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Coordinate transformations for characterization and cluster analysis of spatial configurations in football

: Andrienko, G.; Andrienko, N.; Budziak, G.; Landesberger, T. von; Weber, H.


Frasconi, P.:
Machine learning and knowledge discovery in databases. European conference, ECML PKDD 2016. Pt.3 : Riva del Garda, Italy, September 19-23, 2016; Proceedings
Cham: Springer International Publishing, 2016 (Lecture Notes in Computer Science 9853)
ISBN: 978-3-319-46130-4 (Print)
ISBN: 978-3-319-46131-1 (Online)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <15, 2016, Riva del Garda>
Conference Paper
Fraunhofer IAIS ()

Current technologies allow movements of the players and the ball in football matches to be tracked and recorded with high accuracy and temporal frequency. We demonstrate an approach to analyzing football data with the aim to find typical patterns of spatial arrangement of the field players. It involves transformation of original coordinates to relative positions of the players and the ball with respect to the center and attack vector of each team. From these relative positions, we derive features for characterizing spatial configurations in different time steps during a football game. We apply clustering to these features, which groups the spatial configurations by similarity. By summarizing groups of similar configurations, we obtain representation of spatial arrangement patterns practiced by each team. The patterns are represented visually by density maps built in the teams’ relative coordinate systems. Using additional displays, we can investigate under what conditions each pattern was applied.