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: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...
arXiv:2606. 09289v1 Announce Type: new Abstract: Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases.
arXiv:2602. 12080v2 Announce Type: replace Abstract: Despite recent advances in AI, event data collection in soccer still relies heavily on labor-intensive manual annotation.
arXiv:2603. 15212v2 Announce Type: replace Abstract: Evaluating football player transfers is challenging because player actions depend strongly on tactical systems, teammates, and match context.
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...
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:2606. 06353v1 Announce Type: new Abstract: Machine learning is increasingly employed for the evaluation of football tactics.
Soccer tactics are interactive: an attacking action changes the opponent's defensive problem, and the observed response depends on the multi-agent match state. We introduce SambaGraph, an action--reac...
The paper introduces Graph-to-Grid (G2G), a method that paints continuous‑coordinate player features onto a grid using bilinear interpolation, enabling end‑to‑end training of per‑player encoders for pass surface prediction. On 53,628 World Cup passes, this painting approach improves selection likelihood by about a quarter of a nat compared to raster‑only inputs, with further gains from learned encoders and message passing. The study also shows that painting benefits existing models like SoccerMap and U‑Net, while other variants such as offset channels or raster‑free decoders do not provide similar improvements.
arXiv:2607. 11548v1 Announce Type: cross Abstract: Spatial football metrics such as pitch control assume access to the positions of all 22 players, yet the most widely available source of positional data -- the broadcast main camera -- shows only 10-16 of them at any moment.
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).
arXiv:2608. 19646v1 Announce Type: new Abstract: Visual understanding in sports has emerged as a hot topic in computer vision in recent years.