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This distribution enables distribution-aware attribution with two complementary execution-surplus scores used for analysis and ranking: mean-based and per","title":"Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football","url":"https://arxiv.org/abs/2606.11120","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.11120v1 Announce Type: new \nAbstract: 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). 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