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Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Authors

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.
Original languageEnglish
Title of host publicationProceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015
EditorsJesse Davis, Jan Van Haaren
Number of pages8
Place of PublicationAchen
PublisherSun Site Central Europe (RWTH Aachen University)
Publication date2015
Pages10-17
Publication statusPublished - 2015
Externally publishedYes
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML/PKDD 2015 - Porto, Germany
Duration: 07.09.2015 → 11.09.2015
http://www.ecmlpkdd2018.org/
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=40580&copyownerid=30660

Other publications by the same author(s)

Interactive sequential generative models for team sports

Fassmeyer, D., Cordes, M. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 15 p., 38.

Research output: Journal contributions › Journal articles › Research › peer-review

Joint Item Response Models for Manual and Automatic Scores on Open-Ended Test Items

Bengs, D., Brefeld, U., Kroehne, U. & Zehner, F., 2025, (Accepted/In press) In: Psychometrika.

Research output: Journal contributions › Journal articles › Research › peer-review

Machine Learning and Data Mining for Sports Analytics: 11th International Workshop, MLSA 2024, Vilnius, Lithuania, September 9, 2024, Revised Selected Papers

Brefeld, U. (Editor), Davis, J. (Editor), Van Haaren, J. (Editor) & Zimmermann, A. (Editor), 2025, Cham: Springer Verlag. 119 p. (Communications in Computer and Information Science; vol. 2460)

Research output: Books and anthologies › Conference proceedings › Research

Masked autoencoder for multiagent trajectories

Rudolph, Y. & Brefeld, U., 02.2025, In: Machine Learning. 114, 2, 18 p., 44.

Research output: Journal contributions › Journal articles › Research › peer-review

Self-improvement for Computerized Adaptive Testing

Rudolph, Y., Neubauer, K. & Brefeld, U., 2026, Machine Learning and Knowledge Discovery in Databases - Research Track: European Conference, ECML PKDD 2025, Porto, Portugal, September 15–19, 2025, Proceedings. Ribeiro, R. P., Pfahringer, B., Japkowicz, N., Larrañaga, P., Jorge, A. M., Soares, C., Abreu, P. H. & Gama, J. (eds.). Cham: Springer International Publishing, Vol. 2. p. 70-86 17 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence); vol. 16014).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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