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Dagstuhl Seminar 24081

Computational Approaches to Strategy and Tactics in Sports

( Feb 18 – Feb 23, 2024 )

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Please use the following short url to reference this page: https://www.dagstuhl.de/24081

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Motivation

The past decade has seen a rapid growth in the ability to collect large-scale spatiotemporal data sets about sports. Ideally, such data should be used to inform strategic and tactical decision making. On the one hand, strategy is the long-term planning of training sessions, signing of coaches and athletes, rotation and the plan made before a match/race. On the other hand, tactics are short-term and involve the execution and adaptation to the match/race plan. Having insights into the efficacy and feasibility of strategies and tactics is particularly important and challenging within sports because effective and novel strategy & tactics allow weaker teams or athletes to win against stronger ones. Unfortunately, the size, richness, and complexity of modern spatiotemporal sports data means that automated analysis is essential. Alas, the nature of the data has posed a number of challenges for classic analysis techniques. This has spurred the development of novel statistical and machine learning techniques in order to perform more fine-grained analysis of every action and decision during a competitive event.

In this Dagstuhl Seminar, we aim to bring together sports researchers in academia and industry to understand how they are using machine learning and statistical techniques to analyze strategy and tactics. Understanding, formalizing, discovering, and analyzing strategy & tactics pose a number of key challenges from a computational perspective, which include the following aspects:

  1. From a modeling perspective, what is the best way to represent discovered strategic and tactical concepts?
  2. How can we evaluate the efficacy of different strategies and tactics?
  3. How do you communicate the findings from tactical studies to an interdisciplinary audience?
  4. How can computational approaches be used to support decision making for coaching?
  5. What kind of data and domain knowledge are needed to conduct strategic and tactical studies?

The goal of the seminar is to explore and discuss how we can and should answer these questions.

Copyright Ulf Brefeld, Jesse Davis, Laura de Jong, and Stephanie Kovalchik

Participants

Related Seminars
  • Dagstuhl Seminar 06381: Computer Science in Sport (2006-09-17 - 2006-09-20) (Details)
  • Dagstuhl Seminar 08372: Computer Science in Sport - Mission and Methods (2008-09-07 - 2008-09-10) (Details)
  • Dagstuhl Seminar 11271: Computer Science in Sport - Special emphasis: Football (2011-07-03 - 2011-07-06) (Details)
  • Dagstuhl Seminar 13272: Computer Science in High Performance Sport - Applications and Implications for Professional Coaching (2013-06-30 - 2013-07-03) (Details)
  • Dagstuhl Seminar 15382: Modeling and Simulation of Sport Games, Sport Movements, and Adaptations to Training (2015-09-13 - 2015-09-16) (Details)
  • Dagstuhl Seminar 21411: Machine Learning in Sports (2021-10-10 - 2021-10-15) (Details)

Classification
  • Artificial Intelligence

Keywords
  • sports
  • machine learning
  • spatio-temporal data
  • explainability