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.

Effect of a training week on heart rate variability in elite youth rugby league players.

The aim of this study was to examine the influence of weekly training including a competitive game on heart rate (HR) variability (HRV). Youth players (n=9, age 17-20 years) were monitored during daily supine rest (10 min) and standing (8 min), 5 times over 8 days. Heart rate recordings were analysed for time domain, frequency (e. g. low frequency [LF], high frequency [HF]) domain and non-linear measures of HRV and compared using ANOVA or Friedman's tests. Relationships between HRV and training workloads were examined via Spearman rank rho (ρ) correlation coefficients. Prior to a game, mean HR was significantly increased and remained elevated until 2 days post-game while parasympathetic modulations (HF) were significantly reduced (p<0.05). The supine to standing change in HRV was significantly reduced for up to 4 days post-game (LF/HF ratio, - 1.0±2.9 vs. - 3.0±1.9, p<0.05). These results confirm that prior to a game, players exhibited reduced parasympathetic and/or predominant sympathetic modulation with the game significantly reducing autonomic responses to standing for up to the following 4 days. Identification of day to day fluctuations in HRV may provide a helpful tool for monitoring player workload to maximise training and game performance.

Influence of cycle ergometer type and sex on assessment of 30-second anaerobic capacity and power.

This study examined the influence of cycle ergometer type and sex on assessment of 30-s anaerobic capacity and power. 41 healthy adults performed a 30-s anaerobic cycle test using a mechanically- (ME) and air-braked (AE) ergometer in a randomised order, approximately 7 days apart. Peak heart rate (HR) and rating of perceived exertion were similar between tests with peak HR greater for females compared to males (187.0 ± 9.1 vs. 180.8 ± 9.9 bpm, p<0.05). Peak power (1 100 ± 330 vs. 802 ± 225 W), mean power (793 ± 223 vs. 587 ± 156 W) and total work (23.8 ± 6.7 vs. 17.6 ± 4.7 kJ) were greater for AE compared to ME (p<0.001) and greater for males compared to females (p<0.001). The mean difference for anaerobic capacity and power between AE and ME were similar for males and females (37-41% vs. 33-35%, p>0.05). Peak lactate was greater for AE compared to ME (16.1 ± 3.4 vs. 14.8 ± 2.9 mmol·L (-1); p<0.05) and greater for males compared to females (16.2 ± 3.5 vs. 14.6 ± 2.7 mmol·L (-1); p<0.05). The current study demonstrated that anaerobic power and capacity were substantially greater when assessed using AE compared to the traditional ME with the difference between ergometer types unaffected by sex. Ergometer type should be considered when comparing anaerobic results across populations and/or studies.

The reliability of VO2(peak) determination in healthy females during an incremental arm ergometry test.

This study examined the reliability of an incremental arm ergometry (AE) protocol during peak aerobic power (VO2(peak)) determination in healthy females. Fifteen females completed two incremental AE tests to exhaustion, seven to eleven days apart, using a mechanically braked arm ergometer. The initial work rate was 16W and increased by 16W every two minutes until exhaustion. Significant differences between tests were determined by repeated measures ANOVA, and paired t-tests or Wilcoxon signed-rank tests, where appropriate. Reliability was determined by intraclass correlation coefficients (ICC), typical error, coefficient of variation (CV) and measurement bias/ratio and 95% limits of agreement (LOA). Peak cardio-respiratory responses were similar between tests, except for tidal volume (1.95 +/-0.47 vs. 1.81 +/- 0.41 L, P<0.05). Reliability for peak variables was moderate to high (ICC=0.659-0.941; CV< or =10%) while LOA were considerable for most variables including VO2(peak) (LOA=0.57 L.min(-1)). Similar peak cardio-respiratory responses, low CV and moderate-high ICC confirmed the reliability of the current incremental AE protocol to be similar to that of prior reported protocols for VO2(peak) determination. Substantial within-participant variability (LOA) for respiratory rate and tidal volume was common during the AE protocol and possibly reflects the influence of respiratory entrainment on reliability that requires further investigation.