Using principal component analysis to develop performance indicators in professional rugby league

Nimai Parmar, Nic James, Gary Hearne and Ben Jones

International Journal of Performance Analysis in Sport, 2018, vol. 18, issue 6, 938-949

Abstract: Previous research on performance indicators in rugby league has suggested that dimension reduction techniques should be utilised when analysing sporting data sets with a large number of variables. Forty-five rugby league team performance indicators, from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, were reduced to 10 orthogonal principal components with standardised team scores produced for each component. Forced-entry logistic (match outcome) and linear (point’s difference) regression models were used alongside exhaustive chi-square automatic interaction detection decision trees to determine how well each principle component predicted success. The 10 principal components explained 81.8% of the variance in point’s difference and classified match outcome correctly ~90% of the time. Results suggested that if a team increased “amount of possession” and “making quick ground” component scores, they were more likely to win (β = 15.6, OR = 10.1 and β = 7.8, OR = 13.3) respectively. Decision trees revealed that “making quick ground” was an important predictor of match outcome followed by “quick play” and “amount of possession”. The use of PCA provided a useful guide on how teams can increase their chances of success by improving performances on a collection of variables, instead of analysing variables in isolation.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://hdl.handle.net/10.1080/24748668.2018.1528525 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rpanxx:v:18:y:2018:i:6:p:938-949

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RPAN20

DOI: 10.1080/24748668.2018.1528525

Access Statistics for this article

International Journal of Performance Analysis in Sport is currently edited by Peter O'Donoghue

More articles in International Journal of Performance Analysis in Sport from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().