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-agent trajectories with ball interactions), and a policy over counterfactual actions (sampling pass variants with noise). Building on the first public high-fidelity tracking dataset with 3D ball trajectories from the Bundesliga, we introduce Monte Carlo Pass Search (MCPS), which infers kick parameters for each observe
The increasing availability of high-fidelity sports tracking data and advancements in AI, particularly Monte Carlo Tree Search, are enabling sophisticated analytical tools in sports.
This development showcases the application of advanced AI techniques to complex, dynamic, real-world scenarios, which can have implications for other domains beyond sports.
Football pass evaluation moves from qualitative assessment to quantitative, predictive modeling of outcomes based on simulated trajectories and player interactions.
- · Professional sports teams
- · Sports analytics companies
- · AI/ML researchers in game theory
- · Traditional sports scouts
- · Teams without data science capabilities
Improved strategic decision-making and player development in football through data-driven insights.
Expansion of similar AI simulation and evaluation techniques into other fast-paced, multi-agent environments beyond sports.
Enhanced commercial value and fan engagement in sports through advanced statistical narratives and predictive capabilities.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI