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: Lecture Notes in Computer Science (LNCS); 9853, In: Machine Learning and Knowledge Discovery in Databases, Proceedings, Part II, pp. 27-31,
Springer, European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 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 |
Export: | |
Suche nach Titel in: | TUfind oder in Google |
![]() |
Send an inquiry |
Options (only for editors)
![]() |
Show editorial Details |