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Coordinate Transformations for Characterization and Cluster Analysis of Spatial Configurations in Football

Andrienko, Gennady and Andrienko, Natalia and Budziak, Guido and Landesberger, Tatiana von and Weber, Hendrik (2016):
Coordinate Transformations for Characterization and Cluster Analysis of Spatial Configurations in Football.
In: Machine Learning and Knowledge Discovery in Databases, Proceedings, Part II, Springer, In: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, In: Lecture Notes in Computer Science (LNCS); 9853, DOI: 10.1007/978-3-319-46131-1₆,
[Conference or Workshop Item]

Abstract

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.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Andrienko, Gennady and Andrienko, Natalia and Budziak, Guido and Landesberger, Tatiana von and Weber, Hendrik
Title: Coordinate Transformations for Characterization and Cluster Analysis of Spatial Configurations in Football
Language: English
Abstract:

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.

Title of Book: Machine Learning and Knowledge Discovery in Databases, Proceedings, Part II
Series Name: Lecture Notes in Computer Science (LNCS); 9853
Publisher: Springer
Uncontrolled Keywords: Cluster analysis
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: European Conference, ECML PKDD 2016
Event Location: Riva del Garda, Italy
Event Dates: September 19-23
Date Deposited: 08 May 2019 06:33
DOI: 10.1007/978-3-319-46131-1₆
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