arXiv:2608. 19646v1 Announce Type: new Abstract: Visual understanding in sports has emerged as a hot topic in computer vision in recent years.
arXiv:2609.28049v1 Announce Type: cross Abstract: Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as ev...
Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Ama...
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.
arXiv:2607. 00190v1 Announce Type: cross Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games.
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.
arXiv:2608.23435v1 Announce Type: cross Abstract: Understanding a basketball game requires recognizing events, localizing actions, identifying players, and relating these to structured game knowledge...
arXiv:2606. 11120v1 Announce Type: new Abstract: We recast pass evaluation in football (soccer) as a Monte Carlo Tree Search (MCTS)-like evaluation problem whose components mostly exist in the literature under different names: a value model (possession value), a world model (multi-agent trajectories with ball interactions), and a policy over counterfactual actions (sampling pass variants with noise).
The paper presents a statistical and machine‑learning framework for estimating expected goals (xG) and attributing offensive involvement in professional box lacrosse. Using 1,006 manually annotated shot attempts from 13 Rochester Knighthawks games, the authors evaluate logistic regression, random forest, and extremely randomized trees, finding that a contextual baseline random forest achieves the lowest log loss and Brier score. They also introduce a shot‑based expected assist metric and an exploratory expected pick value (xPV) metric, noting that xPV’s magnitude is indistinguishable from noise but shows a directional pattern in permutation tests.
arXiv:2510. 23216v4 Announce Type: replace Abstract: While several high profile video games have served as testbeds for Deep Reinforcement Learning (DRL), this technique has rarely been employed by the game industry for crafting authentic AI behaviors.
arXiv:2606. 06353v1 Announce Type: new Abstract: Machine learning is increasingly employed for the evaluation of football tactics.
arXiv:2609.25569v1 Announce Type: new Abstract: Soccer tactics are interactive: an attacking action changes the opponent's defensive problem, and the observed response depends on the multi-agent matc...