Differences in the Movement Skills and Physical Qualities of Elite Senior & Academy Rugby League Players.

The aim of the present study was to investigate (a) the differences in the movement skills and physical qualities between academy and senior rugby league players, and (b) the relationships between movement skills and physical qualities. Fifty-five male rugby league players (Senior, n=18; Under 19 n=23; Under 16, n=14) undertook a physical testing battery including anthropometric (stature & body mass), strength (isometric mid-thigh pull; IMTP) and power (countermovement jump; CMJ) qualities, alongside the athletic ability assessment (AAA; comprised of overhead squat, double lunge, single-leg Romanian deadlift, press-up and pull-up exercises). Univariate analysis of variance demonstrated significant (p<0.001) differences in body mass, IMTP peak force, CMJ mean power, and AAA movement skills between groups. The greatest observed differences for total movement skills, peak force and mean power were identified between Under 16 and 19 academy age groups. Spearman's rank correlation coefficients demonstrated a significant moderate (r=0.31) relationship between peak force and total movement skill. Furthermore, trivial (r=0.01) and small (r=0.13; r=0.22) relationships were observed between power qualities and total movement skill. These findings highlight that both movement skills and physical qualities differentiate between academy age groups, and provides comparative data for English senior and academy rugby league players

The Use of Microtechnology to Quantify the Peak Match Demands of the Football Codes: A Systematic Review.

BACKGROUND: Quantifying the peak match demands within the football codes is useful for the appropriate prescription of external training load. Wearable microtechnology devices can be used to identify the peak match demands, although various methodologies exist at present. OBJECTIVES: This systematic review aimed to identify the methodologies and microtechnology-derived variables used to determine the peak match demands, and to summarise current data on the peak match demands in the football codes. METHODS: A systematic search of electronic databases was performed from earliest record to May 2018; keywords relating to microtechnology, peak match demands and football codes were used. RESULTS: Twenty-seven studies met the eligibility criteria. Six football codes were reported: rugby league (n = 7), rugby union (n = 5), rugby sevens (n = 4), soccer (n = 6), Australian Football (n = 2) and Gaelic Football (n = 3). Three methodologies were identified: moving averages, segmental and 'ball in play'. The moving averages is the most commonly used (63%) and superior method, identifying higher peak demands than other methods. The most commonly used variables were relative distance covered (63%) and external load in specified speed zones (57%). CONCLUSION: This systematic review has identified moving averages to be the most appropriate method for identifying the peak match demands in the football codes. Practitioners and researchers should choose the most relevant duration-specific period and microtechnology-derived variable for their specific needs. The code specific peak match demands revealed can be used for the prescription of conditioning drills and training intensity

Sex-biased parental care and sexual size dimorphism in a provisioning arthropod

The diverse selection pressures driving the evolution of sexual size dimorphism (SSD) have long been debated. While the balance between fecundity selection and sexual selection has received much attention, explanations based on sex-specific ecology have proven harder to test. In ectotherms, females are typically larger than males, and this is frequently thought to be because size constrains female fecundity more than it constrains male mating success. However, SSD could additionally reflect maternal care strategies. Under this hypothesis, females are relatively larger where reproduction requires greater maximum maternal effort – for example where mothers transport heavy provisions to nests. To test this hypothesis we focussed on digger wasps (Hymenoptera: Ammophilini), a relatively homogeneous group in which only females provision offspring. In some species, a single large prey item, up to 10 times the mother’s weight, must be carried to each burrow on foot; other species provide many small prey, each flown individually to the nest. We found more pronounced female-biased SSD in species where females carry single, heavy prey. More generally, SSD was negatively correlated with numbers of prey provided per offspring. Females provisioning multiple small items had longer wings and thoraxes, probably because smaller prey are carried in flight. Despite much theorising, few empirical studies have tested how sex-biased parental care can affect SSD. Our study reveals that such costs can be associated with the evolution of dimorphism, and this should be investigated in other clades where parental care costs differ between sexes and species

Synthetic Data as a Strategy to Resolve Data Privacy and Confidentiality Concerns in the Sport Sciences: Practical Examples and an R Shiny Application

There has been a proliferation in technologies in the sport performance environment that collect increasingly largerquantities of athlete data. These data have the potential to be personal, sensitive, and revealing and raise privacy and confidentialityconcerns. A solution may be the use of synthetic data, which mimic the properties of the original data. The aim of this study was toprovide examples of synthetic data generation to demonstrate its practical use and to deploy a freely available web-based R Shinyapplication to generate synthetic data. Methods: Openly available data from 2 previously published studies were obtained,representing typical data sets of (1) field- and gym-based team-sport external and internal load during a preseason period (n = 28)and (2) performance and subjective changes from before to after the posttraining intervention (n = 22). Synthetic data weregenerated using the synthpop package in R Studio software, and comparisons between the original and synthetic data sets weremade through Welch t tests and the distributional similarity standardized propensity mean squared error statistic. Results: Therewere no significant differences between the original and more synthetic data sets across all variables examined in both data sets(P &gt; .05). Further, there was distributional similarity (ie, low standardized propensity mean squared error) between the originalobserved and synthetic data sets. Conclusions: These findings highlight the potential use of synthetic data as a practical solution toprivacy and confidentiality issues. Synthetic data can unlock previously inaccessible data sets for exploratory analysis andfacilitate multiteam or multicenter collaborations. Interested sport scientists, practitioners, and researchers should considerutilizing the shiny web application (SYNTHETIC DATA—available at https://assetlab.shinyapps.io/SyntheticData/)

Identifying the Current State and Improvement Opportunities in the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union

In sporting environments, the knowledge necessary to manage athletes is built on information flows associated with player management processes. In current literature, there are limited case studies available to illustrate how such information flows are optimized. Hence, as the first step of an optimization project, this study aimed to evaluate the current state and the improvement opportunities in the player management information flow executed within the High-Performance Unit (HPU) at a professional rugby union club in England. Guided by a Business Process Management framework, elicitation of the current process architecture illustrated the existence of 18 process units and two core process value chains relating to player management. From the identified processes, the HPU management team prioritized 7 processes for optimization. In-depth details on the current state (As-Is) of the selected processes were extracted from semi-structured, interview-based process discovery and were modeled using Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) standards. Results were presented for current issues in the information flow of the daily training load management process, identified through a thematic analysis conducted on the data obtained mainly from focus group discussions with the main stakeholders (physiotherapists, strength and conditioning coaches, and HPU management team) of the process. Specifically, the current state player management information flow in the HPU had issues relating to knowledge creation and process flexibility. Therefore, the results illustrate that requirements for information flow optimization within the considered environment exist in the transition from data to knowledge during the execution of player management decision-making processes.</p

A four-season study quantifying the weekly external training loads during different between match microcycle lengths in professional rugby league

This study investigated differences in external training load between microcycle lengths and its variation between microcycles, players, and head coaches. Commonly used external training load variables including total-, high-speed- (5–7 m·s -1), and sprint-distance (&gt; 7 m·s -1) alongside combined high acceleration and deceleration distance (&gt; 2 m·s -2). Which were also expressed relative to time were collected using microtechnology within a repeated measures design from 54 male rugby league players from one Super League team over four seasons. 4337 individual observations across ninety-one separate microcycles and six individual microcycle lengths (5 to 10 day) were included. Linear mixed effects models established the differences in training load between microcycle-length and the variation between-microcycles, players and head coaches. The largest magnitude of difference in training load was seen when comparing 5-day with 9-day (ES = 0.31 to 0.53) and 10-day (ES = 0.19 to 0.66) microcycles. The greatest number of differences between microcycles were observed in high- (ES = 0.3 to 0.53) and sprint-speed (ES = 0.2 to 0.42) variables. Between-microcycle variability ranged between 11% to 35% dependent on training load variable. Training load also varied between players (5–65%) and head coaches (6–20%) with most variability existing within high-speed (19–43%) and sprinting (19–65%). Overall, differences in training load between microcycle lengths exist, likely due to manipulation of session duration. Furthermore, training load varies between microcycle, player and head coach.</p

The use of match-based exact movement activities to classify elite rugby league players into positional groups

The cluster analysis of elite rugby league players identified groups of distinct playing positions, that can be referred to as broad positional groups. However, the identified positional groups were based on traditional indicators (physical and technical-tactical) that provided no information about the exact match-based movement activities that led to such similarity grouping and the classification of elite rugby league players into these broad positional groups remains unexplored. Hence, this study finds the best model to classify elite rugby league players into positional groups, using data characterised by movement patterns to uncover the similar movement activities of distinct playing positions within a positional group. Key movement patterns for the positional group classification and differences between the groups were also investigated. A total of 18,173 unique movement patterns were derived from 422 players' GPS data across the 2019 and 2020 seasons, where only 36 were identified as key patterns. The highest classification accuracy of 77.58% using all unique patterns and 74.5% accuracy using the key patterns was achieved, outperforming studies that used traditional indicators. Further analyses, based on key patterns revealed differences between forwards and backs. These findings establish movement patterns as viable indicators to classify rugby league players into positional groups, enabling coaches and trainers to develop position-specific training programs that cater to the unique physical demands of each position, leading to better player development and team performance. Movement patterns are therefore recommended as an alternative approach to quantifying players' external loads and obtaining granular information

Quantification of Head Acceleration Events in Rugby League: An Instrumented Mouthguard and Video Analysis Pilot Study

Instrumented mouthguards (iMG) were used to collect head acceleration events (HAE) in men’s professional rugby league matches. Peak linear acceleration (PLA), peak angular acceleration (PAA) and peak change in angular velocity (∆PAV) were collected using custom-fit iMG set with a 5 g single iMG-axis recording threshold. iMG were fitted to ten male Super League players for thirty-one player matches. Video analysis was conducted on HAE to identify the contact event; impacted player; tackle stage and head loading type. A total of 1622 video-verified HAE were recorded. Approximately three-quarters of HAE (75.7%) occurred below 10 g. Most (98.2%) HAE occurred during tackles (59.3% to tackler; 40.7% to ball carrier) and the initial collision stage of the tackle (43.9%). The initial collision stage resulted in significantly greater PAA and ∆PAV than secondary contact and play the ball tackle stages (p &lt; 0.001). Indirect HAE accounted for 29.8% of HAE and resulted in significantly greater ∆PAV (p &lt; 0.001) than direct HAE, but significantly lower PLA (p &lt; 0.001). Almost all HAE were sustained in the tackle, with the majority occurring during the initial collision stage, making it an area of focus for the development of player protection strategies for both ball carriers and tacklers. League-wide and community-level implementation of iMG could enable a greater understanding of head acceleration exposure between playing positions, cohorts, and levels of play.</p

Defining and quantifying fatigue in the rugby codes

The rugby codes (i.e., rugby union, rugby league, rugby sevens [termed 'rugby']) are teamsports that impose multiple complex physical, perceptual, and technical demands on players which leads to substantial player fatigue post-match. In the post-match period, fatigue manifests through multiple domains and negatively influences recovery. There is, however, currently no definition of fatigue contextualised to the unique characteristics of rugby (e.g., locomotor and collision loads). Similarly, the methods and metrics which practitioners consider when quantifying the components of post-match fatigue and subsequent recovery are not known. The aims of this study were to develop a definition of fatigue in rugby, to determine agreement with this common definition of fatigue, and to outline which methods and metrics are considered important and feasible to implement to quantify post-match fatigue. Subject matter experts (SME) undertook a two-round online Delphi questionnaire (round one; n = 42, round two; n = 23). SME responses in round one were analysed to derive a definition of fatigue, which after discussion and agreement by the investigators, obtained 96% agreement in round two. The SME agreed that fatigue in rugby refers to a reduction in performance- related task ability which is underpinned by time-dependent negative changes within and between cognitive, neuromuscular, perceptual, physiological, emotional, and technical/tactical domains. Further, there were 33 items in the neuromuscular performance, cardio-autonomic, or self-report domains achieved consensus for importance and/or feasibility to implement. Highly rated methods and metrics included countermovement jump force/power (neuromuscular performance), heart rate variability (cardio-autonomic measures), and soreness, mood, stress, and sleep quality (self-reported assessments). A monitoring system including highly-rated fatigue monitoring objective and subjective methods and metrics in rugby is presented. Practical recommendations of objective and subjective measures, and broader considerations for testing and analysing the resulting data in relation to monitoring fatigue are provided.</p

Concurrent validity and between-unit reliability of a foot-mounted inertial measurement unit to measure velocity during team sport activity

The concurrent validity and between-unit reliability of a foot-mounted inertial measurement unit (F-IMU) was investigated during linear and change of direction running drills. Sixteen individuals performed four repetitions of two drills (maximal acceleration and flying 10 m sprint) and five repetitions of a multi-directional movement protocol. Participants wore two F-IMUs (Playermaker) and 10 retro-reflective markers to allow for comparisons to the criterion system (Qualisys). Validity of the F-IMU derived velocity was assessed via root-mean-square error (RMSE), 95% limits of agreement (LoA) and mean difference with 95% confidence interval (CI). Between-unit reliability was assessed via intraclass correlation (ICC) with 90% CI and 95% LoA. The mean difference for instantaneous velocity for all participants and drills combined was −0.048 ± 0.581 m ∙ s −1, the LoA were from −1.09 to −1.186 m ∙ s −1 and RMSE was 0.583 m ∙ s −1. The ICC ranged from 0.84 to 1, with LoA from −7.412 to 2.924 m ∙ s −1. Differences were dependent on the reference speed, with the greatest absolute difference (−0.66 m ∙ s −1) found at velocities above 7 m ∙ s −1. Between-unit reliability of the F-IMU ranges from good to excellent for all locomotor characteristics. Playermaker has good agreement with 3D motion capture for velocity and good to excellent between-unit reliability.</p