Clustering of match running and performance indicators to assess between- and within-playing position similarity in professional rugby league. Issue 15 (3rd August 2022)

• Record Type: Journal Article
• Authors: Dalton-Barron, Nicholas; Palczewska, Anna; Weaving, Dan; Rennie, Gordon; Beggs, Clive; Roe, Gregory; Jones, Ben
• Is Part Of: Journal of sports sciences. Volume 40:Issue 15(2022)
• Page Start: 1712
• Page End: 1721
• Publication Date: 2022-08-03
• Subjects: Feature selection -- hierarchical cluster analysis -- positional groups -- sport analytics -- data visualisation; Sports -- Periodicals; Sports -- Physiological aspects -- Periodicals; Sports -- Psychological aspects -- Periodicals; 612.044
• DOI: 10.1080/02640414.2022.2100781
• Languages: English
• ISSNs: 0264-0414

Abstract

ABSTRACT: 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.