The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review
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Abstract (arXiv)
Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Dec 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 4; 2025: 0; 2026: 1.
9 papers.
- 2021BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modellingcited by 2
- 2023Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolutioncited by 2
- 2024Machine Learning for Soccer Match Result Predictioncited by 2
- 2022Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Indexcited by 1
- 2023Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Treescited by 1
- 2024Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysiscited by 1
- 2024Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecastingcited by 1
- 2024Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learningcited by 1
- 2026NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysiscited by 0
- 2026NCAA Bracket Prediction Using Machine Learning and Combinatorial Fusion Analysis11 Mar 2026
- 2024Optimizing Fantasy Sports Team Selection with Deep Reinforcement Learning26 Dec 2024
- 2024Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting3 May 2024
- 2024Machine Learning for Soccer Match Result Prediction12 Mar 2024
- 2024Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis4 Feb 2024
- 2023Evaluating Soccer Match Prediction Models: A Deep Learning Approach and Feature Optimization for Gradient-Boosted Trees26 Sep 2023
- 2023Who You Play Affects How You Play: Predicting Sports Performance Using Graph Attention Networks With Temporal Convolution29 Mar 2023
- 2022Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index28 Nov 2022
- 2021BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling18 May 2021