Data-Driven Multidimensional Performance Profiling in U10 Tennis Players.

objectiveTalent 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.

methodsA 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

resultsA 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'

conclusionsMultidimensional 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.