Dalton-Barron, Nicholas

Palczewska, Anna

Weaving, Dan

Rennie, Gordon

Beggs, Clive

Roe, Gregory

Jones, Ben

2022-08

<p>This study aimed to determine the similarity between and within positions in professional rugby league in terms of technical performance and match displacement. Here, the analyses were repeated on 3 different datasets which consisted of technical features only, displacement features only, and a combined dataset including both. Each dataset contained 7617 observations from the 2018 and 2019 Super League seasons, including 366 players from 11 teams. For each dataset, feature selection was initially used to rank features regarding their importance for predicting a player’s position for each match. Subsets of 12, 11, and 27 features were retained for technical, displacement, and combined datasets for subsequent analyses. Hierarchical cluster analyses were then carried out on the positional means to find logical groupings. For the technical dataset, 3 clusters were found: (1) props, loose forwards, second-row, hooker; (2) halves; (3) wings, centres, fullback. For displacement, 4 clusters were found: (1) second-rows, halves; (2) wings, centres; (3) fullback; (4) props, loose forward, hooker. For the combined dataset, 3 clusters were found: (1) halves, fullback; (2) wings and centres; (3) props, loose forward, hooker, second-rows. These positional clusters can be used to standardise positional groups in research investigating either technical, displacement, or both constructs within rugby league.</p>

Journal of Sports Sciences, 40(15), p. 1712-1721

1466-447X

0264-0414

https://hdl.handle.net/1959.11/58251

en

Routledge

Attribution 4.0 International

Clustering of match running and performance indicators to assess between- and within-playing position similarity in professional rugby league

Journal Article

Publication