Identifying team style in soccer using formations learned from spatiotemporal tracking data - Bialkowski, A.; Lucey, P.; Yue, Y.; Matthews, I. (IEEE International Conference on Data Mining (ICDM), 2014)
To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., Shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.