The University of Southampton
University of Southampton Institutional Repository

Artificial intelligence for team sports: a survey

Artificial intelligence for team sports: a survey
Artificial intelligence for team sports: a survey
The sports domain presents a number of significant computational challenges for artificial intelligence and machine learning. In this paper we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, these include: match outcome prediction, tactical decision making, player investments, fantasy sports and injury prediction. By assessing the work in these areas we explore how AI is used in the predict match outcomes, and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights a number of strengths, but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and machine learning in team sports.
Beal, Ryan
d9874cb0-bd92-4a16-8576-78d769b41ff7
Norman, Timothy J.
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Beal, Ryan
d9874cb0-bd92-4a16-8576-78d769b41ff7
Norman, Timothy J.
663e522f-807c-4569-9201-dc141c8eb50d
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3

Beal, Ryan, Norman, Timothy J. and Ramchurn, Sarvapali D. (2019) Artificial intelligence for team sports: a survey. Knowledge Engineering Review, 34, [e28]. (doi:10.1017/S0269888919000225).

Record type: Article

Abstract

The sports domain presents a number of significant computational challenges for artificial intelligence and machine learning. In this paper we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, these include: match outcome prediction, tactical decision making, player investments, fantasy sports and injury prediction. By assessing the work in these areas we explore how AI is used in the predict match outcomes, and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights a number of strengths, but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and machine learning in team sports.

Text
Artificial Intelligence for Team Sports: A Survey - Accepted Manuscript
Restricted to Repository staff only until 20 June 2021.
Request a copy
Text
Team-Sport-Survey-KER - Accepted Manuscript
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 2 October 2019
e-pub ahead of print date: 20 December 2019
Published date: 2019

Identifiers

Local EPrints ID: 436900
URI: http://eprints.soton.ac.uk/id/eprint/436900
PURE UUID: 355f1558-cac8-4ab0-99b9-20acbaf77629
ORCID for Timothy J. Norman: ORCID iD orcid.org/0000-0002-6387-4034
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

Catalogue record

Date deposited: 13 Jan 2020 17:33
Last modified: 16 May 2020 00:46

Export record

Altmetrics

Contributors

Author: Ryan Beal
Author: Timothy J. Norman ORCID iD
Author: Sarvapali D. Ramchurn ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×