Classification of playing position in elite junior Australian football using technical skill indicators. Issue 1 (2nd January 2018)
- Record Type:
- Journal Article
- Title:
- Classification of playing position in elite junior Australian football using technical skill indicators. Issue 1 (2nd January 2018)
- Main Title:
- Classification of playing position in elite junior Australian football using technical skill indicators
- Authors:
- Woods, Carl T.
Veale, James
Fransen, Job
Robertson, Sam
Collier, Neil French - 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.
- Is Part Of:
- Journal of sports sciences. Volume 36:Issue 1(2018)
- Journal:
- Journal of sports sciences
- Issue:
- Volume 36:Issue 1(2018)
- Issue Display:
- Volume 36, Issue 1 (2018)
- Year:
- 2018
- Volume:
- 36
- Issue:
- 1
- Issue Sort Value:
- 2018-0036-0001-0000
- 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 - Journal URLs:
- http://www.tandfonline.com/toc/rjsp20/current ↗
http://www.tandfonline.com/ ↗ - DOI:
- 10.1080/02640414.2017.1282621 ↗
- Languages:
- English
- ISSNs:
- 0264-0414
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5066.350000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 5373.xml