Title:Landmark-Based Discrimination of Injury-Associated Athlete-Sessions from Minute-Resolution Multimodal Football Monitoring Data

Abstract:Athlete monitoring data may be recorded minute by minute throughout a match or training session, while injury information may only indicate whether the entire session was injury-associated.
This creates a modelling problem: assigning the same session-level label to every minute would imply that injury status is known at each exact time, even though within-session injury onset is unknown.
Our novelty is a fixed-landmark, one-representation-per-athlete-session formulation that directly addresses this mismatch. Instead of labelling every minute, we construct one representation per athlete-session at each landmark using information observed up to that point. This keeps the target at the session level and avoids unsupported minute-level injury supervision.
A landmark is a fixed time point within the same session, such as 10, 20, or 30 minutes. At each landmark, we assess whether the whole session is injury-associated or non-injury-associated and examine how discrimination changes as more within-session information becomes available.
Using 2020 SoccerMon data, we analyse 3,743 athlete-sessions from 48 elite women's football athletes, including 22 injury-associated sessions from five athletes. We evaluate pre-session, cumulative, dynamic, and combined representations with athlete-disjoint validation, athlete-cluster bootstrap uncertainty, common-cohort sensitivity analysis, alternative negative-athlete fold allocations, equal-athlete weighting, and Logistic Regression, Random Forest, and XGBoost benchmarks.
Primary CUM+DYN Logistic Regression yields ROC-AUC 0.367-0.607 and PR-AUC 0.0080-0.0150 across landmarks, with wide uncertainty. PRE-containing representations show higher point estimates at several landmarks but remain uncertain.

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