Artificial intelligence for team sports: a survey

Искусственный интеллект в командных видах спорта: обзор
Ryan Beal, Timothy J. Norman, Sarvapali D. Ramchurn
2019-01-01

artificial intelligenceinjury predictionmatch outcome predictiontactical decision makingteam sports
Abstract The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). 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, namely 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 to 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 not only 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 ML in team sports.
1
It covers five application areas: match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction.
2
The analysis identifies major research directions, along with strengths and weaknesses of existing models and techniques.
3
The paper outlines open research questions for advancing AI and machine learning in team sports.
4
The reviewed methods support predicting match outcomes and improving teams’ strategic and tactical decision making.
5
The survey reviews AI and machine-learning techniques applied to computational challenges in team sports.

Team sports

Applications of artificial intelligence and machine learning to match outcome prediction, tactical decision making, player investment, fantasy sports, and injury prediction

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2019-01-01
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Ryan Beal
Timothy J. Norman
Sarvapali D. Ramchurn
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