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

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet


  • Carl T. Woods
  • James Veale
  • Job Fransen
  • Sam Robertson
  • Neil Collier
​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.
ZeitschriftJournal of Sports Sciences
Seiten (von - bis)97 - 103
Anzahl der Seiten7
PublikationsstatusErschienen - 02.01.2018