Classification of playing position in elite junior Australian football using technical skill indicators. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Woods, Carl T.; Veale, James; Fransen, Job; Robertson, Sam; Collier, Neil French
• Is Part Of: Journal of sports sciences. Volume 36:Issue 1(2018)
• Page Start: 97
• Page End: 103
• Publication Date: 2018-01-02
• Subjects: Performance analysis -- machine learning -- discriminant analysis -- random forest -- rule induction; Sports -- Periodicals; Sports -- Physiological aspects -- Periodicals; Sports -- Psychological aspects -- Periodicals; 612.044
• DOI: 10.1080/02640414.2017.1282621
• Languages: English
• ISSNs: 0264-0414

Abstract

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.