Multi-sensor fusion outperforms single indicators for fatigue prediction in university soccer players: a machine learning approach

Multi-sensor fusion combined with machine learning (XGBoost, AUC = 0.895) significantly outperforms single-indicator models for fatigue prediction in university soccer players, with wellness score, ACWR, and morning HRV identified as the most important predictive features.