Development and validation of a machine learning model for non-contact injury prediction based on lower limb strength asymmetry in professional football

Abstract

Non-contact injuries significantly impact professional football, yet traditional risk assessment methods demonstrate limited predictive accuracy. This study aimed to develop and validate a machine learning model integrating lower limb strength asymmetry data for injury prediction. A prospective cohort study enrolled 312 professional male football players from six European clubs (2022–2024). Comprehensive isokinetic strength testing, functional assessments, and 12-month injury surveillance were conducted. Four machine learning algorithms (Random Forest, SVM, GBDT, Deep Neural Networks) were developed using nested cross-validation, with performance evaluated through AUPRC, calibration, and clinical utility metrics. Temporal validation was conducted on a subsequent season cohort from the same clubs. During follow-up, 89 players (28.5%) sustained 127 non-contact injuries. Players with knee flexor asymmetry > 15% demonstrated 3.2-fold increased injury hazard (HR: 3.24, 95% CI: 2.18–4.82). The ensemble model achieved superior predictive performance (AUPRC: 0.759) compared to baseline logistic regression (0.589). Observational implementation of risk-stratified interventions was associated with 73% reduction in injury probability within four weeks. Preliminary cost-effectiveness analysis suggested €215,800 net savings per club season. Machine learning models incorporating strength asymmetry data demonstrate improved injury risk prediction performance in professional football. The observational clinical implementation framework suggests potential for effective injury prevention through personalized interventions, though randomized controlled trials are needed to establish causal relationships. These findings represent a step toward precision prevention strategies in sports medicine.