License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.11.9.45
URN: urn:nbn:de:0030-drops-159178
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/15917/
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Brefeld, Ulf ; Davis, Jesse ; Lames, Martin ; Little, James J.
Weitere Beteiligte (Hrsg. etc.): Ulf Brefeld and Jesse Davis and Martin Lames and James J. Little

Machine Learning in Sports (Dagstuhl Seminar 21411)

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dagrep_v011_i009_p045_21411.pdf (6 MB)

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.

BibTeX - Entry

@Article{brefeld_et_al:DagRep.11.9.45,
  author =	{Brefeld, Ulf and Davis, Jesse and Lames, Martin and Little, James J.},
  title =	{{Machine Learning in Sports (Dagstuhl Seminar 21411)}},
  pages =	{45--63},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{9},
  editor =	{Brefeld, Ulf and Davis, Jesse and Lames, Martin and Little, James J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2022/15917},
  URN =		{urn:nbn:de:0030-drops-159178},
  doi =		{10.4230/DagRep.11.9.45},
  annote =	{Keywords: machine learning, artificial intelligence, sports science, computer vision, explanations, visualization, tactics, health, biomechanics}
}

Keywords: machine learning, artificial intelligence, sports science, computer vision, explanations, visualization, tactics, health, biomechanics
Collection: DagRep, Volume 11, Issue 9
Issue Date: 2022
Date of publication: 11.04.2022

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