The Soccer Game, bit by bit: An information-theoretic analysis

TL;DR

This paper models soccer match dynamics using an information-theoretic network approach, revealing how key game events influence player interactions and providing insights for training and strategy development.

Contribution

It introduces a novel information-theoretic framework for analyzing soccer match dynamics through network clustering and spectral analysis, linking game events to player interactions.

Findings

Significant correlation between information distance and key match events.

Increased information distance during player jostling and marking.

Spectral analysis reveals patterns in player interactions over time.

Abstract

We modeled the dynamics of a soccer match based on a network representation where players are nodes discretely clustered into homogeneous groups. Players were grouped by physical proximity, supported by the intuitive notion that competing and same-team players use relative position as a key tactical tool to contribute to the team's objectives. The model was applied to a set of matches from a major European national football league, with players' coordinates sampled at 10Hz, resulting in approx. 60,000 network samples per match. We took an information theoretic approach to measuring distance between samples and used it as a proxy for the game dynamics. Significant correlations were found between measurements and key match events that are empirically known to result in players jostling for position, such as when striving to get unmarked or to mark. These events increase the information distance, while breaks in game play have the opposite effect. By analyzing the frequency spectrum of players' cluster transitions and their corresponding information distance, it is possible to build a comprehensive view of player's interactions, useful for training and strategy development. This analysis can be drilled down to the level of individual players by quantifying their contribution to cluster breakup and emergence, building an overall multi-level map that provides insights into the game dynamics, from the individual player, to the clusters of interacting players, all the way to the teams and their matches.