title: Classification of playing position in elite junior Australian football using technical skill indicators.
abstract: ​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.
authors.affiliation: a Discipline of Sport and Exercise Science , James Cook University , Townsville , Queensland , Australia.
authors.forename: Carl T
authors.initials: CT
authors.lastname: Woods
authors.affiliation: b Talent Pathway, Australian Football League , Melbourne , Victoria.
authors.forename: James
authors.initials: J
authors.lastname: Veale
authors.affiliation: c Faculty of Health , University of Technology Sydney , New South Wales , Sydney , Australia.
authors.forename: Job
authors.initials: J
authors.lastname: Fransen
authors.affiliation: d Institute of Sport, Exercise & Active Living (ISEAL), Victoria University , Melbourne , Australia.
authors.forename: Sam
authors.identifier: http://orcid.org/0000-0002-8330-0011
authors.initials: S
authors.lastname: Robertson
authors.affiliation: e Faculty of Sustainability , Leuphana University, Luneburg , Germany.
authors.forename: Neil French
authors.initials: NF
authors.lastname: Collier
mesh_terms: D000293:Adolescent; D001076:Aptitude; D054874:Athletic Performance / Q000145:classification*; D001315:Australia; D006801:Humans; D009048:Motor Skills / Q000145:classification*; D012916:Soccer / Q000145:classification*; D013647:Task Performance and Analysis
keywords: Performance analysis; discriminant analysis; machine learning; random forest; rule induction