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). Building on the first public high-fidelity tracking dataset with 3D ball trajectories from the Bundesliga, we introduce Monte Carlo Pass Search (MCPS), which infers kick parameters for each observed pass, samples execution variants and option variants, rolls each candidate forward with a ball-conditioned world model until the next ball interaction, and scores outcomes with a learned value model to obtain a distribution over gained value. This distribution enables distribution-aware attribution with two complementary execution-surplus scores used for analysis and ranking: mean-based and percentile-based scores. To make the world model sample-efficient under limited public data, we adapt a discrete-token, autoregressive trajectory generator from autonomous driving (SMART) and show it yields strong best-of-20 forecasting accuracy compared to baselines, while supporting fully hypothetical rollouts for downstream evaluation. We have released model checkpoints and code.

翻译：我们将足球中的传球评估重新构建为蒙特卡罗树搜索（MCTS）风格的评估问题，其组成部分在已有文献中以不同名称存在：价值模型（控球价值）、世界模型（包含球交互的多智能体轨迹）以及反事实动作上的策略（含噪声的传球变体采样）。基于德甲联赛首个公开的高保真三维球轨迹追踪数据集，我们提出了蒙特卡罗传球搜索（MCPS），该方法为每个观测到的传球推断踢球参数，采样执行变体和选项变体，利用球条件世界模型将每个候选传球向前推演至下一次球交互，并通过学习到的价值模型对结果进行评分，从而获得增益价值的分布。该分布能够通过两种互补的执行盈余分数实现分布感知式归因，用于分析和排名：基于均值的分数和基于百分位数的分数。为了在有限的公开数据下提高世界模型的样本效率，我们借鉴了自动驾驶领域的离散令牌自回归轨迹生成器（SMART），并证明其在最佳20次预测准确率上相较于基线方法表现优异，同时可为下游评估支持完全假设性的推演。我们已发布模型检查点和代码。