This study aimed to determine whether machine learning models based on technical performance and not score margin could be used to predict end-of-match outcome of Australian football matches in real-time. If efficacious, these models could be used to generate insights about team performance and support the decision-making of coaches during matches. A database of 168 team technical performance indicators from 829 Australian Football League matches played between 2017 and 2021 was used. Two feature sets (data-driven and data-informed) were used to train and evaluate six models (generalised linear model, random forest and adaboost) on match outcome prediction (Win/Loss) over 120 epochs (a representation of normalised time during each match). All models performed well (mean classification accuracy = 73.5-75.8%) in comparison with a benchmark score-based model (mean classification accuracy = 77.4%). Data-informed feature sets performed better than data-driven in most cases. Classification accuracy was low at the start of a match (45.7-48.8%) but increased to a peak near the end of a match (87.2-92.7%). These findings suggest that any of the employed models can be used to formulate in-match decision support. The model which is best in practice will depend on factors such as time-cost trade-off, feasibility and the perceived value of its suggestions.
Keywords: Performance analysis; decision support; machine learning; performance indicators; sport result prediction; team sport.