4040365
Graph-based approaches for analyzing team interaction on the example of soccer
We present a graph-based approach to analyzing player interaction in team sports. A simple pass-based representation is presented that is subsequently used together with the PageRank algorithm to identify the importance of the players. Aggregating player scores to team values allows for turning our approach into a predictor of the winning team. We report on empirical results on five German Bundesliga seasons.
© Copyright 2015 Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2015 workshop. Published by Department of Computer Science, KU Leuven. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | technical and natural sciences sport games |
| Tagging: | data mining |
| Published in: | Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2015 workshop |
| Language: | English |
| Published: |
Leuven
Department of Computer Science, KU Leuven
2015
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| Online Access: | https://dtai.cs.kuleuven.be/events/MLSA15/papers/mlsa15_submission_3.pdf |
| Pages: | 15-22 |
| Document types: | article |
| Level: | advanced |