Predicting Recovery Status In Professional Male Basketball Players Using Ensemble Machine Learning Approaches

PURPOSE: Recovery status plays a critical role in optimizing training loads and preventing overtraining in athletes. While previous studies have investigated recovery in various athletic populations, there remains a gap in the literature regarding the application of ensemble machine learning models to predict recovery status in professional male basketball players. METHODS: A total of 72 variables were monitored, including daily nutrition intake, PlayerLoad, sleep quality, and patterns, heart rate variability (HRV), and subjective well-being and recovery status, over a 36-week period from 53 professional male basketball players. 23 predictors were finally selected based on the random-forest-based Boruta algorithm. Four ensemble machine learning models — Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Light Gradient Boosting Machine (LightGBM) — were employed to predict recovery status. SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) were utilized to interpret the models and identify the most influential predictors of recovery status. RESULTS: Among the ensemble methods, XGBoost demonstrated the best performance (root mean square error [RMSE]: 7.2 points; R2: 0.79). SHAP and LIME analyses revealed that sleep quality, HRV, and muscle soreness were the most significant predictors of recovery status. Prediction errors varied significantly across individuals, with RMSE ranging from 5.6 to 14.3 points, highlighting the importance of personalized factors in recovery prediction. CONCLUSIONS: XGBoost proves to be an effective tool for predicting recovery status in athletes. SHAP and LIME provide valuable insights into the decision-making processes of these models. The individual variability in prediction accuracy underscores the need for further personalization to enhance model performance.