Predictors of agility in youth basketball: Age-stratified hierarchical regression in 6–13- year-old boys

Predictors of agility in youth basketball: Age-stratified hierarchical regression in 6–13- year-old boys | 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 Research Article Predictors of agility in youth basketball: Age-stratified hierarchical regression in 6–13- year-old boys Gizem Başkaya, Veli Volkan Gürses, Serkan Necati Metin, Ömer Özer, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8025215/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2026 Read the published version in BMC Sports Science, Medicine and Rehabilitation → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Agility in youth basketball reflects the interplay between body dimensions and motor abilities. Age-specific prediction models may inform training and talent identification. Methods: Ninety-eight male players (6–13 y) from a basketball school were classified into 6–9 y and 10–13 y groups. Anthropometry included height, mass, and the triponderal mass index (TMI). Motor performance comprised the 20-m sprint, countermovement jump (CMJ), and the Hexagon test. Agility was assessed with the V-cut (V-CUT) test. Pearson (and, where normality was violated, Spearman) correlations were computed; age-stratified hierarchical regressions identified predictors of V-CUT. Results: V-CUT time correlated strongly and positively with 20-m sprint in both groups (6–9 y: r=.807, p<.001; 10–13 y: r=.619, p<.001) and moderately and negatively with CMJ (6–9 y: r=−.440, p=.001; 10–13 y: r=−.337, p=.007). Associations with TMI were small and non-significant. In regression, adding 20-m sprint markedly increased explained variance (6–9 y: R²=.657; 10–13 y: R²=.387, both p<.001). Final models yielded R²=.659 (6–9 y) and R²=.476 (10–13 y); Hexagon provided additional unique variance only in the older group (ΔR²=.082, p=.009), whereas CMJ contributed minimally once sprint was entered. Conclusion: Sprint speed is the primary determinant of agility (V-CUT) in young basketball players, while multidirectional change-of-direction ability (Hexagon) gains importance from 10–13 y. Anthropometric indices (e.g., TMI) show limited predictive value. These results support emphasizing early sprint development and progressively integrating multidirectional drills in older athletes to inform age-appropriate training and talent identification. youth basketball agility change of direction sprint speed vertical jump hexagon test triponderal mass index Figures Figure 1 Introduction Basketball is one of the most widely played team sports worldwide, engaging millions of young athletes in recreational and competitive contexts. Its dynamic nature requires frequent accelerations, decelerations, and rapid direction changes, making agility a fundamental determinant of performance. These demands make agility a critical determinant of performance, particularly in youth athletes during early developmental stages when physical growth and motor learning interact dynamically [ 1 , 2 ]. Ability defined as to rapidly change direction or velocity in response to a stimulus while maintaining balance and control [ 3 – 6 ]. Contemporary models conceptualize agility as a multifactorial construct influenced by neuromuscular coordination, biomechanical efficiency, and cognitive-perceptual processes [ 4 , 7 – 9 ]. Within this framework, both anthropometric characteristics (e.g., body height, mass, limb length, and segmental ratios) and motor performance attributes (e.g., sprint speed, vertical jump ability, and multidirectional movement capacity) are recognized as key contributors to agility outcomes in youth athletes [ 10 – 13 ]. Understanding the determinants of agility in youth basketball has become a central focus for sports scientists, coaches, and talent development programs [ 6 , 14 – 16 ]. In recent years, sports scientists and coaches have increasingly emphasized the importance of objective and field-based assessments to predict agility and inform individualized training programs for young athletes [ 1 , 17 ]. Over the last decade, numerous studies have examined how anthropometric and motor variables relate to agility and change-of-direction performance in youth sports. Among motor predictors, recent studies have reported moderate to strong associations between sprint performance and agility across sports, including basketball, soccer, and tennis [ 18 , 19 ]. Vertical jump ability and reactive strength have also been linked to agility through their reliance on stretch-shortening cycle efficiency [ 16 , 20 , 21 ]. In contrast, anthropometric measures such as body mass index (BMI) and triponderal mass index (TMI) show mixed associations, with some evidence suggesting that excessive body mass may hinder change-of-direction performance [ 22 ]. In addition to BMI, TMI (weight/height³) has recently been proposed as a more stable indicator of adiposity in pediatric populations [ 23 – 25 ]. Given these advantages, TMI was selected in the present study as a complementary morphological parameter to BMI in predicting agility performance. Despite these insights, significant gaps remain. Most studies focus on single predictors rather than integrating multiple anthropometric and motor variables into regression-based models, limiting their practical value for training and talent identification [ 26 ]. Furthermore, despite significant differences in biological maturation and neuromuscular development between early childhood and early adolescence, there are very few studies examining age-specific determinants of agility [ 1 , 5 , 14 , 27 , 28 ], and there is still a need for research in the literature that aims to highlight these differences. Finally, validated field tests such as the V-cut and Hexagon protocols remain underutilized in predictive modeling, despite their reliability and ecological relevance [ 18 , 29 ]. Methodological limitations also persist in literature. Many studies use small, homogeneous samples, limit generalizability, and overlook confounding factors such as training experience, maturation, or positional roles [ 5 , 12 , 26 , 28 , 30 ]. While laboratory assessments provide precision, they lack ecological validity compared to field-based contexts where agility occurs in real competition. Addressing these shortcomings is essential for developing practical, evidence-based tools to guide individualized training and long-term athlete development. Given this need, research should adopt regression-based models to determine the relative contribution of multiple predictors while accounting for age-specific developmental differences. Such approaches would advance theoretical understanding and provide actionable insights for coaches and talent identification systems. Therefore, grounded in the Sheppard-Young model of agility (neuromuscular and perceptual components), we examined age-specific predictors of preplanned change-of-direction performance (V-CUT) in male basketball players aged 6–13 years. Additionally, by addressing developmental differences in the determinants of agility, we aimed to determine whether predictors differed between two age groups (6–9 and 10–13 years old). We hypothesized that: (H1) linear sprint would be the dominant predictor across ages; (H2) multidirectional coordination (Hexagon) would contribute more in the older group; and (H3) anthropometric indices (including TMI) would show limited predictive value after motor variables. Methods Study Design This study employed a cross-sectional design to examine the predictors of agility performance using anthropometric and motor variables, aiming to develop regression models for youth basketball players. The sample was stratified into two age groups (6-9 years and 10-13 years) to account for developmental differences. Athletes in both age groups train for a total of 180 minutes per week, with two 90-minute sessions. Selected participants, coaches and parents were provided with detailed information about the purpose, duration and requirements of the training, and the necessary approvals were obtained from the participants' families for them to take part. All measurements were taken on two consecutive test days and at the same time of day (between 10:00 and 12:00) to minimise fatigue and ensure optimum performance. This application was also implemented during tests conducted throughout the day, with the tests being ranked taking into account fatigue-inducing factors. All field tests were performed on an indoor hardwood court surface with athletes wearing court shoes. The testing order was fixed: anthropometry, CMJ, and the sprint test were conducted on Day 1, while the Hexagon and V-CUT tests were performed on Day 2. Two trials were administered for each test, with the best value used for analysis and a 2-minute rest period provided between trials. To minimize inter-rater variability, all measurements were conducted by the same trained investigator following standardized testing procedures. Testing environments were controlled to reduce external influences, with the temperature at 23°C (±1 °C). To control for dietary effects, participants were asked to maintain their usual dietary patterns throughout the study. All baseline assessments were conducted two hours after meals, and participants were instructed to refrain from physical exercise on the day of testing. Before data collection, all participants were screened to confirm eligibility based on inclusion and exclusion criteria. This methodological approach was designed to generate a practical and field-based predictive framework for understanding the relationship between anthropometric and motor performance variables and agility in youth basketball players. All baseline assessments were conducted two hours after meals, and participants were instructed to refrain from physical exercise on the day of testing. Study Sample A priori power analysis was conducted using G*Power 3.1.9.2. Assuming three main predictors, the analysis indicated that a minimum of 77 participants would be required to achieve a statistical power of 0.80 at α =.05, effect size f² = 0.15. Therefore, the sample size of 98 athletes in this study exceeded the required threshold. A total of 98 healthy male children participated in the study. All participants voluntarily took part, and because they were underage, written informed consent was obtained from their parents or legal guardians. Inclusion criteria were defined as healthy male basketball players aged between 6 and 13 years. Exclusion criteria included any known chronic diseases, musculoskeletal disorders, or cardiovascular conditions. To determine eligibility, participants were asked to complete a standardized health screening questionnaire, including family medical history. In addition, the researchers confirmed both players' and parents' statements through individual interviews [31, 32]. No participant presenting symptoms related to the exclusion criteria was included in the study. Data collection tools Anthropometric Measurements: Anthropometric variables were assessed following standardized guidelines to ensure precision, validity, and reliability. Body height was measured using a stadiometer (Seca 213; Seca GmbH & Co. KG, Hamburg, Germany) with 0.1 cm accuracy, and body mass was recorded with a segmental body composition monitor (TANITA BC-558 Ironman, Tanita Corporation, Tokyo, Japan) with 0.05 kg accuracy. Participants were measured barefoot and wearing only light clothing. BMI was calculated as body mass (kg)/height (m²), and TMI was computed as body mass (kg)/height³ (m³). Arm and hand span were measured using a flexible anthropometric tape (Seca 201, Seca GmbH & Co. KG, Hamburg, Germany). All anthropometric assessments were performed twice, and the best measure was retained for analysis. V-cut Agility Test (V-CUT): Agility performance was measured using the V-cut test, a widely applied protocol in basketball to assess change-of-direction ability [1, 17] . The test was conducted on a standard basketball court (28 m x 15 m, parquet flooring) located in an indoor sports hall. The test was performed over a 25 m course with four directional changes of 45° every 5 m. Players started between two cones set 0.7 m apart, sprinted through the course, and finished between two cones at the end. Each participant performed two attempts, separated by two minutes of passive rest. Times were recorded to the nearest 0.01 s using electronic timing gates, and the best performance was used in the analyses [29]. The V-cut test followed the protocol outlined by Gonzalo-Skok et al. [17] , involving four 45° cuts. Due to court geometry constraints, a 25 m variant was used. To ensure comparability, a sensitivity analysis was conducted using z-standardized V-CUT times. Hexagon Test: The Hexagon test evaluated multidirectional agility and lower-limb coordination [33] . A hexagon (24 in per side, 120° angles) was marked on the floor with tape. Starting at the center, participants performed continuous two-footed jumps over each side for three complete circuits (18 jumps total), always returning to the center after each jump. Timing began with the starting signal and stopped when the athlete returned to the center at the end of the final circuit. The fastest of the two trials was recorded. Countermovement Jump (CMJ): Vertical jump performance was assessed using the CMJ test [11] . Measurements were taken using the MyJump 2 mobile application, whose validity and reliability have been established by various researchers (ICC=0.997) [34-38] . For the CMJ, athletes were instructed to perform a rapid downward movement from the starting position (approximately 90° knee flexion), followed by a rapid upward movement to jump as high as possible. Participants performed two maximal jumps with arm swing permitted, and jump height (cm) was recorded. The best trial was retained for analysis. Vertical jump ability has been widely used to indicate explosive lower-limb power in youth athletes [11] . 20-m sprint Test: Sprint performance was assessed over 20 m using photocell timing gates (Witty System, Microgate, Bolzano, Italy) positioned 1 m above the ground at the start and finish lines. The players started standing 50 cm behind the first gate with their front feet close to the line. Twenty-meter sprint times were recorded using dual photocell gates positioned at a height of 1.0 m. A standardized standing-start position was adopted, with the lead forefoot placed directly behind the start line and hands free beside the trunk. Two attempts were performed, separated by 2 minutes of rest, and the fastest sprint time was recorded [18] . Health Screening Questionnaire: A specific pediatric Preparticipation Physical Examination (PPE) health history screening questionnaire and a brief semi-structured interview checklist modified from AHA pediatric screening guidance were developed to verify medical history, participation eligibility, and training background in children aged 6-13 years [31, 32]. Item content drew on established PPE recommendations and AHA pediatric screening guidance [39, 40] . Both tools were piloted to ensure clarity. Predictor Variables: Independent variables included anthropometric measures: Height, body mass, BMI, TMI, and motor performance outcomes (20-m sprint time, CMJ height, Hexagon test time). The dependent variable was agility performance, operationalized through the V-cut test, expressed in seconds. Correlations were computed for all measures; regression models were a-priori restricted to TMI, 20-m sprint, CMJ, and Hexagon. Statistical Analysis All data were analyzed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were reported as means and standard deviations. Statistical significance was set at two-tailed α = .05. Distributions were screened with Shapiro-Wilk tests and Q-Q plots; when normality was violated, we reported Spearman’s ρ in addition to Pearson’s r (with 95% CIs from Fisher’s z) to describe bivariate associations with V-CUT. Hierarchical linear regression analyses were run within each age group using four a priori blocks to quantify incremental contributions; Block 1 Anthropometry (TMI), Block 2-20-m sprint, Block 3-CMJ, Block 4-Hexagon. Assumptions were checked on standardized residuals: linearity, normality, homoscedasticity and independence. Multicollinearity was inspected using VIF (acceptable if < 5). Anthropometry was represented by TMI to avoid redundancy with height and mass. We report unstandardized coefficients (B, standard error [SE], 95% CI, t, exact p), standardized coefficients (β), and model quality (R², Adjusted R², ΔR² with Sig. F-change, standard error of the estimate (SEE), overall F with degrees of freedom, and Durbin-Watson. Analyses used complete-case data; influential observations were screened and did not alter inferences. Where computed, internal validation and model selection indices were additionally reported AIC/AICc from residual sum of squares (SSE), sample size (n), and parameter count (k). Training exposure was uniform (2×90 min/week); therefore, it was not entered into the models. Assumptions were verified on studentized residuals; influential points were inspected. Results Descriptive Characteristics Participants were 98 male basketball players aged 6–13 y (n=44 in 6–9 y; n=54 in 10–13 y). Descriptive statistics for anthropometry; body height, body mass, TMI, and performance V-CUT, 20-m sprint, CMJ and Hexagon by age group are presented in the Table 1 and Table 2. These values provide the context for the correlation and regression analyses that follow. <<>> <<>> Correlation Analyses Pearson's correlation coefficients revealed significant associations between motor performance tests and agility (Tables 3 and 4). In the 6-9 age group, V-CUT performance showed a robust positive correlation with 20-m sprint time (r=.807, p<.001) and a moderate negative correlation with CMJ height (r=-.440, p=.001). A small but significant positive correlation was also observed with Hexagon performance (r=.318, p=.018). TMI showed a weak and non-significant association with agility (r =.130, p=.201). In the 10-13 age group, similar trends emerged. V-CUT performance correlated strongly with 20-m sprint time (r=.619, p<.001) and moderately with CMJ (r=-.337, p=.007). However, correlations with TMI (r=.187, p= .092) and Hexagon performance (r=.156, p=.135) were small and non-significant. <<>> <<>> Hierarchical regression (6–9 y) Entering TMI alone explained 1.7% of variance in V-CUT (R²=.017, p=.402). Adding 20-m sprint increased explained variance to 65.7% (R²=.657, p<.001). Subsequent inclusion of CMJ and Hexagon yielded negligible improvements (final model R²=.659, Adj. R²=.624, SEE=0.658 s, DW=2.181). In the final model, 20-m sprint remained the only substantial predictor; other coefficients were small and non-significant. (Table 5). <<>> Based on these findings, The final unstandardized equation was: V-CUT (s) = 1.576 + 1.596·(20-m sprint, s) + 0.006·CMJ (cm) + 0.006·Hexagon (s) − 0.031·TMI (kg·m⁻³). Hierarchical Regression (10–13 y) TMI alone explained 3.5% of variance (R²=.035, p=.183). Adding 20-m sprint increased R² to .387 (p<.001). Including CMJ produced a minimal change (R²=.394), whereas adding Hexagon further improved the model to R² = .476 (Adj. R²=.431, ΔR²=.082, p=.009), indicating unique variance explained by Hexagon in older players. Final model fit indices were SEE=0.734 s and DW=2.264. (Table 6) . <<>> The final unstandardized equation was: V-CUT (s) = −0.199 + 1.352·(20-m sprint, s) + 0.028·CMJ (cm) + 0.104·Hexagon (s) − 0.004·TMI (kg·m⁻³). Sensitivity analyses using z-standardized V-CUT times led to identical inferences (no change in the significance pattern); results not shown. Across both age bands, 20-m sprint consistently explained the largest share of variance in V-CUT. CMJ related to V-CUT at the bivariate level but contributed little once sprint was in the model. Hexagon added explanatory power only in 10–13 y, suggesting an age-related rise in multidirectional coordination demands. TMI showed minimal predictive value in either group. Discussion The present study aimed to identify anthropometric and motor predictors of agility in young basketball players by applying regression-based models across two age groups (6-9 years and 10-13 years). The results revealed that sprint speed was the reliable predictor of agility across both age groups. The findings revealed that anthropometric indices and vertical jump ability contributed minimally. Moreover, multidirectional coordination, measured by the Hexagon test, emerged as an additional predictor in older athletes. <<>> Sprint as the Primary Predictor of Agility Sprint speed accounted for 65.7% of the variance in younger players (aged 6-9) and 38.7% of the variance in the older group (aged 10-13). Additionally, it has been demonstrated that sprint performance showed positive, moderate to high correlations with agility in both age groups (6-9 years: r=.807, p<.001; 10-13 years: r=.619, p<.001). This finding aligns with a substantial body of literature reporting moderate to strong correlations between sprint ability and change-of-direction performance in team sports [18, 19, 30] . Negra et al. (2017) reported large to substantial correlations between sprint tests and agility outcomes (0.53< r <0.85, p<.001; shared variance 28-72%) [20] . Linear sprinting and agility share similar neuromuscular and biomechanical processes, such as force production, ground contact efficiency (short ground contact times), efficient lower limb mechanics, and lower limb power, which may explain the strong relationship between them [27] . Similar trends were reported by Horička & Šimonek (2019), who showed that acceleration (ACC-3 m) accounted for the largest share (71.5%) of reactive agility variance in basketball players [41] . These results reinforce sprint development as a fundamental training goal for young athletes, particularly during sensitive developmental stages. [5, 6, 42]. CMJ and Hexagon performance emerged only as secondary, developmentally dependent predictors. Specifically, CMJ showed small but significant associations, likely reflecting overlapping contributions of explosive strength and stretch-shortening cycle efficiency, while the Hexagon test became a more relevant predictor in older players, indicating the growing role of multidirectional coordination with maturation [1, 21] . This streamlined interpretation emphasizes sprinting as the primary determinant of agility, with CMJ and Hexagon offering additional, context-dependent contributions. Role of Explosive Power, Vertical Jump, and Change of Direction Ability Vertical jump performance showed moderate associations with agility, consistent with previous evidence highlighting the role of stretch-shortening cycle efficiency and explosive power in rapid directional changes [11, 20, 21] . However, in our regression models, its predictive contribution was marginal once sprint speed was included, indicating overlapping neuromuscular demands between sprinting and jumping [8, 12, 43-45] . This suggests that while sprint speed remains the dominant predictor, lower-limb explosive strength contributes meaningfully to agility, particularly in tasks requiring deceleration and reacceleration. Age-specific analyses revealed further nuances. In the 6-9 age group, adding CMJ and Hexagon test performance to the regression model produced only marginal improvements in explained variance, while in the 10-13 group, Hexagon performance contributed more substantially (increased the explained variance to 47.6%). This pattern suggests developmental differences in how multidirectional motor tasks interact with agility. Indeed, small but significant correlations were observed between V-cut and Hexagon performance in the younger cohort (r=.318, p<.018), whereas CMJ was negatively correlated with agility across both age groups (6-9 age: r=-.440, p<.001; 10-13 age: r=-.337, p<.007). These findings are consistent with prior studies reporting negative associations between jump test performance and agility times, reflecting that greater explosive power reduces time-to-completion in change-of-direction tasks [19, 20] . Given the rapid neuromotor reorganization around peak height velocity, the older group’s unique Hexagon contribution likely reflects maturation-related gains in inter-segmental coordination and postural control during multidirectional tasks [8, 44, 46–48] . Biomechanically, this link is supported by the sequence of eccentric, isometric, and concentric contractions inherent to the stretch-shortening cycle, which mirrors the stop-start characteristics of agility movements [3, 49, 50] . While vertical jump ability clearly reflects explosive leg strength, its contribution to predicting agility outcomes appears secondary once sprint performance is accounted for. This aligns with training studies demonstrating that improvements in vertical or squat jump ability yield modest gains in change-of-direction ability, whereas targeted sprint and multidirectional speed training elicit more pronounced effects [51, 52] . Considered as a whole, these results suggest that although explosive power contributes meaningfully to agility, its role is conditional and developmentally dependent. Coaches should focus on sprint training as the primary determinant of agility in younger players, while progressively integrating plyometric and multidirectional drills to enhance explosive strength and movement efficiency in older cohorts. Future work should explore how maturation, sex differences, and perceptual-cognitive factors interact with neuromuscular performance to shape agility trajectories during adolescence [53] . Anthropometric Indices and Morphological Parameters In both cases, TMI alone contributed minimally, while the inclusion of sprint performance substantially increased the predictive value of the models. Contrary to expectations, anthropometric indices such as BMI and TMI accounted for only minimal variance in agility performance. This result supports findings by Muniroglu and Subak (2018), who reported weak associations between morphological traits and agility in children [54] . On the other hand, although previous studies have emphasised that excessive body mass may impair deceleration and re-acceleration [22] , the relatively homogeneous and healthy sample in this study may have minimised the effect of anthropometric variation. In relatively homogeneous youth cohorts, anthropometric indices are therefore expected to add little explanatory power once motor variables are entered. Developmental differences within the studied cohorts may explain the absence of strong associations between triponderal index and agility. Participants in our sample were in the pre-peak height velocity stage, where motor and physiological maturation are ongoing. Pavlinovic et al. (2022a) reported similar findings, attributing the weak associations partly to short-duration test protocols that may not adequately capture the metabolic demands of greater body or fat mass [19] . Other studies have likewise noted inconsistent or negligible relationships between children's anthropometric/body composition indices and preplanned agility tests [30, 39] . For instance, Sekulic et al. (2014) found no significant correlations between anthropometric indices and multiple agility tests, except for modest associations with body mass and the Zig-Zag test [39] . Similarly, Pavlinovic et al. (2022b) reported negligible correlations between body fat and TRAG, with shared variance below 5% [30] . Spasic et al. (2013) also noted that in early adolescent girls, reactive strength and sprint ability, rather than morphology, explained most agility variance (30-64%) [40] . These findings support the conclusion that functional motor skills are more important than morphological parameters in shaping agility performance during childhood and indicate that anthropometric/body composition indices cannot be reliable predictors of agility. Agility Predictors in Basketball: Evidence from Comparative Studies Although our sample differed in age from some prior investigations, comparable studies in basketball provide valuable insights. Horička and Šimonek (2019) examined female players (mean age 21.7 years) and found that acceleration (ACC-3 m) accounted for the most significant proportion of variance (71.5%) in reactive agility, underscoring the centrality of sprint ability [41] . Their results also highlighted significant correlations between V-cut and Hexagon-type agility tests, consistent with the associations observed in our study. They concluded that change-of-direction speed (CODS) and reactive agility should be considered distinct skills, with the relative contribution of motor determinants decreasing as task complexity increases, while cognitive demands become more influential. Similarly, Pavlinovic et al. (2022a) investigated boys and girls aged 11-12 using the Triangle Reactive Agility Test (TRAG) [19] . While anthropometric and body composition indices showed negligible correlations with agility (0-4% shared variance), motor skills such as sprinting, broad jump, and CMJ were significant predictors (7-43% shared variance). In boys, CODS (Triangle-CODS) alone explained 64% of TRAG variance, highlighting again the primacy of motor over morphological factors. These discrepancies with our findings likely reflect methodological differences, as reactive agility protocols incorporate more complex perceptual and decision-making components than preplanned CODS tests. Other studies reinforce this perspective. Spasic et al. (2013) reported that in early adolescent girls (12-13 years), reactive strength and sprint ability were stronger predictors of agility performance than anthropometric traits, with regression models explaining 30-64% of variance across multiple agility tests [40] . França et al. (2022) further showed that lower-body explosive power, assessed by squat jump, predicted agility outcomes more strongly than sprinting in some cohorts [55] . Interestingly, they observed that correlations between explosive power, speed, and agility declined with increasing chronological age, suggesting that maturation moderates these relationships. Our findings partially align with these trends. In our cohort, sprint performance strongly correlated with agility in the younger group (6-9 years, r = .807), but was only moderately associated with older players (10-13 years, r = .619). This attenuation may reflect the influence of growth and biological maturation, as developmental status increasingly shapes agility outcomes during early adolescence. Indeed, prior research has emphasized that the ages 11-14 represent a sensitive period marked by rapid physical and biological change, intensified training exposure, and sport specialization [8, 44, 46-48] . These factors likely contribute to variability in agility predictors across age groups and highlight the importance of accounting for maturational status when interpreting youth performance data. Conclusion and Recommendations Overall, this study provides novel evidence that sprint performance is the primary predictor of agility in young basketball players. At the same time, age-related developmental differences modulate the contribution of additional motor variables. These findings offer valuable insights for talent identification and individualized training strategies in youth basketball, supporting evidence-based practices for coaches and practitioners. Regression-based models incorporating TMI, 20-m sprint, vertical jump, and Hexagon performance successfully predicted agility in both age groups. In the 6-9-year-old players, TMI and 20-m sprint accounted for the most explained variance (65.7%), with vertical jump and Hexagon adding only marginal improvements. In contrast, in the 10-13-year-old group, the model including all four predictors explained 47.6% of variance, indicating a broader interplay of motor determinants at this developmental stage. These models may be supportive tools for predicting agility performance, though further refinement and validation are required before routine application. Importantly, relying solely on anthropometric and physical profiles to determine basketball ability may be insufficient as it overlooks cognitive-perceptual skills. As previous literature has emphasised the increasing role of cognitive abilities as agility tasks become more complex, future research should integrate perceptual-cognitive measurements and reactive agility assessments to improve prediction accuracy and inform more comprehensive training interventions. Practical Implications From a practical perspective, coaches working with players aged 6-9 should focus on short sprint accelerations (5-20 m), coordination ladder exercises, and tagging games that encourage quick starts and stops in a fun environment. These activities help develop neuromuscular coordination and fundamental movement skills, which are essential for future sport-specific performance. Agility performance for players aged 10-13 is influenced by a broader set of variables, including multi-directional movement tasks such as the Hexagon test. Therefore, training for this age group should incorporate basketball-specific agility exercises. Examples include multi-directional short sprints, defensive slides combined with reactive cues (e.g., the coach's signal or the ball's movement), and 1v1 agility competitions where athletes must adapt to their opponent's movements. Furthermore, incorporating decision-making elements such as reacting to passing options or defensive rotations into agility training can better mimic the perceptual-cognitive demands of basketball matches. These recommendations indicate that sprint-based training should remain a fundamental component throughout all stages of development, but that basketball coaches should gradually integrate agility-focused and cognitively enriched exercises as athletes approach adolescence. This approach can enhance the transfer of training effects to on-court performance and support long-term athletic development. Limitations and Future Directions Despite its contributions, this study is not without limitations. First, only healthy male basketball players were included, limiting the findings' generalizability to female athletes or children with health conditions. Accordingly, external validity is restricted to healthy male youth with comparable training exposure. Sex- and age-specific physiological and anthropometric differences may affect performance during testing and, consequently, influence outcomes. Future research should incorporate biological maturation assessments, such as estimating peak height velocity, to better account for growth-related variability in agility performance [56] . Study sample was restricted to players aged 6-13 years and divided into two categories: 6-9 years and 10-13 years. Training exposure and biological maturation were not precisely measured. These variables were not available in the dataset and thus could not be modeled as a separate hierarchical block. To partially mitigate confounding, analyses were stratified by age group and anthropometry was entered prior to motor variables to quantify incremental explanatory power. Future studies should implement PHV-adjusted mixed-effects models and incorporate reactive agility protocols to capture perceptual–cognitive demands that preplanned change-of-direction tests do not assess.We explicitly acknowledge the absence of training and maturity metrics as a limitation and a priority for future data collection. Future research should consider narrower age ranges, include more diverse populations, and incorporate assessments of maturity status to provide more precise information about developmental differences. Secondly, as prior exposure to structured basketball training may influence motor skill efficiency and agility outcomes, the age at which training commences, and accumulated sporting experience should also be taken into account. [57] . Another important avenue is the examination of basketball-specific positional roles (e.g., guard, forward, center), given that physical and agility demand vary across positions [28] . Including these factors would enhance the ecological validity of predictive models and provide more comprehensive insights for coaches and talent identification programs. Thirdly, anthropometric measurements are subject to evaluator dependence and methodological limitations. While the tools used in this study were portable, inexpensive, non-invasive, and widely recognized as valid for estimating body composition, they may not capture regional variations as accurately as advanced techniques. Fourth, agility was assessed exclusively using the V-cut test and predicted by triponderal index, 20-m sprint, vertical jump, and Hexagon test performance. Although justified, this approach may have overlooked other important components of agility, particularly perceptual-cognitive and reactive elements. Current studies also encourage the integration of perceptual-cognitive measurements, such as reactive agility protocols, to capture the decision-making and foresight components that are of great importance in basketball [58, 59] . Finally, the study employed a cross-sectional design, which prevents causal inferences. Moreover, no internal validation techniques, such as k-fold cross-validation or bootstrapping, were applied, which may limit the generalizability of the regression models and risk overfitting. Future studies should adopt longitudinal designs and incorporate robust validation procedures to improve agility prediction models' reliability, stability, and practical applicability. Abbreviations TMI Triponderal Mass Index CMJ Countermovement Jump V-CUT V-cut Test BMI Body Mass Index cm Centimeter kg Kilogram m Meter VIF Variance Inflation Factors TRAG Triangle Reactive Agility Test CODS Change of Direction Speed SD. Standard Deviation PHV Peak Height Velocity Declarations Author contributions All authors have contributed sufficiently to the manuscript and have approved the final version. Concept and design (GB, VGG); Data collection (ÖÖ, KU, OBA); Analysis (VGG, SD); Interpretation (all authors); Draft preparation (GB, VGG, SNM, SD, AEC, AO); Revision (GB, VGG); Final approval (all authors) and acceptance of responsibility (all authors) stages. Funding There is no financial support received for this study. Data availability The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was approved by the Bandırma Onyedi Eylül University Health Sciences Non-Interventional Research Ethics Committee (Meeting Number: 2025-06, Date: 09 July 2025; Ethics committee number 25691463-050.04-2500035819) and was conducted in accordance with the ethical principles of the Declaration of Helsinki. Because all participants were minors (<16 years), written informed consent to participate was obtained from their parents or legal guardians, and age-appropriate information and verbal assent were obtained from the children themselves. Before enrolment, participants, their parents/legal guardians, and coaches were provided with detailed information about the purpose of the research, the procedures, and the possible risks. Participants’ personal information and research data were protected in accordance with confidentiality principles; all data were anonymized and securely stored. Consent for publication Not applicable Competing interests The authors declare no competing interests. References Quílez-Maimón A, Siquier-Coll J, Nadal CA, Clemente FM, González-Fernandez FT. Relationship between talent identification and change of direction in young basketball players. Physical Education Theory and Methodology, 2023; 23(1):133-142. Muehlbauer T, Wagner V, Brueckner D, Schedler S, Schwiertz G, Kiss R, Hagen M. Effects of a blocked versus an alternated sequence of balance and plyometric training on physical performance in youth soccer players. BMC Sports Sci Med Rehabil., 2019:11, 18. Sheppard JM, Young WB. Agility literature review: Classifications, training and testing. Journal of Sports Sciences, 2006; 24(9):919-932. Armstrong R, Greig M. The functional movement screen and modified star excursion balance test as predictors of t-test agility performance in university rugby union and netball players. Physical Therapy in Sport, 2018; 32:21-26. Hader K, Mendez-Villanueva A, Ahmaidi S, Williams,BK, Buchheit M. Changes of direction during high-intensity intermittent runs: neuromuscular and metabolic responses. BMC sports science, medicine and rehabilitation, 2014; 6(1): 2. Raeder C, Kämper M, Praetorius A, Tennler J-S, Schoepp C. Metabolic, cognitive and neuromuscular responses to different multidirectional agility-like sprint protocols in elite female soccer players - a randomised crossover study. BMC Sports Sci Med Rehabil., 2024; 16: 64. Young W, Rayner R, Talpey, S. it's time to change direction on agility research: a call to action. Sports Medicine, 2021; 7(1):12. Yılmaz AK, Akgün S, Salkılıç EK, Anıl B, Akdemir E, Aktaş B, Karaduman E, Kabadayı M. The relationship between agility and lower extremity strength in female basketball players. BMC Sports Sci Med Rehabil., 2025; 17(1):198. Hardaning CS, Alim A, Sukamti ER, Tomoliyus. The relationship between grip strength, agility, and hand eye coordination hand eye coordination on groundstroke ability field tennis athletes. Indonesian Journal of Sport Management, 2025; 5(3):669-678. Cunningham DJ, West DJ, Owen NJ, Shearer DA, Finn CV, Bracken RM, Crewther BT, Scott P, Cook CJ, Kilduff LP. Strength and power predictors of sprinting performance in professional rugby players. J Sports Med Phys Fitness, 2013; 53(2):105-11. Fernández-Fernández J, Granacher U, Sanz-Rivas D, Marín JMS, Hernández-Davó JL, Moya M. Sequencing effects of neuromuscular training on physical fitness in youth elite tennis players. Journal of Strength and Conditioning Research, 2018; 32(3):849-856. Sun M, Soh KG, Cao S, Yaacob AB, Ma S, Ding C. Effects of speed, agility, and quickness training on athletic performance: a systematic review and meta-analysis. BMC Sports Science, Medicine and Rehabilitation, 2025; 17(1):66. Ploypradub C, Chinsongkram B, Thongchaom C, Panyakorn N, Aeksiriloes B. Correlation between strength with agility and grip strength in overweight children aged between 7-12 years old. J Med Health Sci., 2021; 28(2):12-26. Buhmann R, Stuelcken M, Sayers M. Alternatives to common approaches for training change of direction performance: a scoping review. BMC Sports Science, Medicine and Rehabilitation, 2022; 14(1):151. Stofberg J, van Aswegen M, Kramer M. Dynamic change of direction, isometric strength, and psychological readiness in final phase ACLR athletes: an integrated approach. BMC musculoskeletal disorders, 2025; 26(1):808. Sarvaiya PH, Puntambekar A. Correlation between lower limb power andagility on hand grip strength in three different positions in young fencers. International Journal of Innovative Research in Medical Science (IJIRMS), 2022; 7(12):751-755. Gonzalo-Skok O, Tous-Fajardo J, Suarez-Arrones L, Arjol-Serrano JL, Casajus JA, Mendez-Villanueva A. Validity of the V-cut test for young basketball players. International Journal of Sports Medicine, 2015; 36(11):893-899. Hernández-Davó JL, Loturco I, Pereira LA, Cesari R, Pratdesaba J, Madruga-Parera M, Fernández-Fernández J. Relationship between sprint, change of direction, jump, and hexagon test performance in young tennis players. Journal of Sports Science & Medicine, 2021; 20(2):197-205. Pavlinovic V, Foretic N, Versic S, Sekulic D, Liposek S. Predictors of reactive agility in early puberty: A multiple regression gender-stratified study. Children, 2022a; 9(12):1780. Negra Y, Chaabene H, Hammami M, Amara S, Sammoud S, Mkaouer B, Hachana Y. Agility in young athletes: Is it a distinct physical quality?. Journal of Strength and Conditioning Research, 2017; 31(3):727-735. Makhlouf I, Chaouachi A, Chaouachi M, Ben Othman A, Granacher U, Behm DG. Combination of agility and plyometric training provides similar training benefits as combined balance and plyometric training in young soccer players. Frontiers in Physiology, 2018; 9:1611. Sellami M, Makni E, Moalla W, Tarwneh R, Elloumi M. Effect of maturation level on agility performance and fitness predictors in youth soccer players aged 11-18 years. Biology of Sport, 2024; 41(1): 15-25. Cole TJ, Faith MS, Pietrobelli A, Heo M. What is the best measure of adiposity change in growing children: BMI, BMI %, BMI z-score or BMI centile? European Journal of Clinical Nutrition, 2017; 62(3):419-425. Peterson CM, Su H, Thomas DM, et al. Tri-Ponderal mass index vs body mass index in estimating body fat during adolescence. JAMA Pediatr. 2017;171(7):629-636. Zheng M, Lamb KE, Grimes C, Laws R, Bolton K, Ong KK. Rapid weight gain during infancy and subsequent adiposity: A systematic review and meta-analysis of evidence. Obesity Reviews , 2017; 19(3):321-332. Barrera-Domínguez FJ, Almagro BJ, Tornero-Quiñones I, Sáez-Padilla J, Sierra-Robles, Á, Molina-López J. Decisive factors for a greater performance in the change of direction and its angulation in male basketball players. International Journal of Environmental Research and Public Health, 2020; 17(18):65-98. Sekulic D, Spasic M, Esco MR. Predictors of agility performance in college athletes. Journal of Strength and Conditioning Research, 2019; 33(7):1813-1823. Torres-Unda J, Zarrazquin I, Gil J, Ruiz F, Irazusta A, Kortajarena M, Seco J, Irazusta J. Anthropometric, physiological and maturational characteristics in selected elite and non-elite male adolescent basketball players. Journal of Sports Sciences, 2013; 31(2):196-203. Uzgur K, Gürses VV, Kamiş O, Özer Ö. Relationship between V-cut agility performance and sprint, vertical jump and change of direction in preadolescent male basketball players. Sportive, 2025; 8(2):400-411. Pavlinovic V, Spasic M, Foretic N, Kontic D, Zenic N. Differential influence of general anthropometric and motor predictors on preplanned agility in pubescent boys and girls: A multiple regression study. Sport Mont, 2022b; 20(1):75-81. American Academy of Family Physicians, American Academy of Pediatrics, American College of Sports Medicine, American Medical Society for Sports Medicine, American Orthopaedic Society for Sports Medicine, & American Osteopathic Academy of Sports Medicine. (2010). PPE: preparticipation physical evaluation (Vol. 154). D. T. Bernhardt, & W. O. Roberts (Eds.). Elk Grove Village, IL: American Academy of Pediatrics. Maron BJ, Friedman RA, Kligfield P, Levine BD, Viskin S, Chaitman BR, ... & Thompson PD. (2014). Assessment of the 12-lead ECG as a screening test for detection of cardiovascular disease in healthy general populations of young people (12-25 years of age): a scientific statement from the American Heart Association and the American College of Cardiology. Circulation, 2014; 130(15):1303-1334. Beekhuizen KS, Davis MD, Kolber MJ, Cheng MSS. Test-retest reliability and minimal detectable change of the hexagon agility test. Journal of Strength and Conditioning Research, 2009; 23(7):2167-2171. Balsalobre-Fernandez C, Glaister M, Lockey RA. The validity and reliability ofan iPhone app for measuring vertical jump performance. J Sports Sci., 2015; 33(15):1574-1579. Cruvinel-Cabral RM, Oliveira-Silva I, Medeiros AR, Claudino JG, Jiménez-Reyes P, Boullosa DA. The validity and reliability of the “My Jump App” for measuring jump height of the elderly. PeerJ. 2018; 6:e5804. Gallardo-Fuentes F, Gallardo-Fuentes J, Ramirez-Campillo R. et. al. Inter andintra-session reliability and validity of the MyJump app for measuring differ-ent jump actions in trained male and female athletes. J Strength Cond Res., 2016; 30(7):2049-2056. Haynes T, Bishop C, Antrobus M, Brazier J. The validity and reliability of the My Jump 2 app for measuring the reactive strength index and drop jump performance. The Journal of Sports Medicine and Physical Fitness, 2019; 59(2):253-258. Stanton R, Kean CO, Scanlan AT. My Jump for vertical jump assessment. British Journal of Sports Medicine, 2017; 49(17):1157-1158. Sekulic D, Spasic M, Esco MR. Predicting agility performance with other performance variables in pubescent boys: A multiple-regression approach. Perceptual and Motor Skills, 2014; 118(2):447-461. Spasic M, Uljevic O, Coh M, Dzelalija M, Sekulic D. Predictors of agility performance among early pubescent girls. International Journal of Performance Analysis in Sport, 2013; 13(2):480-496. Horička P, Šimonek J. Identification of agility predictors in basketball. Trends in Sport Sciences, 2019; 26(1): 27-32. Nayıroğlu S, Yılmaz AK, Silva AF, Silva R, Nobari H, Clemente FM. Effects of small-sided games and running-based high-intensity interval training on body composition and physical fitness in under-19 female soccer players. BMC Sports Science, Medicine and Rehabilitation, 2022; 14(1):119. Kadlubowski B, Keiner M, Wirth K, Csapo R. Association between sprint and jump performance and maximum strength in standing calf raise or squat in elite youth soccer players. Sports , 2024; 12 (4):87. Acar NE, Umutlu G, Gencer YG, Güven E, Taşman GA. Strength asymmetries and their impact on landing dynamics during countermovement jump and drop jump tests in professional female basketball players. BMC Sports Science, Medicine and Rehabilitation, 2025; 17(1):282. Sammoud S, Negra Y, Bouguezzi R, Ramirez-Campillo R, Moran J, Bishop C, Chaabene H. Effects of plyometric jump training on measures of physical fitness and lower-limb asymmetries in prepubertal male soccer players: a randomized controlled trial. BMC Sports Science, Medicine and Rehabilitation, 2024; 16(1):37. Bayraktar I, Örs BS, Bağcı E, Altunsoy M, Pekel HA. The investigation of approach run in terms of age, gender, bio-motor and technical components on vaulting table. Science of Gymnastics Journal, 2012; 13(2):275-285. Canlı U, Aldhahi MI, Küçük H. Association of physiological performance, physical fitness, and academic achievement in secondary school students. Children, 2024; 11(3):396. Kırıkoğlu N, Pekel AÖ, Ceylan T, Eliöz M, Yavaşoğlu B, Beykümül A, Kurt KA, Yazıcıoğlu Çalışan H, Baykal C, Karaman M, Türkoğlu H, Ceylan L. Differences in foot pressure distribution of males with and without basketball training in early adolescence. Journal of Men’s Health, 2025; 21(5):86-92. Turner AN, Jeffreys I. The stretch-shortening cycle: Proposed mechanisms and methods for enhancement. Strength & Conditioning Journal, 2010; 32(4):87-99. Şahin HM. Relationships between acceleration, agility, and jumping ability in female volleyball players. European Journal of Experimental Biology, 2014; 4(1):303-308. Warneke K, Keiner M, Schiemann S, Lohmann LH, Wirth K. Influence of maximal strength performance in front squat and deadlift on linear sprint and jump performance in male youth elite basketball players. German Journal of Exercise and Sport Research, 2022; 53(1):10. Wang X, Qin X, Ji S, Dong D. Effects of resistance training with blood flow restriction on explosive power of lower limbs: a systematic review and meta-analysis. Journal of Human Kinetics, 2023; 89:259-268. Koźlenia D, Popowczak M, Horička P, Šimonek J, Domaradzki J. Sex-specific relationship patterns between body morphology and maturity status with change of direction and agility in elite adolescent volleyball players. Scientific Reports, 2024; 14:13170. Muniroglu S, Subak EA. Comparison of 5, 10, 30 meters sprint, Modified T-Test, Arrowhead and Illinois agility tests on football referees. Journal of Education and Training Studies, 2018 ; 6(8):70-76. França C, Gouveia É, Caldeira R, Marques A, Martins J, Lopes H, Henriques R, Ihle A. Speed and agility predictors among adolescent male football players. International Journal of Environmental Research and Public Health, 2022; 19(5):28-56. Malina RM, Rogol AD, Cumming SP, Coelho e Silva MJ, Figueiredo AJ. Biological maturation of youth athletes: Assessment and implications. British Journal of Sports Medicine, 2015; 49(13):852-859. Lloyd RS, Oliver JL. The youth physical development model: A new approach to long-term athletic development. Strength & Conditioning Journal, 2012; 34(3):61-72. Young W, Willey, B. Analysis of a reactive agility field test. Journal of Science and Medicine in Sport, 2010; 13(3):376-378. Serpell BG, Young WB, Ford M. Are the perceptual and decision-making components of agility trainable? A preliminary investigation. Journal of Strength and Conditioning Research, 2011; 25(5):1240-1248. Tables Table 1. Characteristics and performance variables of participants aged 6-9 years (n = 44) Variables Mean ± S.D. Min–Max %95 Lower %95 Upper Age (years) 7.30±1.12 6-9 6,99 7.61 Height (cm) 139.72 ± 10.87 114-165 136.79 142.74 Body mass (kg) 36.61 ± 11.14 17-63 33.29 40.02 TMI (kg/m -3 ) 13.16 ± 2.51 9.91-19.05 12.43 14.05 VCUT (s) 10.00 ± 1.07 8.07 – 12.61 9.68 10.30 Hexagon (s) 32.47 ± 7.10 23.62 – 50.00 30.57 34.66 CMJ (cm) 20.25 ± 6.57 9.47 – 35.49 18.56 22.11 20 m Sprint (s) 5.34 ± 0.56 4.32 – 6.81 5.17 5.51 Values are Mean ± SD with range (Min–Max). TMI = triponderal mass index (kg·m⁻³); VCUT = V-cut change-of-direction test time (s); Hexagon = Hexagon agility test time (s); CMJ = countermovement jump height (cm); 20-m sprint = electronically timed 20-m sprint time (s). For time variables (VCUT, Hexagon, 20-m sprint), lower values indicate better performance; for CMJ, higher values indicate better performance. Where reported, 95% CI refers to the confidence interval for the mean. Table 2. Characteristics and performance variables of participants aged 10-13 years (n = 54) Variables Mean ± S.D. Min–Max %95 Lower %95 Upper Age (years) 11.52±1.05 10-13 11.21 11.83 Height (cm) 145.21 ± 17.81 118-189 140.65 150.18 Body mass (kg) 41.37 ± 16.96 21-96 37.27 45.91 TMI (kg/m -3 ) 13.04 ± 2.21 8.54-18.31 12.45 13.56 VCUT (s) 9.24 ± 0.97 7.34 – 11.76 8.97 9.52 Hexagon (s) 18.42 ± 2.78 12.96 – 24.70 17.71 19.12 CMJ (cm) 20.51 ± 6.09 7.48 – 36.51 18.80 22.13 20 m Sprint (s) 5.19 ± 0.57 3.99 – 6.61 5.03 5.34 Values are Mean ± SD with range (Min–Max). TMI = triponderal mass index (kg·m⁻³); VCUT = V-cut change-of-direction test time (s); Hexagon = Hexagon agility test time (s); CMJ = countermovement jump height (cm); 20-m sprint = electronically timed 20-m sprint time (s). For time variables (VCUT, Hexagon, 20-m sprint), lower values indicate better performance; for CMJ, higher values indicate better performance. Where reported, 95% CI refers to the confidence interval for the mean. Table 3. Pearson’s correlation coefficients between V-cut time and performance predictors in children aged 6–9 years old (n = 44) Predicted Variable Vcut TMI ( kg/m³ ) 20m Sprint (sn) CMJ (cm) Hexagon (kg/m 2 ) VCUT r .130 .807 -.440 .318 p .201 .000 ** .001 ** .018 * Values are Pearson’s r with two-tailed p-values showing associations between VCUT (s) and each predictor (TMI, 20 m sprint time, CMJ height, Hexagon time). VCUT = V-cut agility test time (s). TMI = triponderal mass index (kg·m⁻³). CMJ = countermovement jump (cm). Hexagon = hexagon agility test time (s). For time variables (VCUT, 20 m sprint, Hexagon), lower values indicate better performance; for CMJ, higher values indicate better performance. Statistical significance set at α = 0.05 (two-tailed); * p < 0.05; ** p < 0.01; *** p < 0.001. Table 4. Pearson’s correlation coefficients between V-cut time and performance predictors in children aged 10-13 years old (n = 54) Predicted Variable Vcut TMI ( kg/m³ ) 20m Sprint (sn) CMJ (cm) Hexagon (kg/m 2 ) VCUT r .187 .619 -.337 .156 p .092 .000*** .007* .135 Values are Pearson’s r with two-tailed p-values showing associations between VCUT (s) and each predictor (TMI, 20 m sprint time, CMJ height, Hexagon time). VCUT = V-cut agility test time (s). TMI = triponderal mass index (kg·m⁻³). CMJ = countermovement jump (cm). Hexagon = hexagon agility test time (s). For time variables (VCUT, 20 m sprint, Hexagon), lower values indicate better performance; for CMJ, higher values indicate better performance. Statistical significance set at α = 0.05 (two-tailed); * p < 0.05; ** p < 0.01; *** p < 0.001. Table 5. Hierarchical regression predicting VCUT (s) in 6-9 years old; Panel A-Coefficients; Panel B-Model fit (n = 44) (A) Coefficients (final model - Block 4) Predictor B SE 95% CI (LL, UL) t p β Tolerance VIF Intercept 1.576 1.554 -1.567, 4.720 1.014 .317 - - - 20 - m sprint (s) 1.596 0.230 1.131, 2.062 6.937 <.001 .834 .606 1.651 CMJ (cm) 0.006 0.019 -0.033, 0.045 0.315 .754 .037 .625 1.601 Hexagon (s) 0.006 0.015 -0.025, 0.036 0.378 .707 .038 .866 1.154 TMI (kg·m⁻³) -0.031 0.042 -0.117, 0.054 -0.737 .465 -.073 .892 1.122 (B) Model fit (by blocks) Model / Block R² Adj. R² ΔR² Sig. F - change SEE (s) F (df1, df2) p Durbin – Watson AIC (full) AICc (full) Block 1 (TMI) .017 -.007 - - 1.0769 0.717 (1,42) .402 - 133.341 133.634 Block 2 (+ 20-m s) .657 .640 .640 < .001 0.6440 39.234 (2,41) <.001 - 89.029 89.629 Block 3 (+ CMJ) .658 .632 .001 .777 0.6513 25.597 (3,40) <.001 - 90.941 91.966 Block 4 (+Hexago) .659 .624 .001 .707 0.6584 18.822 (4,39) <.001 2.181 92.780 94.359 Panel A reports unstandardized coefficients (B) with standard errors (SE), 95% confidence intervals (CI), t, exact p, and standardized coefficients (β). Collinearity is summarized by Tolerance and VIF. Panel B shows model fit by blocks: R², Adjusted R², ΔR² (Sig. F-change), SEE (s), overall F with df, and Durbin–Watson. Time measures (VCUT, 20-m sprint, Hexagon) are in seconds (s); CMJ in centimeters (cm); TMI = triponderal mass index (kg·m⁻³). For values rounding to .000, report p < .001. Table 6. Hierarchical regression predicting VCUT (s) in 10-13 years old; Panel A-Coefficients; Panel B-Model fit (n = 54) (A) Coefficients (final model - Block 4) Predictor B SE 95% CI (LL, UL) t p β Tolerance VIF Intercept -0.199 1.993 -4.205, 3.807 -0.100 .921 - - - 20 - m sprint (s) 1.352 0.250 0.849, 1.855 5.405 <.001 .791 .521 1.920 CMJ (cm) 0.028 0.023 -0.018, 0.074 1.212 .232 .173 .545 1.833 Hexagon (s) 0.104 0.038 0.028, 0.180 2.708 .009 .297 .929 1.076 TMI (kg·m⁻³) -0.004 0.052 -0.109, 0.101 -0.077 .939 -.009 .816 1.226 (B) Model fit (by blocks) Model / Block R² Adj. R² ΔR² Sig. F - change SEE (s) F (df1, df2) p Durbin–Watson AIC (full) AICc (full) Block 1 (TMI) .035 .016 - - 0.9662 1.819 (1, 50) .183 - 145.952 146.197 Block 2 (+ 20-m s) .387 .362 .352 < .001 0.7777 15.486 (2, 49) <.001 - 124.338 124.838 Block 3 (+ CMJ) .394 .356 .007 .466 0.7814 10.407 (3, 48) <.001 - 125.755 126.607 Block 4 (+Hexago) .476 .431 .082 .009 0.7344 10.670 (4, 47) <.001 2.264 120.214 121.518 Panel A reports unstandardized coefficients (B) with standard errors (SE), 95% confidence intervals (CI), t, exact p, and standardized coefficients (β). Collinearity is summarized by Tolerance and VIF. Panel B shows model fit by blocks: R², Adjusted R², ΔR² (Sig. F-change), SEE (s), overall F with df, and Durbin–Watson. Time measures (VCUT, 20-m sprint, Hexagon) are in seconds (s); CMJ in centimeters (cm); TMI = triponderal mass index (kg·m⁻³). For values rounding to .000, report p < .001. Additional Declarations No competing interests reported. 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15:34:00","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141152,"visible":true,"origin":"","legend":"","description":"","filename":"fa6dc25346024fefadfd90f0faa3a5db1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8025215/v1/62373b6727f884a3d07be74a.xml"},{"id":98420012,"identity":"bdf4e436-3058-404e-863f-cbb661c84ddc","added_by":"auto","created_at":"2025-12-17 15:34:00","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138706,"visible":true,"origin":"","legend":"","description":"","filename":"fa6dc25346024fefadfd90f0faa3a5db1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8025215/v1/f0e26562e4ac8b6049ce2746.xml"},{"id":98441688,"identity":"ec47f214-996f-44e0-af3d-e76ab55d0611","added_by":"auto","created_at":"2025-12-17 17:05:42","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150472,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8025215/v1/459fc4f96604a867cb9dd86f.html"},{"id":98420005,"identity":"6f15a5a0-4748-4da0-ad46-97d71cc6a87a","added_by":"auto","created_at":"2025-12-17 15:33:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExplained variance by hierarchical blocks across age groups.\u003c/strong\u003e\u003cbr\u003e\nStacked bars show the incremental variance explained (ΔR²) by each block; TMI , 20-m sprint, CMJ, and Hexagon for the 6-9 y and 10–13 y models. Total model fit (Total R²) is annotated above each bar. Sprint accounts for the largest share of explained variance in both groups; Hexagon adds unique variance only in 10-13 y, consistent with increasing multidirectional coordination demands. Values correspond to Tables 5-6.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8025215/v1/ebc1a4c1a9bf90d8f21eec56.png"},{"id":104739612,"identity":"ade8bece-12b4-4d1a-bc77-ef1336f99846","added_by":"auto","created_at":"2026-03-16 16:10:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1716740,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8025215/v1/65973a11-9bc2-45da-bd5f-e52b64a2652b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictors of agility in youth basketball: Age-stratified hierarchical regression in 6–13- year-old boys","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBasketball is one of the most widely played team sports worldwide, engaging millions of young athletes in recreational and competitive contexts. Its dynamic nature requires frequent accelerations, decelerations, and rapid direction changes, making agility a fundamental determinant of performance. These demands make agility a critical determinant of performance, particularly in youth athletes during early developmental stages when physical growth and motor learning interact dynamically [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbility defined as to rapidly change direction or velocity in response to a stimulus while maintaining balance and control [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Contemporary models conceptualize agility as a multifactorial construct influenced by neuromuscular coordination, biomechanical efficiency, and cognitive-perceptual processes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Within this framework, both anthropometric characteristics (e.g., body height, mass, limb length, and segmental ratios) and motor performance attributes (e.g., sprint speed, vertical jump ability, and multidirectional movement capacity) are recognized as key contributors to agility outcomes in youth athletes [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Understanding the determinants of agility in youth basketball has become a central focus for sports scientists, coaches, and talent development programs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In recent years, sports scientists and coaches have increasingly emphasized the importance of objective and field-based assessments to predict agility and inform individualized training programs for young athletes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the last decade, numerous studies have examined how anthropometric and motor variables relate to agility and change-of-direction performance in youth sports. Among motor predictors, recent studies have reported moderate to strong associations between sprint performance and agility across sports, including basketball, soccer, and tennis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Vertical jump ability and reactive strength have also been linked to agility through their reliance on stretch-shortening cycle efficiency [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, anthropometric measures such as body mass index (BMI) and triponderal mass index (TMI) show mixed associations, with some evidence suggesting that excessive body mass may hinder change-of-direction performance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition to BMI, TMI (weight/height\u0026sup3;) has recently been proposed as a more stable indicator of adiposity in pediatric populations [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Given these advantages, TMI was selected in the present study as a complementary morphological parameter to BMI in predicting agility performance.\u003c/p\u003e \u003cp\u003eDespite these insights, significant gaps remain. Most studies focus on single predictors rather than integrating multiple anthropometric and motor variables into regression-based models, limiting their practical value for training and talent identification [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, despite significant differences in biological maturation and neuromuscular development between early childhood and early adolescence, there are very few studies examining age-specific determinants of agility [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and there is still a need for research in the literature that aims to highlight these differences. Finally, validated field tests such as the V-cut and Hexagon protocols remain underutilized in predictive modeling, despite their reliability and ecological relevance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethodological limitations also persist in literature. Many studies use small, homogeneous samples, limit generalizability, and overlook confounding factors such as training experience, maturation, or positional roles [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While laboratory assessments provide precision, they lack ecological validity compared to field-based contexts where agility occurs in real competition. Addressing these shortcomings is essential for developing practical, evidence-based tools to guide individualized training and long-term athlete development. Given this need, research should adopt regression-based models to determine the relative contribution of multiple predictors while accounting for age-specific developmental differences. Such approaches would advance theoretical understanding and provide actionable insights for coaches and talent identification systems.\u003c/p\u003e \u003cp\u003eTherefore, grounded in the Sheppard-Young model of agility (neuromuscular and perceptual components), we examined age-specific predictors of preplanned change-of-direction performance (V-CUT) in male basketball players aged 6\u0026ndash;13 years. Additionally, by addressing developmental differences in the determinants of agility, we aimed to determine whether predictors differed between two age groups (6\u0026ndash;9 and 10\u0026ndash;13 years old). We hypothesized that: (H1) linear sprint would be the dominant predictor across ages; (H2) multidirectional coordination (Hexagon) would contribute more in the older group; and (H3) anthropometric indices (including TMI) would show limited predictive value after motor variables.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a cross-sectional design to examine the predictors of agility performance using anthropometric and motor variables, aiming to develop regression models for youth basketball players. The sample was stratified into two age groups (6-9 years and 10-13 years) to account for developmental differences. Athletes in both age groups train for a total of 180 minutes per week, with two 90-minute sessions. Selected participants, coaches and parents were provided with detailed information about the purpose, duration and requirements of the training, and the necessary approvals were obtained from the participants\u0026apos; families for them to take part. All measurements were taken on two consecutive test days and at the same time of day (between 10:00 and 12:00) to minimise fatigue and ensure optimum performance.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis application was also implemented during tests conducted throughout the day, with the tests being ranked taking into account fatigue-inducing factors.\u0026nbsp;All field tests were performed on an indoor hardwood court surface with athletes wearing court shoes. The testing order was fixed: anthropometry, CMJ, and the sprint test were conducted on Day 1, while the Hexagon and V-CUT tests were performed on Day 2. Two trials were administered for each test, with the best value used for analysis and a 2-minute rest period provided between trials.\u0026nbsp;To minimize inter-rater variability, all measurements were conducted by the same trained investigator following standardized testing procedures. Testing environments were controlled to reduce external influences, with the temperature at 23\u0026deg;C (\u0026plusmn;1 \u0026deg;C). To control for dietary effects, participants were asked to maintain their usual dietary patterns throughout the study.\u0026nbsp;All baseline assessments were conducted two hours after meals, and participants were instructed to refrain from\u0026nbsp;physical exercise on the day of testing. Before data collection, all participants were screened to confirm eligibility based on inclusion and exclusion criteria. This methodological approach was designed to generate a practical and field-based predictive framework for understanding the relationship between anthropometric and motor performance variables and agility in youth basketball players. All baseline assessments were conducted two hours after meals, and participants were instructed to refrain from physical exercise on the day of testing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Sample\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA priori power analysis was conducted using G*Power 3.1.9.2. Assuming three main predictors, the analysis indicated that a minimum of 77 participants would be required to achieve a statistical power of 0.80 at \u0026alpha; =.05, effect size \u003cem\u003ef\u0026sup2;\u003c/em\u003e = 0.15. Therefore, the sample size of 98 athletes in this study exceeded the required threshold.\u003c/p\u003e\n\u003cp\u003eA total of 98 healthy male children participated in the study. All participants voluntarily took part, and because they were underage, written informed consent was obtained from their parents or legal guardians. Inclusion criteria were defined as healthy male basketball players aged between 6 and 13 years. Exclusion criteria included any known chronic diseases, musculoskeletal disorders, or cardiovascular conditions. To determine eligibility, participants were asked to complete a standardized health screening questionnaire, including family medical history. In addition, the researchers confirmed both players\u0026apos; and parents\u0026apos; statements through individual interviews \u003cstrong\u003e[31, 32].\u003c/strong\u003e No participant presenting symptoms related to the exclusion criteria was included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric Measurements:\u0026nbsp;\u003c/strong\u003eAnthropometric variables were assessed following standardized guidelines to ensure precision, validity, and reliability. Body height was measured using a stadiometer (Seca 213; Seca GmbH \u0026amp; Co. KG, Hamburg, Germany) with 0.1 cm accuracy, and body mass was recorded with a segmental body composition monitor (TANITA BC-558 Ironman, Tanita Corporation, Tokyo, Japan) with 0.05 kg accuracy. Participants were measured barefoot and wearing only light clothing. BMI was calculated as body mass (kg)/height (m\u0026sup2;), and TMI was computed as body mass (kg)/height\u0026sup3; (m\u0026sup3;). Arm and hand span were measured using a flexible anthropometric tape (Seca 201, Seca GmbH \u0026amp; Co. KG, Hamburg, Germany). All anthropometric assessments were performed twice, and the best measure was retained for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eV-cut Agility Test (V-CUT):\u0026nbsp;\u003c/strong\u003eAgility performance was measured using the V-cut test, a widely applied protocol in basketball to assess change-of-direction ability \u003cstrong\u003e[1, 17]\u003c/strong\u003e. The test was conducted on a standard basketball court (28 m x 15 m, parquet flooring) located in an indoor sports hall. The test was performed over a 25 m course with four directional changes of 45\u0026deg; every 5 m. Players started between two cones set 0.7 m apart, sprinted through the course, and finished between two cones at the end. Each participant performed two attempts, separated by two minutes of passive rest. Times were recorded to the nearest 0.01 s using electronic timing gates, and the best performance was used in the analyses \u003cstrong\u003e[29].\u003c/strong\u003e The V-cut test followed the protocol outlined by Gonzalo-Skok et al. \u003cstrong\u003e[17]\u003c/strong\u003e, involving four 45\u0026deg; cuts. Due to court geometry constraints, a 25 m variant was used. To ensure comparability, a sensitivity analysis was conducted using z-standardized V-CUT times.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHexagon Test:\u0026nbsp;\u003c/strong\u003eThe Hexagon test evaluated multidirectional agility and lower-limb coordination \u003cstrong\u003e[33]\u003c/strong\u003e. A hexagon (24 in per side, 120\u0026deg; angles) was marked on the floor with tape. Starting at the center, participants performed continuous two-footed jumps over each side for three complete circuits (18 jumps total), always returning to the center after each jump. Timing began with the starting signal and stopped when the athlete returned to the center at the end of the final circuit. The fastest of the two trials was recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCountermovement Jump (CMJ):\u0026nbsp;\u003c/strong\u003eVertical jump performance was assessed using the CMJ test \u003cstrong\u003e[11]\u003c/strong\u003e. Measurements were taken using the MyJump 2 mobile application, whose validity and reliability have been established by various researchers (ICC=0.997) \u003cstrong\u003e[34-38]\u003c/strong\u003e. For the CMJ, athletes were instructed to perform a rapid downward movement from the starting position (approximately 90\u0026deg; knee flexion), followed by a rapid upward movement to jump as high as possible. Participants performed two maximal jumps with arm swing permitted, and jump height (cm) was recorded. The best trial was retained for analysis. Vertical jump ability has been widely used to indicate explosive lower-limb power in youth athletes \u003cstrong\u003e[11]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e20-m sprint Test:\u0026nbsp;\u003c/strong\u003eSprint performance was assessed over 20 m using photocell timing gates (Witty System, Microgate, Bolzano, Italy) positioned 1 m above the ground at the start and finish lines. The players started standing 50 cm behind the first gate with their front feet close to the line. Twenty-meter sprint times were recorded using dual photocell gates positioned at a height of 1.0 m. A standardized standing-start position was adopted, with the lead forefoot placed directly behind the start line and hands free beside the trunk. Two attempts were performed, separated by 2 minutes of rest, and the fastest sprint time was recorded \u003cstrong\u003e[18]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Screening Questionnaire:\u003c/strong\u003e A specific pediatric Preparticipation Physical Examination (PPE) health history screening questionnaire and a brief semi-structured interview checklist modified from AHA pediatric screening guidance were developed to verify medical history, participation eligibility, and training background in children aged 6-13 years \u003cstrong\u003e[31, 32].\u003c/strong\u003e Item content drew on established PPE recommendations and AHA pediatric screening guidance \u003cstrong\u003e[39, 40]\u003c/strong\u003e. Both tools were piloted to ensure clarity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictor Variables:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eIndependent variables included anthropometric measures: Height, body mass, BMI, TMI, and motor performance outcomes (20-m sprint time, CMJ height, Hexagon test time). The dependent variable was agility performance, operationalized through the V-cut test, expressed in seconds.\u0026nbsp;Correlations were computed for all measures; regression models were a-priori restricted to TMI, 20-m sprint, CMJ, and Hexagon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were analyzed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were reported as means and standard deviations. Statistical significance was set at two-tailed \u0026alpha; = .05. Distributions were screened with Shapiro-Wilk tests and Q-Q plots; when normality was violated, we reported Spearman\u0026rsquo;s \u0026rho; in addition to Pearson\u0026rsquo;s r (with 95% CIs from Fisher\u0026rsquo;s z) to describe bivariate associations with V-CUT.\u003c/p\u003e\n\u003cp\u003eHierarchical linear regression analyses were run within each age group using four a priori blocks to quantify incremental contributions; Block 1 Anthropometry (TMI), Block 2-20-m sprint, Block 3-CMJ, Block 4-Hexagon. Assumptions were checked on standardized residuals: linearity, normality, homoscedasticity and independence. Multicollinearity was inspected using VIF (acceptable if \u0026lt; 5). Anthropometry was represented by TMI to avoid redundancy with height and mass. We report unstandardized coefficients (B, standard error [SE], 95% CI, t, exact p), standardized coefficients (\u0026beta;), and model quality (R\u0026sup2;, Adjusted R\u0026sup2;, \u0026Delta;R\u0026sup2; with Sig. F-change, standard error of the estimate (SEE), overall F with degrees of freedom, and Durbin-Watson. Analyses used complete-case data; influential observations were screened and did not alter inferences. Where computed, internal validation and model selection indices were additionally reported AIC/AICc from residual sum of squares (SSE), sample size (n), and parameter count (k). Training exposure was uniform (2\u0026times;90 min/week); therefore, it was not entered into the models. Assumptions were verified on studentized residuals; influential points were inspected.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were 98 male basketball players aged 6\u0026ndash;13 y (n=44 in 6\u0026ndash;9 y; n=54 in 10\u0026ndash;13 y). Descriptive statistics for anthropometry; body height, body mass, TMI, and performance V-CUT, 20-m sprint, CMJ and Hexagon by age group are presented in the Table 1 and Table 2. These values provide the context for the correlation and regression analyses that follow.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;\u0026lt; Table 1 about here \u0026gt;\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;\u0026lt; Table 2 about here \u0026gt;\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson\u0026apos;s correlation coefficients revealed significant associations between motor performance tests and agility (Tables 3 and 4). In the 6-9 age group, V-CUT performance showed a robust positive correlation with 20-m sprint time (r=.807, p\u0026lt;.001) and a moderate negative correlation with CMJ height (r=-.440, p=.001). A small but significant positive correlation was also observed with Hexagon performance (r=.318, p=.018). TMI showed a weak and non-significant association with agility (r =.130, p=.201). In the 10-13 age group, similar trends emerged. V-CUT performance correlated strongly with 20-m sprint time (r=.619, p\u0026lt;.001) and moderately with CMJ (r=-.337, p=.007). However, correlations with TMI (r=.187, p= .092) and Hexagon performance (r=.156, p=.135) were small and non-significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;\u0026lt; Table 3 about here \u0026gt;\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;\u0026lt; Table 4 about here \u0026gt;\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHierarchical regression (6\u0026ndash;9 y)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Entering TMI alone explained 1.7% of variance in V-CUT (R\u0026sup2;=.017, p=.402). Adding 20-m sprint increased explained variance to 65.7% (R\u0026sup2;=.657, p\u0026lt;.001). Subsequent inclusion of CMJ and Hexagon yielded negligible improvements (final model R\u0026sup2;=.659, Adj. R\u0026sup2;=.624, SEE=0.658 s, DW=2.181). In the final model, 20-m sprint remained the only substantial predictor; other coefficients were small and non-significant. (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;\u0026lt; Table 5 about here \u0026gt;\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on these findings, The final unstandardized equation was:\u003c/p\u003e\n\u003cp\u003eV-CUT (s) = 1.576 + 1.596\u0026middot;(20-m sprint, s) + 0.006\u0026middot;CMJ (cm) + 0.006\u0026middot;Hexagon (s) \u0026minus; 0.031\u0026middot;TMI (kg\u0026middot;m⁻\u0026sup3;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHierarchical Regression (10\u0026ndash;13 y)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMI alone explained 3.5% of variance (R\u0026sup2;=.035, p=.183). Adding 20-m sprint increased R\u0026sup2; to .387 (p\u0026lt;.001). Including CMJ produced a minimal change (R\u0026sup2;=.394), whereas adding Hexagon further improved the model to R\u0026sup2; = .476 (Adj. R\u0026sup2;=.431, \u0026Delta;R\u0026sup2;=.082, p=.009), indicating unique variance explained by Hexagon in older players. Final model fit indices were SEE=0.734 s and DW=2.264.\u003cem\u003e\u0026nbsp;(Table 6)\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp;\u003cstrong\u003e\u0026lt;\u0026lt;\u0026lt; Table 6 about here \u0026gt;\u0026gt;\u0026gt;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe final unstandardized equation was:\u003c/p\u003e\n\u003cp\u003eV-CUT (s) = \u0026minus;0.199 + 1.352\u0026middot;(20-m sprint, s) + 0.028\u0026middot;CMJ (cm) + 0.104\u0026middot;Hexagon (s) \u0026minus; 0.004\u0026middot;TMI (kg\u0026middot;m⁻\u0026sup3;).\u003c/p\u003e\n\u003cp\u003eSensitivity analyses using z-standardized V-CUT times led to identical inferences (no change in the significance pattern); results not shown. Across both age bands, 20-m sprint consistently explained the largest share of variance in V-CUT. CMJ related to V-CUT at the bivariate level but contributed little once sprint was in the model. Hexagon added explanatory power only in 10\u0026ndash;13 y, suggesting an age-related rise in multidirectional coordination demands. TMI showed minimal predictive value in either group.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to identify anthropometric and motor predictors of agility in young basketball players by applying regression-based models across two age groups (6-9 years and 10-13 years). The results revealed that sprint speed was the reliable predictor of agility across both age groups.\u0026nbsp;The findings revealed that anthropometric indices and vertical jump ability contributed minimally. Moreover, multidirectional coordination, measured by the Hexagon test, emerged as an additional predictor in older athletes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;\u0026lt;\u0026lt; Figure 1 about here \u0026gt;\u0026gt;\u0026gt;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSprint as the Primary Predictor of Agility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSprint speed accounted for 65.7% of the variance in younger players (aged 6-9) and 38.7% of the variance in the older group (aged 10-13). Additionally, it has been demonstrated that sprint performance showed positive, moderate to high correlations with agility in both age groups (6-9 years: r=.807, p\u0026lt;.001; 10-13 years: r=.619, p\u0026lt;.001). This finding aligns with a substantial body of literature reporting moderate to strong correlations between sprint ability and change-of-direction performance in team sports \u003cstrong\u003e[18, 19, 30]\u003c/strong\u003e. Negra et al. (2017) reported large to substantial correlations between sprint tests and agility outcomes (0.53\u0026lt; r \u0026lt;0.85, p\u0026lt;.001; shared variance 28-72%) \u003cstrong\u003e[20]\u003c/strong\u003e. Linear sprinting and agility share similar neuromuscular and biomechanical processes, such as force production, ground contact efficiency (short ground contact times), efficient lower limb mechanics, and lower limb power, which may explain the strong relationship between them \u003cstrong\u003e[27]\u003c/strong\u003e. Similar trends were reported by Horička \u0026amp; \u0026Scaron;imonek (2019), who showed that acceleration (ACC-3 m) accounted for the largest share (71.5%) of reactive agility variance in basketball players \u003cstrong\u003e[41]\u003c/strong\u003e. These results reinforce sprint development as a fundamental training goal for young athletes, particularly during sensitive developmental stages.\u003cstrong\u003e\u0026nbsp;[5, 6, 42].\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCMJ and Hexagon performance emerged only as secondary, developmentally dependent predictors. Specifically, CMJ showed small but significant associations, likely reflecting overlapping contributions of explosive strength and stretch-shortening cycle efficiency, while the Hexagon test became a more relevant predictor in older players, indicating the growing role of multidirectional coordination with maturation \u003cstrong\u003e[1, 21]\u003c/strong\u003e. This streamlined interpretation emphasizes sprinting as the primary determinant of agility, with CMJ and Hexagon offering additional, context-dependent contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of Explosive Power, Vertical Jump, and Change of Direction Ability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVertical jump performance showed moderate associations with agility, consistent with previous evidence highlighting the role of stretch-shortening cycle efficiency and explosive power in rapid directional changes \u003cstrong\u003e[11, 20, 21]\u003c/strong\u003e. However, in our regression models, its predictive contribution was marginal once sprint speed was included, indicating overlapping neuromuscular demands between sprinting and jumping \u003cstrong\u003e[8, 12, 43-45]\u003c/strong\u003e. This suggests that while sprint speed remains the dominant predictor, lower-limb explosive strength contributes meaningfully to agility, particularly in tasks requiring deceleration and reacceleration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAge-specific analyses revealed further nuances. In the 6-9 age group, adding CMJ and Hexagon test performance to the regression model produced only marginal improvements in explained variance, while in the 10-13 group, Hexagon performance contributed more substantially\u0026nbsp;(increased the explained variance to 47.6%).\u0026nbsp;This pattern suggests developmental differences in how multidirectional motor tasks interact with agility. Indeed, small but significant correlations were observed between V-cut and Hexagon performance in the younger cohort (r=.318, p\u0026lt;.018), whereas CMJ was negatively correlated with agility across both age groups (6-9 age: r=-.440, p\u0026lt;.001; 10-13 age: r=-.337, p\u0026lt;.007). These findings are consistent with prior studies reporting negative associations between jump test performance and agility times, reflecting that greater explosive power reduces time-to-completion in change-of-direction tasks \u003cstrong\u003e[19, 20]\u003c/strong\u003e. Given the rapid neuromotor reorganization around peak height velocity, the older group\u0026rsquo;s unique Hexagon contribution likely reflects maturation-related gains in inter-segmental coordination and postural control during multidirectional tasks \u003cstrong\u003e[8, 44, 46\u0026ndash;48]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBiomechanically, this link is supported by the sequence of eccentric, isometric, and concentric contractions inherent to the stretch-shortening cycle, which mirrors the stop-start characteristics of agility movements \u003cstrong\u003e[3, 49, 50]\u003c/strong\u003e. While vertical jump ability clearly reflects explosive leg strength, its contribution to predicting agility outcomes appears secondary once sprint performance is accounted for. This aligns with training studies demonstrating that improvements in vertical or squat jump ability yield modest gains in change-of-direction ability, whereas targeted sprint and multidirectional speed training elicit more pronounced effects \u003cstrong\u003e[51, 52]\u003c/strong\u003e. Considered as a whole, these results suggest that although explosive power contributes meaningfully to agility, its role is conditional and developmentally dependent. Coaches should focus on sprint training as the primary determinant of agility in younger players, while progressively integrating plyometric and multidirectional drills to enhance explosive strength and movement efficiency in older cohorts. Future work should explore how maturation, sex differences, and perceptual-cognitive factors interact with neuromuscular performance to shape agility trajectories during adolescence \u003cstrong\u003e[53]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric Indices and Morphological Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn both cases, TMI alone contributed minimally, while the inclusion of sprint performance substantially increased the predictive value of the models. Contrary to expectations, anthropometric indices such as BMI and TMI accounted for only minimal variance in agility performance. This result supports findings by Muniroglu and Subak (2018), who reported weak associations between morphological traits and agility in children \u003cstrong\u003e[54]\u003c/strong\u003e. On the other hand, although previous studies have emphasised that excessive body mass may impair deceleration and re-acceleration \u003cstrong\u003e[22]\u003c/strong\u003e, the relatively homogeneous and healthy sample in this study may have minimised the effect of anthropometric variation. In relatively homogeneous youth cohorts, anthropometric indices are therefore expected to add little explanatory power once motor variables are entered.\u003c/p\u003e\n\u003cp\u003eDevelopmental differences within the studied cohorts may explain the absence of strong associations between triponderal index and agility. Participants in our sample were in the pre-peak height velocity stage, where motor and physiological maturation are ongoing. Pavlinovic et al. (2022a) reported similar findings, attributing the weak associations partly to short-duration test protocols that may not adequately capture the metabolic demands of greater body or fat mass \u003cstrong\u003e[19]\u003c/strong\u003e. Other studies have likewise noted inconsistent or negligible relationships between children\u0026apos;s anthropometric/body composition indices and preplanned agility tests \u003cstrong\u003e[30, 39]\u003c/strong\u003e. For instance, Sekulic et al. (2014) found no significant correlations between anthropometric indices and multiple agility tests, except for modest associations with body mass and the Zig-Zag test \u003cstrong\u003e[39]\u003c/strong\u003e. Similarly, Pavlinovic et al. (2022b) reported negligible correlations between body fat and TRAG, with shared variance below 5% \u003cstrong\u003e[30]\u003c/strong\u003e. Spasic et al. (2013) also noted that in early adolescent girls, reactive strength and sprint ability, rather than morphology, explained most agility variance (30-64%) \u003cstrong\u003e[40]\u003c/strong\u003e. These findings support the conclusion that functional motor skills are more important than morphological parameters in shaping agility performance during childhood and indicate that anthropometric/body composition indices cannot be reliable predictors of agility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgility Predictors in Basketball: Evidence from Comparative Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough our sample differed in age from some prior investigations, comparable studies in basketball provide valuable insights. Horička and \u0026Scaron;imonek (2019) examined female players (mean age 21.7 years) and found that acceleration (ACC-3 m) accounted for the most significant proportion of variance (71.5%) in reactive agility, underscoring the centrality of sprint ability \u003cstrong\u003e[41]\u003c/strong\u003e. Their results also highlighted significant correlations between V-cut and Hexagon-type agility tests, consistent with the associations observed in our study. They concluded that change-of-direction speed (CODS) and reactive agility should be considered distinct skills, with the relative contribution of motor determinants decreasing as task complexity increases, while cognitive demands become more influential.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, Pavlinovic et al. (2022a) investigated boys and girls aged 11-12 using the Triangle Reactive Agility Test (TRAG) \u003cstrong\u003e[19]\u003c/strong\u003e.\u0026nbsp;While anthropometric and body composition indices showed negligible correlations with agility (0-4% shared variance), motor skills such as sprinting, broad jump, and CMJ were significant predictors (7-43% shared variance). In boys, CODS (Triangle-CODS) alone explained 64% of TRAG variance, highlighting again the primacy of motor over morphological factors. These discrepancies with our findings likely reflect methodological differences, as reactive agility protocols incorporate more complex perceptual and decision-making components than preplanned CODS tests.\u003c/p\u003e\n\u003cp\u003eOther studies reinforce this perspective. Spasic et al. (2013) reported that in early adolescent girls (12-13 years), reactive strength and sprint ability were stronger predictors of agility performance than anthropometric traits, with regression models explaining 30-64% of variance across multiple agility tests \u003cstrong\u003e[40]\u003c/strong\u003e. Fran\u0026ccedil;a et al. (2022) further showed that lower-body explosive power, assessed by squat jump, predicted agility outcomes more strongly than sprinting in some cohorts \u003cstrong\u003e[55]\u003c/strong\u003e. Interestingly, they observed that correlations between explosive power, speed, and agility declined with increasing chronological age, suggesting that maturation moderates these relationships.\u003c/p\u003e\n\u003cp\u003eOur findings partially align with these trends. In our cohort, sprint performance strongly correlated with agility in the younger group (6-9 years, r = .807), but was only moderately associated with older players (10-13 years, r = .619). This attenuation may reflect the influence of growth and biological maturation, as developmental status increasingly shapes agility outcomes during early adolescence. Indeed, prior research has emphasized that the ages 11-14 represent a sensitive period marked by rapid physical and biological change, intensified training exposure, and sport specialization \u003cstrong\u003e[8, 44, 46-48]\u003c/strong\u003e. These factors likely contribute to variability in agility predictors across age groups and highlight the importance of accounting for maturational status when interpreting youth performance data.\u003c/p\u003e"},{"header":"Conclusion and Recommendations","content":"\u003cp\u003eOverall, this study provides novel evidence that sprint performance is the primary predictor of agility in young basketball players. At the same time, age-related developmental differences modulate the contribution of additional motor variables. These findings offer valuable insights for talent identification and individualized training strategies in youth basketball, supporting evidence-based practices for coaches and practitioners.\u003c/p\u003e\n\u003cp\u003eRegression-based models incorporating TMI, 20-m sprint, vertical jump, and Hexagon performance successfully predicted agility in both age groups. In the 6-9-year-old players, TMI and 20-m sprint accounted for the most explained variance (65.7%), with vertical jump and Hexagon adding only marginal improvements. In contrast, in the 10-13-year-old group, the model including all four predictors explained 47.6% of variance, indicating a broader interplay of motor determinants at this developmental stage. These models may be supportive tools for predicting agility performance, though further refinement and validation are required before routine application.\u003c/p\u003e\n\u003cp\u003eImportantly, relying solely on anthropometric and physical profiles to determine basketball ability may be insufficient as it overlooks cognitive-perceptual skills. As previous literature has emphasised the increasing role of cognitive abilities as agility tasks become more complex, future research should integrate perceptual-cognitive measurements and reactive agility assessments to improve prediction accuracy and inform more comprehensive training interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom a practical perspective, coaches working with players aged 6-9 should focus on short sprint accelerations (5-20 m), coordination ladder exercises, and tagging games that encourage quick starts and stops in a fun environment. These activities help develop neuromuscular coordination and fundamental movement skills, which are essential for future sport-specific performance.\u003c/p\u003e\n\u003cp\u003eAgility performance for players aged 10-13 is influenced by a broader set of variables, including multi-directional movement tasks such as the Hexagon test. Therefore, training for this age group should incorporate basketball-specific agility exercises. Examples include multi-directional short sprints, defensive slides combined with reactive cues (e.g., the coach\u0026apos;s signal or the ball\u0026apos;s movement), and 1v1 agility competitions where athletes must adapt to their opponent\u0026apos;s movements. Furthermore, incorporating decision-making elements such as reacting to passing options or defensive rotations into agility training can better mimic the perceptual-cognitive demands of basketball matches.\u003c/p\u003e\n\u003cp\u003eThese recommendations indicate that sprint-based training should remain a fundamental component throughout all stages of development, but that basketball coaches should gradually integrate agility-focused and cognitively enriched exercises as athletes approach adolescence. This approach can enhance the transfer of training effects to on-court performance and support long-term athletic development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite its contributions, this study is not without limitations. First, only healthy male basketball players were included, limiting the findings\u0026apos; generalizability to female athletes or children with health conditions. Accordingly, external validity is restricted to healthy male youth with comparable training exposure. Sex- and age-specific physiological and anthropometric differences may affect performance during testing and, consequently, influence outcomes. Future research should incorporate biological maturation assessments, such as estimating peak height velocity, to better account for growth-related variability in agility performance\u0026nbsp;\u003cstrong\u003e[56]\u003c/strong\u003e. Study sample was restricted to players aged 6-13 years and divided into two categories: 6-9 years and 10-13 years.\u0026nbsp;Training exposure and biological maturation were not precisely measured. These variables were not available in the dataset and thus could not be modeled as a separate hierarchical block. To partially mitigate confounding, analyses were stratified by age group and anthropometry was entered prior to motor variables to quantify incremental explanatory power. Future studies should implement PHV-adjusted mixed-effects models and incorporate reactive agility protocols to capture perceptual\u0026ndash;cognitive demands that preplanned change-of-direction tests do not assess.We explicitly acknowledge the absence of training and maturity metrics as a limitation and a priority for future data collection. Future research should consider narrower age ranges, include more diverse populations, and incorporate assessments of maturity status to provide more precise information about developmental differences.\u003c/p\u003e\n\u003cp\u003eSecondly, as prior exposure to structured basketball training may influence motor skill efficiency and agility outcomes, the age at which training commences, and accumulated sporting experience should also be taken into account. \u003cstrong\u003e[57]\u003c/strong\u003e. Another important avenue is the examination of basketball-specific positional roles (e.g., guard, forward, center), given that physical and agility demand vary across positions \u003cstrong\u003e[28]\u003c/strong\u003e. Including these factors would enhance the ecological validity of predictive models and provide more comprehensive insights for coaches and talent identification programs.\u003c/p\u003e\n\u003cp\u003eThirdly, anthropometric measurements are subject to evaluator dependence and methodological limitations. While the tools used in this study were portable, inexpensive, non-invasive, and widely recognized as valid for estimating body composition, they may not capture regional variations as accurately as advanced techniques.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourth, agility was assessed exclusively using the V-cut test and predicted by triponderal index, 20-m sprint, vertical jump, and Hexagon test performance. Although justified, this approach may have overlooked other important components of agility, particularly perceptual-cognitive and reactive elements. Current studies also encourage the integration of perceptual-cognitive measurements, such as reactive agility protocols, to capture the decision-making and foresight components that are of great importance in basketball \u003cstrong\u003e[58, 59]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFinally, the study employed a cross-sectional design, which prevents causal inferences. Moreover, no internal validation techniques, such as k-fold cross-validation or bootstrapping, were applied, which may limit the generalizability of the regression models and risk overfitting. Future studies should adopt longitudinal designs and incorporate robust validation procedures to improve agility prediction models\u0026apos; reliability, stability, and practical applicability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTMI Triponderal Mass Index\u003c/p\u003e\n\u003cp\u003eCMJ Countermovement Jump\u003c/p\u003e\n\u003cp\u003eV-CUT V-cut Test\u003c/p\u003e\n\u003cp\u003eBMI Body Mass Index \u003c/p\u003e\n\u003cp\u003ecm Centimeter \u003c/p\u003e\n\u003cp\u003ekg Kilogram\u003c/p\u003e\n\u003cp\u003em Meter\u003c/p\u003e\n\u003cp\u003eVIF Variance Inflation Factors\u003c/p\u003e\n\u003cp\u003eTRAG Triangle Reactive Agility Test\u003c/p\u003e\n\u003cp\u003eCODS Change of Direction Speed\u003c/p\u003e\n\u003cp\u003eSD. Standard Deviation\u003c/p\u003e\n\u003cp\u003ePHV Peak Height Velocity\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have contributed sufficiently to the manuscript and have approved the final version. Concept and design (GB, VGG); Data collection (\u0026Ouml;\u0026Ouml;, KU, OBA); Analysis (VGG, SD); Interpretation (all authors); Draft preparation (GB, VGG, SNM, SD, AEC, AO); Revision (GB, VGG); Final approval (all authors) and acceptance of responsibility (all authors) stages.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no financial support received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Bandırma Onyedi Eyl\u0026uuml;l University Health Sciences Non-Interventional Research Ethics Committee (Meeting Number: 2025-06, Date: 09 July 2025; Ethics committee number 25691463-050.04-2500035819) and was conducted in accordance with the ethical principles of the Declaration of Helsinki. Because all participants were minors (\u0026lt;16 years), written informed consent to participate was obtained from their parents or legal guardians, and age-appropriate information and verbal assent were obtained from the children themselves. Before enrolment, participants, their parents/legal guardians, and coaches were provided with detailed information about the purpose of the research, the procedures, and the possible risks. Participants\u0026rsquo; personal information and research data were protected in accordance with confidentiality principles; all data were anonymized and securely stored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eQu\u0026iacute;lez-Maim\u0026oacute;n A, Siquier-Coll J, Nadal CA, Clemente FM, Gonz\u0026aacute;lez-Fernandez FT. Relationship between talent identification and change of direction in young basketball players. Physical Education Theory and Methodology, 2023; 23(1):133-142.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMuehlbauer T, Wagner V, Brueckner D, Schedler S, Schwiertz G, Kiss R, Hagen M. Effects of a blocked versus an alternated sequence of balance and plyometric training on physical performance in youth soccer players. BMC Sports Sci Med Rehabil., 2019:11, 18.\u003c/li\u003e\n \u003cli\u003eSheppard JM, Young WB. Agility literature review: Classifications, training and testing. Journal of Sports Sciences, 2006; 24(9):919-932.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArmstrong R, Greig M. The functional movement screen and modified star excursion balance test as predictors of t-test agility performance in university rugby union and netball players. Physical Therapy in Sport, 2018; 32:21-26.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHader K, Mendez-Villanueva A, Ahmaidi S, Williams,BK, Buchheit M. Changes of direction during high-intensity intermittent runs: neuromuscular and metabolic responses. BMC sports science, medicine and rehabilitation, 2014; 6(1): 2.\u003c/li\u003e\n \u003cli\u003eRaeder C, K\u0026auml;mper M, Praetorius A, Tennler J-S, Schoepp C. Metabolic, cognitive and neuromuscular responses to different multidirectional agility-like sprint protocols in elite female soccer players - a randomised crossover study. BMC Sports Sci Med Rehabil., 2024; 16: 64.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYoung W, Rayner R, Talpey, S. it\u0026apos;s time to change direction on agility research: a call to action. Sports Medicine, 2021; 7(1):12.\u003c/li\u003e\n \u003cli\u003eYılmaz AK, Akg\u0026uuml;n S, Salkılı\u0026ccedil; EK, Anıl B, Akdemir E, Aktaş B, Karaduman E, Kabadayı M. The relationship between agility and lower extremity strength in female basketball players. BMC Sports Sci Med Rehabil., 2025; 17(1):198.\u003c/li\u003e\n \u003cli\u003eHardaning CS, Alim A, Sukamti ER, Tomoliyus. The relationship between grip strength, agility, and hand eye coordination hand eye coordination on groundstroke ability field tennis athletes. Indonesian Journal of Sport Management, 2025; 5(3):669-678.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCunningham DJ, West DJ, Owen NJ, Shearer DA, Finn CV, Bracken RM, Crewther BT, Scott P, Cook CJ, Kilduff LP. Strength and power predictors of sprinting performance in professional rugby players. J Sports Med Phys Fitness, 2013; 53(2):105-11.\u003c/li\u003e\n \u003cli\u003eFern\u0026aacute;ndez-Fern\u0026aacute;ndez J, Granacher U, Sanz-Rivas D, Mar\u0026iacute;n JMS, Hern\u0026aacute;ndez-Dav\u0026oacute; JL, Moya M. Sequencing effects of neuromuscular training on physical fitness in youth elite tennis players. Journal of Strength and Conditioning Research, 2018; 32(3):849-856.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSun M, Soh KG, Cao S, Yaacob AB, Ma S, Ding C. Effects of speed, agility, and quickness training on athletic performance: a systematic review and meta-analysis. BMC Sports Science, Medicine and Rehabilitation, 2025; 17(1):66.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePloypradub C, Chinsongkram B, Thongchaom C, Panyakorn N, Aeksiriloes B. Correlation between strength with agility and grip strength in overweight children aged between 7-12 years old. J Med Health Sci., 2021; 28(2):12-26.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBuhmann R, Stuelcken M, Sayers M. Alternatives to common approaches for training change of direction performance: a scoping review. BMC Sports Science, Medicine and Rehabilitation, 2022; 14(1):151.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStofberg J, van Aswegen M, Kramer M. Dynamic change of direction, isometric strength, and psychological readiness in final phase ACLR athletes: an integrated approach. BMC musculoskeletal disorders, 2025; 26(1):808.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSarvaiya PH, Puntambekar A. Correlation between lower limb power andagility on hand grip strength in three different positions in young fencers. International Journal of Innovative Research in Medical Science (IJIRMS), 2022; 7(12):751-755.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eGonzalo-Skok O, Tous-Fajardo J, Suarez-Arrones L, Arjol-Serrano JL, Casajus JA, Mendez-Villanueva A. Validity of the V-cut test for young basketball players. International Journal of Sports Medicine, 2015; 36(11):893-899.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHern\u0026aacute;ndez-Dav\u0026oacute; JL, Loturco I, Pereira LA, Cesari R, Pratdesaba J, Madruga-Parera M, Fern\u0026aacute;ndez-Fern\u0026aacute;ndez J. Relationship between sprint, change of direction, jump, and hexagon test performance in young tennis players. Journal of Sports Science \u0026amp; Medicine, 2021; 20(2):197-205.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePavlinovic V, Foretic N, Versic S, Sekulic D, Liposek S. Predictors of reactive agility in early puberty: A multiple regression gender-stratified study. Children, 2022a; 9(12):1780.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNegra Y, Chaabene H, Hammami M, Amara S, Sammoud S, Mkaouer B, Hachana Y. Agility in young athletes: Is it a distinct physical quality?. Journal of Strength and Conditioning Research, 2017; 31(3):727-735.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMakhlouf I, Chaouachi A, Chaouachi M, Ben Othman A, Granacher U, Behm DG. Combination of agility and plyometric training provides similar training benefits as combined balance and plyometric training in young soccer players. Frontiers in Physiology, 2018; 9:1611.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSellami M, Makni E, Moalla W, Tarwneh R, Elloumi M. Effect of maturation level on agility performance and fitness predictors in youth soccer players aged 11-18 years. Biology of Sport, 2024; 41(1): 15-25.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCole TJ, Faith MS, Pietrobelli A, Heo M. What is the best measure of adiposity change in growing children: BMI, BMI %, BMI z-score or BMI centile? European Journal of Clinical Nutrition, 2017; 62(3):419-425.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePeterson CM,\u0026nbsp;Su H,\u0026nbsp;Thomas DM, et al. Tri-Ponderal mass index vs body mass index in estimating body fat during adolescence. \u003cem\u003eJAMA Pediatr.\u003c/em\u003e 2017;171(7):629-636.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZheng M, Lamb KE, Grimes C, Laws R, Bolton K, Ong KK. Rapid weight gain during infancy and subsequent adiposity: A systematic review and meta-analysis of evidence. Obesity Reviews\u003cem\u003e,\u003c/em\u003e 2017; 19(3):321-332.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBarrera-Dom\u0026iacute;nguez FJ, Almagro BJ, Tornero-Qui\u0026ntilde;ones I, S\u0026aacute;ez-Padilla J, Sierra-Robles, \u0026Aacute;, Molina-L\u0026oacute;pez J. Decisive factors for a greater performance in the change of direction and its angulation in male basketball players. International Journal of Environmental Research and Public Health, 2020; 17(18):65-98.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSekulic D, Spasic M, Esco MR. \u0026nbsp;Predictors of agility performance in college athletes. Journal of Strength and Conditioning Research, 2019; 33(7):1813-1823.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTorres-Unda J, Zarrazquin I, Gil J, Ruiz F, Irazusta A, Kortajarena M, Seco J, Irazusta J. Anthropometric, physiological and maturational characteristics in selected elite and non-elite male adolescent basketball players. Journal of Sports Sciences, 2013; 31(2):196-203.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eUzgur K, G\u0026uuml;rses VV, Kamiş O, \u0026Ouml;zer \u0026Ouml;. Relationship between V-cut agility performance and sprint, vertical jump and change of direction in preadolescent male basketball players. Sportive, 2025; 8(2):400-411.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePavlinovic V, Spasic M, Foretic N, Kontic D, Zenic N. Differential influence of general anthropometric and motor predictors on preplanned agility in pubescent boys and girls: A multiple regression study. Sport Mont, 2022b; 20(1):75-81.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAmerican Academy of Family Physicians, American Academy of Pediatrics, American College of Sports Medicine, American Medical Society for Sports Medicine, American Orthopaedic Society for Sports Medicine, \u0026amp; American Osteopathic Academy of Sports Medicine. (2010). PPE: preparticipation physical evaluation (Vol. 154). D. T. Bernhardt, \u0026amp; W. O. Roberts (Eds.). Elk Grove Village, IL: American Academy of Pediatrics.\u003c/li\u003e\n \u003cli\u003eMaron BJ, Friedman RA, Kligfield P, Levine BD, Viskin S, Chaitman BR, ... \u0026amp; Thompson PD. (2014). Assessment of the 12-lead ECG as a screening test for detection of cardiovascular disease in healthy general populations of young people (12-25 years of age): a scientific statement from the American Heart Association and the American College of Cardiology. Circulation, 2014; 130(15):1303-1334.\u003c/li\u003e\n \u003cli\u003eBeekhuizen KS, Davis MD, Kolber MJ, Cheng MSS. Test-retest reliability and minimal detectable change of the hexagon agility test. Journal of Strength and Conditioning Research, 2009;\u003cem\u003e\u0026nbsp;\u003c/em\u003e23(7):2167-2171.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBalsalobre-Fernandez C, Glaister M, Lockey RA. The validity and reliability ofan iPhone app for measuring vertical jump performance. J Sports Sci., 2015; 33(15):1574-1579.\u003c/li\u003e\n \u003cli\u003eCruvinel-Cabral RM, Oliveira-Silva I, Medeiros AR, Claudino JG, Jim\u0026eacute;nez-Reyes P, Boullosa DA. The validity and reliability of the \u0026ldquo;My Jump App\u0026rdquo; for measuring jump height of the elderly. PeerJ. 2018; 6:e5804.\u003c/li\u003e\n \u003cli\u003eGallardo-Fuentes F, Gallardo-Fuentes J, Ramirez-Campillo R. et. al. Inter andintra-session reliability and validity of the MyJump app for measuring differ-ent jump actions in trained male and female athletes. J Strength Cond Res., 2016; 30(7):2049-2056.\u003c/li\u003e\n \u003cli\u003eHaynes T, Bishop C, Antrobus M, Brazier J. The validity and reliability of the My Jump 2 app for measuring the reactive strength index and drop jump performance. The Journal of Sports Medicine and Physical Fitness, 2019; 59(2):253-258.\u003c/li\u003e\n \u003cli\u003eStanton R, Kean CO, Scanlan AT. My Jump for vertical jump assessment. British Journal of Sports Medicine, 2017; 49(17):1157-1158.\u003c/li\u003e\n \u003cli\u003eSekulic D, Spasic M, Esco MR. Predicting agility performance with other performance variables in pubescent boys: A multiple-regression approach. Perceptual and Motor Skills, 2014; 118(2):447-461.\u003c/li\u003e\n \u003cli\u003eSpasic M, Uljevic O, Coh M, Dzelalija M, Sekulic D. Predictors of agility performance among early pubescent girls. International Journal of Performance Analysis in Sport, 2013; 13(2):480-496.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHorička P, \u0026Scaron;imonek J. Identification of agility predictors in basketball. Trends in Sport Sciences, 2019; 26(1): 27-32.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eNayıroğlu S, Yılmaz AK, Silva AF, Silva R, Nobari H, Clemente FM. Effects of small-sided games and running-based high-intensity interval training on body composition and physical fitness in under-19 female soccer players. BMC Sports Science, Medicine and Rehabilitation, 2022; 14(1):119.\u003c/li\u003e\n \u003cli\u003eKadlubowski B, Keiner M, Wirth K, Csapo R. Association between sprint and jump performance and maximum strength in standing calf raise or squat in elite youth soccer players. \u003cem\u003eSports\u003c/em\u003e\u003cem\u003e,\u003c/em\u003e 2024; \u003cem\u003e12\u003c/em\u003e(4):87.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAcar NE, Umutlu G, Gencer YG, G\u0026uuml;ven E, Taşman GA. Strength asymmetries and their impact on landing dynamics during countermovement jump and drop jump tests in professional female basketball players. BMC Sports Science, Medicine and Rehabilitation, 2025; 17(1):282.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSammoud S, Negra Y, Bouguezzi R, Ramirez-Campillo R, Moran J, Bishop C, Chaabene H. Effects of plyometric jump training on measures of physical fitness and lower-limb asymmetries in prepubertal male soccer players: a randomized controlled trial. BMC Sports Science, Medicine and Rehabilitation, 2024; 16(1):37.\u003c/li\u003e\n \u003cli\u003eBayraktar I, \u0026Ouml;rs BS, Bağcı E, Altunsoy M, Pekel HA. The investigation of approach run in terms of age, gender, bio-motor and technical components on vaulting table. Science of Gymnastics Journal, 2012; 13(2):275-285.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCanlı U, Aldhahi MI, K\u0026uuml;\u0026ccedil;\u0026uuml;k H. Association of physiological performance, physical fitness, and academic achievement in secondary school students. Children, 2024; 11(3):396.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKırıkoğlu N, Pekel A\u0026Ouml;, Ceylan T, Eli\u0026ouml;z M, Yavaşoğlu B, Beyk\u0026uuml;m\u0026uuml;l A, Kurt KA, Yazıcıoğlu \u0026Ccedil;alışan H, Baykal C, Karaman M, T\u0026uuml;rkoğlu H, Ceylan L. Differences in foot pressure distribution of males with and without basketball training in early adolescence. Journal of Men\u0026rsquo;s Health, 2025; 21(5):86-92.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTurner AN, Jeffreys I. The stretch-shortening cycle: Proposed mechanisms and methods for enhancement. Strength \u0026amp; Conditioning Journal, 2010; 32(4):87-99.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eŞahin HM. Relationships between acceleration, agility, and jumping ability in female volleyball players. European Journal of Experimental Biology, 2014; 4(1):303-308.\u003c/li\u003e\n \u003cli\u003eWarneke K, Keiner M, Schiemann S, Lohmann LH, Wirth K. Influence of maximal strength performance in front squat and deadlift on linear sprint and jump performance in male youth elite basketball players. German Journal of Exercise and Sport Research, 2022; 53(1):10.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWang X, Qin X, Ji S, Dong D. Effects of resistance training with blood flow restriction on explosive power of lower limbs: a systematic review and meta-analysis. Journal of Human Kinetics, 2023; 89:259-268.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKoźlenia D, Popowczak M, Horička P, \u0026Scaron;imonek J, Domaradzki J. Sex-specific relationship patterns between body morphology and maturity status with change of direction and agility in elite adolescent volleyball players. Scientific Reports, 2024; 14:13170.\u003c/li\u003e\n \u003cli\u003eMuniroglu S, Subak EA. Comparison of 5, 10, 30 meters sprint, Modified T-Test, Arrowhead and Illinois agility tests on football referees. Journal of Education and Training Studies, 2018\u003cem\u003e;\u003c/em\u003e 6(8):70-76.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eFran\u0026ccedil;a C, Gouveia \u0026Eacute;, Caldeira R, Marques A, Martins J, Lopes H, Henriques R, Ihle A. Speed and agility predictors among adolescent male football players. International Journal of Environmental Research and Public Health, 2022; 19(5):28-56.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMalina RM, Rogol AD, Cumming SP, Coelho e Silva MJ, Figueiredo AJ. Biological maturation of youth athletes: Assessment and implications. British Journal of Sports Medicine, 2015; 49(13):852-859.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLloyd RS, Oliver JL. The youth physical development model: A new approach to long-term athletic development. Strength \u0026amp; Conditioning Journal, 2012; 34(3):61-72.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYoung W, Willey, B. Analysis of a reactive agility field test. Journal of Science and Medicine in Sport, 2010; 13(3):376-378.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSerpell BG, Young WB, Ford M. Are the perceptual and decision-making components of agility trainable? A preliminary investigation. Journal of Strength and Conditioning Research, 2011; 25(5):1240-1248. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics and performance variables of participants aged 6-9 years (n = 44)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; S.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u0026ndash;Max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95 Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e7.30\u0026plusmn;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6,99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e7.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e139.72 \u0026plusmn; 10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e114-165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e136.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e142.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e36.61 \u0026plusmn; 11.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e17-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e33.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e40.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMI (kg/m\u003csup\u003e-3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13.16 \u0026plusmn; 2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9.91-19.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e14.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCUT (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e10.00 \u0026plusmn; 1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e8.07 \u0026ndash; 12.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e10.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHexagon (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e32.47 \u0026plusmn; 7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e23.62 \u0026ndash; 50.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e34.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e20.25 \u0026plusmn; 6.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9.47 \u0026ndash; 35.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e18.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20 m Sprint (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5.34 \u0026plusmn; 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.32 \u0026ndash; 6.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 567px;\"\u003e\n \u003cp\u003eValues are Mean \u0026plusmn; SD with range (Min\u0026ndash;Max). TMI = triponderal mass index (kg\u0026middot;m⁻\u0026sup3;); VCUT = V-cut change-of-direction test time (s); Hexagon = Hexagon agility test time (s); CMJ = countermovement jump height (cm); 20-m sprint = electronically timed 20-m sprint time (s). For time variables (VCUT, Hexagon, 20-m sprint), lower values indicate better performance; for CMJ, higher values indicate better performance. Where reported, 95% CI refers to the confidence interval for the mean.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Characteristics and performance variables of participants aged 10-13 years (n = 54)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; S.D.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u0026ndash;Max\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%95 Upper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e11.52\u0026plusmn;1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e11.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e145.21 \u0026plusmn; 17.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e118-189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e140.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e150.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody mass (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e41.37 \u0026plusmn; 16.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e21-96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e37.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e45.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMI (kg/m\u003csup\u003e-3\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e13.04 \u0026plusmn; 2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8.54-18.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e13.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCUT (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e9.24 \u0026plusmn; 0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.34 \u0026ndash; 11.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e9.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHexagon (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e18.42 \u0026plusmn; 2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12.96 \u0026ndash; 24.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e19.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e20.51 \u0026plusmn; 6.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.48 \u0026ndash; 36.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e18.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e22.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20 m Sprint (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e5.19 \u0026plusmn; 0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3.99 \u0026ndash; 6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e5.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 567px;\"\u003e\n \u003cp\u003eValues are Mean \u0026plusmn; SD with range (Min\u0026ndash;Max). TMI = triponderal mass index (kg\u0026middot;m⁻\u0026sup3;); VCUT = V-cut change-of-direction test time (s); Hexagon = Hexagon agility test time (s); CMJ = countermovement jump height (cm); 20-m sprint = electronically timed 20-m sprint time (s). For time variables (VCUT, Hexagon, 20-m sprint), lower values indicate better performance; for CMJ, higher values indicate better performance. Where reported, 95% CI refers to the confidence interval for the mean.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s correlation coefficients between V-cut time and performance predictors in children aged 6\u0026ndash;9 years old (n = 44)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"513\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Variable Vcut\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003ekg/m\u0026sup3;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20m Sprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(sn)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHexagon\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVCUT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.001\u003csup\u003e**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.018\u003csup\u003e*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003eValues are Pearson\u0026rsquo;s r with two-tailed p-values showing associations between VCUT (s) and each predictor (TMI, 20 m sprint time, CMJ height, Hexagon time). VCUT = V-cut agility test time (s). TMI = triponderal mass index (kg\u0026middot;m⁻\u0026sup3;). CMJ = countermovement jump (cm). Hexagon = hexagon agility test time (s). For time variables (VCUT, 20 m sprint, Hexagon), lower values indicate better performance; for CMJ, higher values indicate better performance. Statistical significance set at \u0026alpha; = 0.05 (two-tailed); * \u0026nbsp;p \u0026lt; 0.05; \u0026nbsp;** p \u0026lt; 0.01; *** p \u0026lt; 0.001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003ePearson\u0026rsquo;s correlation coefficients between V-cut time and performance predictors in children aged 10-13 years old (n = 54)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"513\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredicted Variable Vcut\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003ekg/m\u0026sup3;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20m Sprint\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(sn)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHexagon\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 28px;\"\u003e\n \u003cp\u003eVCUT\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e-.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.000***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e.007*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 100px;\"\u003e\n \u003cp\u003eValues are Pearson\u0026rsquo;s r with two-tailed p-values showing associations between VCUT (s) and each predictor (TMI, 20 m sprint time, CMJ height, Hexagon time). VCUT = V-cut agility test time (s). TMI = triponderal mass index (kg\u0026middot;m⁻\u0026sup3;). CMJ = countermovement jump (cm). Hexagon = hexagon agility test time (s). For time variables (VCUT, 20 m sprint, Hexagon), lower values indicate better performance; for CMJ, higher values indicate better performance. Statistical significance set at \u0026alpha; = 0.05 (two-tailed); * \u0026nbsp;p \u0026lt; 0.05; \u0026nbsp;** p \u0026lt; 0.01; *** p \u0026lt; 0.001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eHierarchical regression predicting VCUT (s) in 6-9\u0026nbsp;years old; Panel A-Coefficients; Panel B-Model fit (n = 44)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(A) Coefficients (final model\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Block 4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI (LL, UL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTolerance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e1.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-1.567, 4.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003em sprint (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1.131, 2.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.033, 0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHexagon (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.025, 0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMI (kg\u0026middot;m⁻\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.117, 0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"top\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(B) Model fit (by blocks)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel / Block\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj. R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. F\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003echange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEE (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(df1, df2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDurbin\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e\u003cstrong\u003eWatson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC (full)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAICc (full)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 1 (TMI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e-.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.0769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.717 \u0026nbsp; (1,42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e133.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e133.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 2 \u0026nbsp; (+ 20-m s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e39.234 (2,41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e89.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e89.629\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 3 \u0026nbsp; (+ CMJ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25.597 (3,40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e90.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e91.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 4 (+Hexago)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.6584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18.822 (4,39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e92.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 46px;\"\u003e\n \u003cp\u003e94.359\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" valign=\"top\" style=\"width: 613px;\"\u003e\n \u003cp\u003ePanel A reports unstandardized coefficients (B) with standard errors (SE), 95% confidence intervals (CI), t, exact p, and standardized coefficients (\u0026beta;). Collinearity is summarized by Tolerance and VIF. Panel B shows model fit by blocks: R\u0026sup2;, Adjusted R\u0026sup2;, \u0026Delta;R\u0026sup2; (Sig. F-change), SEE (s), overall F with df, and Durbin\u0026ndash;Watson. Time measures (VCUT, 20-m sprint, Hexagon) are in seconds (s); CMJ in centimeters (cm); TMI = triponderal mass index (kg\u0026middot;m⁻\u0026sup3;). For values rounding to .000, report p \u0026lt; .001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6.\u0026nbsp;\u003c/strong\u003eHierarchical regression predicting VCUT (s) in 10-13\u0026nbsp;years old; Panel A-Coefficients; Panel B-Model fit (n = 54)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" style=\"width: 613px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(A) Coefficients (final model\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Block 4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI (LL, UL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTolerance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e1.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-4.205, 3.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003em sprint (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.849, 1.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.791\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.920\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCMJ (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.018, 0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHexagon (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.028, 0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTMI (kg\u0026middot;m⁻\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e-0.109, 0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e1.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 586px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(B) Model fit (by blocks)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel / Block\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdj. R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;R\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSig. F\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003echange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEE (s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(df1, df2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDurbin\u0026ndash;Watson\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIC (full)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAICc (full)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 1 (TMI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.9662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.819 \u0026nbsp; \u0026nbsp; \u0026nbsp;(1, 50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e145.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e146.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 2 \u0026nbsp; (+ 20-m s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.7777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e15.486 \u0026nbsp; \u0026nbsp;(2, 49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e124.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e124.838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 3 \u0026nbsp; (+ CMJ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.7814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.407 \u0026nbsp; \u0026nbsp;(3, 48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e125.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e126.607\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlock 4 (+Hexago)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.7344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e10.670 \u0026nbsp; \u0026nbsp;(4, 47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e120.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 46px;\"\u003e\n \u003cp\u003e121.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\" style=\"width: 613px;\"\u003e\n \u003cp\u003ePanel A reports unstandardized coefficients (B) with standard errors (SE), 95% confidence intervals (CI), t, exact p, and standardized coefficients (\u0026beta;). Collinearity is summarized by Tolerance and VIF. Panel B shows model fit by blocks: R\u0026sup2;, Adjusted R\u0026sup2;, \u0026Delta;R\u0026sup2; (Sig. F-change), SEE (s), overall F with df, and Durbin\u0026ndash;Watson. Time measures (VCUT, 20-m sprint, Hexagon) are in seconds (s); CMJ in centimeters (cm); TMI = triponderal mass index (kg\u0026middot;m⁻\u0026sup3;). For values rounding to .000, report p \u0026lt; .001.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"youth basketball, agility, change of direction, sprint speed, vertical jump, hexagon test, triponderal mass index","lastPublishedDoi":"10.21203/rs.3.rs-8025215/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8025215/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Agility in youth basketball reflects the interplay between body dimensions and motor abilities. Age-specific prediction models may inform training and talent identification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Ninety-eight male players (6–13 y) from a basketball school were classified into 6–9 y and 10–13 y groups. Anthropometry included height, mass, and the triponderal mass index (TMI). Motor performance comprised the 20-m sprint, countermovement jump (CMJ), and the Hexagon test. Agility was assessed with the V-cut (V-CUT) test. Pearson (and, where normality was violated, Spearman) correlations were computed; age-stratified hierarchical regressions identified predictors of V-CUT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e V-CUT time correlated strongly and positively with 20-m sprint in both groups (6–9 y: r=.807, p\u0026lt;.001; 10–13 y: r=.619, p\u0026lt;.001) and moderately and negatively with CMJ (6–9 y: r=−.440, p=.001; 10–13 y: r=−.337, p=.007). Associations with TMI were small and non-significant. In regression, adding 20-m sprint markedly increased explained variance (6–9 y: R²=.657; 10–13 y: R²=.387, both p\u0026lt;.001). Final models yielded R²=.659 (6–9 y) and R²=.476 (10–13 y); Hexagon provided additional unique variance only in the older group (ΔR²=.082, p=.009), whereas CMJ contributed minimally once sprint was entered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Sprint speed is the primary determinant of agility (V-CUT) in young basketball players, while multidirectional change-of-direction ability (Hexagon) gains importance from 10–13 y. Anthropometric indices (e.g., TMI) show limited predictive value. These results support emphasizing early sprint development and progressively integrating multidirectional drills in older athletes to inform age-appropriate training and talent identification.\u003c/p\u003e","manuscriptTitle":"Predictors of agility in youth basketball: Age-stratified hierarchical regression in 6–13- year-old boys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 15:33:52","doi":"10.21203/rs.3.rs-8025215/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-02T06:05:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-01T12:52:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T20:07:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T07:00:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"227098478304333883801692118995463859441","date":"2025-12-15T19:25:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25359680310019488996402465719859103416","date":"2025-12-15T03:59:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306168413483405551386133780598640944432","date":"2025-12-13T19:50:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T11:20:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T21:24:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-01T14:44:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T16:49:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-11-27T16:41:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-sports-science-medicine-and-rehabilitation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ssmr","sideBox":"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ssmr/default.aspx","title":"BMC Sports Science, Medicine and Rehabilitation","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89a34500-8055-4c5a-856b-3026351c0c6f","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T16:05:25+00:00","versionOfRecord":{"articleIdentity":"rs-8025215","link":"https://doi.org/10.1186/s13102-026-01594-z","journal":{"identity":"bmc-sports-science-medicine-and-rehabilitation","isVorOnly":false,"title":"BMC Sports Science, Medicine and Rehabilitation"},"publishedOn":"2026-03-09 15:58:56","publishedOnDateReadable":"March 9th, 2026"},"versionCreatedAt":"2025-12-17 15:33:52","video":"","vorDoi":"10.1186/s13102-026-01594-z","vorDoiUrl":"https://doi.org/10.1186/s13102-026-01594-z","workflowStages":[]},"version":"v1","identity":"rs-8025215","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8025215","identity":"rs-8025215","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}