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Football pass evaluation enhanced with 3D trajectory generation

Researchers have developed a new method called Monte Carlo Pass Search (MCPS) to evaluate passes in football using 3D trajectory generation. This approach treats pass evaluation as a Monte Carlo Tree Search problem, incorporating a value model for possession and a world model for multi-agent trajectories. The system infers kick parameters, samples execution variants, and uses a learned value model to score outcomes, enabling distribution-aware attribution. AI

IMPACT Introduces a novel method for analyzing sports data using AI, potentially improving player evaluation and strategy.

RANK_REASON The cluster contains an academic paper detailing a new research method. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.CV →

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COVERAGE [1]

  1. 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…