Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football
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.