026thinsp;=\\u0026thinsp;low, 2\\u0026thinsp;=\\u0026thinsp;medium, 3\\u0026thinsp;=\\u0026thinsp;high) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Testosterone and Cortisol were analyzed by ELISA and computed as TCR (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), representing anabolic-catabolic balance (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAthletes completed a wellness questionnaire corresponding to a Mini-P OMS using validated 10-point Likert scales assessing Self-reported psychological states including: Tiredness, Sleep Quality, Energy Level, Stress Level, Calmness, Anger, Muscle Soreness (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSleep Time Duration was categorically and assessed based on previous night's sleep broken down in three time scales: i) 7\\u0026ndash;9 hours (49.2%), 5\\u0026ndash;7 hours (43.0%) and less than 5 hours (5.5%) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 | Match-day Metrics\\u003c/h2\\u003e \\u003cp\\u003eThe Field metrics on Match Day are collected per athlete and summarized in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e capturing the average field metrics for the season. The team showed a balanced distribution of Game metrics expressed in % points (2-Point Field Goals, 3-Point Field Goals and Free Throws) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) or absolute numbers (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) aligned with their league rating.\\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\\u003eData values obtained for other game metrics in absolute numbers.\\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=\\\"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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGame Metric\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStd Dev\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eRange\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStarters\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFouls Committed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;5.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFouls Suffered\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.84\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;9.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2PT Made\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;7.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2PT Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;13.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eScoring Plays\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;2.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3PT Made\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;6.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3PT Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;11.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFT Made\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;10.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFT Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;11.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOff Rebounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;7.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDef Rebounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;10.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal Rebounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;13.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlocks Given\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;2.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlocks Received\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;2.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTurnovers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;6.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSteals\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;3.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAssists\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;11.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeague Rating\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.00\\u0026ndash;29.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOER Rating\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.00\\u0026ndash;2.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlus/Minus\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-1.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-35.00\\u0026ndash;27.00\\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=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 | Associations among independent variables\\u003c/h2\\u003e \\u003cp\\u003ePrior to conducting regression analyses to examine the relationships between recovery variables and game performance metrics, we assessed multicollinearity among the 12 predictor variables (4 biomarkers and 8 self-reported measures). Pearson correlations, Variance Inflation Factor (VIF), condition number analysis, and eigenvalue decomposition were employed to comprehensively evaluate the collinearity structure among the recovery variables.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.4.1 | Correlations among bio-psycho-physical variables\\u003c/h2\\u003e \\u003cp\\u003eFigure 2A shows the correlation matrix for the 12 predictor variables (4 biomarkers and 8 self-reported measures), computed using Pearson correlations. The multicollinearity assessment indicated a moderate overall risk, which was deemed manageable for subsequent regression analyses. Of the 66 possible variable pairs, 35 (53%) showed statistically significant correlations (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) (Fig.\\u0026nbsp;2A). Among these, seven pairs exhibited strong correlations (|r| \\u0026ge; 0.5), while six pairs demonstrated moderate correlations (0.3 \\u0026le; |r| \\u0026lt; 0.5) (Fig.\\u0026nbsp;2A). The observed correlations among recovery variables were largely expected and conceptually meaningful. The strongest correlation was found between Stress level and Anger (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.69), reflecting the psychological overlap between these emotional states. Similarly, Stress level showed a strong negative correlation with Calmness (r = -0.64), as these represent opposite ends of the arousal-affect spectrum. Sleep quality and Sleep Time Score were positively correlated (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.63), which is expected given both variables capture aspects of sleep recovery. The correlation between Tiredness and Muscle Soreness (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.59) suggests a physical fatigue cluster where athletes experiencing muscular discomfort also report higher subjective tiredness. The negative correlation between Cortisol and TCR (r = -0.56) is mathematically expected since TCR is calculated using Cortisol in the denominator.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec26\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.4.1 | Collinearity among bio-psycho-physical variables\\u003c/h2\\u003e \\u003cp\\u003eMulticollinearity occurs when predictor variables are highly correlated, which can inflate standard errors, reduce statistical power, and render coefficient estimates unstable and difficult to interpret. We employed multiple diagnostic approaches Variance Inflation Factor (VIF), condition number analysis, and eigenvalue decomposition to comprehensively evaluate the collinearity structure among recovery variables as shown in Fig.\\u0026nbsp;2B. All 12 variables had VIF values below the commonly accepted threshold of 5, with the highest VIF observed for Stress level (VIF\\u0026thinsp;=\\u0026thinsp;2.66) and the lowest for Salivary Nitrates (VIF\\u0026thinsp;=\\u0026thinsp;1.08) (Fig.\\u0026nbsp;2B). The VIF analysis confirmed that multicollinearity was within acceptable limits for all predictor variables. VIF quantifies how much the variance of a regression coefficient is inflated due to linear dependence with other predictors. A VIF value of 1 indicates no correlation with other variables, while values exceeding 5 or 10 are typically considered problematic. In the present study, all VIF values ranged from 1.08 to 2.66, indicating that no single predictor was excessively collinear with the remaining variables. Consistent with this, all tolerance values (1/VIF) exceeded 0.2, further supporting the absence of severe multicollinearity.\\u003c/p\\u003e \\u003cp\\u003eAdditional diagnostic measures corroborated these findings. The condition number of the predictor correlation matrix was 3.96, well below the threshold of 30 that would indicate problematic multicollinearity. Eigenvalue analysis revealed no values below 0.1, which would have suggested near-linear dependencies among predictors. These results collectively indicate that although the recovery variables share some common variance, the degree of multicollinearity does not threaten the validity of regression analyses.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 | Associations among independent variables and Field Metrics\\u003c/h2\\u003e \\u003cp\\u003eSpearman Correlation analysis was used to assess associations among self-reported psycho-physical measures, recovery fluid biomarkers, and 23 game metrics to investigate the multifaceted influence on basketball field outcomes (Fig.\\u0026nbsp;3A and 3B). Composite metrics such as EFF, Plus/Minus and PIR/min were excluded from the correlation analysis to avoid inflation.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec28\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.5.1 | Recovery \\u0026amp; Overload markers\\u003c/h2\\u003e \\u003cp\\u003eMuscle soreness exhibited the highest number of significant correlations (n\\u0026thinsp;=\\u0026thinsp;14), all positive in direction, indicating that greater reported fatigue was consistently associated with enhanced game performance metrics (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and Fig.\\u0026nbsp;3A). The strongest positive correlations were observed were observed with contact-related metrics: fouls suffered (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.43, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and FT attempted (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.42, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), followed by FT made (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.37, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Rebounding metrics showed consistent positive associations, including total rebounds (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.36, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), defensive rebounds (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.32, p\\u0026thinsp;=\\u0026thinsp;0.001), and offensive rebounds (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.29, p\\u0026thinsp;=\\u0026thinsp;0.003). Shooting variables demonstrated moderate correlations with 2PT attempted (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.30, p\\u0026thinsp;=\\u0026thinsp;0.002) and 2PT made (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.27, p\\u0026thinsp;=\\u0026thinsp;0.006). Performance indicators including, scoring plays (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.26, p\\u0026thinsp;=\\u0026thinsp;0.007), and points scored (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.26, p\\u0026thinsp;=\\u0026thinsp;0.007) were also positively associated. Weaker but significant correlations were found with FT percentage (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.21, p\\u0026thinsp;=\\u0026thinsp;0.030), blocks given (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.21, p\\u0026thinsp;=\\u0026thinsp;0.031), and fouls committed (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.20, p\\u0026thinsp;=\\u0026thinsp;0.037).\\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\\u003eData values obtained for Top 10 Significant Correlations found in Pearson and Spearman correlation analysis.\\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=\\\"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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRecovery Variable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eField Metric\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003er\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMuscle_Soreness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFouls_Suffered\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMuscle_Soreness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFT_Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnger\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTurnovers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnger\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3PT_Made\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAnger\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3PT_Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTiredness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal_Rebounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMuscle_Soreness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFT_Made\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMuscle_Soreness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal_Rebounds\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTiredness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFT_Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTiredness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2PT_Attempted\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\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\\u003eTiredness exhibited the second highest number of significant correlations (n\\u0026thinsp;=\\u0026thinsp;13) all positive in direction, indicating that greater reported fatigue was consistently associated with enhanced game performance metrics. (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and Fig.\\u0026nbsp;3). The strongest associations were observed with rebounding variables: total rebounds (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.37, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), offensive rebounds (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.35, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and defensive rebounds (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.31, p\\u0026thinsp;=\\u0026thinsp;0.001). Free throw metrics also demonstrated robust positive correlations, including FT attempted (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.36, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and FT made (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.27, p\\u0026thinsp;=\\u0026thinsp;0.005). Shooting involvement showed moderate positive associations, with 2PT attempted (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.35, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 2PT made (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.28, p\\u0026thinsp;=\\u0026thinsp;0.004). Physical contact metrics were positively correlated, including fouls suffered (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.34, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and blocks received (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.28, p\\u0026thinsp;=\\u0026thinsp;0.005). Additional significant positive correlations were found with points scored (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.009), starter status (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.011), and blocks given (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.21, p\\u0026thinsp;=\\u0026thinsp;0.033).\\u003c/p\\u003e \\u003cp\\u003eSleep Time Score exhibited 13 significant correlations, all negative in direction, representing a paradoxical finding wherein longer sleep duration was associated with reduced game performance metrics (Fig.\\u0026nbsp;3). The strongest negative correlation was observed with shooting variables: 2PT made (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.33, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and 2PT attempted (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.32, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Physical engagement metrics demonstrated consistent negative associations, including fouls suffered (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.32, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), starter status (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.31, p\\u0026thinsp;=\\u0026thinsp;0.001), and total rebounds (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.30, p\\u0026thinsp;=\\u0026thinsp;0.002). Scoring metrics were negatively correlated, including points (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.30, p\\u0026thinsp;=\\u0026thinsp;0.002) and FT attempted (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.29, p\\u0026thinsp;=\\u0026thinsp;0.003). Additional negative correlations were found with offensive rebounds (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.29, p\\u0026thinsp;=\\u0026thinsp;0.002), FT made (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.26, p\\u0026thinsp;=\\u0026thinsp;0.008), defensive rebounds (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.009), blocks received (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.22, p\\u0026thinsp;=\\u0026thinsp;0.021), and minutes played (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.21, p\\u0026thinsp;=\\u0026thinsp;0.032).\\u003c/p\\u003e \\u003cp\\u003eSleep Quality demonstrated only one significant correlation: a negative association with blocks received (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.20, p\\u0026thinsp;=\\u0026thinsp;0.037; Fig.\\u0026nbsp;3). The limited associations suggest that subjective sleep quality may be less relevant to game performance than sleep duration on Recovery days.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec29\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.5.2 | Psychophysical Metrics\\u003c/h2\\u003e \\u003cp\\u003eAnger emerged as the most consistent negative predictor across multiple game metrics (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e and Fig.\\u0026nbsp;3). The most robust negative association was observed with turnovers (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.41, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), indicating that lower anger levels were associated with increased ball-handling activity. Three-point shooting metrics showed consistent strong negative correlations: 3PT made (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.39, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), 3PT attempted (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.39, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and 3PT percentage (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.34, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e \\u003cp\\u003ePlaying involvement metrics demonstrated moderate negative associations, including minutes played (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.31, p\\u0026thinsp;=\\u0026thinsp;0.001), assists (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.31, p\\u0026thinsp;=\\u0026thinsp;0.001), and fouls committed (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.31, p\\u0026thinsp;=\\u0026thinsp;0.001). Additional negative correlations were found with points scored (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.28, p\\u0026thinsp;=\\u0026thinsp;0.004) and starter status (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.011). These findings suggest that elevated anger levels are consistently associated with reduced offensive engagement and perimeter shooting performance.\\u003c/p\\u003e \\u003cp\\u003eCalmness exhibited eight significant correlations, predominantly positive (n\\u0026thinsp;=\\u0026thinsp;7), representing an inverse pattern to Anger (Fig.\\u0026nbsp;3). The strongest associations were with three-point shooting metrics: 3PT percentage (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.33, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), 3PT made (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.32, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and 3PT attempted (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.29, p\\u0026thinsp;=\\u0026thinsp;0.002). Playmaking and game involvement showed positive associations with assists (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.009), fouls committed (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.011), turnovers (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.22, p\\u0026thinsp;=\\u0026thinsp;0.025), and minutes played (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.20, p\\u0026thinsp;=\\u0026thinsp;0.040). Conversely, calmness was negatively associated with blocks given (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.23, p\\u0026thinsp;=\\u0026thinsp;0.017), suggesting that heightened composure may reduce defensive shot-blocking aggression.\\u003c/p\\u003e \\u003cp\\u003eStress level demonstrated four significant correlations, all negative in direction (Fig.\\u0026nbsp;3). The strongest negative association was observed with fouls committed (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.24, p\\u0026thinsp;=\\u0026thinsp;0.015), followed by 3PT percentage (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.22, p\\u0026thinsp;=\\u0026thinsp;0.027), turnovers (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.20, p\\u0026thinsp;=\\u0026thinsp;0.044), and assists (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.19, p\\u0026thinsp;=\\u0026thinsp;0.047). This pattern suggests that elevated stress levels may impair both perimeter shooting accuracy and playmaking involvement.\\u003c/p\\u003e \\u003cp\\u003eEnergy Level showed only one significant correlation: a negative association with blocks given (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.21, p\\u0026thinsp;=\\u0026thinsp;0.028) (Fig.\\u0026nbsp;3). The paucity of significant associations indicates that self-reported energy levels on Recovery days may not directly translate to game performance metrics.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec30\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.5.3 | Salivary Steroidal Hormones and Markers\\u003c/h2\\u003e \\u003cp\\u003eTestosterone demonstrated three significant correlations with mixed directionality (Fig.\\u0026nbsp;3). A positive association was observed with fouls committed (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.22, p\\u0026thinsp;=\\u0026thinsp;0.025), consistent with testosterone's role in promoting aggressive behavior. Conversely, negative correlations were found with blocks given (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.25, p\\u0026thinsp;=\\u0026thinsp;0.008) and FT percentage (r\\u0026thinsp;=\\u0026thinsp;\\u0026minus;\\u0026thinsp;0.23, p\\u0026thinsp;=\\u0026thinsp;0.019), suggesting potential trade-offs between hormonal arousal and fine motor precision.\\u003c/p\\u003e \\u003cp\\u003eCortisol exhibited two significant positive correlations: blocks received (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.28, p\\u0026thinsp;=\\u0026thinsp;0.004) and steals (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.23, p\\u0026thinsp;=\\u0026thinsp;0.017) (Fig.\\u0026nbsp;3). These associations suggest that elevated cortisol levels may enhance alertness and reactive defensive behaviors, potentially reflecting an adaptive stress response that promotes active engagement in physical play.\\u003c/p\\u003e \\u003cp\\u003eTCR, commonly interpreted as an indicator of anabolic-catabolic balance, demonstrated no significant correlations with any field performance metric on Recovery days (Fig.\\u0026nbsp;3). This null finding suggests that the hormonal balance measured during recovery periods may not directly predict subsequent game performance, or that the relationship between TCR and performance is mediated by other factors not captured in the current analysis.\\u003c/p\\u003e \\u003cp\\u003eSalivary Nitrates demonstrated one significant positive correlation with steals (r\\u0026thinsp;=\\u0026thinsp;+\\u0026thinsp;0.23, p\\u0026thinsp;=\\u0026thinsp;0.017) (Fig.\\u0026nbsp;3). Nitric oxide, derived from dietary nitrates, enhances blood flow and oxygen delivery to working muscles. This association suggests that adequate nitrate availability may support the sustained high-intensity efforts required for successful defensive plays.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec31\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 | Stratification analysis\\u003c/h2\\u003e \\u003cp\\u003eA stratification analysis was performed to examine the associations between recovery metrics, biomarkers, and four key efficiency outcomes in basketball: Plus/Minus (+/-), Efficiency (EFF), Player Impact Rating per minute (PIR/min), and minutes played (MIN). The dataset comprised 145 recovery observations with available performance data (n\\u0026thinsp;=\\u0026thinsp;139 for EFF and PIR/min due to missing values). Each outcome was stratified into tertiles (Low, Medium, High) to identify non-linear relationships between predictor variables and performance levels.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec32\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.6.1 | Distribution of Outcome Metrics\\u003c/h2\\u003e \\u003cp\\u003eTertile stratification evidenced the distribution of the strata per metric outcome (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA). The Plus/Minus metric showed a near-normal distribution centered slightly below zero (tertile thresholds: Low \\u0026lt; -2.048, Medium\\u0026thinsp;\\u0026minus;\\u0026thinsp;2.048 to 7.000, High\\u0026thinsp;\\u0026ge;\\u0026thinsp;7.000), reflecting balanced positive and negative on-court contributions. Efficiency (EFF) displayed a right-skewed distribution (tertile thresholds: Low\\u0026thinsp;\\u0026lt;\\u0026thinsp;5.000, Medium 5.000 to 12.000, High\\u0026thinsp;\\u0026ge;\\u0026thinsp;12.000), with most observations concentrated in the lower efficiency range. PIR/min exhibited a relatively uniform distribution across tertiles (Low\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.281, Medium 0.281 to 0.539, High\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.539), suggesting consistent variability in player impact. Minutes played (MIN) showed a bimodal pattern (tertile thresholds: Low\\u0026thinsp;\\u0026lt;\\u0026thinsp;14.952, Medium 14.952 to 25.000, High\\u0026thinsp;\\u0026ge;\\u0026thinsp;25.000), distinguishing between limited and substantial playing time.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec33\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.6.2 | Significant Predictors of Performance Outcomes\\u003c/h2\\u003e \\u003cp\\u003eNon-parametric tests were conducted to identify variables with statistically significant differences across strata considering the 8 recovery self-reported metrics [Muscle Soreness, Anger, Calmness, Stress Level, Energy Level, Tiredness, Sleep Quality, and Sleep Time Score] (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB) and 4 biological variables: Nitrates, Cortisol (nmol/L), Testosterone (nmol/L), and TCR, psychological metrics (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC). Plus/Minus performance showed significant associations with psychological recovery dimensions (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Anger emerged as the strongest predictor (H\\u0026thinsp;=\\u0026thinsp;10.66, p\\u0026thinsp;=\\u0026thinsp;0.0049, ε\\u0026sup2; = 0.076, medium effect), with players in the low Plus/Minus tertile reporting lower anger levels (M\\u0026thinsp;=\\u0026thinsp;2.10) compared to those in the medium tertile (M\\u0026thinsp;=\\u0026thinsp;3.26). Stress also demonstrated a significant association (H\\u0026thinsp;=\\u0026thinsp;7.73, p\\u0026thinsp;=\\u0026thinsp;0.021, ε\\u0026sup2; = 0.055, small effect), following a similar pattern with lowest stress in the low Plus/Minus group (M\\u0026thinsp;=\\u0026thinsp;3.00) and highest in the medium tertile (M\\u0026thinsp;=\\u0026thinsp;3.65). These findings suggest that affective state, particularly anger and stress, may influence net on-court contribution, though the relationships were non-linear across performance strata.\\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\\u003eAssociations between recovery-related variables and Plus/Minus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\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=\\\"left\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eε\\u0026sup2;\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEffect size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eΔ High\\u0026ndash;Low\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAnger\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e10.66\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0049\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.076\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStress\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7.73\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.021\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.055\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTiredness\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e5.19\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.075\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.037\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.61\\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\\u003eEfficiency performance was significantly associated with sleep-related recovery and muscle condition (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Sleep time showed the strongest association with EFF (H\\u0026thinsp;=\\u0026thinsp;11.57, p\\u0026thinsp;=\\u0026thinsp;0.0031, ε\\u0026sup2; = 0.086, medium effect), with an inverse relationship where the low EFF tertile reported the highest sleep duration (M\\u0026thinsp;=\\u0026thinsp;0.97 hrs above baseline) compared to the high EFF tertile (M\\u0026thinsp;=\\u0026thinsp;0.89 hrs above baseline). Muscle soreness demonstrated a positive association (H\\u0026thinsp;=\\u0026thinsp;6.09, p\\u0026thinsp;=\\u0026thinsp;0.048, ε\\u0026sup2; = 0.045, small effect), with higher EFF tertiles reporting greater muscle soreness (Low: M\\u0026thinsp;=\\u0026thinsp;3.43; High: M\\u0026thinsp;=\\u0026thinsp;4.29).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAssociations between recovery-related variables and Efficiency (EFF)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\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=\\\"left\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eε\\u0026sup2;\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEffect size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eΔ High\\u0026ndash;Low\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSleep time\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0031\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.086\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMuscle soreness\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.048\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.86\\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\\u003ePIR/min emerged as the outcome metric most sensitive to multiple recovery dimensions, showing seven significant associations spanning subjective recovery measures and physiological biomarkers (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Muscle soreness was the strongest predictor (H\\u0026thinsp;=\\u0026thinsp;10.97, p\\u0026thinsp;=\\u0026thinsp;0.0042, ε\\u0026sup2; = 0.081, medium effect), with the high PIR/min tertile reporting substantially greater soreness (M\\u0026thinsp;=\\u0026thinsp;4.60) compared to the low tertile (M\\u0026thinsp;=\\u0026thinsp;3.53). Stress demonstrated a significant but non-linear association (H\\u0026thinsp;=\\u0026thinsp;8.65, p\\u0026thinsp;=\\u0026thinsp;0.013, ε\\u0026sup2; = 0.064, medium effect), with the medium tertile showing lowest stress levels (M\\u0026thinsp;=\\u0026thinsp;2.59). Tiredness showed a positive gradient across tertiles (H\\u0026thinsp;=\\u0026thinsp;7.74, p\\u0026thinsp;=\\u0026thinsp;0.021, ε\\u0026sup2; = 0.057, small effect), with higher PIR/min associated with greater tiredness (Low: M\\u0026thinsp;=\\u0026thinsp;3.36; High: M\\u0026thinsp;=\\u0026thinsp;4.26). Calmness was also significantly associated (H\\u0026thinsp;=\\u0026thinsp;7.01, p\\u0026thinsp;=\\u0026thinsp;0.030, ε\\u0026sup2; = 0.052, small effect). Among physiological biomarkers (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e), salivary nitrates (H\\u0026thinsp;=\\u0026thinsp;6.84, p\\u0026thinsp;=\\u0026thinsp;0.033, ε\\u0026sup2; = 0.050, small effect) and cortisol (H\\u0026thinsp;=\\u0026thinsp;6.17, p\\u0026thinsp;=\\u0026thinsp;0.046, ε\\u0026sup2; = 0.045, small effect) showed significant associations with PIR/min, though both displayed non-monotonic patterns across tertiles. Sleep time again showed an inverse association (H\\u0026thinsp;=\\u0026thinsp;6.12, p\\u0026thinsp;=\\u0026thinsp;0.047, ε\\u0026sup2; = 0.045, small effect). These findings suggest that PIR/min integrates neuromuscular, psychological, and endocrine recovery dimensions, potentially serving as a comprehensive indicator of physiological readiness.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAssociations between recovery-related variables and Player Impact Rating per minute (PIR/min)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\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=\\\"left\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eε\\u0026sup2;\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEffect size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eΔ High\\u0026ndash;Low\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMuscle soreness\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e10.97\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0042\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.081\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;1.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStress\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e8.65\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.013\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.064\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e3.50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTiredness\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7.74\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.0209\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.057\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNitrates\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e6.84\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.033\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.050\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCortisol\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e6.17\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.046\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.045\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e16.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e19.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e15.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.56\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSleep time\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e6.12\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.047\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.045\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026minus;0.06\\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\\u003eMinutes played showed seven significant associations, with the strongest effects observed for sleep and psychological variables (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). Sleep time was the most robust predictor across all outcomes (H\\u0026thinsp;=\\u0026thinsp;18.47, p\\u0026thinsp;=\\u0026thinsp;0.0001, ε\\u0026sup2; = 0.131, medium effect), with an inverse relationship where players with fewer minutes reported higher sleep duration (Low: M\\u0026thinsp;=\\u0026thinsp;0.97 hrs above baseline) compared to those with more minutes (High: M\\u0026thinsp;=\\u0026thinsp;0.88 hrs above baseline). Stress (H\\u0026thinsp;=\\u0026thinsp;12.85, p\\u0026thinsp;=\\u0026thinsp;0.002, ε\\u0026sup2; = 0.091, medium effect) and anger (H\\u0026thinsp;=\\u0026thinsp;10.94, p\\u0026thinsp;=\\u0026thinsp;0.004, ε\\u0026sup2; = 0.078, medium effect) both showed strong negative associations with playing time, with the high minutes tertile reporting substantially lower stress (M\\u0026thinsp;=\\u0026thinsp;2.78 vs. 3.80 in low tertile) and anger (M\\u0026thinsp;=\\u0026thinsp;2.14 vs. 3.15 in low tertile). Calmness demonstrated a positive association (H\\u0026thinsp;=\\u0026thinsp;7.46, p\\u0026thinsp;=\\u0026thinsp;0.024, ε\\u0026sup2; = 0.053, small effect), increasing across tertiles (Low: M\\u0026thinsp;=\\u0026thinsp;6.02; High: M\\u0026thinsp;=\\u0026thinsp;7.16). Tiredness showed a positive gradient (H\\u0026thinsp;=\\u0026thinsp;6.42, p\\u0026thinsp;=\\u0026thinsp;0.040, ε\\u0026sup2; = 0.046, small effect) and calmness was also significant (H\\u0026thinsp;=\\u0026thinsp;7.46, p\\u0026thinsp;=\\u0026thinsp;0.023, ε\\u0026sup2; = 0.054, small effect). Among salivary biomarkers, testosterone was significantly associated with minutes played (H\\u0026thinsp;=\\u0026thinsp;8.00, p\\u0026thinsp;=\\u0026thinsp;0.018, ε\\u0026sup2; = 0.056, small effect), with the medium and high tertiles showing elevated testosterone levels (Medium: M\\u0026thinsp;=\\u0026thinsp;0.89; High: M\\u0026thinsp;=\\u0026thinsp;0.85) compared to the low tertile (M\\u0026thinsp;=\\u0026thinsp;0.71). These findings indicate that minutes played is sensitive to both psychological state and endocrine function, suggesting that coaches\\u0026rsquo; playing time decisions may implicitly integrate multiple dimensions of athlete readiness.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAssociations between recovery-related variables and minutes played\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\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=\\\"left\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eH\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eε\\u0026sup2;\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eEffect size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eΔ High\\u0026ndash;Low\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSleep Time Score\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e18.47\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.000\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.131\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-0.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eStress level\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e12.85\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.002\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.091\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-1.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAnger\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e10.94\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.078\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMedium\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-1.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTestosterone nmol/L\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e8.00\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.018\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.056\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCalmness\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7.46\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.024\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.053\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e7.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;1.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTiredness\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e6.42\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.040\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.046\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eSmall\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e+\\u0026thinsp;0.88\\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 \\u003c/div\\u003e\"},{\"header\":\"4. DISCUSSION\",\"content\":\"\\u003cdiv id=\\\"Sec35\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Principal Findings\\u003c/h2\\u003e \\u003cp\\u003eThe purpose of this study was to examine associations between biological, psychological, and physical recovery markers and game performance in professional basketball across a competitive season. Within a high-performance context (top-8 league standing; 56.3% win rate), several key findings emerged. First, subjective indicators of tiredness and muscle soreness were positively associated with performance metrics reflecting competitive engagement. Second, sleep-related variables showed inverse associations with several performance indicators, contrary to prevailing assumptions in the literature. Third, psychological states demonstrated clear domain-specific relationships with performance, particularly for shooting-related outcomes. Fourth, hormonal biomarkers were selectively associated with offensive and defensive behaviors. Finally, a multivariate model incorporating recovery-related variables provided modest but meaningful discrimination between performance strata.\\u003c/p\\u003e \\u003cp\\u003eCollectively, these findings highlight the complex, non-linear, and context-dependent nature of recovery\\u0026ndash;performance relationships in elite basketball.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec36\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Physical Recovery Markers as as Proxies for Competitive Load\\u003c/h2\\u003e \\u003cp\\u003eSelf-reported tiredness and muscle soreness were positively associated with minutes played, free throw activity, and interior play actions. Within this cohort, these subjective markers appear to reflect \\u003cb\\u003eexposure to competitive demands\\u003c/b\\u003e rather than impaired readiness.\\u003c/p\\u003e \\u003cp\\u003eThis interpretation is consistent with frameworks suggesting that moderate physiological and perceptual strain may occur alongside maintained performance in elite athletes operating within their adaptive capacity (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). Muscle soreness likely reflects the eccentric loading and physical contact inherent to rebounding and interior play (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThese associations should not be interpreted as evidence that fatigue or soreness enhances performance. Rather, they indicate that subjective recovery markers may co-vary with playing role and match exposure, underscoring the importance of contextualized and longitudinal interpretation in applied monitoring.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec37\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Sleep Metrics and the Recovery\\u0026ndash;Performance Paradox\\u003c/h2\\u003e \\u003cp\\u003eSleep duration and perceived sleep quality were inversely associated with several performance indicators. These findings contrast with controlled experimental evidence demonstrating performance benefits of sleep extension (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e), suggesting that \\u003cb\\u003econtextual factors inherent to elite competition\\u003c/b\\u003e may influence observed associations in applied settings.\\u003c/p\\u003e \\u003cp\\u003eSeveral explanations remain plausible, including reverse causality related to playing time, the distinction between sleep duration and sleep quality or timing (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e), and potential non-linear relationships between sleep duration and performance (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). Given the observational design, these explanations cannot be disentangled.\\u003c/p\\u003e \\u003cp\\u003eAccordingly, these results indicate that sleep duration alone may be an insufficient marker of readiness in professional basketball, and should be interpreted alongside playing exposure, individual baselines, and longitudinal trends.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec38\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Psychological States and Task-Specific Performance Demands\\u003c/h2\\u003e \\u003cp\\u003ePsychological states demonstrated task-specific associations with performance outcomes. Anger was negatively associated with shooting efficiency and playing time, consistent with evidence linking elevated negative affect to impaired attentional control and decision-making (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e). Calmness was positively associated with three-point shooting and assists, aligning with optimal arousal models for precision-based tasks (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eStress showed differentiated associations, negatively related to perimeter shooting but positively associated with interior scoring and defensive actions. This pattern is consistent with prior evidence that elevated arousal may differentially affect precision and reactive performance demands(\\u003cspan additionalcitationids=\\\"CR38\\\" citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThese findings support the interpretation that psychological readiness is domain-specific, rather than uniformly beneficial or detrimental across performance outcomes.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec39\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5 Hormonal Biomarkers and Behavioral Tendencies\\u003c/h2\\u003e \\u003cp\\u003eHormonal biomarkers were selectively associated with behavioral performance indicators. Testosterone was associated with markers of engagement and risk-taking, consistent with prior work linking testosterone to dominance-related behaviors (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). However, its negative association with plus/minus suggests that increased individual activity does not necessarily translate to improved team outcomes.\\u003c/p\\u003e \\u003cp\\u003eCortisol was positively associated with defensive actions and interior scoring, consistent with evidence that moderate cortisol elevations may coincide with heightened vigilance and reactivity (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e). The TCR showed limited associations, suggesting restricted utility as an acute performance marker in this context.\\u003c/p\\u003e \\u003cp\\u003eSalivary nitrates were modestly associated with assists and steals, in line with literature linking nitric oxide bioavailability to vascular and cognitive-motor processes (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e). Given the observational design, these associations should be interpreted cautiously.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec40\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.6 Multivariate weighted stratification and Practical Considerations\\u003c/h2\\u003e \\u003cp\\u003eThe stratification analysis extended the univariate findings by examining how recovery-related variables discriminate across tertiles of performance outcomes. This approach revealed several patterns with practical implications for athlete monitoring and performance prediction in elite basketball.\\u003c/p\\u003e \\u003cp\\u003eThe four performance outcomes demonstrated distinct sensitivity to recovery-related variables. Minutes played and PIR/min each showed seven significant predictors (35% of tests), indicating that these metrics integrate multiple dimensions of athlete readiness. In contrast, Plus/Minus demonstrated only two significant predictors (10% of tests), suggesting that this team-oriented metric may be less sensitive to individual recovery states or more strongly influenced by contextual factors such as opponent quality, lineup composition, and game situation.\\u003c/p\\u003e \\u003cp\\u003eThe predominance of sleep time as a predictor across multiple outcomes (H\\u0026thinsp;=\\u0026thinsp;18.47 for MIN, H\\u0026thinsp;=\\u0026thinsp;11.57 for EFF, H\\u0026thinsp;=\\u0026thinsp;6.12 for PIR/min) underscores its cross-cutting relevance, though the inverse associations observed suggest compensatory rather than causal relationships. Similarly, psychological variables (anger, stress, calmness, tiredness) collectively accounted for the majority of significant associations, particularly for MIN and Plus/Minus, reinforcing the interpretation that affective state is a critical component of performance readiness in basketball.\\u003c/p\\u003e \\u003cp\\u003eThe finding that muscle soreness was the strongest predictor of PIR/min (H\\u0026thinsp;=\\u0026thinsp;10.97, p\\u0026thinsp;=\\u0026thinsp;0.0042, ε\\u0026sup2; = 0.081) but showed weaker associations with other outcomes suggests that this variable may be particularly informative for metrics reflecting physical engagement and competitive intensity. This pattern aligns with the interpretation in section \\u003cspan refid=\\\"Sec36\\\" class=\\\"InternalRef\\\"\\u003e4.2\\u003c/span\\u003e that muscle soreness serves as a proxy for competitive load exposure rather than impairment.\\u003c/p\\u003e \\u003cp\\u003eThe stratification approach revealed predominantly non-monotonic patterns across tertiles, including U-shaped, inverted U-shaped, and threshold effects. For instance, stress showed lowest levels in the medium PIR/min tertile (M\\u0026thinsp;=\\u0026thinsp;2.59) compared to both low (M\\u0026thinsp;=\\u0026thinsp;3.38) and high (M\\u0026thinsp;=\\u0026thinsp;3.50) tertiles, suggesting an optimal arousal zone for performance impact. Similarly, anger demonstrated non-linear associations with Plus/Minus, with the medium tertile showing highest anger levels (M\\u0026thinsp;=\\u0026thinsp;3.26) despite intermediate performance. These non-linear patterns underscore a critical limitation of linear correlation approaches and highlight the value of stratification methods for identifying threshold effects and optimal ranges. From an applied perspective, these findings suggest that athlete monitoring systems should incorporate individualized reference ranges and consider within-person changes relative to baseline, rather than relying on universal cut-points or group norms.\\u003c/p\\u003e \\u003cp\\u003eAcross all outcomes, subjective recovery metrics (tiredness, muscle soreness, stress, anger, calmness, sleep quality) accounted for 43.8% of significant findings (14 of 32 tests), substantially outperforming biomarkers (18.8%; 3 of 16 tests). This pattern is consistent with prior evidence suggesting that self-report measures capture integrated information about physiological, psychological, and contextual factors that may not be reflected in isolated biomarkers (44,45). However, the significant associations observed for testosterone, cortisol, and nitrates (particularly for PIR/min and MIN) indicate that physiological markers provide complementary information beyond subjective perceptions. The modest effect sizes observed for biomarkers (ε\\u0026sup2; = 0.045\\u0026ndash;0.056) suggest that these variables contribute incremental predictive value when combined with subjective measures, rather than serving as standalone indicators.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec41\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.7 Limitation and Directions\\u003c/h2\\u003e \\u003cp\\u003eSeveral methodological limitations should be acknowledged when interpreting the findings of this study, while also pointing toward clear directions for future research.\\u003c/p\\u003e \\u003cp\\u003eFirst, the observational and correlational design precludes causal inference. Although meaningful associations were identified between recovery-related variables and performance outcomes, these relationships may reflect reverse causality, bidirectional effects, or the influence of unmeasured confounders. Experimental approaches, including randomized controlled trials manipulating recovery strategies (e.g., sleep optimization, psychological regulation techniques, or nutritional interventions), are necessary to establish causal mechanisms and determine the efficacy of targeted interventions.\\u003c/p\\u003e \\u003cp\\u003eSecond, the study was conducted within a single professional team, which limits the generalizability of the findings. Recovery\\u0026ndash;performance relationships may differ across competition levels, tactical systems, cultural environments, and training loads. Future studies should include multi-team and multi-league cohorts to assess the robustness and external validity of the observed patterns.\\u003c/p\\u003e \\u003cp\\u003eThird, measurement timing and temporal resolution represent important constraints. Recovery variables were assessed at single daily time points, typically on the morning of game day, which may not adequately capture the dynamic and non-linear nature of recovery processes. Longitudinal designs incorporating repeated daily measurements across training, competition, and recovery cycles would allow for a more nuanced understanding of within-athlete fluctuations, delayed effects, and cumulative load responses.\\u003c/p\\u003e \\u003cp\\u003eFourth, several key variables\\u0026mdash;particularly psychological states and wellness indicators\\u0026mdash;relied on self-report measures, which are inherently subject to response bias, social desirability effects, and individual differences in perception. While self-reported data remain valuable in applied sport settings, future research would benefit from integrating more objective assessment tools, such as actigraphy for sleep, ecological momentary assessment for mood, and sensor-based measures of physical load.\\u003c/p\\u003e \\u003cp\\u003eFifth, performance metrics were not adjusted for contextual factors such as opponent strength, game tempo, tactical role, or situational demands (e.g., score margin, home vs. away games). These contextual variables can substantially influence performance statistics and may moderate recovery\\u0026ndash;performance relationships. Multilevel or hierarchical modeling approaches that account for player-, game-, and team-level variance would provide more precise and ecologically valid estimates.\\u003c/p\\u003e \\u003cp\\u003eSixth, while salivary biomarkers offer a practical and non-invasive monitoring solution, they present inherent biological limitations. Single-sample assessments do not capture diurnal rhythms, acute fluctuations, or tissue-level hormonal activity, potentially attenuating observed associations. Future studies should incorporate repeated sampling protocols or complementary physiological measures to better characterize endocrine dynamics.\\u003c/p\\u003e \\u003cp\\u003eLooking forward, several avenues for future research emerge. Within-athlete longitudinal monitoring across multiple seasons would help identify individualized recovery\\u0026ndash;performance profiles and optimal readiness thresholds. Mechanistic studies integrating physiological, psychological, and neurocognitive assessments could clarify how recovery variables differentially influence specific performance domains. Position-specific analyses are also warranted, given the distinct physical and cognitive demands placed on guards, forwards, and centers. Additionally, examining team-level dynamics, including how individual recovery status aggregates to influence collective performance, would support a more systems-oriented understanding of elite sport.\\u003c/p\\u003e \\u003cp\\u003eFinally, advanced modeling approaches, including machine learning and non-linear predictive frameworks, may better capture complex interactions among recovery variables than traditional linear models. Such approaches hold promise for improving predictive accuracy and supporting more personalized, data-driven athlete management strategies.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. CONCLUSIONS\",\"content\":\"\\u003cp\\u003eThis comprehensive investigation of recovery-performance relationships in professional basketball revealed a complex, multifaceted picture that challenges several conventional assumptions. Contrary to expectations, physical recovery markers (tiredness, muscle soreness) showed positive associations with performance indicators, likely reflecting competitive engagement rather than performance impairment. Sleep metrics demonstrated unexpected inverse relationships with performance, highlighting the complexity of sleep-performance dynamics and the importance of considering sleep quality, timing, and contextual factors beyond absolute duration.\\u003c/p\\u003e \\u003cp\\u003ePsychological states demonstrated domain-specific effects, with calmness enhancing precision tasks (three-point shooting) and anger impairing multiple performance domains. Hormonal biomarkers showed selective associations with specific behavioral patterns, with testosterone relating to aggressive engagement, cortisol enhancing defensive vigilance, and nitrates potentially supporting cognitive-motor performance.\\u003c/p\\u003e \\u003cp\\u003eStratification analysis incorporating 12 recovery and biomarker variables achieved modest but meaningful discrimination between performance strata with sleep variables contributing most substantially (30.5%), followed by psychological indicators (22.9%) and hormonal biomarkers (17.7%). The modest predictive effect highlights that while recovery monitoring provides valuable information, athletic performance is multifactorially determined by numerous factors beyond physiological readiness.\\u003c/p\\u003e \\u003cp\\u003eThese findings have important practical implications for athlete monitoring and performance optimization. Recovery data should be interpreted contextually, considering individual baselines, performance demands, and the distinction between markers of competitive engagement versus performance impairment. Psychological preparation should target emotional states appropriate for specific performance demands, and sleep monitoring should emphasize quality and circadian alignment rather than duration alone.\\u003c/p\\u003e \\u003cp\\u003eFuture research employing experimental designs, longitudinal monitoring, and mechanistic investigations will further clarify the complex relationships between recovery and performance in basketball, ultimately informing more effective, individualized approaches to athlete management and performance optimization.\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eCONFLICT OF INTEREST\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare no conflicts of interest.\\u003c/p\\u003e \\u003c/p\\u003e\\u003cp\\u003e \\u003ch2\\u003eORCID\\u003c/h2\\u003e \\u003cp\\u003e[Author ORCID identifiers to be specified]\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eFUNDING\\u003c/h2\\u003e \\u003cp\\u003eThe study is supported by the Booster Grant for Life Sciences of the Regione Autonoma Friuli Venezia Giulia (CUP: G97H24001640002).\\u003c/p\\u003e\\u003ch2\\u003eAUTHOR CONTRIBUTIONS\\u003c/h2\\u003e \\u003cp\\u003eConceptualization (LA, LB, FC), Data curation (LB, LA, SZ), Formal analysis (LA, GC, SJ, RD), Funding acquisition (LA, DC), Investigation (GC, LB, LA, RD, SZ), Methodology (GC, RD, LB, SZ), Project administration (LA, LB), Resources (DC, LB, LA, FC), Software (SZ, SJ), Supervision (LA, LB, DC), Validation (FC), Visualization (LA, GC), Writing \\u0026ndash; original draft (LA, GC, SJ), Writing \\u0026ndash; review \\u0026amp; editing (LA, GC, SJ, FC, LB, RD, SZ)\\u003c/p\\u003e\\u003ch2\\u003eACKNOWLEDGMENTS\\u003c/h2\\u003e \\u003cp\\u003eAll authors acknowledge that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation, and state that results of the present study do not constitute endorsement by ACSM.\\u003c/p\\u003e \\u003cp\\u003eWe thank Giulia Cordaro at CRI for her technical support and Paolo Soave for his administrative assistance at CRI.\\u003c/p\\u003e\\u003ch2\\u003eDATA AVAILABILITY STATEMENT\\u003c/h2\\u003e \\u003cp\\u003eData can be released on request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eKellmann M, Bertollo M, Bosquet L, Brink M, Coutts AJ, Duffield R et al (2018) Recovery and performance in sport: Consensus statement. Int J Sports Physiol Perform 13(2):240\\u0026ndash;245\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHalson SL (2014) Monitoring training load to understand fatigue in athletes. Sports Med 44(Suppl 2S2):S139\\u0026ndash;S147\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eClaudino JG, Capanema D, de O TV, Serr\\u0026atilde;o JC, Machado Pereira AC, Nassis GP (2019) Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review. Sports Med Open 5(1):28\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSeshadri DR, Li RT, Voos JE, Rowbottom JR, Alfes CM, Zorman CA et al (2019) Wearable sensors for monitoring the physiological and biochemical profile of the athlete. NPJ Digit Med 2(1):72\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCrewther BT, Cook C, Cardinale M, Weatherby RP, Lowe T (2011) Two emerging concepts for elite athletes: the short-term effects of testosterone and cortisol on the neuromuscular system and the dose-response training role of these endogenous hormones. Sports Med 41(2):103\\u0026ndash;123\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDuclos M, Tabarin A (2016) Exercise and the hypothalamo-pituitary-adrenal axis. Front Horm Res 47:12\\u0026ndash;26\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFinn KJ, Ransone J, Martinez M (2019) Salivary biomarkers in college female basketball players during the late competition season. Med Sci Sports Exerc 51(6S):27\\u0026ndash;28\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTomescu V, Bellar D (2019) Seasonal changes in salivary biomarkers and psychomotor function among elite fencers. Med Sci Sports Exerc 51(6S):324\\u0026ndash;324\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDeminice R, Sicchieri T, Pay\\u0026atilde;o PO, Jord\\u0026atilde;o AA (2010) Blood and salivary oxidative stress biomarkers following an acute session of resistance exercise in humans. Int J Sports Med 31(9):599\\u0026ndash;603\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLochbaum M, Zanatta T, Kirschling D, May E (2021) The profile of moods states and athletic performance: A meta-analysis of published studies. Eur J Investig Health Psychol Educ 11(1):50\\u0026ndash;70\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCarr\\u0026eacute; JM, Olmstead NA (2015) Social neuroendocrinology of human aggression: examining the role of competition-induced testosterone dynamics. Neuroscience 286:171\\u0026ndash;186\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePapacosta E, Gleeson M, Nassis GP (2013) Salivary hormones, IgA, and performance during intense training and tapering in judo athletes. J Strength Cond Res 27(9):2569\\u0026ndash;2580\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eObmiński Z, Stupnicki R (1997) Comparison of the testosterone-to-cortisol ratio values obtained from hormonal assays in saliva and serum. J Sports Med Phys Fit 37(1):50\\u0026ndash;55\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTownsend JR, Hart TL, Haynes JT 4th, Woods CA, Toy AM, Pihera BC et al (2022) Influence of dietary nitrate supplementation on physical performance and body composition following offseason training in Division I athletes. J Diet Suppl 19(4):534\\u0026ndash;549\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBeedie CJ, Terry PC, Lane AM (2000) The profile of mood states and athletic performance: Two meta-analyses. J Appl Sport Psychol 12(1):49\\u0026ndash;68\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRobazza C, Bortoli L (2007) Perceived impact of anger and anxiety on sporting performance in rugby players. Psychol Sport Exerc 8(6):875\\u0026ndash;896\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHanin YL (2012) Emotions in Sport: Current Issues and Perspectives. Handbook of Sport Psychology. John Wiley \\u0026amp; Sons, Inc., Hoboken, NJ, USA, pp 31\\u0026ndash;58\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTeigen KH, Yerkes-Dodson (1994) A law for all seasons. Theory Psychol 4(4):525\\u0026ndash;547\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiang WM, Xiao J, Ren FF, Chen ZS, Li CR, Bai ZM et al (2023) Acute effect of breathing exercises on muscle tension and executive function under psychological stress. Front Psychol 14:1155134\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeldmanHall O, Raio CM, Kubota JT, Seiler MG, Phelps EA (2015) The effects of social context and acute stress on decision making under uncertainty. Psychol Sci 26(12):1918\\u0026ndash;1926\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThorpe RT, Strudwick AJ, Buchheit M, Atkinson G, Drust B, Gregson W (2017) The influence of changes in acute training load on daily sensitivity of morning-measured fatigue variables in elite soccer players. Int J Sports Physiol Perform 12(Suppl 2):S2107\\u0026ndash;S2113\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSaw AE, Main LC, Gastin PB (2016) Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med 50(5):281\\u0026ndash;291\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFullagar HHK, Skorski S, Duffield R, Hammes D, Coutts AJ, Meyer T (2015) Sleep and athletic performance: the effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Med 45(2):161\\u0026ndash;186\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWalsh NP, Halson SL, Sargent C, Roach GD, N\\u0026eacute;d\\u0026eacute;lec M, Gupta L et al (2020) Sleep and the athlete: narrative review and 2021 expert consensus recommendations. Br J Sports Med 55(7):356\\u0026ndash;368\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEpidemiology and biostatistics: An introduction to clinical research, 2nd edition. Med Sci Sports Exerc (2020) ;52(2):523\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCroux C, Filzmoser P, Joossens K (2005) Robust linear discriminant analysis for multiple groups: Influence and classification efficiencies. SSRN Electron J [Internet]. ; Available from: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://dx.doi.org/10.2139/ssrn.876896\\u003c/span\\u003e\\u003cspan address=\\\"10.2139/ssrn.876896\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMcEwen BS, Wingfield JC (2003) The concept of allostasis in biology and biomedicine. Horm Behav 43(1):2\\u0026ndash;15\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNieman DC (2010) Mental fatigue impairs physical performance in humans. Year B Sports Med 2010:145\\u0026ndash;146\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCheung K, Hume PA, Maxwell L (2003) Delayed onset muscle soreness. Sports Med 33(2):145\\u0026ndash;164\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMah CD, Mah KE, Kezirian EJ, Dement WC (2011) The effects of sleep extension on the athletic performance of collegiate basketball players. Sleep 34(7):943\\u0026ndash;950\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVitale KC, Owens R, Hopkins SR, Malhotra A (2019) Sleep hygiene for optimizing recovery in athletes: Review and recommendations. Int J Sports Med 40(8):535\\u0026ndash;543\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChaput JP, Dutil C, Sampasa-Kanyinga H (2018) Sleeping hours: what is the ideal number and how does age impact this? Nat Sci Sleep 10:421\\u0026ndash;430\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEasterbrook JA (1959) The effect of emotion on cue utilization and the organization of behavior. Psychol Rev 66(3):183\\u0026ndash;201\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLane AM, Beedie CJ, Devonport TJ, Stanley DM (2011) Instrumental emotion regulation in sport: relationships between beliefs about emotion and emotion regulation strategies used by athletes. Scand J Med Sci Sports 21(6):e445\\u0026ndash;e451\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEldadi O, Tenenbaum G (2025) Team cognition (TC) in sport: Foundations, development, and performance implications. Psychol Sport Exerc 80(102927):102927\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eConcepcion RY (2004) Foundations of sport and exercise psychology, 3rd edition. Med Sci Sports Exerc. ;1449\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDerakshan N, Eysenck MW (2009) Anxiety, processing efficiency, and cognitive performance. Eur Psychol 14(2):168\\u0026ndash;176\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEysenck MW, Calvo MG (1992) Anxiety and performance: The processing efficiency theory. Cogn Emot 6(6):409\\u0026ndash;434\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRohleder N, Beulen SE, Chen E, Wolf JM, Kirschbaum C (2007) Stress on the dance floor: the cortisol stress response to social-evaluative threat in competitive ballroom dancers. Pers Soc Psychol Bull 33(1):69\\u0026ndash;84\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWingfield JC, Hegner RE, Dufty AM Jr, Ball GF (1990) The challenge hypothesis: Theoretical implications for patterns of testosterone secretion, mating systems, and breeding strategies. Am Nat 136(6):829\\u0026ndash;846\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCarr\\u0026eacute; JM, Geniole SN, Ortiz TL, Bird BM, Videto A, Bonin PL (2017) Exogenous testosterone rapidly increases aggressive behavior in dominant and impulsive men. Biol Psychiatry 82(4):249\\u0026ndash;256\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSalvador A, Costa R (2009) Coping with competition: neuroendocrine responses and cognitive variables. Neurosci Biobehav Rev 33(2):160\\u0026ndash;170\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJones AM, Thompson C, Wylie LJ, Vanhatalo A (2018) Dietary nitrate and physical performance. Annu Rev Nutr 38(1):303\\u0026ndash;328\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eImpellizzeri FM, Marcora SM, Coutts AJ (2019) Internal and external training load: 15 years on. Int J Sports Physiol Perform 14(2):270\\u0026ndash;273\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[{\"identity\":\"15fba243-f938-4141-8d38-abecca9327d9\",\"identifier\":\"10.13039/501100009874\",\"name\":\"Regione Autonoma Friuli Venezia Giulia\",\"awardNumber\":\"G97H24001640002\",\"order_by\":0}],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"VitalizeDx-Eu Personalized Care\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"athletic performance optimization, physiological biomarkers, TCR, salivary nitrates, stratification analysis, machine learning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8815351/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8815351/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch3\\u003ePurpose\\u003c/h3\\u003e\\n\\u003ch3\\u003eThis study investigated relationships between recovery-related biomarkers and game performance in professional basketball players to identify actionable indicators for optimizing athletic performance.\\u003c/h3\\u003e\\n\\u003ch3\\u003eMethods\\u003c/h3\\u003e\\n\\u003ch3\\u003eThirteen professional male basketball players were monitored longitudinally over 15 weeks during games at 3-4 day intervals. Twelve independent variables—four salivary biomarkers (testosterone, cortisol, testosterone-to-cortisol ratio, salivary nitrates) and eight self-reported measures (anger, calmness, stress, energy, muscle soreness, tiredness, sleep duration, sleep quality)—were examined against 26 game performance metrics. Stratification analysis on four key outcomes (Plus/Minus, Efficiency, Player Impact Rating per minute, minutes played) identified non-linear relationships and predictor importance.\\u003c/h3\\u003e\\n\\u003ch3\\u003eResults\\u003c/h3\\u003e\\n\\u003ch3\\u003eAmong the 69 significant correlations among recovery variables and game performance metrics, \\u0026nbsp;Tiredness and muscle soreness showed the strongest positive relationships, accounting for 39.1% of significant correlations. Sleep duration was the most consistent negative predictor (13 inverse correlations). Stratification revealed 20 significant associations with predominantly non-linear patterns. Sleep duration best predicted minutes played (H=18.47, p=0.0001, ε²=0.131); muscle soreness best predicted PIR/min (H=10.97, p=0.0042, ε²=0.081). Psychological variables primarily influenced playing time and Plus/Minus. Salivary biomarkers showed small but significant effects. Subjective recovery metrics demonstrated superior predictive value (43.8%) versus biomarkers (18.8%).\\u003c/h3\\u003e\\n\\u003ch3\\u003eConclusions\\u003c/h3\\u003e\\n\\u003cp\\u003eRecovery-performance relationships in elite basketball are complex, non-linear, and context-dependent. Subjective markers provide more informative readiness assessment than isolated biomarkers. Inverse sleep duration-performance associations suggest compensatory mechanisms, emphasizing individualized interpretation. Practical monitoring should prioritize daily self-reports, player-specific reference ranges, and integrate recovery data with training load contextual factors.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Case Study using Physiological and Wellness Indicators for Performance Optimization in Basketball\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-10 11:37:58\",\"doi\":\"10.21203/rs.3.rs-8815351/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"8a4de97a-c371-42ba-86ea-030b83558672\",\"owner\":[],\"postedDate\":\"February 10th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":62506565,\"name\":\"Sports Medicine and Kinesiology\"}],\"tags\":[],\"updatedAt\":\"2026-02-10T11:37:58+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-10 11:37:58\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8815351\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8815351\",\"identity\":\"rs-8815351\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}