C Wedding1, C T Woods2, W H Sinclair3, M A Gomez4, A S Leicht3. 1. Sport and Exercise Science, James Cook University, Australia. Electronic address: corey.wedding@my.jcu.edu.au. 2. Institute of Health and Sport, Victoria University, Australia. 3. Sport and Exercise Science, James Cook University, Australia. 4. Faculty of Physical Activity and Sport Sciences - INEF, Polytechnic University of Madrid, Spain.
Abstract
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 η2=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.
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 η2=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.