New Method Uses AI to Evaluate Football Passes Through Simulated Scenarios
Researchers have developed Monte Carlo Pass Search (MCPS), a system that uses AI trajectory generation to evaluate and compare soccer passes in three dimensions. The method frames pass evaluation as a Monte Carlo Tree Search problem, combining a possession value model, a multi-agent world model, and counterfactual pass sampling, built on the first public high-fidelity 3D ball tracking dataset from the Bundesliga. The work could advance data-driven player and tactical analysis in professional football by enabling richer, distribution-aware attribution of passing decisions.
A team of researchers has proposed Monte Carlo Pass Search (MCPS), a framework that recasts soccer pass evaluation as a Monte Carlo Tree Search (MCTS)-style problem using three core components: a possession value model, a multi-agent trajectory world model with ball interactions, and a policy for sampling counterfactual pass variants. The system infers kick parameters for observed passes, generates execution and option variants, rolls each candidate forward using a ball-conditioned world model, and scores outcomes to produce a distribution of gained value. Two complementary scoring metrics — mean-based and percentile-based execution-surplus scores — allow both analysis and player ranking. To address limited public data availability, the authors adapted SMART, a discrete-token autoregressive trajectory generator originally developed for autonomous driving, demonstrating strong best-of-20 forecasting accuracy relative to baselines. The work is grounded in the first publicly available high-fidelity 3D ball tracking dataset from the German Bundesliga, and model checkpoints and code have been released. The paper has been accepted to the CVSports Workshop at CVPR 2026.
What's missing
The paper does not detail the size or specific seasons covered by the Bundesliga 3D tracking dataset, nor does it discuss how well MCPS generalizes to other leagues or playing styles beyond the Bundesliga. The study also does not address potential biases introduced by the SMART model's autonomous driving origins when applied to football contexts.
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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