Classification of playing position in elite junior Australian football using technical skill indicators

Woods, CT; Veale, J; Fransen, J; Robertson, S; Collier, NF

Classification of playing position in elite junior Australian football using technical skill indicators

Woods, CT Veale, J Fransen, J

Robertson, S Collier, NF

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Publication Type:: Journal Article
Citation:: Journal of Sports Sciences, 2018, 36 (1), pp. 97 - 103
Issue Date:: 2018-01-02

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Field	Value	Language
dc.contributor.author	Woods, CT	en_US
dc.contributor.author	Veale, J	en_US
dc.contributor.author	Fransen, J https://orcid.org/0000-0003-3355-1848	en_US
dc.contributor.author	Robertson, S	en_US
dc.contributor.author	Collier, NF	en_US
dc.date.issued	2018-01-02	en_US
dc.identifier.citation	Journal of Sports Sciences, 2018, 36 (1), pp. 97 - 103	en_US
dc.identifier.issn	0264-0414	en_US
dc.identifier.uri	http://hdl.handle.net/10453/94302
dc.description.abstract	© 2017 Informa UK Limited, trading as Taylor & Francis Group. In team sport, classifying playing position based on a players’ expressed skill sets can provide a guide to talent identification by enabling the recognition of performance attributes relative to playing position. Here, elite junior Australian football players were a priori classified into 1 of 4 common playing positions; forward, midfield, defence, and ruck. Three analysis approaches were used to assess the extent to which 12 in-game skill performance indicators could classify playing position. These were a linear discriminant analysis (LDA), random forest, and a PART decision list. The LDA produced classification accuracy of 56.8%, with class errors ranging from 19.6% (midfielders) to 75.0% (ruck). The random forest model performed at a slightly worse level (51.62%), with class errors ranging from 27.8% (midfielders) to 100% (ruck). The decision list revealed 6 rules capable of classifying playing position at accuracy of 70.1%, with class errors ranging from 14.4% (midfielders) to 100% (ruck). Although the PART decision list produced the greatest relative classification accuracy, the technical skill indicators reported were generally unable to accurately classify players according to their position using the 3 analysis approaches. This player homogeneity may complicate recruitment by constraining talent recruiter’s ability to objectively recognise distinctive positional attributes.	en_US
dc.relation.ispartof	Journal of Sports Sciences	en_US
dc.relation.isbasedon	10.1080/02640414.2017.1282621	en_US
dc.subject.classification	Sport Sciences	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Task Performance and Analysis	en_US
dc.subject.mesh	Aptitude	en_US
dc.subject.mesh	Motor Skills	en_US
dc.subject.mesh	Soccer	en_US
dc.subject.mesh	Adolescent	en_US
dc.subject.mesh	Australia	en_US
dc.subject.mesh	Athletic Performance	en_US
dc.title	Classification of playing position in elite junior Australian football using technical skill indicators	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	36	en_US
utslib.for	1106 Human Movement and Sports Sciences	en_US
utslib.for	1302 Curriculum and Pedagogy	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Health
pubs.organisational-group	/University of Technology Sydney/Strength - CHSP - Health Services and Practice
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

© 2017 Informa UK Limited, trading as Taylor & Francis Group. In team sport, classifying playing position based on a players’ expressed skill sets can provide a guide to talent identification by enabling the recognition of performance attributes relative to playing position. Here, elite junior Australian football players were a priori classified into 1 of 4 common playing positions; forward, midfield, defence, and ruck. Three analysis approaches were used to assess the extent to which 12 in-game skill performance indicators could classify playing position. These were a linear discriminant analysis (LDA), random forest, and a PART decision list. The LDA produced classification accuracy of 56.8%, with class errors ranging from 19.6% (midfielders) to 75.0% (ruck). The random forest model performed at a slightly worse level (51.62%), with class errors ranging from 27.8% (midfielders) to 100% (ruck). The decision list revealed 6 rules capable of classifying playing position at accuracy of 70.1%, with class errors ranging from 14.4% (midfielders) to 100% (ruck). Although the PART decision list produced the greatest relative classification accuracy, the technical skill indicators reported were generally unable to accurately classify players according to their position using the 3 analysis approaches. This player homogeneity may complicate recruitment by constraining talent recruiter’s ability to objectively recognise distinctive positional attributes.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/94302