Identifying playing styles in football using topic modeling

Abstract Football clubs analyze large amounts of data in an attempt to improve their performance and gain a competitive advantage over rivals. Several attempts have been made in formulating, detecting, and measuring team-based indicators. One team-based indicator popular with football analysts and managers, is a so-called playing style. Analysts at all levels of the game regularly use the term playing style to better understand the complexity of football matches and team tactics. A formal definition, let alone a proper quantification, of a playing style is typically not provided. In this paper, we introduce a method for quantifying a team’s playing style based on match event data. In particular, we define playing styles based on the location and patterns of a team’s consecutive actions with the ball. Specifically, we apply Latent Dirichlet allocation to ball movement patterns to obtain distributions over such ball movement patterns with similar structure; that is, the playing styles. Using our method, a team’s playing style is represented by a “style” vector, that summarizes the playing style in an interpretable way. We apply our methodology to a publicly available data set, and illustrate how the resulting playing styles can be used in practice.