Discerning tactical patterns for professional soccer teams: An enhanced topic model with applications

Analyzing team tactics plays an important role in the professional soccer industry. Recently, the progressing ability to track the mobility of ball and players makes it possible to accumulate extensive match logs, which open a venue for better tactical analysis. However, traditional methods for tactical analysis largely rely on the knowledge and manual labor of domain experts. To this end, in this paper we propose an unsupervised approach to automatically discerning the typical tactics, i.e., tactical patterns, of soccer teams through mining the historical match logs. To be specific, we first develop a novel model named Team Tactic Topic Model (T³M) for learning the latent tactical patterns, which can model the locations and passing relations of players simultaneously. Furthermore, we demonstrate several potential applications enabled by the proposed T³M, such as automatic tactical pattern discovery, pass segment annotation, and spatial analysis of player roles. Finally, we implement an intelligent demo system to empirically evaluate our approach based on the data collected from La Liga 2013-2014. Indeed, by visualizing the results obtained from T³M, we can successfully observe many meaningful tactical patterns and interesting discoveries, such as using which tactics a team is more likely to score a goal and how a team's playing tactic changes in sequential matches across a season.