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English(EN) Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football

AI利用三维轨迹生成评估足球传球

研究人员开发了一种名为蒙特卡洛传球搜索(MCPS)的新方法,利用三维轨迹生成来评估足球比赛中的球员传球。该方法将传球评估视为一个蒙特卡洛树搜索问题,包含一个价值模型、一个用于多主体交互的世界模型以及一个用于生成传球变体的策略。该系统利用德甲联赛的高保真数据集,并改编了来自自动驾驶领域的自回归轨迹生成器来预测结果并归因传球成功率。 AI

影响 为足球比赛中客观的球员表现评估引入了一种新颖的AI驱动的方法。

排序理由 该集群包含一篇详细介绍用于体育分析的新颖AI方法的学术论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Andrew Kang, Priya Narasimhan ·

    Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football

    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…

  2. arXiv cs.CV TIER_1 English(EN) · Priya Narasimhan ·

    Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football

    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…