New Method Uses AI to Evaluate Football Passes Through Simulated Scenarios
Researchers have developed Monte Carlo Pass Search (MCPS), a new technique that evaluates soccer passes by simulating thousands of alternative outcomes using artificial intelligence. The method combines a value model, a world model that predicts player and ball movements, and a policy for generating pass variations, building on a new public dataset of 3D ball tracking data from the Bundesliga. This approach could help analysts and coaches better understand pass quality and decision-making in football.
A new research paper introduces Monte Carlo Pass Search (MCPS), an AI-based system for evaluating soccer passes by simulating counterfactual scenarios. The method recasts pass evaluation as a Monte Carlo Tree Search problem, combining three key components: a learned value model that assesses possession value, a world model that predicts multi-agent trajectories with ball interactions, and a policy for sampling pass variants. The researchers adapted a trajectory generation model from autonomous driving research (SMART) to efficiently forecast player movements and ball outcomes. The system infers kick parameters from observed passes, samples execution and option variants, simulates each candidate forward until the next ball interaction, and scores outcomes using the value model to produce a distribution of gained value. The authors released model checkpoints and code alongside the paper, and built their work on a newly public high-fidelity tracking dataset with 3D ball trajectories from the Bundesliga.
What's missing
The paper does not discuss potential limitations of the approach, such as how well the world model generalizes to different leagues or playing styles, computational requirements for real-time application, or validation against human expert evaluations of pass quality.
What different sources said
- arXiv cs.AICenter
Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football
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