Background: Athlete monitoring is considered an important component of youth athlete development and talent pathway management; however, ecologically valid longitudinal evidence within national tennis development systems remains limited. Purpose: Therefore, the present study examined temporal trends in physical performance among junior tennis players within a national talent development system. Methods: This retrospective repeated-measures monitoring study included 127 testing observations from 67 junior tennis players collected between 2021 and 2025. The testing battery assessed sprint speed, multidirectional movement, lower- and upper-body power, flexibility, and muscular endurance. Linear mixed-effects models were conducted for each performance variable, with testing year, chronological age, and sex included as fixed effects and athlete ID as a random intercept. Sensitivity analyses were performed using a repeated-athlete subsample and by including national ranking position as an additional covariate. Results: Significant year effects were observed for sprint performance and plank endurance. Sprint performance was fastest in 2021 and significantly slower in 2025, with similar findings in sensitivity analyses. The sprint difference between 2021 and 2025 (∼0.24 s) suggests a potentially meaningful difference in performance; however, its practical significance within junior tennis remains to be determined. Standing long jump and flexibility did not demonstrate statistically significant year effects, although both variables approached statistical significance. In contrast, multidirectional movement, push-up performance, and medicine ball throw performance remained relatively stable across testing years. Conclusions: Physical performance trends in junior tennis appeared to be non-linear and capacity-specific rather than uniform across performance domains. These findings highlight the value of multi-year monitoring within national talent development systems for identifying developmental variability and informing individualized and maturation-sensitive athlete development strategies in junior tennis.

Objective: Talent identification in youth tennis remains challenging because current assessment approaches often evaluate physical, technical, and cognitive characteristics separately rather than within an integrated multidimensional framework. The present study aimed to identify multidimensional performance profiles in U10 tennis players by integrating technical, physical-motor, and reaction-based cognitive performance characteristics within a single analytical framework. Methods: A total of 113 players participating in the Hungarian Tennis Federation’s U10 Talent Identification Festival were assessed using a multidimensional performance battery. Following data cleaning, 113 participants were included in the analyses. Principal component analysis was used to examine the dimensional structure of the assessment battery, whereas k-means clustering was performed separately on the ten standardized performance variables. The optimal number of clusters was determined using complementary validation procedures, including the elbow method, average silhouette width, and gap statistic. Differences between the two performance profiles were examined using Welch’s t-tests with Hedges’ g effect sizes, while associations of profile membership with birth quarter and sex were assessed using chi-square analyses. An age-adjusted sensitivity analysis was additionally performed to evaluate the influence of chronological age on the clustering solution. Results: A two-cluster solution was retained, comprising a higher-performing profile (n = 48) and a lower-performing profile (n = 65). The higher-performing profile demonstrated better performance across all ten technical, physical-motor, and reaction-based measures, with the largest standardized differences observed for throwing speed (Hedges’ g = 1.43), technique (g = 1.42), and 15-m sprint performance (|g| = 1.38). Chronological age differed significantly between profiles, with participants in the higher-performing profile being older (9.41 vs. 8.80 years; Welch’s t(102.94) = 4.24, p < 0.001; Hedges’ g = 0.80). However, the two-cluster solution remained highly stable after age adjustment (bootstrap Jaccard coefficients = 0.937 and 0.946), with 85.0% correspondence in cluster membership. Neither birth quarter (χ²(3) = 0.28, p = 0.964) nor sex (χ²(1) = 3.19, p = 0.074) was significantly associated with profile membership. Conclusions: Multidimensional profiling identified two distinct performance profiles in U10 tennis players, primarily reflecting a broad gradient in current technical, physical-motor, and reaction-based performance. Although chronological age contributed to profile membership, the persistence of the cluster structure after age adjustment suggests that age did not fully account for the observed performance heterogeneity. These profiles should be interpreted as exploratory descriptions of current multidimensional performance rather than fixed talent categories or predictors of future development. In practice, multidimensional assessment may help coaches identify relative strengths and weaknesses across performance domains and support individualised player development; however, profile membership should not be used as a stand-alone criterion for talent selection or deselection. Longitudinal studies are needed to establish their developmental stability and predictive validity.

The aim of this study was to examine the relative contribution of physical performance, executive functions, and competitive anxiety to competitive success in junior tennis players. A total of 39 national-level junior athletes (20 males, 19 females) participated in the study. Physical performance was assessed using a standardized test battery including a 20 m sprint, standing long jump, agility test, and shuttle run. Executive functions were measured using the Adult Executive Functioning Inventory (ADEXI), while competitive anxiety was assessed with the Competitive State Anxiety Inventory-2 (CSAI-2). Competitive success was operationalized using ranking points. Hierarchical multiple regression analysis was conducted using log-transformed ranking points as the dependent variable. Age and sex explained 71.3% of the variance in LogRanking (R² = 0.713, p < 0.001). The addition of physical performance variables provided a modest, non-significant increase in explained variance (ΔR² = 0.068, p = 0.064). Executive functions (ΔR² = 0.006, p = 0.645) and competitive anxiety (ΔR² = 0.008, p = 0.801) did not provide additional explanatory power. In the final model, age and standing long jump were significant predictors of LogRanking. These findings suggest that ranking-based competitive success in junior tennis is strongly influenced by age-related and competition-exposure factors. Physical performance showed a limited additional contribution, while executive functions and competitive anxiety did not explain further variance in this sample.

Mental health in athletes is shaped by both interpersonal and intrapersonal psychological resources. This study examined whether the association between athletes’ perceptions of the coach–athlete relationship and emotional, psychological, and social well-being showed statistically significant indirect associations through competitive state self-confidence. A cross-sectional sample of 211 adult Hungarian athletes (149 women and 62 men; mean age = 23.39 years, SD = 3.58) completed validated Hungarian measures of the coach–athlete relationship, competitive state self-confidence, and positive mental health. Participants were recruited online by distributing the questionnaire directly to athletes and through sports clubs. Three separate single-mediator models were estimated. Athletes’ perceptions of the coach–athlete relationship were positively associated with competitive state self-confidence (β = 0.41, p < 0.001), which was positively associated with emotional (β = 0.26, p < 0.001), psychological (β = 0.30, p < 0.001), and social well-being (β = 0.19, p = 0.010). Significant indirect associations through competitive state self-confidence were observed for emotional (β = 0.11, p = 0.002), psychological (β = 0.12, p < 0.001), and social well-being (β = 0.08, p = 0.016), whereas the corresponding direct associations were not statistically significant. These findings indicate that the observed associations were statistically consistent with indirect associations through competitive state self-confidence. However, the cross-sectional design does not establish temporal ordering or a causal mediating mechanism. Longitudinal and intervention-based research is needed to determine the temporal direction of these associations.