Interpretable Decision Support for Next-Morning Soreness in Elite Women’s Football

This paper presents a retrospective proof-of-concept development and preliminary evaluation of an interpretable decision-support system for daily fatigue-risk management in one elite women’s football club. The system integrates morning wellness, GPS-derived external load, and a daily wellness-duration internal-load proxy at the player-day level. It combines a player-day integration layer, an interpretable predictive layer, and a recommendation layer that returns one of three staff-facing actions: Reduce, Maintain, or Progress. The predictive model outputs a calibrated probability of elevated next-morning self-reported soreness. The target is a subjective questionnaire outcome, not an injury, medical diagnosis, or objective marker of recovery. The decision-support layer maps this probability to a three-state recommendation, informed by a review threshold, operational guardrails, and staff oversight. Using retrospective monitoring data from two competitive seasons (2024/25 and 2025/26) in a single professional team, we evaluated the proposed approach using rolling-origin temporal validation, leave-one-player-out cross-validation, and between-season validation. To separate genuine predictive signal from the day-to-day persistence of soreness, we report a baseline ladder ranging from a trivial persistence rule to the full model, with bootstrap confidence intervals for performance differences. Under rolling-origin validation across 16 monthly folds, the final logistic regression model achieved a mean ROC-AUC of 0.759 (SD=0.089). Critically, a model excluding current soreness still outperformed the persistence baseline (ROC-AUC 0.738 vs. 0.721), and the isolated contribution of current soreness was modest but reliable (ΔROC-AUC =+0.044, 95% CI [+0.027,+0.059]). Between-season validation (train: 2024/25; test: 2025/26) yielded an ROC-AUC of 0.801. The three-state recommendation layer separated outcomes monotonically, with observed next-morning soreness rates of 0.054 for Progress, 0.217 for Maintain, and 0.326 for Reduce (p<0.001 for the Progress-versus-Maintain contrast). These preliminary findings support the feasibility of the proposed approach within the club studied. However, because the model requires complete wellness, GPS, and proxy data, it operates only on the fully monitored on-pitch stratum (3386 of 17,703 player-days); the reported performance therefore applies to this stratum rather than to a typical player-day, and prospective evaluation and external validation by independent teams are required before broader implementation can be considered.