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

Woods, Carl T., Veale, James, Fransen, Job, Robertson, Sam, and Collier, Neil French (2017) Classification of playing position in elite junior Australian football using technical skill indicators. Journal of Sports Sciences, 36 (1). pp. 97-103.

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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.

Item ID: 46982
Item Type: Article (Research - C1)
ISSN: 1466-447X
Keywords: performance analysis, machine learning, discriminant analysis, random forest, rule induction
Date Deposited: 31 Jan 2017 05:31
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1106 Human Movement and Sports Science > 110699 Human Movement and Sports Science not elsewhere classified @ 100%
SEO Codes: 92 HEALTH > 9299 Other Health > 929999 Health not elsewhere classified @ 100%
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