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Computational Approaches to Strategy and Tactics in Sports (Dagstuhl Seminar 24081)

Authors: Ulf Brefeld, Jesse Davis, Laura de Jong, and Stephanie Kovalchik

Published in: Dagstuhl Reports, Volume 14, Issue 2 (2024)

Abstract

One of the most challenging and interesting aspects in sports are Strategy and Tactics. In this interdisciplinary Dagstuhl Seminar, we aimed to develop a computational understanding of these concepts in an interdisciplinary setting with researchers and practitioners from Machine Learning, Statistics, and Sports. The seminar was organized around the themes "Discovery", "Evaluation", and "Communication" that were introduced with tutorial and overview style talks about the key concepts to facilitate a common ground among researchers with different backgrounds. These were augmented by more in-depth presentations on specific problems or techniques. Besides several topical discussions in larger groups, there were two panel discussions dealing with differences between individual and team sports and bringing computational analytics into practice, respectively.

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Ulf Brefeld, Jesse Davis, Laura de Jong, and Stephanie Kovalchik. Computational Approaches to Strategy and Tactics in Sports (Dagstuhl Seminar 24081). In Dagstuhl Reports, Volume 14, Issue 2, pp. 164-181, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{brefeld_et_al:DagRep.14.2.164,
  author =	{Brefeld, Ulf and Davis, Jesse and de Jong, Laura and Kovalchik, Stephanie},
  title =	{{Computational Approaches to Strategy and Tactics in Sports (Dagstuhl Seminar 24081)}},
  pages =	{164--181},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{14},
  number =	{2},
  editor =	{Brefeld, Ulf and Davis, Jesse and de Jong, Laura and Kovalchik, Stephanie},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.14.2.164},
  URN =		{urn:nbn:de:0030-drops-205023},
  doi =		{10.4230/DagRep.14.2.164},
  annote =	{Keywords: AI, machine learning, sports, team, athletes, strategy, tactics}
}

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DOI: 10.4230/DagRep.11.9.45

Machine Learning in Sports (Dagstuhl Seminar 21411)

Authors: Ulf Brefeld, Jesse Davis, Martin Lames, and James J. Little

Published in: Dagstuhl Reports, Volume 11, Issue 9 (2022)

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.

Cite as

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)

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@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/entities/document/10.4230/DagRep.11.9.45},
  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}
}

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Documents authored by Davis, Jesse

Computational Approaches to Strategy and Tactics in Sports (Dagstuhl Seminar 24081)

Abstract

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Machine Learning in Sports (Dagstuhl Seminar 21411)

Abstract

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