Examining the evolution and classification of player position using performance indicators in the National Rugby League during the 2015-2019 seasons

Wedding, C., Woods, C.T., Sinclair, W.H., Gomez, M.A., and Leicht, A.S. (2020) Examining the evolution and classification of player position using performance indicators in the National Rugby League during the 2015-2019 seasons. Journal of Science and Medicine in Sport, 23 (9). pp. 891-896.

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Objectives: This study aimed to: 1) examine recent seasonal changes in performance indicators for different National Rugby League (NRL) playing positions; and 2) determine the accuracy of performance indicators to classify and discriminate positional groups in the NRL.

Design: Retrospective, longitudinal analysis of individual performance metrics.

Methods: 48 performance indicators (e.g. passes, tackles) from all NRL games during the 2015-2019 seasons were collated for each player's match-related performance. The following analyses were conducted with all data: (i) one-way ANOVA to identify seasonal changes in performance indicators; (ii) principal component analysis (PCA) to group performance indicators into factors; (iii) two-step cluster analysis to classify playing positions using the identified factors; and (iv) discriminant analysis to discriminate the identified playing positions.

Results: ANOVA showed significant differences in performance indicators across seasons (F = 2.3-687.7; p = 0-0.05; partial eta(2) = 0.00-0.075). PCA pooled all performance indicators and identified 14 factors that were included in the two-step cluster analysis (average silhouette = 0.5) that identified six positional groups: forwards, 26.7%, adjustables, 17.2%, interchange, 23.2%, backs, 20.9%, interchange forwards, 5.5% and utility backs, 6.5%. Lastly, discriminant analysis revealed five discriminant functions that differentiated playing positions.

Conclusions: Results indicated that player's performance demands across different playing positions did significantly change over recent seasons (2015-2019). Cluster analysis yielded a high-level of accuracy relative to playing position, identifying six clusters that best discriminated positional groups. Unsupervised analytical approaches may provide sports scientists and coaches with meaningful tools to evaluate player performance and future positional suitability in RL.

Item ID: 64237
Item Type: Article (Research - C1)
ISSN: 1878-1861
Keywords: Team sports, Sport analytics, Classification, Data visualisation, Performance analysis
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Copyright Information: (C) 2020 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Funders: Australian Government Research Training Scholarship, North Queensland Toyota Cowboys, James Cook University, Ministry of Education, Culture and Sport of Spain (MECS)
Projects and Grants: MECS mobility grant "Salvador de Madariaga" PRX18/00098
Date Deposited: 02 Sep 2020 07:30
FoR Codes: 42 HEALTH SCIENCES > 4207 Sports science and exercise > 420799 Sports science and exercise not elsewhere classified @ 100%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970111 Expanding Knowledge in the Medical and Health Sciences @ 100%
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