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This study quantified and compared the collision and non-collision match characteristics across age categories (i.e. U12, U14, U16, U18, Senior) for both amateur and elite playing standards from Tier 1 rugby union nations (i.e. England, South Africa, New Zealand). Two-hundred and one male matches (5911 min ball-in-play) were coded using computerised notational analysis, including 193,708 match characteristics (e.g. 83,688 collisions, 33,052 tackles, 13,299 rucks, 1006 mauls, 2681 scrums, 2923 lineouts, 44,879 passes, 5568 kicks). Generalised linear mixed models with post-hoc comparisons and cluster analysis compared the match characteristics by age category and playing standard. Overall significant differences (p < 0.001) between age category and playing standard were found for the frequency of match characteristics, and tackle and ruck activity. The frequency of characteristics increased with age category and playing standard except for scrums and tries that were the lowest at the senior level. For the tackle, the percentage of successful tackles, frequency of active shoulder, sequential and simultaneous tackles increased with age and playing standard. For ruck activity, the number of attackers and defenders were lower in U18 and senior than younger age categories. Cluster analysis demonstrated clear differences in all and collision match characteristics and activity by age category and playing standard. These findings provide the most comprehensive quantification and comparison of collision and non-collision activity in rugby union demonstrating increased frequency and type of collision activity with increasing age and playing standard. These findings have implications for policy to ensure the safe development of rugby union players throughout the world. The safety of rugby union, especially the tackle, has previously been questioned but limited data are available to understand the collision and non-collision match characteristics between different age categories and playing standards. The frequency of collision and non-collision match characteristics increase with age and playing standard except for the frequency of scrums and tries which are lowest at the Senior Elite level. The activity of the tackle and ruck are also different between age categories and playing standards. Hierarchical cluster analysis demonstrated clear differences in all and collision match characteristics between junior (i.e. U12, U14, U16), and amateur (i.e. U18 and senior) and elite (i.e. U18 and senior) playing levels. Governing bodies and practitioners should be aware of the differences in collision and non-collision match characteristics by age and playing standard, when reviewing future versions of rugby union. [ABSTRACT FROM AUTHOR]

This study aims to (a) quantify the movement patterns during rugby league match-play and (b) identify if differences exist by levels of competition within the movement patterns and units through the sequential movement pattern (SMP) algorithm. Global Positioning System data were analysed from three competition levels; four Super League regular (regular-SL), three Super League (semi-)Finals (final-SL) and four international rugby league (international) matches. The SMP framework extracted movement pattern data for each athlete within the dataset. Between competition levels, differences were analysed using linear discriminant analysis (LDA). Movement patterns were decomposed into their composite movement units; then Kruskal–Wallis rank-sum and Dunn post-hoc were used to show differences. The SMP algorithm found 121 movement patterns comprised mainly of "walk" and "jog" based movement units. The LDA had an accuracy score of 0.81, showing good separation between competition levels. Linear discriminant 1 and 2 explained 86% and 14% of the variance. The Kruskal–Wallis found differences between competition levels for 9 of 17 movement units. Differences were primarily present between regular-SL and international with other combinations showing less differences. Movement units which showed significant differences between competition levels were mainly composed of low velocities with mixed acceleration and turning angles. The SMP algorithm found 121 movement patterns across all levels of rugby league match-play, of which, 9 were found to show significant differences between competition levels. Of these nine, all showed significant differences present between international and domestic, whereas only four found differences present within the domestic levels. This study shows the SMP algorithm can be used to differentiate between levels of rugby league and that higher levels of competition may have greater velocity demands. Highlights This study shows that movement patterns and movement units can be used to investigate team sports through the application of the SMP framework One hundred and twenty-one movement patterns were found to be present within rugby league match-play, with the walk- and jog-based movement units most prevalent. No movement pattern was unique to a single competition level. Further analysis revealed that the majority of movement units analysed had significant differences between international and domestic rugby league, whereas only four movement units (i.e. f,m,n,q) had significant differences within the two domestic rugby league levels. International rugby league had higher occurrences of the movement patterns consisting of higher velocity movement units (ie. T,S,y). This suggests that international rugby league players may need greater high velocity exposure in training. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the relationship between matchday wellness status and a technical–tactical performance construct during rugby match-play. One hundred and thirty-three male rugby union players (73 forwards and 60 backs) from five under-18 national squads who participated in the under-18 Six Nations competition completed a subjective wellness questionnaire on each matchday morning. Players subjectively rated each item (sleep quality, fatigue, muscle soreness, stress and mood) on a five-point Likert scale to calculate their daily wellness status (i.e. difference between matchday and baseline perceived wellness). Technical–tactical performance during match-play was quantified by coding individual key performance indicators (e.g. number of carries, number of tackles). Partial least squares correlation analysis (PLSCA) was employed to compute the latent variables of perceived wellness status (X matrix) and technical–tactical performance (Y matrix) for each player observation (n = 271). The latent variables are a construct of each variable group, enabling higher dimensional data to be visualised more simply. Linear mixed-effect models were later conducted to assess the relationships between the latent variables. The effect of perceived wellness status on technical–tactical performance was statistically significant in forwards (p =.042), not statistically significant in backs (p =.120) and accounted for 4.9% and 1.9% variance in the technical–tactical performance construct, respectively. The findings of this study suggest that perceived wellness status can influence technical–tactical match performance, but the practical significance of these findings should be interpreted with caution given the amount of variance in technical–tactical performance accounted by the models. [ABSTRACT FROM AUTHOR]