Discovering Team Structures in Soccer from Spatiotemporal Data

In teamsports like soccer, utilizing tracking data for analysis is challenging due to the dynamic andmulti-agent nature of the data. The biggest issue surrounds the changing of positions or roles between players on a frame-to-framebasis, which causes misalignment of the data and makes it difficult to perform team analysis. In this paper, we present an unsupervised method to learn a formation template which allows us to align the tracking data at the frame level. Not only does this approach give important contextual information to facilitate large-scale analysis (e.g., we know when a player is in the left-wing position compared to left-back), it also yields the teamstructure or formation which serves as a strong descriptor for identifying a team's style. The utility of the approach is demonstrated on a full season of player and ball tracking data from a professional soccer league consisting of over 21.5 million frames of player tracking data.