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. 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:2605.01234v2 Announce Type: replace
Abstract: We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides $140+$ hours of reconstructed singles and doubles gameplay from mon...
By Nima Rahmanian, Daniel Kienzle, Thomas Gossard, Dvij Kalaria, Rainer Lienhart, Shankar Sastry
arXiv:2606. 09327v1 Announce Type: cross Abstract: Football event data constitute a rich spatiotemporal source for quantitative analysis of player actions in team sports.
By Weiran Yang, Daniel Memmert, Maximilian Klemp-Weins
arXiv:2609.26923v1 Announce Type: cross
Abstract: Cricket is one of the most celebrated sports world-wide, and technological advancement has become deeply embedded in how the modern game is analyzed...
By Sourav Shome, M. D. Ashiquzzaman Rahad, Rameswar Debnath
arXiv:2610.07223v1 Announce Type: new
Abstract: Cricket, often referred to as the "gentleman's game," adheres to a strict rule set for both batsmen and bowlers, where each delivery can significantly...
By Debopom Sutradhar, Niful Islam, Sudipto Mondal, Tasmima Hossain Jamim, Jubaer Muhammad Shufol, Swakkhar Shatabda
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 paper introduces EventNet, a two‑stage pipeline that uses 2D keypoints of players, table corners, and the ball to detect key events in table tennis videos. First, a keypoint transformer condenses the pose and ball information into a robust representation; second, a transformer encoder predicts how close each frame is to the next and previous ball‑racket contact using a novel temporal cosine‑like target signal. Experiments on Latte‑MV and TTHQ datasets show high accuracy, with an F1 score of 91.16% and a mean frame deviation of 0.42 on Latte‑MV, and 73.08% / 1.16 on TTHQ.
By Rainer Lienhart, Daniel Kienzle, Shin'ichi Satoh, Anastasiia Bilinska
arXiv:2608.29563v1 Announce Type: cross
Abstract: School coaches prepare for opponents with game film and intuition. The analytics tools of professional teams stay out of reach. We ask how far public...
By Yibo Gong, Cong Guo, Jiacheng Ding
arXiv:2510. 20454v2 Announce Type: replace Abstract: Intransitive player dominance, where player A beats B, B beats C, but C beats A, is common in competitive tennis.
By Lawrence Clegg, John Cartlidge
arXiv:2505. 24415v2 Announce Type: replace-cross Abstract: Automated evaluation of movement quality can enhance physiotherapeutic treatment and sports training by providing objective, real-time feedback.
By Andreas Spilz, Heiko Oppel, Michael Munz
arXiv:2607. 21267v1 Announce Type: new Abstract: Comprehensive basketball video understanding requires resolving not only what event occurs, but also who is responsible and when the key evidence appears.
By Yu Zhang, Jiayuan Rao, Haoning Wu, Weidi Xie