Discovering team structures in soccer from spatiotemporal data - Bialkowski, A.; Lucey, P.; Carr, P.; Matthews, I.; Sridharan, S.; Fookes, C. (IEEE Transactions on Knowledge and Data Engineering, 2016)
In team sports like soccer, utilizing tracking data for analysis is challenging due to the dynamic and multi-agent nature of the data. The biggest issue surrounds the changing of positions or "roles" between players on a frame-to-frame basis, 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 team structure 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.