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The aims were to determine the relationship(s) between match-play external load and post-match neuromuscular fatigue as latent constructs, the contribution of the specific measured variables to these latent constructs, and how these differ between forwards and backs in elite rugby union. Forty-one elite male rugby union players (22 forwards and 19 backs) from the same international rugby union team were tested, with data included from the 2020 and 2021 international seasons (11 matches; 146 player appearances). Player's match-play external loads were quantified using microtechnology (for locomotor activities) and video analysis (for collision actions). Neuromuscular fatigue was quantified using countermovement jump tests on force plates which were conducted ~ 24 to 48 hours pre- and post-match. Partial least squares correlation (PLSC) leave one variable out (LOVO) procedure established the relative variable contribution to both external load (X matrix) and neuromuscular fatigue (Y matrix) constructs. Linear mixed-effects models were then constructed to determine the variance explained by the latent scores applied to the variables representing these constructs. For external load, both locomotor and collision variables were identified for the forwards and the backs, although the identified variables differed between groups. For neuromuscular fatigue, jump height was identified as a high contributor for the forwards and the backs, with concentric impulse and reactive strength index high contributors only for the backs. The explained variance between the external load and neuromuscular fatigue latent constructs at the individual player level was 4.4% and 32.2% in the forwards and the backs models, respectively. This discrepancy may be explained by differences in match-play external loads and/or the specificity of the tests to measure indicators of fatigue. These may differ due to, for example, the activities undertaken in the different positional groups. [ABSTRACT FROM AUTHOR]

Using a pre-post-test design, this study evaluated the impact of implementing a standard on the reliability of player management decision-making within a professional rugby union environment. Five practitioners from a High-Performance Unit (HPU) rated 22 instances of Global Positioning System (GPS)–based external training load information of 14 players across the 2021–2022 season. This rating was whether a peak/trough/normal exposure in load had occurred. The ratings were repeated at four time points (separated by 2 weeks) before (Pre1, Pre2) and after (Post1, Post2) implementing a consensus statement as a subjective standard (using a dashboard) developed previously within the same environment to identify peaks/troughs in player external training loads. Inter-rater agreement between individuals at each voting round was assessed using Light's Kappa, while pre-post-standard intra-rater agreement was determined from Cohen's Kappa (both with 95% confidence intervals). Changes to dashboard usability from implementing the standard were assessed by administering the System Usability Scale to 11 HPU staff at the four time points. Pre-standard moderate inter-rater agreement (Pre1: 0.53 (0.36–0.69), Pre2: 0.60 (0.42–0.77)) increased to almost perfect agreement (Post1: 0.74 (0.57–0.89), Post2: 0.90 (0.79–1)) post-standard. The intra-rater agreement of 2/5 participants was almost perfect post-standard, while it remained within substantial levels for the others. A linear mixed model (χ 2(3) = 8.85, p = 0.03) illustrated a slight increase in dashboard usability after incorporating the standard (Pre1: 84.09, Pre2: 81.36; Post1: 87.73, Post2: 87.27). Overall, the results highlighted that the subjective standard enhanced reliability of practitioner agreement for the selected decision. [ABSTRACT FROM AUTHOR]

Purpose: Despite the known health and wellbeing benefits of taking part in sport for children and adolescents, it is reported that sports participation declines during adolescence. The purpose of this study was to explore current organized youth sport participation rates across Europe for both males and females and update current understanding. Method: Sport participation registration data was collected for 18 sports from 27 countries. In total, participation data was collected from over 5 million young people from Under 8s (U8s) to Under 18s (U18s). Differences in the participation rates between age categories were investigated using a generalized linear mixed effects model. Results: Overall, males were four times more likely to participate in organised youth sport than females' participants, with this trend apparent across all age categories and across most sports. There was a significant decrease across sports in participation rates for males during adolescence from U14-U16 and U16-U18. There was a significant decrease in participation rates for females from U14-U16 for most sports except but an increase in participation rates from U16-U18 for 12 out of 18 sports. Soccer (1262%), wrestling (391%) and boxing (209%) were the sports that had greater male sport participation rates. In contrast, dance sports (86%) and volleyball (63%) had more female participants than males. This research shows male sports participation is significantly greater than female in youth sport across Europe. Conclusion: Furthermore, findings showed that for both male and female participants, participation rates increased from U8-U14 for the majority of sports followed by reduced participation rates during adolescence. Findings of this research can be used by national governing bodies and sporting organizations to inform youth sport participation initiatives. [ABSTRACT FROM AUTHOR]

Determining key performance indicators and classifying players accurately between competitive levels is one of the classification challenges in sports analytics. A recent study applied Random Forest algorithm to identify important variables to classify rugby league players into academy and senior levels and achieved 82.0% and 67.5% accuracy for backs and forwards. However, the classification accuracy could be improved due to limitations in the existing method. Therefore, this study aimed to introduce and implement feature selection technique to identify key performance indicators in rugby league positional groups and assess the performances of six classification algorithms. Fifteen and fourteen of 157 performance indicators for backs and forwards were identified respectively as key performance indicators by the correlation-based feature selection method, with seven common indicators between the positional groups. Classification results show that models developed using the key performance indicators had improved performance for both positional groups than models developed using all performance indicators. 5-Nearest Neighbour produced the best classification accuracy for backs and forwards (accuracy = 85% and 77%) which is higher than the previous method's accuracies. When analysing classification questions in sport science, researchers are encouraged to evaluate multiple classification algorithms and a feature selection method should be considered for identifying key variables. [ABSTRACT FROM AUTHOR]

Standards are pivotal for generating the evidence required to manage players in professional sport environments like rugby union. Resultantly, using a three-step qualitative approach, this study aimed to formulate a consensus as a subjective standard for evidence generation pertaining to player management. The consensus statement intended to identify evidence on peaks/troughs in player external training loads using Global Positioning System (GPS)-based information in the High-Performance Unit (HPU) of a Gallagher Premiership rugby union club. Initially, a systematic review adhering to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) framework was conducted to unravel the factors considered (literature-based cues) when identifying peaks/troughs in player external training loads using GPS information. Next, thematic analysis conducted on the data obtained from 7 semi-structured interviews with HPU staff highlighted that they consider 6 factors with 38 elements (practitioner-based cues) during player external training load management. Thereafter, guided by the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument and by utilising selected elements representing 4/6 factors (healthy player, GPS information, longitudinal durations and practitioner judgements on information), a consensus among practitioners for identifying peaks/troughs in player external training loads was developed with the participation of five HPU members using the nominal group technique (NGT). Practitioners reached an agreement with regard to 12 indicators to subjectively identify peaks/troughs in player external training loads within the considered environment. [ABSTRACT FROM AUTHOR]

The aim was to assess concurrent validity and test–retest reliability of spatiotemporal gait parameters from a thoracic-placed inertial measurement unit (IMU) in lab- (Phase One) and field-based (Phase Two) conditions. Spatiotemporal gait parameters were compared (target speeds 3, 5 and 7.5 m·s−1) between a 100 Hz IMU and an optical measurement system (OptoJump Next, 1000 hz) in 14 trained individuals (Phase One). Additionally, 29 English Premier League football players performed weekly 3 × 60 m runs (5 m·s−1; observations = 1227; Phase Two). Mixed effects modelling assessed the effect of speed on agreement between systems (Phase One) and test–retest reliability (Phase Two). IMU step time showed strong agreement (<0.3%) regardless of individual or running speed. Direction of mean biases up to 40 ms for contact and flight time depended on the running speed and individual. Step time, length and frequency were most reliable (coefficient of variation = 1.3-1.4%) but confounded by running speed. Step time, length and frequency derived from a thoracic-placed IMU can be used confidently. Contact time could be used if bias is corrected for each individual. To optimise test–retest reliability, a minimum running distance of 40 m is needed to ensure 10 constant-speed steps is gathered. [ABSTRACT FROM AUTHOR]

Rule changes within football-code team sports aim to improve performance, enhance player welfare, increase competitiveness, and provide player development opportunities. This manuscript aimed to review research investigating the effects of rule changes in football-code team sports. A systematic search of electronic databases (PubMed, ScienceDirect, CINAHL, MEDLINE, and SPORTDiscus) was performed to August 2023; keywords related to rule changes, football-code team sports, and activity type. Studies were excluded if they failed to investigate a football-code team sport, did not quantify the change of rule, or were review articles. Forty-six studies met the eligibility criteria. Four different football codes were reported: Australian rules football ( n  = 4), rugby league ( n  = 6), rugby union ( n  = 16), soccer ( n  = 20). The most common category was physical performance and match-play characteristics ( n  = 22). Evidence appears at a high risk of bias partly due to the quasi-experimental nature of included studies, which are inherently non-randomised, but also due to the lack of control for confounding factors within most studies included. Rule changes can result in unintended consequences to performance (e.g., longer breaks in play) and effect player behaviour (i.e., reduce tackler height in rugby) but might not achieve desired outcome (i.e., unchanged concussion incidence). Coaches and governing bodies should regularly and systematically investigate the effects of rule changes to understand their influence on performance and injury risk. It is imperative that future studies analysing rule changes within football codes account for confounding factors by implementing suitable study designs and statistical analysis techniques.

Player movement in rugby league is complex, being spatiotemporal and multifaceted. Modeling this complexity to provide robust measures of player activity and load has proved difficult, with important aspects of player movement yet to be considered. These include the influence of time-varying covariates on player activity and the combination of different dimensions of player movement. Few studies have simultaneously categorized player activity into different activity states and investigated factors influencing the transition between states, or compared player activity and load profiles between matches and training. This study applied hidden Markov models (HMMs)-a data-driven, multivariate approach-to rugby league training and match GPS data to i) demonstrate how HMMs can combine multiple variables in a data-driven way to effectively categorize player movement states, ii) investigate the influence of two time-varying covariates, score difference and elapsed match time on player activity states, and iii) compare player activity and load profiles within and between training and match modalities. HMMs were fitted to player GPS, accelerometer and heart rate data of one English Super League team across 60 training sessions and 35 matches. Distinct activity states were detected for both matches and training, with transitions between states in matches influenced by score difference and elapsed time and clear differences in activity and load profiles between training and matches. HMMs can model the complexity of player movement to effectively profile player activity and load in rugby league and have the potential to facilitate new research across several sports.