Identifying team style in soccer using formations learned from spatiotemporal tracking data

Bialkowski, Alina, Lucey, Patrick J., Carr, Peter, Yue, Yisong, Sridharan, Sridha, & Matthews, Iain (2014) Identifying team style in soccer using formations learned from spatiotemporal tracking data. In 9th International Workshop on Spatial and Spatio-Temporal Data Mining, 14 December 2014, Shenzhen, China.


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.

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ID Code: 78125
Item Type: Conference Paper
Refereed: Yes
Additional Information: 9th International Workshop on Spatial and Spatio-Temporal Data Mining is part of the IEEE International Conference on Data Mining (ICDM 2014)
Additional URLs:
Keywords: Spatiotemporal Tracking Data, Sports Analytics, Team Identity, Style, Formation, Soccer
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the author]
Deposited On: 29 Oct 2014 22:31
Last Modified: 30 Jan 2015 04:59

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