DagRep.11.9.45.pdf
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Machine Learning in Sports (Dagstuhl Seminar 21411)
Authors and all authors of the abstracts in this report
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Issue:
Dagstuhl Reports, Volume 11, Issue 9
Volume: Dagstuhl Reports, Volume 11
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-04-11
Cite AsGet BibTex
Ulf Brefeld, Jesse Davis, Martin Lames, and James J. Little. Machine Learning in Sports (Dagstuhl Seminar 21411). In Dagstuhl Reports, Volume 11, Issue 9, pp. 45-63, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/DagRep.11.9.45
https://doi.org/10.4230/DagRep.11.9.45
Abstract
Data about sports have long been the subject of research and analysis by sports scientists. The increasing size and availability of these data have also attracted the attention of researchers in machine learning, computer vision and artificial intelligence. However, these communities rarely interact. This seminar aimed to bring together researchers from these areas to spur an interdisciplinary approach to these problems. The seminar was organized around five different themes that were introduced with tutorial and overview style talks about the key concepts to facilitate knowledge exchange among researchers with different backgrounds and approaches to data-based sports research. These were augmented by more in-depth presentations on specific problems or techniques. There was a panel discussion by practitioners on the difficulties and lessons learned about putting analytics into practice. Finally, we came up with a number of conclusions and next steps.
Subject Classification
ACM Subject Classification
- Computing methodologies → Machine learning
- Computing methodologies → Computer vision
Keywords
- machine learning
- artificial intelligence
- sports science
- computer vision
- explanations
- visualization
- tactics
- health
- biomechanics
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