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Real time prediction of match outcomes in Australian football

journal contribution
posted on 2023-10-03, 02:00 authored by Mitchell (Mitch) AaronsMitchell (Mitch) Aarons, CM Young, Lyndell BruceLyndell Bruce, Dan DwyerDan Dwyer
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.

History

Journal

Journal of Sports Sciences

Pagination

1-11

Location

England

ISSN

0264-0414

eISSN

1466-447X

Language

en

Publication classification

C1 Refereed article in a scholarly journal

Publisher

Informa UK Limited