Classification of playing position in elite junior Australian football using technical skill indicators |
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| Authors: | Carl T Woods James Veale Job Fransen Sam Robertson Neil French Collier |
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| Institution: | 1. Discipline of Sport and Exercise Science, James Cook University, Townsville, Queensland, Australia;2. Talent Pathway, Australian Football League, Melbourne, Victoria;3. Faculty of Health, University of Technology Sydney, New South Wales, Sydney, Australia;4. Institute of Sport, Exercise &5. Active Living (ISEAL), Victoria University, Melbourne, Australia;6. Faculty of Sustainability, Leuphana University, Luneburg, Germany |
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| 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. |
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| Keywords: | Performance analysis machine learning discriminant analysis random forest rule induction |
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