The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review

TL;DR

This review analyzes two decades of research on machine learning methods for predicting team sport outcomes, highlighting effective algorithms, evaluation strategies, and future research directions.

Contribution

It provides a comprehensive survey of ML techniques in team sports, identifying successful algorithms, evaluation methods, and research gaps from 1996 to 2019.

Findings

Certain ML algorithms are more successful in specific sports.

Evaluation strategies significantly impact reported accuracy.

Predictability varies across different team sports.

Abstract

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.