The paper introduces a landmark-based approach for classifying injury-associated athlete sessions using minute-resolution multimodal football monitoring data. Instead of labeling every minute, it creates a single representation per athlete-session at fixed time points (landmarks) that incorporates information up to that moment, thereby aligning the session-level injury label with the data. Experiments on 3,743 elite women's football athlete-sessions from the 2020 SoccerMon dataset evaluate various representation strategies and machine learning models, reporting ROC-AUC values between 0.367 and 0.607 and PR-AUC values between 0.0080 and 0.0150, with notable uncertainty across landmarks.
By Evangelos Chatzidimitriou, Konstantinos Tserpes
The paper introduces a landmark‑based approach to classify injury‑associated athlete sessions using minute‑resolution multimodal football monitoring data. Instead of labeling every minute, it creates a single representation per athlete‑session at fixed time points (landmarks) based on information up to that moment, thereby aligning the session‑level injury label with the data. Experiments on 3,743 elite women’s football sessions show that cumulative and dynamic logistic regression models achieve ROC‑AUCs between 0.367 and 0.607, though results carry wide uncertainty.
The paper introduces PART, a multimodal predictive framework for tennis that combines physiological, training, sleep, questionnaire, jump, and video data from nine collegiate players to assess overall wellness, injury risk, physical capability, and playing style. Using machine learning and deep learning, PART provides holistic athlete assessments and forecasts specific injury risks to body areas such as elbows and knees. Evaluation shows strong performance in predicting wellness and injury risk, with potential benefits for recreational players who often injure themselves due to poor technique.
By Francisco Erramuspe Alvarez, Shobharani Polasa, Weihao Qu, Jay Wang, Ling Zheng
arXiv:2606. 28570v1 Announce Type: cross Abstract: Athlete assessment is a critical process for tracking physical progress and identifying elite talent.
By Deep Ghosal, Ishani Sen, Wazib Ansar, Amlan Chakrabarti
arXiv:2608. 14776v1 Announce Type: new Abstract: Collegiate running in the United States generates thousands of race results annually in cross country and track and field, yet no large-scale dataset has been publicly available for research.
By Jonathan A. Karr Jr., Ryan M. Fryer, Ben Darden, Nicholas Pell, Kayla Ambrose, Evan Hall, Ramzi K. Bualuan, Nitesh V. Chawla
arXiv:2607. 01838v1 Announce Type: new Abstract: Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms of semantic feature groups rather than individual channels.
By Emmanuel C. Chukwu, Rianne M. Schouten, Monique Tabak, Mykola Pechenizkiy