  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.
