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
AI Soccer Analyst is a mixed‑initiative system that guides analysts through six revisable stages—Data Understanding, Problem Definition, Structured Planning, Execution, Evidence‑Grounded Reporting, and Interaction and Refinement—to produce soccer data analyses. A formative study with five analysts shaped design goals around automation, verifiability, human control, and accessibility, while a task‑based evaluation with 16 participants showed that 33 of 48 tasks met completion criteria and participants reported high quality, reliability, and verifiability of outputs. Interaction logs revealed that domain knowledge emerged during clarification, planning, and refinement, demonstrating that stage‑aware human‑AI collaboration can produce inspectable, revisable, and verifiable analyses while keeping domain experts in control.
By Calvin Yeung, Keisuke Fujii
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:2607. 26061v1 Announce Type: new Abstract: Pre-match tactical decision-making in professional football relies heavily on subjective expert analysis and identity-based scouting systems that cannot generalize to unseen teams.
By Mouad Zemzoumi, Amine Abouaomar
arXiv:2606. 09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage.
By Parth Agrawal, Ronit, Sagar Kumar, Aashish Bhambri
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
The study introduces a framework that reconciles minute‑resolution athlete monitoring data with injury labels that are only available at the session level. By creating fixed‑time landmarks (10, 20, 30, 40, 50, and 60 minutes) and generating a single representation per athlete‑session up to each landmark, the authors evaluate several machine‑learning models and data‑augmentation strategies on elite women’s football data. Results show that discrimination varies across landmarks, with TabPFN outperforming Logistic Regression at later landmarks but not consistently beating Random Forest, and that synthetic augmentation offers benefits only in specific conditions.
By Evangelos Chatzidimitriou, Konstantinos Tserpes
Skill Profiling with Attributable Reasoning (SPAR) is a wearable system that uses an eight‑IMU garment and pressure‑insole sensors to classify boxing punches as expert or novice. It provides multi‑tier explanations—joint‑level attributions for analysts, counterfactual kinetic‑chain layers for coaches, and plain‑language narratives for athletes—to enable actionable feedback. In a study of 17 participants and 4,713 punches, SPAR achieved a leave‑one‑participant‑out AUC of 0.842, and thematic analysis of coach interviews identified six key feedback themes.
By Nibraas Khan, Hanchen David Wang, Enya Bullard, Ritam Ghosh, Ruj Haan, Aarav Agrawal, Meiyi Ma, Nilanjan Sarkar
The paper investigates whether open‑source Vision‑Language Models (VLMs) can perform zero‑shot action quality assessment (AQA) on Olympic diving videos. Using the AQA‑7 benchmark, the authors propose a regression framework that combines VLM‑generated semantic reasoning, phase‑level sub‑scores, TF‑IDF vectorization, dimensionality reduction, and ensemble learning to predict final competition scores. While individual VLMs achieve moderate Spearman correlations (<0.32), the ensemble approach boosts performance to 0.67, demonstrating that VLM‑derived textual reasoning features are more informative than raw numerical sub‑scores for AQA.
whyItMatters":"The study shows that VLMs can serve as explainable, semi‑automated tools for evaluating sports performance, potentially aiding expert judging in complex, subjective Olympic events."
By Henry O. Velesaca, David Freire-Obregon, Luigi Miranda, Abel Reyes-Angulo
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. 24573v1 Announce Type: new Abstract: Large language models (LLMs) increasingly support decisions about uncertain future events, yet evaluating their ability to forecast real-world outcomes remains difficult.
By Jonas Schr\"oder, Jonas Schweisthal, Oliver M\"uller, Markus Weinmann, Stefan Feuerriegel