足球数据分析团队终于有了可落地的3D传球评估工具——MCPS用MCTS框架量化每次传球的执行盈余，做战术分析或球员评估的可以直接用开源代码和模型。
该研究将足球传球评估重新定义为蒙特卡洛树搜索（MCTS）问题，利用已有的价值模型、世界模型和反事实动作策略。基于德甲首个公开高保真3D球轨迹数据集，提出Monte Carlo Pass Search（MCPS），为每个观察到的传球推断踢球参数，采样执行变体和选项变体，用球条件世界模型滚动预测至下一次触球，并通过学习到的价值模型评分获得价值分布。该分布支持两种互补的执行盈余分数（基于均值和百分位数）用于分析和排名。为在有限公开数据下提高世界模型样本效率，改编了自动驾驶领域的离散令牌自回归轨迹生成器（SMART），在最佳20次预测准确率上优于基线，并支持完全假设性滚动用于下游评估。已发布模型检查点和代码。
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 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.