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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

Standard

Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer. / Brandt, Markus; Brefeld, Ulf.
Proceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015. ed. / Jesse Davis; Jan Van Haaren. Achen: Sun Site Central Europe (RWTH Aachen University), 2015. p. 10-17 (CEUR Workshop Proceedings; Vol. 1970).

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

Harvard

Brandt, M & Brefeld, U 2015, Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer. in J Davis & JV Haaren (eds), Proceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015. CEUR Workshop Proceedings, vol. 1970, Sun Site Central Europe (RWTH Aachen University), Achen, pp. 10-17, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML/PKDD 2015, Porto, Germany, 07.09.15. <https://dtai.cs.kuleuven.be/events/MLSA15/papers/mlsa15_submission_3.pdf>

APA

Brandt, M., & Brefeld, U. (2015). Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer. In J. Davis, & J. V. Haaren (Eds.), Proceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015 (pp. 10-17). (CEUR Workshop Proceedings; Vol. 1970). Sun Site Central Europe (RWTH Aachen University). https://dtai.cs.kuleuven.be/events/MLSA15/papers/mlsa15_submission_3.pdf

Vancouver

Brandt M, Brefeld U. Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer. In Davis J, Haaren JV, editors, Proceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015. Achen: Sun Site Central Europe (RWTH Aachen University). 2015. p. 10-17. (CEUR Workshop Proceedings).

Bibtex

@inbook{c0fccdde455e49b29135eba14d23aff8,
title = "Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer",
abstract = "We present a graph-based approach to analyzing player interactionin team sports. A simple pass-based representation is presentedthat 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.",
keywords = "Informatics, Business informatics",
author = "Markus Brandt and Ulf Brefeld",
year = "2015",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "Sun Site Central Europe (RWTH Aachen University)",
pages = "10--17",
editor = "Jesse Davis and Haaren, {Jan Van}",
booktitle = "Proceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015",
address = "Germany",
note = "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML/PKDD 2015, ECML/PKDD Conference 2015 ; Conference date: 07-09-2015 Through 11-09-2015",
url = "http://www.ecmlpkdd2018.org/, http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=40580&copyownerid=30660",

}

RIS

TY - CHAP

T1 - Graph-based Approaches for Analyzing Team Interaction on the Example of Soccer

AU - Brandt, Markus

AU - Brefeld, Ulf

PY - 2015

Y1 - 2015

N2 - We present a graph-based approach to analyzing player interactionin team sports. A simple pass-based representation is presentedthat 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.

AB - We present a graph-based approach to analyzing player interactionin team sports. A simple pass-based representation is presentedthat 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.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85034998605&partnerID=8YFLogxK

M3 - Article in conference proceedings

T3 - CEUR Workshop Proceedings

SP - 10

EP - 17

BT - Proceedings of the 2th Workshop on Machine Learning and Data Mining for Sports Analytics - MLSA 2015

A2 - Davis, Jesse

A2 - Haaren, Jan Van

PB - Sun Site Central Europe (RWTH Aachen University)

CY - Achen

T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECML/PKDD 2015

Y2 - 7 September 2015 through 11 September 2015

ER -

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., Jorge, A. M., Soares, C., Gama, J., Pfahringer, B., Japkowicz, N., Larrañaga, P. & Abreu, P. H. (eds.). Cham: Springer International Publishing, Vol. 2. p. 70-86 17 p. (Lecture Notes in Computer Science; vol. 16014 LNCS).

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

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