Study State Dynamics of Team Passing Networks in Soccer Games.

Abstract

Complex networks have been widely used in studying collective behaviours in soccer sports, such as examining tactical strategies, recognizing team characteristics, and discovering topological determinants for high team performance. The passing network of a team evolves and displays different temporal patterns, that are strongly linked to team status, tactical strategies, attacking/defending transitions, etc. Nevertheless, existing research has not illuminated the state dynamics of team passing networks, whereas similar methods have been extensively used in examining the dynamical brain networks constructed from human brain neuroimage data. This study aims to investigate the state dynamics of team passing networks in soccer sports. The introduced method incorporates multiple techniques, including sliding time window, network modeling, graph distance measure, clustering, and cluster validation. The final match of the FIFA World Cup 2018 was taken as an example, and the state dynamics of teams Croatia and France were analyzed respectively. Additionally, the effects of the time windows and graph distance measures on the results were briefly discussed. This study presents a novel outlook on examining the dynamics of team passing networks, as it facilitates the recognition of important team states or state transitions in soccer and other team ball-passing sports for further analysis.