An Interpretable Machine Learning Framework for Athlete Motor Profiling Using Multi-Domain Field Assessments: A Proof-of-Concept Study

Early detection of modifiable motor deficits is essential for safe, long-term athletic development, yet most field screens provide only binary risk scores. We therefore designed a practical and interpretable profiling system that classifies youth athletes into one of four functional categories—Functionally Weak, Strength-Deficient, Stability-Deficient, or No Clear Dysfunction—using three common assessments: Functional Movement Screen, hand-held dynamometry, and Y-Balance Test. A total of 46 youth athletes aged 11–16 years participated in the study, including 37 male soccer players (13.3 ± 1.6 y) in the development cohort and 9 handball players (5 male, 4 female; 12.8 ± 0.7 y) in the external validation group. Expert rules based on FMS quartiles and ≤−0.5 SD Z-scores for strength or balance generated the reference labels. The random forest model achieved 81% cross-validated accuracy (with balanced performance across classes) and 89% accuracy on the external handball group, exceeding the performance of the decision tree model. SHAP analysis confirmed that model predictions were driven by domain relevant variables rather than demographics. An accompanying web-based application automatically generates personalized reports, visualizations, and targeted training recommendations, making the system directly usable by coaches and clinicians. Rather than merely predicting injury, this field-ready framework delivers actionable, profile-based guidance to support informed decision making in athlete development. Further validation in larger, sport-diverse cohorts is needed to assess its generalizability and long-term value in practice.