arXiv Machine Learning By Lawrence Clegg, John Cartlidge

Capturing Intransitive Dominance in Tennis Forecasting: A Graph Neural Network Approach

Read the original on arXiv Machine Learning →

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.

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arXiv Machine Learning
Sep 10

Forecasting the Winner of a Live Tennis Match

arXiv:2609.07617v1 Announce Type: new Abstract: With the rise of live sports betting in recent years, tennis forecasting has expanded from pre-match prediction to models that update win probabilities...

By Charles Xie, Aneesh Muppidi
arXiv Machine Learning
Aug 4

Isotonic Bradley-Terry Model for Paired Comparison Data

arXiv:2608. 02081v1 Announce Type: new Abstract: In this paper, we study prediction problems for paired comparison data, for example, predicting the win probability between two unmatched players and ranking all the players according to the order of their strengths by using win probability data between two matched players.

By Ryoya Yamasaki
arXiv AI
Sep 7

Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection

The paper introduces the Hierarchical Possession-aware Graph Pointer Network (HPGPN) for selecting pass receivers in football analytics. HPGPN treats the task as a variable-size candidate prediction problem, modeling current player interactions, local event context, and possession-level temporal dynamics using a graph representation. Experiments on public football event and freeze-frame datasets show that HPGPN improves pass receiver selection performance, with ablation studies confirming the value of graph-based interaction modeling, fixed event context, and dual-branch dynamic possession-history modeling.

By Jingyi Wang, Da Li, Kaixin Wang, Zhangqin Huang
arXiv Machine Learning
Aug 27

Multimodal Injury Risk Prediction in Tennis

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