Abstract:
Analyzing playing style is a recurring task within soccer analytics that plays a crucial role in club activities such as player scouting and match preparation. It involves identifying and summarizing prototypical behaviors of teams and players that reoccur both within and across matches. Current techniques for analyzing playing style are often hindered by the sparsity of event stream data (i.e., the same player rarely performs the same action in the same location more than once). This paper proposes SoccerMix, a soft clustering technique based on mixture models that enables a novel probabilistic representation for soccer actions. SoccerMix overcomes the sparsity of event stream data by probabilistically grouping together similar actions in a data-driven manner. We show empirically how SoccerMix can capture the playing style of both teams and players and present an alternative view of a team’s style that focuses not on the team’s own actions, but rather on how the team forces its opponents to deviate from their usual playing style.
