Abstract
Background: Youth football players experience rapid physical and biological changes while being exposed to high training loads, increasing performance demands and musculoskeletal injury risk. Current evaluations often analyze anthropometric, physiological, and biological domains separately, and few studies integrate these dimensions using machine-learning (ML) approaches. Objective: To provide a multidimensional assessment of elite youth football players and investigate how anthropometric, physical, and biological markers jointly relate to performance through classical statistics and ML. Methods: One hundred elite players (14-18 years) underwent standardized anthropometric, physical, and laboratory assessments. Analyses included descriptive statistics, ANOVA/MANOVA, PCA, factor analysis, composite biological indices, and ML models (linear regression, SVR) with 5-fold cross-validation. K-means clustering explored hidden adaptation phenotypes. Results: Older players showed higher weight and BMI, physical testing revealed consistent limb asymmetry (~5%), and biological markers remained within reference ranges. PCA and factor analysis extracted latent structural and metabolic domains. Linear regression predicted performance with R(2) ≈ 0.59, while SVR underperformed. K-means identified three adaptation phenotypes. Conclusions: Performance and resilience arise from interactions between structural, functional, and biological domains. Interpretable ML methods enhance individualized monitoring, early risk detection, and evidence-based injury-prevention strategies.