Modelling match outcome in Australian football: improved accuracy with large databases

Young, Christopher, Luo, Wei, Gastin, Paul, Tran, Jacqueline and Dwyer, Daniel 2019, Modelling match outcome in Australian football: improved accuracy with large databases, International journal of computer science in sport, vol. 18, no. 1, pp. 80-92, doi: 10.2478/ijcss-2019-0005.

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Title Modelling match outcome in Australian football: improved accuracy with large databases
Author(s) Young, ChristopherORCID iD for Young, Christopher orcid.org/0000-0001-6615-1950
Luo, WeiORCID iD for Luo, Wei orcid.org/0000-0002-4711-7543
Gastin, PaulORCID iD for Gastin, Paul orcid.org/0000-0003-2320-7875
Tran, Jacqueline
Dwyer, DanielORCID iD for Dwyer, Daniel orcid.org/0000-0002-8177-7262
Journal name International journal of computer science in sport
Volume number 18
Issue number 1
Start page 80
End page 92
Total pages 13
Publisher Sciendo
Place of publication Warsaw, Poland
Publication date 2019
ISSN 1684-4769
1684-4769
Summary © 2019 C. Young et al., published by Sciendo 2019. Mathematical models that explain match outcome, based on the value of technical performance indicators (PIs), can be used to identify the most important aspects of technical performance in team field-sports. The purpose of this study was to evaluate several methodological opportunities, to enhance the accuracy of this type of modelling. Specifically, we evaluated the potential benefits of 1) modelling match outcome using an increased number of seasons and PIs compared with previous reports, 2) how to identify eras where technical performance characteristics were stable and 3) the application of a novel feature selection method. Ninety-one PIs across sixteen Australian Football (AF) League seasons were analysed. Change-point and Segmented Regression analyses were used to identify eras and they produced similar but non-identical outcomes. A feature selection ensemble method identified the most valuable 45 PIs for modelling. The use of a larger number of seasons for model development lead to improvement in the classification accuracy of the models, compared with previous studies (88.8 vs 78.9%). This study demonstrates the potential benefits of large databases when creating models of match outcome and the pitfalls of determining whether there are eras in a longitudinal database.
Language eng
DOI 10.2478/ijcss-2019-0005
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2019, C. Young et al.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30129832

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