Abstract
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.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Software
Reference129 articles.
1. Production Efficiency: The Case of Professional Basketball
2. Yue, Y. , Lucey, P. , Carr, P. , Bialkowski, A. & Matthews, I. 2014. Learning fine-grained spatial models for dynamic sports play prediction. In IEEE International Conference on Data Mining, 670–679.
3. A two-stage Bayesian model for predicting winners in major league baseball;Yang;Journal of Data Science,2004
4. Visualizing a Team's Goal Chances in Soccer from Attacking Events: A Bayesian Inference Approach
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