Dr Cameron Owen

Purpose: The aims of this study were to (1) assess the concurrent validity of global positioning systems (GPSs) against a radar device to measure sprinting force–velocity (F–v) profiles and (2) evaluate the interunit reliability of 10-Hz GPS devices (Vector S7, Catapult Innovations). Methods: Sixteen male elite U18 rugby union players (178.3 [7.6] cm; 78.3 [13.2] kg) participated. Two 50-m sprints interspersed with at least 5 minutes of recovery were completed to obtain input (maximal sprint speed and acceleration time constant τ) and output (theoretical maximal horizontal force, sprinting speed, and horizontal power) F–v profile variables. Sprint running speed was concurrently measured with a radar and 2 GPS units placed on the upper back of each player. Concurrent validity and interunit reliability analyses were performed. Results: Moderate to nearly perfect correlations were observed between radar and GPS-derived F–v variables, with small to large typical errors. Trivial to small coefficients of variation were found regarding the GPS interunit reliability. Conclusion: The GPS devices tested in this study represent a valid and reliable alternative to a radar device when assessing sprint acceleration F–v profiles in team-sport players.

This study assessed the effects of percentage-based training (%1RM), rating of perceived exertion (RPE), repetitions in reserve (RIR), and velocity-based training (VBT) on (a) acute kinematic outputs, perceptions of effort, and changes in neuromuscular function during resistance training; and (b) neuromuscular fatigue and perceptions of soreness 24 hours after exercise. In a randomized crossover design, 15 subjects completed a %1RM, RPE, RIR, and VBT training condition involving a fatiguing protocol followed by 5 sets of the free-weight back squat and bench press at 70% of 1 repetition maximum. Subjects returned at 24 hours to assess neuromuscular fatigue and perceived soreness. Percentage-based training and RPE allowed the smallest volume loads, with %1RM prescription causing sets to be regularly taken to failure. Alternatively, RIR and VBT allowed greater maintenance of training volume. Velocity-based training had the most accurate training prescription, with all sets being within 5% of the intended starting velocity, while the RPE and %1RM prescriptive methods caused subjects to train with loads that were increasingly inaccurate. The RPE prescriptive method had the lowest reported values for differential RPE while the %1RM had the greatest change across the session. At no point were there between-group differences in measures of neuromuscular fatigue or perceived soreness. These findings demonstrate that autoregulatory prescriptive methods can be used to mitigate the risk of training to failure, ensure accurate training prescription that can maintain training volume, and enhance within-training kinematic outputs without altering neuromuscular fatigue or perceptions of soreness.

The aim of this study was to investigate the effects of traditional, rest redistribution, and velocity-based repeated sprint training methods on repeated sprint performance, perceived effort, heart rate, and changes in force-velocity-power (FVP) profiles in male semiprofessional athletes. In a randomized crossover design, a traditional (2 sets of 6 repetitions [TRAD]), 2 different rest redistribution (4 sets of 3 repetitions [RR4] and 12 sets of 1 repetition [RR12]), and a 5% velocity loss (VL5%) (12 repetitions, with sets terminated when a 5% reduction in mean velocity had occurred) condition were completed. Mean and peak velocity, mean heart rate, and differential ratings of perceived exertion (dRPE) were measured throughout each session, while horizontal FVP profiles were assessed presession and postsession. The RR4 and RR12 conditions allowed the greatest maintenance of velocity, while the RR4, RR12, and VL5% had a moderate, significantly greater mean heart rate than the traditional condition. Trivial, nonsignificant differences between all conditions were observed in dRPE of the legs and breathlessness and FVP profiles. These findings indicate that rest redistribution can allow for greater maintenance of sprint velocity and heart rate, without altering perceived effort during repeated sprint training. In addition, velocity-loss thresholds may be a feasible method of prescription if athletes have diverse physical qualities and reductions in sprint performance during repeated sprint training are undesirable. Practitioners should consider these outcomes when designing repeated sprint training sessions because the strategic use of these methods can alter sprint performance and internal load without changing perceptions of intensity.

To support the understanding, preparation, and long-term development of youth rugby players, the accurate measurement of their physical qualities is vital. This chapter summarises how anthropometry, body composition, strength, power, speed, agility and change-of-direction, aerobic and anaerobic capacity, and athletic movement skills are measured within youth rugby players and discusses the accuracy and reliability of these methods. Furthermore, the implications of using these different testing methods within research are considered. Due to the large discrepancies in testing outcomes between rugby players of similar ages, this chapter will provide recommendations for accurate and reproducible testing of youth rugby players. Additionally, future research directions are provided that will enhance the understanding of youth rugby player development.

Objectives: Describe the injury and illness incidence, severity and burden during the men's, women's and wheelchair Rugby League World Cup (RLWC). Design: Retrospective cohort epidemiological study. Methods: Injury and illness diagnosis and estimated return-to-play duration following consensus definitions were reported for men's (n = 16 teams), women's (n = 8 teams) and wheelchair (n = 8 teams) players during the 2021 RLWC. Results: Match injury incidence per 1000 player-hours was 91.2 (95 % CI; 74.0 to 111.1, n = 98) injuries for men, 115.4 (95 % CI; 88.1 to 148.5, n = 60) injuries for women, and 80.0 (95 % CI; 45.7 to 129.9, n = 16) injuries for wheelchair, with the mean severity of 17, 10 and 8 days. Training injury incidence per 1000 player-hours was 2.4 (95 % CI; 1.3 to 3.9, n = 15) injuries for men, 1.7 (95 % CI; 0.5 to 4.3, n = 4) injuries for women, and 6.4 (95 % CI; 2.1 to 15.0, n = 5) injuries for wheelchair. Match concussion incidence per 1000 player-hours was 11.2 (95 % CI; 5.8 to 19.5, n = 12) concussions for men, 19.2 (95 % CI; 9.2 to 35.4, n = 10) concussions for women, and 10.0 (95 % CI; 1.2 to 36.1, n = 2) concussions for wheelchair. There were 13, 5 and 3 episodes of illness in men, women and wheelchair. Conclusions: Injury incidence was highest in women, whereas the highest severity was found in men. Concussion incidence was highest in women. The match injury incidence at the RLWC was similar to domestic elite rugby league. Different factors, such as variations in physical characteristics, rule differences, and travel could have contributed to injury and illness observations.

This book is a comprehensive guide to these protocols and to the key issues relating to physiological testing.

Rugby sevens is a demanding sport that requires extensive physical preparation. Travel and logistical challenges in rugby sevens mean that coaches often have limited contact time with players, but must ensure adequate physical, technical and tactical preparation. Tactical periodisation (TP) presents a potential solution by simultaneously developing these aspects of performance, but this concept has not been empirically tested. To investigate the effectiveness of TP, microtechnology devices were used to measure total distance, high-speed distance, maximum velocity, mean acceleration, PlayerLoad and collisions in a group of international sevens rugby players (n=22) during four tournaments and two training camps. Differences in the mean and peak demands of matches and training session types (volume, quality, speed, collision) were determined using linear mixed models and effect sizes (ES) with 95% confidence intervals. Volume and quality training types simulated mean and peak match demands effectively with only PlayerLoad demonstrating a practically important reduction from match exertion (match vs. quality ES = -0.97, 95%CI -1.17 to -0.77). Speed training exceeded the peak high-speed running demands of matches over durations from 1 to 5 minutes (ES range 1.78 to 2.54). These results demonstrate that training guided by tactical periodization principles represents an effective method of preparation during the competition period.

Background:Globally, sports are proactively aiming to reduce concussions and head acceleration events (HAEs) given the potential dose-response association with neurodegenerative diseases. No data exists regarding HAEs from training or commonly performed drills, which is important, as arguably the training environment is more modifiable than matches. Therefore, this study aimed to describe the HAEs during common training drills used in rugby league.Methodology:Fifteen male academy rugby league players from a professional Super League club participated. Players participated in three training sessions, with 7 standardised drills, designed in consultation with experienced coaches, completed in the same order in each session. Players wore a custom-fitted instrumented mouthguard (iMG) and each session was filmed. An iMG capture framework was developed andapplied to synchronise and process the iMG and video data to verify the HAEs occurring in a drill. The probability of a HAE being observed in a drill was estimated using binomial logistic regression and exceedance probabilities using ordinal mixed effectsregression.Results:1402 (93 ± 50 per player) drill observations were recorded, which resulted in approximately 133 observed HAEs (9 ± 8 per player). 130 HAEs were analysed further (wrestle = 48, tackler = 59, ball-carrier = 23). Standing wrestle had the highest overall probability of HAE occurrence of 41.3% (CI = 31.0 –52.3%) than the other drills (range: 0.67 –14.3%). HAE exposure was greater for tacklers than ball-carriers. Increasing the distance of the drill, e.g., tackle shield hit 1m (1.3% [0.5 –3.4]) vs 3m (9.0%[6.2 –12.8]), increased the probability of a HAE being observed. All drills were observed to have an exceedance probability of experiencing an HAE ≥25 g ~0.0% (CI = 0.0 –0.8%), except for standing wrestle 1.0% (CI = 0.2 -4.1%).Conclusion:For the first time the findings from this study offer insights into HAE exposure from various common training drills in rugby league. While the overall chance of high-magnitude HAEs was relatively low, the contextual and constraint-based variability in HAE exposure between drills demonstrates the need for practitioners to consider how manipulating constraints may affect HAE exposure and accumulation.

ABSTRACT

Background Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby. Methods This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts. Tackle events from professional rugby league matches were coded as either head-on-head or non-head-on-head impacts. These included non-televised standard-definition and televised high-definition video clips to train (n=341) and test (n=670) the framework. A computer vision framework consisting of two deep learning networks, an object detection algorithm and three-dimensional Convolutional Neural Networks, was employed and compared with the analyst-coded criterion. Sensitivity, specificity and positive predictive value were reported. Results The overall performance evaluation of the framework to classify head-on-head impacts against manual coding had a sensitivity, specificity and positive predictive value (95% CIs) of 68% (58% to 78%), 84% (78% to 88%) and 0.61 (0.54 to 0.69) in standard-definition clips, and 65% (55% to 75%), 84% (79% to 89%) and 0.61 (0.53 to 0.68) in high-definition clips. Conclusion The study introduces a novel computer vision framework for head-on-head impact detection. Governing bodies may also use the framework in real time, or for retrospective analysis of historical videos, to establish head-on-head rates and evaluate prevention strategies. Future work should explore the application of the framework to other head-contact mechanisms and also the utility in real time to identify potential events for clinical assessment.

Concussions in contact sports are challenging for athletes, health professionals and sporting bodies to prevent, detect and manage. Design of interventions for primary prevention, early recognition of concussion and continuing to improve postconcussion management are essential for protecting athletes and promoting brain health. Over the last decade, there have been advancements in video technology for analysing head impact events and improvements in the clinical management of concussions. This study protocol describes how researchers, clinicians and staff from the Australasian National Rugby League (NRL) have brought these advancements together and developed a database of videos with head impact events and clinical outcomes. The intended outputs from this work will enhance the understanding of head impact events in NRL, from biomechanical and gameplay factors to concussion and return to play outcomes. Publishing this protocol increases the transparency of this large-scale effort to better identify head impacts and their relationship to concussions and player movement behaviour to contextualise these variables to generate new knowledge and support the reproducibility of these emerging findings. Between 2017 and 2023, over 5250 head contact cases were recorded in the database, from which >1700 head injury assessments were performed, and >600 concussions were diagnosed. Future studies using these data are planned to inform both primary and secondary injury prevention initiatives, such as risk analysis and prediction of game scenarios that result in concussion, as well as investigation of features and factors that help to inform the duration of recovery and return to play.

Sports-related concussions (SRCs) typically occur when the brain is hyperthermic. Acute head-and-neck cooling should, therefore, reduce the brain’s metabolic demands, with the potential to improve recovery following an SRC. Elite ice hockey players who underwent head-and-neck cooling after sustaining a concussion (SRC) showed reduced return-to-play times, although further investigation is warranted. This paper aims to describe the methods proposed for investigating the clinical effects and feasibility of acute head-and-neck cooling in elite male rugby union players.

A quasi-experimental study will be conducted in two professional male rugby competitions (clusters): the United Rugby Championship ‘intervention group’ and the PREM Rugby ‘standard care group’. Both groups will follow World Rugby’s standardised, graduated return-to-play concussion management protocols. In addition to this, within 30 min of the SRC, the intervention group will be offered head-and-neck cooling for 45 min. The quantitative phase of the study will collect return-to-play times and clinical outcomes in both groups (sample size calculated: 100 concussions per cohort). The qualitative phase will explore the experiences of players and medical teams with the intervention. Intention-to-treat and per-protocol analyses, using appropriate regression modelling techniques, will adjust for possible confounders between the two groups, and thematic content analysis will be employed in the analysis of the respective phases.

The Clinical Observed Outcomes with Local HEad-neck cooling After Diagnosed concussions study will provide evidence regarding acute head-and-neck cooling as a potential adjunct treatment to current concussion management in elite male rugby union.

Different talent development (TDE) environments exhibit varying training practices in the rugby league talent identity and development systems (TIDS), which may influence rates of talent development and subsequent productivity of each TDE. This study aimed to compare physical qualities and rates of physical development between different rugby league TDEs within the same TIDS, alongside differences between groups of TDEs based on their level of productivity. A sample of 261 youth rugby league players from six academy teams (i.e., TDEs) within the professional TIDS were tested as part of a league-wide fitness testing battery for measures of anthropometrics, strength, power, speed, and cardiovascular fitness. Linear mixed models revealed medium, significant differences in maximum sprint velocity at the beginning of the season (η 2  = 0.05, p = 0.03) and large, significant differences in the development of prone Yo-Yo IR1 distance over time (η 2  = 0.14–0.18, p < 0.001) between TDEs. No significant differences between groups of TDEs based on their productivity were found. These findings indicate that possible variability in the practices of TDEs mostly leads to small or trivial differences in physical qualities and physical development. Differences in physical qualities and physical development do not appear to relate to the productivity of TDEs, therefore TDEs should focus on holistic development to maximise productivity.

This study aimed to quantify the frequency of individual and team contact events during rugby union match play in top domestic and international men’s and women’s competitions. Analyst‐coded player individual and team contact event types (tackles, carries, attacking rucks and defensive rucks, lineouts, scrums and mauls) from the 2022/2023 rugby union season were analysed from top domestic and international competitions across the world using generalised linear mixed models. For both women’s and men’s rugby, competitions generally had similar numbers of contact events per playing position. Where differences were observed, most ranged between 0.5 and six per contact event per full game equivalent (FGE). Similar trends were observed when comparing women’s to men’s rugby. However, within‐game accumulation of these different contact events for certain positional groups may have a significant impact (e.g., a front five player called up from a Farah Palmer Cup team to play in WXV1 could be involved in as much as 6 more attacking rucks, 3 more tackles and 5 more mauls per game on average). Furthermore, the small differences between competitions per FGE may accrue across matches and thus result in far greater exposures across a season (e.g., a front five player in Premiership Rugby may make 48 more tackles over 20 matches than in Top 14 on average). Although a high proportion of contact events per FGE were similar between competitions and sexes per playing position, differences that were observed may have important implications for players transitioning between competitions and the long‐term exposure of players to higher‐risk contact events.

Background: There is growing concern that exposure to head acceleration events may be associated with potential long-term health consequences. Rugby league is a contact sport involving a high number of collisions, and therefore has a high risk of head accelerations. It is therefore important to quantify head acceleration exposure in rugby league. Instrumented mouthguards (iMGs) are a validated means for quantifying head acceleration events (HAEs) and have been implemented within men’s rugby league, however HAEs are yet to be quantified within women’s rugby league. Accordingly, this study implemented iMGs across teams participating in the Women’s Super League competition, with the aim of describing HAEs during matches. Methodology: Seven elite women’s rugby league teams were provided with iMGs, resulting in the collection of iMG data from 84 players, across 116 player matches. In-vivo HAEs were approximated using linear and angular kinematics measured by accelerometers and gyroscopes embedded within iMGs. Peak linear acceleration (PLA; g) and peak angular acceleration (PAA; rad/s2) were calculated to approximate the magnitude of each HAE. Validated machine learning classification algorithms were used to remove false positive events from the dataset. Results: Across 116 player matches, 1389 HAEs were recorded. The median (IQR) number of HAEs per player match was 7 (3 to 18) HAEs per player match. The median (IQR) HAE magnitude was 12 (8.6 to 18.1) g and 982 (657 to 1,723) rad/s2, for PLA and PAA, respectively. Towards the higher end of magnitudes, the 95th percentile magnitude was 36.8 g and 3,740 rad/s2. Conclusion: For the first time, HAEs have been quantified in women’s rugby league matches. Overall, the number of head accelerations per player match is lower than previously reported in men’s rugby league, while the distribution of HAE magnitudes also seems lower.

Background: Head acceleration events (HAEs) are a source of concern across sport due to potential negative long-term brain health in athletes exposed to them. Tackle height is highlighted as a possible factor for risk mitigation in rugby codes. This study aimed to identify the probability of the ball-carrier and tackler receiving a HAE for a given tackle height and estimate the potential impact of changes in tackle height. Methodology: A prospective observational cohort study was conducted during the men’s elite rugby league Super League 2023 season (12 teams, 94 players, 702 player matches). HAEs recorded from instrumented mouthguards were linked to ball-carries and tackles confirmed via video. Events were then labelled by tackle height (i.e., contact on ball-carrier; head/neck, shoulder, upper torso, abdomen, shorts, upper leg and lower leg). Only initial collision HAEs were analysed. Ordinal mixed-effects regression models provided exceedance probabilities for peak linear acceleration (recorded, >10g, >25g, >40g, >55g and >70g) and peak angular acceleration (recorded, >1000rads/s2, >2000 rads/s2, >3000 rads/s2, >4000 rads/s2, and >5000 rads/s2). Differences in initial HAEs were simulated across a range of tackle height distributions using the probabilities and the total number of tackles across the season. Results: The probability of a ball-carrier and tackler recording an initial HAE were 13.4% and 24.2%. The greatest exceedance probabilities for the ball-carrier were initial impact to the head/neck: 35.5% recorded, 4.0% >25g, 13.6% >2000 rads/s2. For other impact locations, ball-carrier HAE probability was 20% at all tackle heights except impact to the ball-carriers head/neck (12.2%). The highest probability for the tackler was contact with the shorts (recorded; 30.9%, >25g; 3.0%, >2000 rads/s2; 11.7%). When 40% of tackles were redistributed from the shoulder to lower parts of the body evenly, the estimated number of HAEs reduced from 40,292 to 35,358. Conclusion: The probability of receiving a HAE for the tackler and ball-carrier differs by overall probability and tackle height. Consequently, simulating the redistribution of tackles below the line of the shoulder suggests there could be a lower number of initial HAE observed across a season.

Background:In collision sports, like rugby union, there is a growing interest in the long-term effects of head acceleration events (HAEs) on brain health. Current methods for understanding HAE exposure have focused on using “inferential variability” as opposed to “outcome variability”. This study aims to use simulation to evaluate outcome variability and provide expected HAE reference ranges in men’s and women’s rugby union across a micro-(weekly), meso-(monthly) and macro-(annual) cycle.Methodology:A prospective observational study was conducted in rugby union players from two professional men’s and two semi-professional women’s competitions. A total of 982 players were included across 132 training weeks and 365 matches. Generalised linear mixed models were used to estimate the count of HAEs, HAEs >25g and >2,000 rads/s2 across training contact types and match-play. Simulations of model estimates, accounting for player and weekly variation, were used to provide reference ranges of expected HAE counts, using current world rugby contact guidelines. Meso-cycles were simulated for players in three categories; high (30 matches), moderate (20 matches) and low (10 matches) match exposure.Results:For both sexes within a micro-and meso-cycle, the reference ranges between positions overlap despite differences in the median expected HAE exposures (e.g., >25g HAEs: male forwards 4 [0-10] vs. male backs 2 [0-8]). Where differences are present, forwards have greater expected HAE counts and variation (indicated by a wider distribution). Meso-cycles simulations identified a clear differentiation in distributions of expected HAEs between all match exposure levels. Generally, more matches playedresulted in higher reference ranges of HAEs, but some low match exposure simulations had a higher HAE count than some high match exposure simulations.Conclusion:The results show wide variability in “normal” weekly, monthly and annual HAE exposures. These reference ranges can be used by practitioners to identify individual players that are exposed to a large number of HAEs and serve as a baseline for future policy change regarding match and training exposure limits.

Purpose: Sleep is recognized as an important recovery strategy, yet little is known regarding its impact on postmatch fatigue. The aims of this study were to (1) describe sleep and postmatch fatigue, (2) understand how sleep is affected by contextual and match factors, and (3) assess how changes in sleep can affect postmatch fatigue. Methods: Twenty-three male rugby union players were monitored across 1 season (N = 71 player–match observations). Actigraphy was used during preseason to establish baseline sleep quality and quantity. Sleep was then measured 1 and 2 days after each match day (MD + 1 and MD + 2). Global positioning systems, notational analysis, and rating of perceived exertion represented external and internal load from matches. Subjective wellness and a standardized run were used to characterize postmatch fatigue 2 days prior (baseline) and at MD + 1 and MD + 2. Linear mixed models established the magnitude of change (effect size [ES]) between baseline, MD + 1, and MD + 2 for sleep and postmatch fatigue. Stepwise forward selection analysis ascertained the effect of match load on sleep and the effect of sleep on postmatch fatigue. Each analysis was combined with magnitude-based decisions. Results: Sleep characteristics and neuromuscular and perceptual postmatch fatigue were negatively affected at MD + 1 and MD + 2 (ES = small to very large). Kickoff and travel time had the greatest effect on sleep (ES = small). Wellness and soreness were influenced by sleep (fall-asleep time and fragmentation index) and collisions, respectively (ES = small). Conclusion: Sleep quality and quantity were affected independently of the match load (ie, running activity) sustained, and changes in sleep marginally affected postmatch fatigue.

Background Rugby union match demands are complex, requiring the development of multiple physical qualities concurrently. Quantifying the physical qualities of age grade rugby union players is vital for practitioners to support athlete preparation and long-term development. Aim This systematic review aimed to identify the methods used to quantify the physical qualities of male age grade (≤ Under-20) rugby union players, present the normative values for physical qualities, and compare physical qualities between age grades and positions. Methods Electronic databases were systematically reviewed from the earliest record to November 2019 using key words relating to sex, age, sport and physical testing. Results Forty-two studies evaluated the physical qualities of age grade rugby union players. Seventy-five tests were used to quantify body composition, muscular strength, muscular power, linear speed, change of direction ability, aerobic capacity and anaerobic endurance. Thirty-one studies met the eligibility criteria to present the physical qualities. Physical qualities differentiate between age groups below Under-16, while differences in older age groups (Under-16 to Under-20) are not clear. Positional differences are present with forwards possessing greater height, body mass, body fat percentage and strength while backs are faster and have greater aerobic capacities. Conclusions A wide variety of tests are used to assess physical qualities limiting between study comparisons. Although differences in older age grades are unclear, older age groups (Under-19-20) generally performed better in physical tests. Positional differences are associated with match demands where forwards are exposed to less running but a greater number of collisions. Practitioners can use the results from this review to evaluate the physical qualities of age grade rugby union players to enhance training prescription, goal setting and player development. Future research should consider the use of national standardised testing batteries due to the inconsistency in testing methods and small samples limiting the reporting of positional differences.

The profiling (including measurement, analysis and evaluation) of the physical qualities of youth rugby players is vital for practitioners to support athlete preparation and long-term development. This chapter summarises the relevant research detailing how physical qualities are measured within youth rugby players. This includes a summary of methods that assess anthropometry and body composition, strength, power, speed, agility and change of direction, aerobic and anaerobic capacity, and movement skills. The second part of the chapter presents the practical application of a national standardised fitness assessment for 14–19-year-old rugby league players. This section highlights how the national standardised fitness assessment was (1) designed, (2) conducted, and (3) analysed, reported and evaluated using the ProPQ (Profiling Physical Qualities) Tool. The ProPQ Tool provides an interactive data analysis tool that can be used by academy managers, coaches and sport science staff for enhancing the physical development of rugby players. The chapter concludes with a range of recommendations and implications for practice for profiling the physical qualities of youth rugby players.

Objectives: Identify tackle characteristics associated with concussions in male professional rugby league. Design: Case-control study. Methods: Tackles resulting in 196 clinically diagnosed concussions and 6592 non-concussive tackles were analysed, from the men's rugby league Super League between 2018 and 2022. Eleven tackle characteristics were coded for each tackle, and Firth penalised logistic regression models were employed to identify influential variables through forward stepwise selection. Three multivariate models were produced; all (i.e., ball-carrier and tackler), tackler, and ball-carrier concussions. Results: Of the 196 concussions, 70 % occurred to the tackler and 30 % to the ball-carrier. Initial impact location on the ball-carrier was identified as a predictor in all models, specifically the shorts, upper- and lower-leg (OR 9.1–12.3, compared to shoulder) for tacklers and head/neck (OR 66.1, compared to shoulder) for ball-carriers. Tackler head placement in front of the ball-carrier (OR 8.5, compared to away from the body) and a ball-carrier leading arm in any position (OR 4.8–22.1, compared to no leading arm) provided the greatest odds of a tackler concussion. For player's body position the greatest risk of concussion for all players was observed when both players were falling/diving (OR 8.8, compared to both players upright). One (OR 4.9, compared to two) and four (OR 3.7, compared to two) defender tackles provide the greatest odds for all concussions. Conclusions: Concussion prevention strategies should aim to reduce head impacts by deterring initial contact with the ball-carrier's head/neck. Tackle technique should prioritise making initial impact with the torso and avoid the head being in front of the ball-carrier and any leading arms.

The study aimed to illustrate how contact (from match‐event data) and head acceleration event (HAE) (from instrumented mouthguard [iMG]) data can be combined to inform match limits within rugby. Match‐event data from one rugby union and rugby league season, including all competitive matches involving players from the English Premiership and Super League, were used. Playing exposure was summarised as full game equivalents (FGE; total minutes played/80). Expected contact and HAE exposures at arbitrary thresholds were estimated using match‐event and iMG data. Generalised linear models were used to identify differences in contact and HAE exposure per FGE. For 30 FGEs, forwards had greater contact than backs in rugby union (n = 1272 vs. 618) and league (n = 1569 vs. 706). As HAE magnitude increased, the differences between positional groups decreased (e.g., rugby union; n = 34 and 22 HAE >40 g for forwards and backs playing 30 FGEs). Currently, only a relatively small proportion of rugby union (2.5%) and league (7.3%) players exceeded 25 FGEs. Estimating contact and HAEs per FGE allows policymakers to prospectively plan and model estimated overall and position‐specific loads over a season and longer term. Reducing FGE limits by a small amount would currently only affect contact and HAE exposure for a small proportion of players who complete the most minutes. This may be beneficial for this cohort but is not an effective HAE and contact exposure reduction strategy at a population level, which requires individual player management. Given the positional differences, FGE limits should exist to manage appropriate HAE and contact exposure.

Background The evaluation of physical qualities in talent identification and development systems is vital and commonplace in supporting youth athletes towards elite sport. However, the complex and dynamic development of physical qualities in addition to temporal challenges associated with the research design, such as unstructured data collection and missing data, requires appropriate statistical methods to be applied in research to optimise the understanding and knowledge of long-term physical development. Aim To collate and evaluate the application of methodological and statistical methods used in studies investigating the development of physical qualities within youth athletes. Methods Electronic databases were systematically searched form the earliest record to June 2021 and reference lists were hand searched in accordance with the PRISMA guidelines. Studies were included if they tested physical qualities over a minimum of 3 timepoints, were observational in nature and used youth sporting populations. Results Forty articles met the inclusion criteria. The statistical analysis methods applied were qualitatively assessed against the theoretical underpinnings (i.e. multidimensional development, non-linear change and between and within athlete change) and temporal challenges (i.e. time variant and invariant variables, missing data, treatment of time and repeated measures) encountered with longitudinal physical testing research. Multilevel models were implemented most frequently (50%) and the most appropriately used statistical analysis method when qualitatively compared against the longitudinal challenges. Independent groups ANOVA, MANOVA and X were also used, yet failed to address any of the challenges posed within longitudinal physical testing research. Conclusions This methodological review identified the statistical methods currently employed within longitudinal physical testing research and addressed the theoretical and temporal challenges faced in longitudinal physical testing research with varying success. The findings can be used to support the selection of statistical methods when evaluating the development of youth athletes through the consideration of the challenges presented.

Background This study simulated the effect of reducing contact training duration on overall in-season head acceleration event (HAE) exposure within men’s and women’s rugby union. Methods Players ( n  = 982) from two professional men’s and two semi-professional women’s competitions wore instrumented mouthguards in training and match-play for one season. Generalised linear mixed models were used to estimate the in-season weekly HAE exposures per position, sex and contact type. Simulation of modelled estimates evaluated the impact of reducing contact load guidelines by 25%, 50% and 75% (scenario 1), and replacing full contact training with controlled contact (scenario 2) or non-contact (scenario 3) training for different seasonal match exposures. Previously established contact load guidelines were used as a reference point. Results HAEs were decreased by a maximum of 3.2 per week (0–95 HAEs per season; 0–23%). In scenario 1, the decrease in HAEs was disproportionately smaller than the reduction in contact training duration (e.g. 23.7% reduction in overall rugby minutes for 7% decrease in HAEs). Scenario 2 decreased HAEs similarly to scenario 1 but with no reduction in contact time. Scenario 3 decreased HAEs proportionally with contact time reductions (e.g. 8.9% decrease in HAEs >10 g for 9.6% reduction in overall rugby minutes). Conclusions HAEs were reduced in all scenarios, but the reduction was relatively small due to the low overall rate of HAEs in training. Policymakers should be aware of the tradeoffs involved in any change. Managing individuals with higher HAE exposures may be more appropriate than reducing contact training guidelines.

This study quantified and compared the movement characteristics of elite domestic and international netball match-play, including fifteen individual players who compete at both levels. Microtechnology data were collected across 75 matches in a league-wide study from players (n = 113) competing in the Netball Superleague (elite domestic) and from international players (n = 23) in 22 international matches. Players were categorised according to the seven playing positions. Accelerometer-derived variables were analysed per whole-match and per quarter, for both absolute (i.e., volume) and relative to duration (i.e., intensity [per minute]) values. The median playing duration ranged across positions from 23.6 to 42.4 minutes at international and 31.6 to 48.1 minutes at domestic level. International matches were greater than elite domestic competition for relative variables across all positions. Moderate to large effect sizes (1.00–1.50) were found between playing levels for PlayerLoadTM per minute (AU·min-1). Significant decreases in both absolute and relative variables were observed across quarters for both competition levels. The movement characteristics are position dependent, with greater absolute characteristics at domestic level across whole-match analysis, but greater relative characteristics at international level. These findings provide practitioners with information to guide training prescription, return-to-play protocols, and transitioning athletes between levels of competition.

Objectives Identify the frequency, propensity, and factors related to tackle events which result in contact with the head in elite-level women's rugby league. Design Prospective video analysis study. Methods Video footage from 59 Women's Super League matches were analysed (n = 14,378 tackle events). All tackle events were coded as no head contact or head contact. Other independent variables included: area contacting head, impacted player, concussion outcome, penalty outcome, round of competition, time in match and team standard. Results There were 83.0 ± 20.0 (propensity 304.0/1000 tackle events) head contacts per match. The propensity of head contact was significantly greater for the tackler than ball-carrier (178.5 vs. 125.7/1000 tackle events; incident rate ratio 1.42, 95 % confidence interval 1.34 to 1.50). Head contacts occurring from an arm, shoulder, and head occurred significantly more than any other contact type. The propensity of concussions was 2.7/1000 head contacts. There was no significant influence of team standard or time in match on the propensity of head contacts. Conclusions The observed head contacts can inform interventions, primarily focusing on the tackler not contacting the ball-carrier's head. The tackler's head should also be appropriately positioned to avoid contact with the ball-carrier's knee (highest propensity for concussion). The findings are consistent with other research in men's rugby. Law modifications and/or enforcement (reducing the number of un-penalised head contacts), concurrent with coaching interventions (optimising head placement or reducing the head being contacted) may help minimise head contact risk factors for women's rugby league.

Background Head acceleration events (HAEs) are an increasing concern in collision sports owing to potential negative health outcomes. Objectives The objective of this study is to describe the probabilities of HAEs in tackles of differing heights and body positions in elite men’s and women’s rugby union. Methods Instrumented mouthguards (iMGs) were worn in men’s (n = 24 teams, 508 players, 782 observations) and women’s (n = 26 teams, 350 players, 1080 observations) rugby union matches. Tackle height (i.e. point of contact on ball-carrier) and body positions of tacklers and ball-carriers were labelled for all tackles in which a player wore an iMG. HAEs from the initial impact were identified. Mean player, tackler and ball-carrier exceedance probabilities for various peak linear and angular acceleration thresholds were estimated from ordinal mixed-effects models. Results Contact with ball-carriers’ head/neck resulted in the highest mean HAE probabilities for both sexes. The probability of an HAE to the ball-carrier decreased as tackle height lowered. The highest probability for the tackler was initial contact to the ball-carriers upper leg. Body position influenced the probability of HAEs, with falling/diving ball-carriers resulting in higher mean probabilities. When a player, regardless of role, was bent-at-waist, elevated HAE probabilities were observed in men’s competitions. Women’s data demonstrated similar probabilities of an HAE for all body positions. Conclusions Initial contact to the ball-carrier’s head/neck had the highest chance of an HAE, whilst role-specific differences are apparent for different tackle heights and body positions. Future player-welfare strategies targeting contact events should therefore consider HAE mechanisms along with current literature.

This study aimed to quantify and compare mean head acceleration event (HAE) incidence within and between men's and women's rugby union competitions; quantify the incidence of HAEs during all contact‐events and describe individual player incidence. Players competing during the 2022/2023 season in women's (337 players; Premiership Women's Rugby, Farah Palmer Cup) and men's (371 players; Premiership Rugby, Currie Cup and Super Rugby) competitions wore instrumented mouthguards (iMGs). Mean HAE incidences using peak linear (PLA) and peak angular acceleration (PAA) were quantified by sex, positional groups and individual players per competition and for contact‐events across a range of magnitude thresholds. Within positional groups, there was high between‐player variability, with some players experiencing up to a 3‐fold greater mean HAE incidence than their positional average. Per full‐game equivalent (FGE), men had significantly higher HAE incidences in most positional groups and HAE magnitude thresholds compared to women ranging from approximately 0.11–3.44 HAEs per FGE. Incidence of HAEs (PLA > 25 g) per FGE was lowest in scrums (0.00–0.04/FGE) and highest for tackles and ball carries (0.21–1.97/FGE) in both women and men, whereas mauling was a frequent source of HAEs for men's back row (0.95/FGE). No significant differences were observed between competitions for most positional groups and HAE magnitude thresholds in both men and women. Per FGE, HAE incidences were similar within, but significant differences were apparent between men's and women's players. The scrum had the lowest HAE incidence of all contact‐events. Individual players can show large variation from the mean, emphasising the importance of HAE mitigation strategies that include individual player monitoring and management processes.

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing standard (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and standards. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

Objectives Assess the validity and feasibility of current instrumented mouthguards (iMGs) and associated systems. Methods Phase I; four iMG systems (Biocore-Football Research Inc (FRI), HitIQ, ORB, Prevent) were compared against dummy headform laboratory criterion standards (25, 50, 75, 100 g). Phase II; four iMG systems were evaluated for on-field validity of iMG-triggered events against video-verification to determine true-positives, false-positives and false-negatives (20±9 player matches per iMG). Phase III; four iMG systems were evaluated by 18 rugby players, for perceptions of fit, comfort and function. Phase IV; three iMG systems (Biocore-FRI, HitIQ, Prevent) were evaluated for practical feasibility (System Usability Scale (SUS)) by four practitioners. Results Phase I; total concordance correlation coefficients were 0.986, 0.965, 0.525 and 0.984 for Biocore-FRI, HitIQ, ORB and Prevent. Phase II; different on-field kinematics were observed between iMGs. Positive predictive values were 0.98, 0.90, 0.53 and 0.94 for Biocore-FRI, HitIQ, ORB and Prevent. Sensitivity values were 0.51, 0.40, 0.71 and 0.75 for Biocore-FRI, HitIQ, ORB and Prevent. Phase III; player perceptions of fit, comfort and function were 77%, 6/10, 55% for Biocore-FRI, 88%, 8/10, 61% for HitIQ, 65%, 5/10, 43% for ORB and 85%, 8/10, 67% for Prevent. Phase IV; SUS (preparation-management) was 51.3–50.6/100, 71.3–78.8/100 and 83.8–80.0/100 for Biocore-FRI, HitIQ and Prevent. Conclusion This study shows differences between current iMG systems exist. Sporting organisations can use these findings when evaluating which iMG system is most appropriate to monitor head acceleration events in athletes, supporting player welfare initiatives related to concussion and head acceleration exposure.

The quantification and development of physical qualities of youth rugby players is vital to support athlete preparation and long-term development. This chapter summarises and presents the research quantifying the physical qualities and their development of male youth rugby union and rugby league players and compares between age grades and playing positions, whilst considering the effect on career attainment and coaches’ perspectives. A range of research presents the physical qualities of youth rugby players, including stature, body mass, body fat percentage, muscular strength, muscular power, linear speed, change of direction, and aerobic capacity. Differences are apparent by age grade and position. However, the research has several limitations, including the presentation of small sample sizes and lack of consistency in the measures used. Future research should consider the use of national standardised testing batteries due to the inconsistency in testing methods and small samples limiting the reporting of positional differences. Practitioners can use the results from this review to evaluate the physical qualities of youth rugby players to enhance training prescription and goal setting.

Understanding the locomotor characteristics of competition can help rugby league (RL) coaches optimise training prescription. To date, no research exists on the locomotor characteristics of women’s RL. The aim was to compare whole match and peak locomotor characteristics of women’s RL competition at international (RL World Cup [WRLWC]) and domestic level (Super League [WSL]). Microtechnology data were collected from 58 players from 3-WSL clubs and 1-WRLWC team. Participants were classified into forwards (n = 30) and backs (n = 28). Partial least squares correlation analysis established which variables were important to discriminate between the level of competition (international vs. domestic) and positional group (forwards vs. backs). Linear mixed-effects models estimated the differences between standards of competition and positional group for those variables. International forwards were most likely exposed to greater peak 1-min average acceleration (standardised mean difference = 1.23 [0.42 to 2.04]) and peak 3-min average acceleration (1.13 [0.41 to 1.85]) than domestic forwards. International backs likely completed greater peak 1-min average acceleration (0.83 [0.08 to 1.58]) than domestic backs and possibly greater high-speed-distances (0.45 [−0.17 to 1.07]). Findings highlight the need for positional specific training across levels to prepare representative players for the increased match characteristics of international competition.

Recognising and removing players with suspected sport-related concussions is crucial for community sports. Objectives Quantify rates and factors associated with non-reporting of concussion symptoms in community rugby league. Methods Overall, 484 community rugby league players aged ≥18 years and 965 parents of rugby league players aged <18 years completed an online survey, regarding concussion history, knowledge, prevalence and reasons for non-reporting of concussion, long-term implications and perceptions of concussion. Results Thirty-five percent of players aged ≥18 years and 22% of parents of players aged <18 years reported at least one concussion in the last two seasons. Forty-three percent of players aged ≥18 years and 5% of parents of players aged<18 years surveyed stated they did not report concussion-related symptoms sustained during 2020 and 2021 seasons. The two most common reasons for non-reporting of concussion symptoms were‘didn’t want to be ruled out of a match’and‘didn’t want to let down the team’. Players aged ≥18 years who received external coaching pressures around concussion were more likely to not report concussion symptoms. Over 40% of parents and players were concerned about the potential long-term implications. Ten percent of players aged ≥18 years and 7% of parents of players aged <18 years would encourage their family members/children to not play rugby league. Conclusions Non-reporting rates of suspected concussion symptoms in adult community players were twice as high as in professional rugby league, with similar reasons (wanting to play and not letting the team down). Engaging coaches to prioritise brain health and providing broader and appropriate education on concussion should be focused on, given the concerns reported by community players and parents.

The measurement, analysis, and reporting of physical qualities within sport is vital for practitioners to support athlete development. However, several challenges exist to support this process (e.g., establishing comparative data, managing large data sets) within sport. This article presents 7 challenges associated with physical testing in sport and offers solutions to overcome them. These solutions are supported by a description of the Profiling Physical Qualities (ProPQ) tool. The ProPQ tool uses advanced data analysis, visualization, and interactive elements, to enhance stakeholders' use of data to optimize player development and coaching practices. The ProPQ is currently used across rugby league in England.

Objectives: To quantify the incidence of concussion and compare between playing level in male rugby league. Design: Retrospective cohort Methods: Between 2016 and 2022, medically diagnosed concussions in Super League, Championship, and Academy competitions were reported to the Rugby Football League via club medical staff. Anonymised data were analysed using generalized linear mixed-effects models by season, month, and between competitions. Results: Overall, 1,403 concussions were identified from 104,209 player-match hours. Concussion incidence for Super League, Championship, and Academy was 15.5, 10.5, and 14.3 per 1,000 player-match hours, respectively. Championship concussion incidence was significantly lower than the Super League (p<0.001) and Academy (p<0.001). No significant differences were identified between year for Super League (range: 13.3 to 18.8 per 1,000 player-match hours) and Championship (range: 8.4 to 12.1 per 1,000 player-match hours). In Academy (range: 9.6 to 20.5 per 1,000 player-match hours), concussion incidence was significantly greater in 2021 compared to earlier years (2016, p=0.01 and 2017, p=0.03). No significant differences were identified between months for any competition. Conclusions: The incidence of concussion is greater in Super League and Academy compared to the Championship. Academy concussion incidence has increased over time. Different factors between and within competitions, such as changes to medical standards and knowledge, could have influenced the identification and diagnosis of concussion.

ABSTRACT

The structure of a talent identification and development system (TIDS), in terms of its starting, entry, and exit points is an important consideration for sporting organisations. Early talent identification decisions can be ineffective due to unpredictable and individually variable talent development. Physical qualities are a key contributor to performance in rugby league. Therefore, understanding physical development differences between age groups can inform the structure of the rugby league TIDS by highlighting key phases of development. Between‐player variability in physical development must also be considered to understand the generalisability of age‐group trends. Consequently, this study aimed to compare rates of physical development between annual age groups (i.e., U15, 16, 17, 18) in 261 youth rugby league players from multiple clubs, considering individual differences in development rates. Latent growth curve analysis was used to model rates of physical development for size (i.e., height, mass), strength, power, speed, and cardiovascular fitness in each age group. Results showed that U15s had significantly faster rates of development for body size and strength qualities compared with all older age groups, with large between‐player variability. No differences were apparent between age groups for power, speed, or cardiovascular fitness. These findings suggest that early talent identification and (de)selection decisions may ignore the potential development of body size and strength qualities, which occurs at individually variable rates. Such findings can inform the structure and design of the rugby league TIDS by highlighting expected rates of physical development based on players' age groups.

Instrumented mouthguards (iMGs) are a novel technology being used within rugby to quantify head acceleration events. Understanding practitioners' perceptions of the barriers and facilitators to their use is important to support implementation and adoption. This study assessed men's and women's rugby union and league iMG managers' perceptions of staff and player interest in the technology, data and barriers to use. Forty‐six iMG managers (men's rugby union and league n = 20 and n = 9 and women's rugby union and league n = 7 and n = 10) completed an 18‐question survey. Perceived interest in data varied across staff roles with medical staff being reported as having the most interest. The iMG devices were perceived as easy to use but uncomfortable. Several uses of data were identified, including medical applications, player monitoring and player welfare. The comfort, size and fit of the iMG were reported as the major barriers to player use. Time constraints and a lack of understanding of data were barriers to engagement with the data. Continued education on how iMG data can be used is required to increase player and staff buy‐in, alongside improving comfort of the devices. Studies undertaken with iMGs investigating player performance and welfare outcomes will make data more useful and increase engagement.

This study aimed to quantify contact-events and associated head acceleration event (HAE) probabilities in semi-elite women's rugby union. Instrumented mouthguards (iMGs) were worn by players competing in the 2023 Farah Palmer Cup season (13 teams, 217 players) during 441 player-matches. Maximum peak linear acceleration (PLA) and peak angular acceleration (PAA) per-event were used as estimates of in vivo HAE (HAEmax), linked to video analysis-derived contact-events and analysed using mixed-effects regression. Back-rows had the highest number of contact-events per full-match (44.1 [41.2 to 47.1]). No differences were apparent between front-five and centres, or between half-backs and outside-backs. The probability of higher HAEmax occurring was greatest in ball-carries, followed by tackles, defensive rucks and attacking rucks. Probability profiles were similar between positions but the difference in contact-events for each position influenced HAEmax exposure. Overall, most HAEmax were relatively low. For example, the probability of a back-row experiencing a PLA HAEmax ≥25g was 0.045 (0.037-0.054) for ball carries (1 in every 22 carries), translating to 1 in every 2.3 full games. This study presents the first in-depth analysis of contact-events and associated HAEmax in semi-elite women's rugby union. The HAEmax profiles during contact-events can help inform both policy and research into injury mitigation strategies.

Objective To (1) systematically review the literature to identify which match-related risk factors and mechanisms of rugby tackle events result in musculoskeletal injury; concussion; head injury assessments; and head impacts or head accelerations; and (2) identify the perceived importance and feasibility of potential intervention strategies for tackle-related injury reduction in the rugby codes. Design A systematic search was performed using PRISMA guidelines. Risk factors/mechanisms associated with tackle injuries across the rugby codes were extracted. After extraction, 50 international rugby experts participated in a Delphi poll. Via content analysis, expert-recommended risk factors/mechanisms were developed. In round two, experts rated all risk factors and mechanisms for importance to injury risk. In round three, the feasibility of law changes, coach and player education and training as interventions to reduce injury risk for each injury risk factor/mechanism deemed important during round two were rated. Data sources PubMed [MEDLINE], Scopus, SPORTDiscus [EBSCOhost] and CINAHL. Eligibility criteria Eligible studies included cohort, observational and cross-sectional designs, that included male or female rugby union, league, or sevens players. Results Thirty-seven eligible studies were identified, with 138 injury risk factors/mechanisms extracted. 70% of the studies were rated ‘high quality’, with 30% moderate quality. Thirty-eight new risk factors/mechanisms were recommended by the expert group, eight being identified as important and highly feasible for modification by an intervention strategy. ‘The tackler placing their head on the incorrect side of the ball carrier’ was described as the most important mechanism, with ‘training’ and ‘coach/player education’ thought to be highly feasible interventions. Conclusion Numerous risk factors or mechanisms associated with tackle-related injury appear important and modifiable, helping to guide interventions to reduce injury risk in the rugby tackle.

Different talent development (TDE) environments exhibit varying training practices in the rugby league talent identity and development systems (TIDS), which may influence rates of talent development and subsequent productivity of each TDE. This study aimed to compare physical qualities and rates of physical development between different rugby league TDEs within the same TIDS, alongside differences between groups of TDEs based on their level of productivity. A sample of 261 youth rugby league players from six academy teams (i.e., TDEs) within the professional TIDS were tested as part of a league-wide fitness testing battery for measures of anthropometrics, strength, power, speed, and cardiovascular fitness. Linear mixed models revealed medium, significant differences in maximum sprint velocity at the beginning of the season (η 2  = 0.05, p = 0.03) and large, significant differences in the development of prone Yo-Yo IR1 distance over time (η 2  = 0.14–0.18, p < 0.001) between TDEs. No significant differences between groups of TDEs based on their productivity were found. These findings indicate that possible variability in the practices of TDEs mostly leads to small or trivial differences in physical qualities and physical development. Differences in physical qualities and physical development do not appear to relate to the productivity of TDEs, therefore TDEs should focus on holistic development to maximise productivity.

The original article has been updated to add the missing Electronic Supplemental Material.

Purpose Head acceleration events (HAEs) are a growing concern in contact sports, prompting two rugby governing bodies to mandate instrumented mouthguards (iMGs). This has resulted in an influx of data imposing financial and time constraints. This study presents two computational methods that leverage a dataset of video-coded match events: cross-correlation synchronisation aligns iMG data to a video recording, by providing playback timestamps for each HAE, enabling analysts to locate them in video footage; and post-synchronisation event matching identifies the coded match event (e.g. tackles and ball carries) from a video analysis dataset for each HAE, this process is important for calculating the probability of match events resulting in HAEs. Given the professional context of iMGs in rugby, utilising commercial sources of coded match event datasets may expedite iMG analysis. Methods Accuracy and validity of the methods were assessed via video verification during 60 rugby matches. The accuracy of cross-correlation synchronisation was determined by calculating synchronisation error, whilst the validity of post-synchronisation event matching was evaluated using diagnostic accuracy measures (e.g. positive predictive value [PPV] and sensitivity). Results Cross-correlation synchronisation yielded mean synchronisation errors of 0.61–0.71 s, with all matches synchronised within 3 s’ error. Post-synchronisation event matching achieved PPVs of 0.90–0.95 and sensitivity of 0.99–1.00 for identifying correct match events for SAEs. Conclusion Both methods achieved high accuracy and validity with the data sources used in this study. Implementation depends on the availability of a dataset of video-coded match events; however, integrating commercially available video-coded datasets offers the potential to expedite iMG analysis, improve feedback timeliness, and augment research analysis.

Rugby league has a relatively high injury risk, with the tackle having the greatest injury propensity. The number of tackles players engage in, prior to injurious tackles may influence injury risk, which has yet to be investigated. Therefore, this study investigated if rugby league players are involved in more tackles (as either tackler or ball carrier) (i) in the 10 minutes, or (ii) 1-min periods prior to an injurious tackle-event, (iii) differences for ball carriers vs. tacklers, and (iv) forwards vs. backs. Video analysis was utilised to quantify the number and rate of tackles in the 10-min periods prior to 61 tackle-related injuries. One thousand two hundred and eighty 10-min periods where players were not injured, were used as matched-controls. Generalized mixed linear models were used to analyse mean total and rate for tackles. Injured players were involved in significantly fewer tackles during the 10-min period, yet significantly more tackles during the final minute prior to the injurious tackle-event, compared to non-injured players. There were no differences between ball carriers vs. tacklers during the 10-min period. Both injured position groups were involved in significantly more tackles in the final minute. Additional match data sources are needed to further inform injury preventive strategies of tackle events.

The aim of this study was to investigate the difference in head acceleration event (HAE) incidence between training and match‐play in women's and men's players competing at the highest level of domestic rugby union globally. Players from Women's (Premiership Women's Rugby, Farah Palmer Cup) and Men's (Premiership Rugby, Currie Cup) rugby union competitions wore instrumented mouthguards during matches and training sessions during the 2022/2023 seasons. Peak linear (PLA) and angular (PAA) acceleration were calculated from each HAE and included within generalized linear mixed‐effects models. The incidence of HAEs was significantly greater in match‐play compared to training for all magnitude thresholds in both forwards and backs, despite players spending approximately 1.75–2.5 times more time in training. For all HAEs (PLA > 5 g and PAA > 400 rad/s2), incidence rate ratios (IRRs) for match versus training ranged from 2.80 (95% CI: 2.38–3.30; men's forwards) to 4.00 (3.31–4.84; women's forwards). At higher magnitude thresholds (PLA > 25 g; PAA > 2000 rad/s2), IRRs ranged from 3.64 (2.02–6.55; PAA > 2000 rad/s2 in men's backs) to 11.70 (6.50–21.08; PAA > 2000 rad/s2 in women's forwards). Similar trends were observed in each competition. Players experienced significantly more HAEs during match‐play than training, particularly at higher magnitude thresholds. Where feasible, HAE mitigation strategies may have more scope for HAE reduction if targeted at match‐play, particularly where higher magnitude HAEs are the primary concern. However, the number of HAEs associated with different training drills requires exploration to understand if HAEs can be reduced in training, alongside optimizing match performance (e.g., enhancing contact technique).

To compare the probability of tackle success (the tackler preventing the ball‐carrier and ball from progressing towards the tackler try‐line) when contacting the ball‐carrier at different heights (shoulder, mid‐torso and legs) for different types of tackles (active, passive, smother and arm) while accounting for other tackler situational factors within seven playing levels. Video footage of 271 male rugby union matches were analysed across seven playing groups (Under [U] 12, n = 25 matches; U14, n = 35; U16, n = 39; U18 Amateur n = 39; U18 Elite n = 38; Senior Amateur, n = 40 and Senior Elite, n = 50) across England, New Zealand, South Africa, Portugal and USA (a total of 51,106 tackles). A multi‐level logistic regression model with tackle success as the outcome variable and first point of contact and type of tackle as the explanatory variables were computed. Included in the model as cofounders were the situational variables tackle direction, tackle sequence, number of players in the tackle and attacker intention. Post‐estimation marginal effects were used to calculate the probabilities (expressed as a percentage %) of tackle success for each interaction between tackle type (active shoulder, smother, passive shoulder and arm) and the first point of contact (shoulder, mid‐torso and legs). The probability of tackle success in relation to where the ball‐carrier is contacted varied by tackle type and within each age group. The probabilities (Pr) for contacting the shoulder versus mid‐torso at the senior levels (elite and amateur) did not differ in relation to tackle success (for instance, for active shoulder tackles within senior elite; shoulder Pr 86% 95% CI 82–89 and mid‐torso Pr 82% 95% CI 77–86), whereas at the junior levels, contacting the shoulder had a higher probability than other points of contact. Active shoulder tackles had the highest probability of tackle success across the different playing levels across the different contact heights, whereas arm tackles had the lowest probability (for instance, for mid‐torso tackles within senior elite, active Pr 82% 95% CI 77–86 vs. arm Pr 69% 95% CI 64–75). Coaches and practitioners can use this information to improve tackle training design and planning within the different age groups and facilitate player development.

Interpreting the physical qualities of youth athletes is complex due to the effects of growth, maturation and development. This study aimed to evaluate the effect of position, chronological age, relative age and maturation on the physical qualities of elite male academy rugby union players. 1,424 participants (n=2,381 observations) from nine Rugby Football Union Regional Academies prospectively completed a physical testing battery at three time points, across three playing seasons. Anthropometrics, body composition, muscular power, muscular strength, speed, aerobic capacity and running momentum were assessed. Positional differences were identified for all physical qualities. The largest effect sizes were observed for the associations between chronological age (d=0.65 to 0.73) and maturation (d=-0.77 to -0.69) and body mass related variables (i.e. body mass and running momentum). Relative strength, maximum velocity and aerobic capacity were the only models to include two fixed effects with all other models including at least three fixed effects (i.e. position and a combination of chronological age, relative age and maturation). These findings suggest a multidimensional approach considering position, chronological age, relative age and maturation is required to effectively assess the physical qualities of male age grade rugby union players. Therefore practitioners should use regression equations rather than traditional descriptive statistic tables to provide individualised normative comparisons thus enhancing the application of testing results for talent identification and player development.

Purpose: To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis. Methods: Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups. Results: Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = −1.83) were identified between U18 academy and international backs within PC1. Conclusion: Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.

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.

Abstract

Body composition assessments in professional male football often lack sport-specific evidence, risking mismanagement of player health and performance. This study described dual-energy X-ray absorptiometry (DXA)-derived values by playing position, ethnicity, competition level, and seasonal timepoints. A total of 343 players (mean ± SD: age = 22.6 ± 4.6 years; stature = 182.0 ± 6.9 cm; body mass = 79.1 ± 8.6 kg) from the English Premier League (n = 76) and English Football League (n = 267) completed 939 scans over a 10-year period (2014–2024) using DXA (Lunar iDXA, GE Healthcare), with repeat measurements taken across the season. Players were sub-classified as Goalkeepers (n = 32), Central Defenders (n = 55), Wide Defenders (n = 64), Central Midfielders (n = 73), Wide Midfielders (n = 62), and Forwards (n = 57). Body composition ranges specific to position were identified for bone mass (3.5–4.2 kg), lean mass (61.2–69.6 kg), fat mass (9.1–13.5 kg), and percentage body fat (11.6–15.4%). Significant differences in bone, lean, and fat mass were observed between playing positions, ethnicity, and league level ( p  < 0.050). Across a single season, fat-free mass increased significantly, while fat mass decreased (both: p  < 0.001), indicating positive physiological adaptations from moderate body mass increases rather than performance concerns. These findings indicate that body fat values above the commonly cited < 10% threshold are regularly observed in elite male footballers, suggesting the need for more individualised targets over generic team-wide standards. Providing the largest criterion-measured dataset for professional male footballers, this study supports athlete-centred, position-specific decision-making to optimise player health and performance.

Objectives To compare match injury incidence, severity and burden in men's and women's elite rugby league. Design A prospective cohort epidemiological study. Methods Time loss match injury data were collected from all men's (11,301 exposure hours) and women's (5,244 exposure hours) Super League clubs. Results Injury incidence and burden were not different between men and women (mean [95 % CI]; 54 [45 to 65] vs. 60 [49 to 74] per 1000 match-hours; p = 0.39, and 2332 [1844 to 2951] vs. 1951 [1560 to 2440] days lost per 1000 match-hours; p = 0.26). However, injury severity was greater for men than women (42 [35–50] vs. 35 [29 to 42]; p = 0.01). Lower limbs accounted for 54 % and 52 % of injuries for men and women, with the head/face the most frequently injured location due to concussion (12 [10 to 15] and 10 [8 to 14] per 1000 match-hours for men and women). Injuries to the knee had the greatest burden for men and women (708 [268–1868] and 863 [320–2328] days lost per 1000 match-hours). Being tackled was the most common injury mechanism for men and women (28 % and 38 %) with greater burden (p < 0.01) than other injury mechanisms. Conclusions Male and female rugby league players have similar injury incidence and burden; however, injury severity was higher in men. Head/face injuries have the highest injury incidence and knee injuries have the highest burden. These injuries should be the focus for prevention initiatives at a league (via laws), player, and coach level, with equal and specific focus for both men's and women's rugby league players.

Objectives Report two-years of training injury data in senior and academy professional rugby league. Design Prospective cohort study. Method Match and training time-loss injuries and exposure data were recorded from two-seasons of the European Super League competition. Eleven/12 (2021) and 12/12 (2022) senior and 8/12 (2021) and 12/12 (2022) academy teams participated. Training injuries are described in detail and overall match injuries referred to for comparison only. Results 224,000 training exposure hours were recorded with 293 injuries at the senior (mean [95 % confidence interval]; 3[2–3] per 1000 h) and 268 academy level (2 [2–3] per 1000 h), accounting for 31 % and 40 % of all injuries (i.e., matches and training). The severity of training injuries (senior: 35 [30–39], academy: 36 [30–42] days-lost) was similar to match injuries. Lower-limb injuries had the greatest injury incidence at both levels (senior: 1.85 [1.61–2.12], academy: 1.28 [1.08–1.51] per 1000 h). Head injuries at the academy level had greater severity (35 [25–45] vs. 18 [12–14] days-lost; p < 0.01) and burden (17 [16–18] vs. 4[4–5] days-lost per 1000 h; p = 0.02) than senior level. At the senior level, the incidence of contact injuries was lower than non-contact injuries (risk ratio: 0.29 [0.09–0.88], p = 0.02). Conclusion Training injuries accounted for about a third of injuries, with similar injury severity to match-play. Within training there is a higher rate of non-contact vs. contact injuries. Whilst current injury prevention interventions target matches, these data highlight the importance of collecting high quality training injury data to develop and evaluate injury prevention strategies in training

Objectives Describe head acceleration events (HAEs) experienced by professional male rugby union players during tackle, ball‐carry, and ruck events using instrumented mouthguards (iMGs). Design Prospective observational cohort. Methods Players competing in the 2023 Currie Cup (141 players) and Super Rugby (66 players) seasons wore iMGs. The iMG‐recorded peak linear acceleration (PLA) and peak angular acceleration (PAA) were used as in vivo HAE approximations and linked to contact‐event data captured using video analysis. Using the maximum PLA and PAA per contact event (HAEmax), ordinal mixed‐effects regression models estimated the probabilities of HAEmax magnitude ranges occurring, while accounting for the multilevel data structure. Results As HAEmax magnitude increased the probability of occurrence decreased. The probability of a HAEmax ≥15g was 0.461 (0.435–0.488) (approximately 1 in every 2) and ≥45g was 0.031 (0.025–0.037) (1 in every 32) during ball carries. The probability of a HAEmax >15g was 0.381 (0.360–0.404) (1 in every 3) and >45g 0.019 (0.015–0.023) (1 in every 53) during tackles. The probability of higher magnitude HAEmax occurring was greatest during ball carries, followed by tackles, defensive rucks and attacking rucks, with some ruck types having similar profiles to tackles and ball carries. No clear differences between positions were observed. Conclusion Higher magnitude HAEmax were relatively infrequent in professional men's rugby union players. Contact events appear different, but no differences were found between positions. The occurrence of HAEmax was associated with roles players performed within contact events, not their actual playing position. Defending rucks may warrant greater consideration in injury prevention research.

BACKGROUND: Elite rugby players experience poor sleep quality and quantity. This lack of sleep could compromise post-exercise recovery. Therefore, it appears central to encourage sleep in order to improve recovery kinetics. However, the effectiveness of an acute ergogenic strategy such as sleep extension on recovery has yet to be investigated among athletes. AIM: To compare the effects of a single night of sleep extension to an active recovery session (CON) on post-exercise recovery kinetics. METHODS: In a randomised cross-over design, 10 male rugby union players participated in two evening training sessions (19:30) involving collision activity, 7-days apart. After each session, participants either extended their sleep to 10 hours or attended an early morning recovery session (07:30). Prior to (PRE), immediately after (POST 0 hour [h]), 14h (POST 14) and 36h (POST 36) post training, neuromuscular, perceptual and cognitive measures of fatigue were assessed. Objective sleep parameters were monitored two days before the training session and over the two-day recovery period. RESULTS: The training session induced substantial decreases in countermovement jump mean power and wellness across all time points, while heart rate recovery decreased at POST 0 in both conditions. Sleep extension resulted in greater total sleep time (effect size [90% confidence interval]: 5.35 [4.56 to 6.14]) but greater sleep fragmentation than CON (2.85 [2.00 to 3.70]). Between group differences highlight a faster recovery of cognitive performance following sleep extension (-1.53 [-2.33 to -0.74]) at POST 14, while autonomic function (-1.00 [-1.85 to -0.16]) and upper-body neuromuscular function (-0.78 [-1.65 to 0.08]) were better in CON. However, no difference in recovery status between groups was observed at POST 36. CONCLUSION: The main finding of this study suggests that sleep extension could affect cognitive function positively but did not improve neuromuscular function the day after a late exercise bout.

Abstract

Player profiling can aid talent identification and development by highlighting strengths and weaknesses, and evaluation training interventions. However, there is currently no consensus in rugby league on the qualities, skills, and characteristics (i.e., factors) which should be profiled, or the methods to use to assess these factors. Consequently, the aims of this two-part study were to 1) establish the most common factors and methods for profiling rugby league players, through a systematic scoping review, and 2) develop consensus on the factors and methods experts believe should be used when profiling rugby league players. In Part 1, a systematic scoping review of studies profiling rugby league players was conducted according to the PRISMA guideline for Scoping Reviews. In Part 2, a panel of 32 experts were invited to participate in a sequential three-round Delphi consensus, used to identify the factors that they believed should be profiled in rugby league players and associated methods of assessment. Part 1 identified 370 studies, which assessed varying numbers of factors from five higher order themes; physical (n=247, 67%), health-related (n=129, 35%), other (n=60, 16%; e.g., playing experience, level of education), technical-tactical (n=58, 16%), and psychological (n=25, 7%). Only 3% of these studies featured female participants (n=11). In Part 2, 120 factors were initially identified, of which 85 reached consensus (≥70% agreement). This included 22 physical, 22 psychological, 20 technical-tactical, 15 health-related, and six player information factors. Collectively, these findings evidence the multidimensional nature of talent in rugby league, highlighting a range of factors across several domains that should be considered when identifying and monitoring talent in the sport. Furthermore, technical-tactical and psychological factors were identified as areas for future research, due to the large number of factors which reached consensus in these areas and the comparatively low amount of research conducted in them.

ABSTRACT

This study aimed to describe the incidence of head acceleration events (HAEs) during pitch‐based in‐season training and matches in professional male rugby league. Data were recorded using instrumented mouthguards from 108 players (70 forwards and 38 backs) at nine Super League teams (2024 season), resulting in 468 player‐training sessions and 665 player‐matches included. Peak linear and angular acceleration were calculated from each HAE and analyzed using generalized linear mixed‐effects models. During the 468 player‐training sessions, 814 HAEs above the lowest magnitude threshold (5  g and 400 rad.s −2 ) were observed and the mean HAE incidence rate per player‐hour was 1.52 (95% confidence intervals; 1.34–1.70). This was substantially lower than matches (25.78 [23.28–28.27] per player‐hour) with HAE incidence being 17 times greater during matches compared to training (incidence rate ratio 16.96 [14.92–19.01]). Higher magnitude HAEs had a lower incidence in both training and matches (e.g., > 25  g 0.04 [0.02–0.06] and 2.01 [1.79–2.24] per player‐hour). Out of 468 player‐training sessions, 307 (~66%) had no HAEs > 10  g and 441 (~94%) had no HAEs > 25  g . Overall, the incidence rates of HAEs during training were low and substantially lower than match‐play. However, a small proportion of relatively high in magnitude HAEs do occur during training, which could be the target of prevention interventions in training. However, given the different HAE rates between training and matches, interventions targeting matches (e.g., law modifications or reduced exposure) would have a larger effect on reducing HAEs for players than training interventions.

The purpose of this study was to quantify the total energy expenditure (TEE) of international female rugby union players. Fifteen players were assessed over 14-days throughout an international multi-game tournament, which represented two consecutive one-match microcycles. Resting metabolic rate (RMR) and TEE were assessed by indirect calorimetry and doubly labelled water, respectively. Physical activity level (PAL) was estimated (TEE:RMR). Mean RMR, TEE, and PAL were 6.60 ± 0.93 MJ.day-1, 13.51 ± 2.28 MJ.day-1 and 2.0 ± 0.3 AU, respectively. There was no difference in TEE (13.74 ± 2.31 vs. 13.92 ± 2.10 MJ.day-1; p = 0.754), or PAL (2.06 ± 0.26 AU vs. 2.09 ± 0.23 AU; p = 0.735) across microcycles, despite substantial decreases in training load (total distance: -8088 m, collisions: -20 n, training duration: -252 min). After correcting for body composition, there was no difference in TEE (13.80 ± 1.74 vs. 13.16 ± 1.97 adj. MJ.day-1, p = 0.190), RMR (6.49 ± 0.81 vs. 6.73 ± 0.83 adj. MJ.day-1, p = 0.633) or PAL (2.15 ± 0.14 vs 1.87 ± 0.26 AU, p = 0.090) between forwards and backs. For an injured participant (n = 1), TEE reduced by 1.7 MJ.day-1 from pre-injury. For participants with illness (n = 3), TEE was similar to pre-illness (+0.49 MJ.day-1). The energy requirements of international female rugby players were consistent across one-match microcycles. Forwards and backs had similar adjusted energy requirements. These findings are critical to inform the dietary guidance provided to female rugby players.

Abstract

This is the first study to assess longitudinal changes in anthropometric, physiological, and physical qualities of international women’s rugby league players. Thirteen forwards and 11 backs were tested three times over a 10-month period. Assessments included: standing height and body mass, body composition measured by dual x-ray absorptiometry (DXA), a blood panel, resting metabolic rate (RMR) assessed by indirect calorimetry, aerobic capacity (i.e., VLO 2 max) evaluated by an incremental treadmill test, and isometric force production measured by a force plate. During the pre-season phase, lean mass increased significantly by ∼2% for backs (testing point 1: 47 kg; testing point 2: 48 kg) and forwards (testing point 1: 50 kg; testing point 2: 51 kg) (p = ≤ 0.05). Backs significantly increased their VLO 2 max by 22% from testing point 1 (40 ml·kg −1 ·min −1 ) to testing point 3 (49 ml·kg −1 ·min −1 ) (p = ≤ 0.04). The VLO 2 max of forwards increased by 10% from testing point 1 (41 ml·kg −1 ·min −1 ) to testing point 3 (45 ml·kg −1 ·min −1 ), however this change was not significant (p = ≥ 0.05). Body mass (values represent the range of means across the three testing points) (backs: 68 kg; forwards: 77-78 kg), fat mass percentage (backs: 25-26%; forwards: 30-31%), resting metabolic rate (backs: 7 MJ·day −1 ; forwards: 7 MJ·day −1 ), isometric mid-thigh pull (backs: 2106-2180 N; forwards: 2155-2241 N), isometric bench press (backs: 799-822 N; forwards: 999-1024 N), isometric prone row (backs: 625-628 N; forwards: 667-678 N) and bloods (backs: ferritin 21-29 ug/L, haemoglobin 137-140 g/L, iron 17-21 umol/L, transferrin 3 g/L, transferring saturation 23-28%; forwards: ferritin 31-33 ug/L, haemoglobin 141-145 g/L, iron 20-23 umol/L, transferrin 3 g/L, transferrin saturation 26-31%) did not change (p = ≥ 0.05). This study provides novel longitudinal data which can be used to better prepare women rugby league players for the unique demands of their sport, underpinning female athlete health.

Participation in women’s rugby league has been growing since the foundation of the English women’s rugby league Super League in 2017. However, the evidence base to inform women’s rugby league remains sparse. This study provides the largest quantification of anthropometric and physical qualities of women’s rugby league players to date, identifying differences between positions (forwards & backs) and playing level (Women’s Super League [WSL] vs. International). The height, weight, body composition, lower body strength, jump height, speed and aerobic capacity of 207 players were quantified during the pre-season period. Linear mixed models and effects sizes were used to determine differences between positions and levels. Forwards were significantly (p < 0.05) heavier (forwards: 82.5 ± 14.8kg; backs: 67.7 ± 9.2kg) and have a greater body fat % (forwards: 37.7 ± 6.9%; backs: 30.4 ± 6.3%) than backs. Backs had significantly greater lower body power measured via jump height (forwards: 23.5 ± 4.4cm; backs: 27.6 ± 4.9cm), speed over 10m (forwards: 2.12 ± 0.14s; backs: 1.98 ± 0.11s), 20m (forwards: 3.71 ± 0.27s; backs: 3.46 ± 0.20s), 30m (forwards: 5.29 ± 0.41s; backs: 4.90 ± 0.33s), 40m (forwards: 6.91 ± 0.61s; backs: 6.33 ± 0.46s) and aerobic capacity (forwards: 453.4 ± 258.8m; backs: 665.0 ± 298.2m) than forwards. Additionally, international players were found to have greater anthropometric and physical qualities in comparison to their WSL counterparts. This study adds to the limited evidence base surrounding the anthropometric and physical qualities of elite women’s rugby league players. Comparative values for anthropometric and physical qualities are provided which practitioners may use to evaluate the strengths and weaknesses of players, informing training programs to prepare players for the demands of women’s rugby league.

Objective Within women’s rugby league (n=12 teams), we (1) identified modifiers for head-to-head contacts informed by sport partners (eg, players, coaches, match officials); (2) compared head-to-head contact and concussion rates to the previous two seasons following a one-season tackle technique coaching intervention and (3) explored barriers and enablers of the intervention. Methods A multi-method design was used. Part 1: Mitigation strategies were identified by sport partners reviewing footage of head-to-head contacts, informing the development of a coach-targeted tackle technique intervention. Part 2 evaluated the intervention, comparing head-to-head contact and concussion incidence rates (IRs). Interviews with coaches and players (n=6) explored barriers and enablers to effective implementation and compliance with the intervention. Results Sport partners reported tacklers were more responsible for head-to-head contacts and lowering the tackle height was the most frequently suggested mitigation strategy preintervention and postintervention. Head-to-head contact rates were significantly lower during the intervention than preintervention (IR 59; 95% CI 56 to 62 vs IR 28; 95% CI 25 to 30/1000 tackle events); however, concussion rates showed no difference. Perceived barriers to the intervention included underdeveloped physical and technical foundations of players, lack of knowledge and understanding of the intervention and its purpose, and the environmental context and lack of resources in women’s rugby league. Beliefs about the consequences of the tackle and concussion were perceived as barriers and enablers. Conclusions Head-to-head contact rates were significantly lower; however, concussion rates did not decrease following a tackle technique coaching intervention. Reduced head-to-head contacts are potentially due to an increased focus on head injury reduction and increased player/coach awareness and support.

This is the first study to assess longitudinal changes in anthropometric, physiological, and physical qualities of international women’s rugby league players. Thirteen forwards and 11 backs were tested three times over a 10-month period. Assessments included: standing height and body mass, body composition measured by dual x-ray absorptiometry (DXA), a blood panel, resting metabolic rate (RMR) assessed by indirect calorimetry, aerobic capacity (i.e.,V˙O2max) evaluated by an incremental treadmill test, and isometric force production measured by a force plate. During the pre-season phase, lean mass increased significantly by ~2% for backs (testing point 1: 47 kg; testing point 2: 48 kg) and forwards (testing point 1: 50 kg; testing point 2: 51 kg) (p = ≤ 0.05). Backs significantly increased their V˙O2max by 22% from testing point 1 (40 ml kg-1 min-1) to testing point 3 (49 ml kg-1 min-1) (p = ≤ 0.04). The V˙O2max of forwards increased by 10% from testing point 1 (41 ml kg-1 min-1) to testing point 3 (45 ml kg-1 min-1), however this change was not significant (p = ≥ 0.05). Body mass (values represent the range of means across the three testing points) (backs: 68 kg; forwards: 77–78 kg), fat mass percentage (backs: 25–26%; forwards: 30–31%), resting metabolic rate (backs: 7 MJ day-1; forwards: 7 MJ day-1), isometric mid-thigh pull (backs: 2106–2180 N; forwards: 2155–2241 N), isometric bench press (backs: 799–822 N; forwards: 999–1024 N), isometric prone row (backs: 625–628 N; forwards: 667–678 N) and bloods (backs: ferritin 21–29 ug/L, haemoglobin 137–140 g/L, iron 17–21 umol/L, transferrin 3 g/L, transferring saturation 23–28%; forwards: ferritin 31–33 ug/L, haemoglobin 141–145 g/L, iron 20–23 umol/L, transferrin 3 g/L, transferrin saturation 26–31%) did not change (p = ≥ 0.05). This study provides novel longitudinal data which can be used to better prepare women rugby league players for the unique demands of their sport, underpinning female athlete health.

To establish the criterion-assessed energy and fluid requirements of female netball players, 13 adult players from a senior Netball Super League squad were assessed over 14 days in a cross-sectional design, representing a two- and one-match microcycle, respectively. Total energy expenditure (TEE) and water turnover (WT) were measured by doubly labeled water. Resting and activity energy expenditure were measured by indirect calorimetry and Actiheart, respectively. Mean 14-day TEE was 13.46 ± 1.20 MJ day−1 (95% CI, 12.63–14.39 MJ day−1). Resting energy expenditure was 6.53 ± 0.60 MJ day−1 (95% CI, 6.17–6.89 MJ day−1). Physical activity level was 2.07 ± 0.19 arbitrary units (AU) (95% CI, 1.95–2.18 AU). Mean WT was 4.1 ± 0.9 L day−1 (95% CI, 3.6–4.7 L day−1). Match days led to significantly greater TEE than training (+2.85 ± 0.70 MJ day−1; 95% CI, +1.00– +4.70 MJ day−1; p = 0.002) and rest (+4.85 ± 0.70 MJ day−1; 95% CI, +3.13–+6.56 MJ day−1; p < 0.001) days. Matches led to significantly greater energy expenditure (+1.85 ± 1.27 MJ; 95% CI, +0.95–+2.76 MJ day−1; p = 0.001) than court-based training sessions. There was no significant difference in TEE (+0.03 ± 0.35 MJ day−1; 95% CI, −0.74–+0.80 MJ day−1; p = 0.936) across weeks. Calibrated Actiheart 5 monitors underestimated TEE (−1.92 ± 1.21 MJ day−1). Energy and fluid turnover were greatest on match days, followed by training and rest days, with no difference across weeks. This study provides criterion-assessed energy and fluid requirements to inform dietary guidance for female netball players.