Analysis of styles of play according to season and end of season rank in the National Rugby League.

OBJECTIVES: This study aimed to identify styles of play in the National Rugby League (NRL) relative to season and end of season rank (position on the NRL ladder) across the 2015-2019 seasons. DESIGN: Retrospective, longitudinal analysis of performance indicators. METHODS: Forty-eight performance indicators (e.g. runs, tackles) from all NRL teams and matches during the 2015-2019 seasons (n=2010) were quantified. Principal component analysis (PCA) was then used to identify styles of play based on dimensions (Factors) of performance indicators. Multivariate analysis of covariance (MANCOVA) was then used to explain these emergent styles of play relative to 'season' and 'end of season rank'. RESULTS: The PCA revealed nine Factors (six attacking, two defensive and one contested style) accounting for ∼51% of seasonal team performance variance. These nine Factors differed across 'seasons', with four showing an effect against 'end of season rank'. From these four, two Factors (ball possession and player efforts) impacted upon the combined effects of 'season' and 'end of season rank'. CONCLUSIONS: The PCA identified nine Factors reflecting a spread of attacking, defensive and contested styles of play within the NRL. These styles differed relative to season and a team's end of season ranking. These results may assist practitioners with the recognition of more contemporary styles of play in the NRL, enabling the development of strategies to exploit competition trends.

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

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.