Interpretable Low-Dimensional Modeling of Spatiotemporal Agent States for Decision Making in Football Tactics

TL;DR

This paper develops low-dimensional, interpretable models using spatiotemporal data to analyze football tactics, enabling better decision-making by capturing key game state variables with practical and understandable features.

Contribution

It introduces a novel rule-based modeling approach that uses key spatiotemporal variables for tactical analysis, improving interpretability over existing complex models.

Findings

Distance to the ball is a key predictor of pass success.

Player space score significantly influences pass outcomes.

The model achieves high accuracy in predicting pass success.

Abstract

Understanding football tactics is crucial for managers and analysts. Previous research has proposed models based on spatial and kinematic equations, but these are computationally expensive. Also, Reinforcement learning approaches use player positions and velocities but lack interpretability and require large datasets. Rule-based models align with expert knowledge but have not fully considered all players' states. This study explores whether low-dimensional, rule-based models using spatiotemporal data can effectively capture football tactics. Our approach defines interpretable state variables for both the ball-holder and potential pass receivers, based on criteria that explore options like passing. Through discussions with a manager, we identified key variables representing the game state. We then used StatsBomb event data and SkillCorner tracking data from the 2023$/$24 LaLiga season to train an XGBoost model to predict pass success. The analysis revealed that the distance between the player and the ball, as well as the player's space score, were key factors in determining successful passes. Our interpretable low-dimensional modeling facilitates tactical analysis through the use of intuitive variables and provides practical value as a tool to support decision-making in football.