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Multifactorial analysis of factors influencing elite Australian football match outcomes: a machine learning approach

Authors
  • Fahey-Gilmour, J.1,
  • Dawson, B.1
  • Peeling, P.1,
  • Heasman, J.
  • Rogalski, B.
  • 1 University of Western Australia, Australia , (Australia)
Type
Published Article
Journal
International Journal of Computer Science in Sport
Publisher
Sciendo
Publication Date
Dec 01, 2019
Volume
18
Issue
3
Pages
100–124
Identifiers
DOI: 10.2478/ijcss-2019-0020
Source
De Gruyter
Keywords
License
Green

Abstract

In Australian football (AF), few studies have assessed combinations of pre- game factors and their relation to game outcomes (win/loss) in multivariable analyses. Further, previous research has mostly been confined to association-based linear approaches and post-game prediction, with limited assessment of predictive machine learning (ML) models in a pre-game setting. Therefore, our aim was to use ML techniques to predict game outcomes and produce a hierarchy of important (win/loss) variables. A total of 152 variables (79 absolute and 73 differentials) were used from the 2013–2018 Australian Football League (AFL) seasons. Various ML models were trained (cross-validation) on the 2013–2017 seasons with the–2018 season used as an independent test set. Model performance varied (66.5-73.3% test set accuracy), although the best model (glmnet – 73.3%) rivalled bookmaker predictions in the same period (70.9%). The glmnet model revealed measures of team quality (a player-based rating and a team-based) in their relative form as the most important variables for prediction. Models that contained in-built feature selection or could model non-linear relationships generally performed better. These findings show that AFL game outcomes can be predicted using ML methods and provide a hierarchy of predictors that maximize the chance of winning.

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