Using principal component analysis to develop performance indicators in professional rugby league
Parmar, Nimai 
ORCID: https://orcid.org/0000-0001-5540-123X, James, Nic ORCID: https://orcid.org/0000-0002-4181-9501, Hearne, Gary ORCID: https://orcid.org/0000-0003-2146-4878 and Jones, Ben
(2018)
Using principal component analysis to develop performance indicators in professional rugby league.
International Journal of Performance Analysis in Sport, 18
(6)
.
pp. 938-949.
ISSN 2474-8668
[Article]
(doi:10.1080/24748668.2018.1528525)
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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.
| Item Type: | Article |
|---|---|
| Research Areas: | A. > School of Science and Technology A. > School of Science and Technology > London Sport Institute A. > School of Science and Technology > London Sport Institute > Performance Analysis at the London Sport Institute |
| Item ID: | 25291 |
| Notes on copyright: | This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Performance Analysis in Sport on 03 Oct 2018, available online: http://www.tandfonline.com/10.1080/24748668.2018.1528525 |
| Useful Links: | |
| Depositing User: | Nimai Parmar |
| Date Deposited: | 04 Oct 2018 12:19 |
| Last Modified: | 29 Nov 2022 19:31 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/25291 |
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