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.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
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. 09289v1 Announce Type: new Abstract: Understanding tactical organisation of association football, hereafter referred to as football, requires identifying distinct match phases.
arXiv:2608. 12926v1 Announce Type: cross Abstract: Traditional player evaluation in professional handball relies on basic box-score metrics or heuristic indices, which fail to credit the multi-player build-up chain.
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).
RoboCup has always been a scenario to develop systems that solve real-world problems. Driven by the main goal of playing against the 2050 FIFA World Cup champions, the RoboCup Soccer leagues need to constantly measure how the research community is progressing.
arXiv:2606. 06353v1 Announce Type: new Abstract: Machine learning is increasingly employed for the evaluation of football tactics.