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

ObjectivesThis 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.DesignRetrospective, longitudinal analysis of performance indicators.MethodsForty-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’.ResultsThe 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’.ConclusionsThe 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

ObjectivesThis 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.DesignRetrospective, longitudinal analysis of individual performance metrics.Methods48 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.ResultsANOVA 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.ConclusionsResults 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.     

A comparison of game-play characteristics between elite youth and senior Australian National Rugby League competitions

Objectives

To compare game-play characteristics between elite youth and senior Australian National Rugby League (NRL) competitions.

A comparison of muscle damage, soreness and performance following a simulated contact and non-contact team sport activity circuit

The aim was to compare the effect of a simulated team sport activity circuit (reflective of the activity demands of Australian football) either with or without body ‘contact’ on muscle soreness, damage, and performance when the circuit was repeated 48 h later. Eleven male, team-sport athletes completed a ‘non-contact’ (NCON) and a ‘contact’ (CON) version of the team sport activity circuit in a crossover design with at least 1 week between trials. The effect of CON and NCON on repeated 15 m sprint and vertical jump performance was assessed by completing the same version of the circuit 48 h after the initial trial. The effect on perceived soreness and blood markers of muscle damage and inflammation was also determined. Subsequent performance was affected to a greater extent by CON, with both best and mean sprint times significantly slower 48 h following CON (p < 0.05), while performance was maintained after NCON. Best and mean vertical jump performance was significantly impaired following CON (p < 0.05), while only best vertical jump was affected by NCON (p < 0.05). Perceived soreness and pressure sensitivity were elevated following both NCON and CON (p < 0.001); however, the increase in soreness was greater with CON (p = 0.012). Both CON and NCON resulted in elevated serum creatine kinase, myoglobin and lactate dehydrogenase, while c-reactive protein increased following CON but not NCON. In conclusion, Greater perceived soreness and decrements in performance of the simulated team sport activity circuit when repeated 48 h later were observed following CON.     

The relationship between match performance indicators and outcome in Australian Football

Objectives

To identify novel insights about performance in Australian Football (AF), by modelling the relationships between player actions and match outcomes. This study extends and improves on previous studies by utilising a wider range of performance indicators (PIs) and a longer time frame for the development of predictive models.