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AI evaluates football passes using 3D trajectory generation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method called Monte Carlo Pass Search (MCPS) to evaluate player passes in football using 3D trajectory generation. This approach treats pass evaluation as a Monte Carlo Tree Search problem, incorporating a value model, a world model for multi-agent interactions, and a policy for generating pass variants. The system utilizes a high-fidelity dataset from the Bundesliga and adapts an autoregressive trajectory generator from autonomous driving to forecast outcomes and attribute pass success. AI

IMPACT Introduces a novel AI-driven methodology for objective player performance evaluation in football.

RANK_REASON The cluster contains a research paper detailing a novel AI methodology for sports analytics.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI evaluates football passes using 3D trajectory generation

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The cluster contains a research paper detailing a novel AI methodology for sports analytics.
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COVERAGE [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…

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