Experimental validation of a multidimensional talent scouting model integrating physical, technical, and psychological indicators in youth athletes

Background and Purpose Talent identification in youth sport has traditionally relied on isolated physical performance measures, which often fail to capture the complexity of long-term athletic potential. Growing evidence suggests that talent is a multidimensional construct influenced by physical, technical, and psychological factors; however, experimental validation of integrated scouting models remains limited. This study aimed to experimentally validate a multidimensional talent scouting model integrating physical, technical, and psychological indicators in youth athletes and to compare its classification accuracy with a traditional physical-based scouting approach. Materials and Methods A quasi-experimental design was employed involving 120 youth athletes (aged 13–16 years) from regional development programs. Physical performance (20 m sprint, countermovement jump, Yo-Yo IR1), technical skills (skill execution accuracy and decision-making), and psychological characteristics (motivation, resilience, competitive confidence) were assessed using standardized and validated instruments. Talent classification accuracy of the multidimensional model was compared with a traditional scouting model using multivariate and regression analyses. Results The multidimensional model demonstrated higher classification accuracy than the traditional model (78.3% vs. 62.5%), with greater sensitivity (81.0% vs. 59.1%) and specificity (75.6% vs. 65.4%). Physical (β = 0.41, p < 0.001), technical (β = 0.47, p < 0.001), and psychological indicators (β = 0.29, p = 0.003) all contributed significantly to talent classification. Significant interaction effects were observed among domains. Conclusions Integrating physical, technical, and psychological indicators provides a more accurate and holistic approach to youth talent identification. Multidimensional scouting models may enhance decision-making, reduce selection bias, and better support long-term athlete development.