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OBJECTIVES: Describe the highest frequency and variability for tackle events in rugby league. Investigate seasonal differences in total tackle events per match over a seven-year period. DESIGN: Retrospective observational. METHODS: Tackle events (i.e., ball carrier events [attacker] and tackler involvements [defender]) from 864 male professional rugby league players competing in 1176 Super League matches from 2014 to 2020 were included. A series of linear mixed effect models were used to determine the frequency and variability during peak 1-, 3-, 5-, 10-, 20-, 40-min and whole-match tackle events per player per match at a positional group level. Differences between seasons for the total number of tackle events per match were compared using a one-way analysis of variance and with Tukey's honestly significant difference test. RESULTS: Tackle events were greatest for Props (51.5 [47.7-55.4] per match). Within-players, between-matches, and between-seasons variability was

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.

Despite the International System of Units (SI), as well as several publications guiding researchers on correct use of terminology, there continues to be widespread misuse of mechanical terms such as ‘work’ in sport and exercise science. A growing concern is the misuse of the term ‘load’. Terms such as ‘training load’ and ‘PlayerLoad’ are popular in sport and exercise science vernacular. However, a ‘load’ is a mechanical variable which, when used appropriately, describes a force and therefore should be accompanied with the SI-derived unit of the newton (N). It is tempting to accept popular terms and nomenclature as scientific. However, scientists are obliged to abide by the SI and must pay close attention to scientific constructs. This communication presents a critical reflection on the use of the term ‘load’ in sport and exercise science. We present ways in which the use of this term breaches principles of science and provide practical solutions for ongoing use in research and practice.

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.

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.

Quantifying differences in locomotor characteristics of training between two competition levels and between training days within elite female soccer players.Foot-mounted inertial measurement unit (Playermaker) data were collected from 293 players from three Women's Super League (WSL; n = 76) and eight Women's Championship (WC; n = 217) teams over a 28-week period. Data were analysed using partial least squares correlation analysis to identify relative variable importance and linear mixed effects models to identify magnitude of effects.WSL players performed more high-speed running distance (HSR;5.29 m∙sMD-4 is a key training day discriminating between competitive level. HSR and SpD volume and intensity is tapered in WSL and WC players, however there is less clear taper of ACC or DEC. As such, WC teams could increase the volume and intensity of HSR on MD-4 to mimic locomotor activities of those at a higher standard.

OBJECTIVES: To (i) quantify the differences in locomotor and technical characteristics between different drill categories in female soccer and (ii) explore the training drill distributions between different standards of competition. METHODS: Technical (ball touches, ball releases, high-speed ball releases) and locomotor data (total distance, high-speed running distance [>5.29 m∙s-1]) were collected using foot-mounted inertial measurement units from 458 female soccer players from three Women's Super League (WSL; n = 76 players), eight Women's Championship (WC; n = 217) and eight WSL Academy (WSLA; n = 165) teams over a 28-week period. Data were analysed using general linear mixed effects. RESULTS: Across all standards, the largest proportion of time was spent in technical (TEC) (WSL = 38%, WC = 28%, WSLA = 29%) and small-sided extensive games (SSGe) (WSL = 20%, WC = 31%, WSLA = 30%) drills. WSL completed more TEC and tactical (TAC) training whilst WC and WSLA players completed more SSGe and possession (POS) drills. Technical drills elicited the highest number of touches, releases and the highest total distance and high-speed activity. Position-specific drills elicited the lowest number of touches and releases and the lowest total distance. When the technical and locomotor demand of each drill were made relative to time, there were limited differences between drills, suggesting drill duration was the main moderating factor. CONCLUSION: Findings provide novel understanding of the technical and locomotor demands of different drill categories in female soccer. These results can be used by coaches and practitioners to inform training session design.

Rugby league carries a high injury incidence with 61% of injuries occurring at tackles. The ball carrier has a higher injury incidence than the defender, therefore understanding mechanisms occurring during injurious tackles are important. Given the dynamic, open nature of tackling, characteristics influencing tackle outcome likely encompass complex networks of dependencies. This study aims to identify important classifying characteristics of the tackle related to ball carrier injurious and non-injurious events in rugby league and identify the characteristics capability to correctly classify those events. Forty-one ball carrier injuries were identified and 205 matched non-injurious tackles were identified as controls. Each case and control were analysed retrospectively through video analysis. Random forest models were built to (1) filter tackle characteristics possessing relative importance for classifying tackles resulting in injurious/non-injurious outcomes and (2) determine sensitivity and specificity of tackle characteristics to classify injurious and non-injurious events. Six characteristics were identified to possess relative importance to classify injurious tackles. This included 'tackler twisted ball carrier's legs when legs were planted on ground', 'the tackler and ball carrier collide heads', 'the tackler used body weight to tackle ball carrier', 'the tackler has obvious control of the ball carrier' 'the tackler was approaching tackle sub-maximally' and 'tackler's arms were below shoulder level, elbows were flexed'. The study identified tackle characteristics that can be modified in attempt to reduce injury. Additional injury data are needed to establish relationship networks of characteristics and analyse specific injuries. Sensitivity and specificity results of the random forest were 0.995 and 0.525.

This study examined the relative contribution of exercise duration and intensity to team-sport athlete's training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load [sRPE-TL], individualised training impulse, total distance, BodyLoad™; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad™). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC

Quantifying the highest intensity of competition (the maximal intensity period [MIP]) for varying durations in team sports has been used to identify training targets to inform the preparation of players. However, its usefulness has recently been questioned since it may still underestimate the training intensity required to produce specific physiological adaptations. Within this conceptual review, we aimed to: (i) describe the methods used to determine the MIP; (ii) compare the data obtained using MIP or whole-match analysis, considering the influence of different contextual factors; (iii) rationalise the use of the MIP in team sports practice and (iv) provide limitations and future directions in the area. Different methods are used to determine the MIP, with MIP values far greater than those derived from averaging across the whole match, although they could be affected by contextual factors that should be considered in practice. Additionally, while the MIP might be utilised during sport-specific drills, it is inappropriate to inform the intensity of interval-based, repeated sprint and linear speed training modes. Lastly, MIP does not consider any variable of internal load, a major limitation when informing training practice. In conclusion, practitioners should be aware of the potential use or misuse of the MIP.

ObjectivesAssess the validity and feasibility of current instrumented mouthguards (iMGs) and associated systems.MethodsPhase 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.ResultsPhase 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.ConclusionThis 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.

This study aims to (a) quantify the movement patterns during rugby league match-play and (b) identify if differences exist by levels of competition within the movement patterns and units through the sequential movement pattern (SMP) algorithm. Global Positioning System data were analysed from three competition levels; four Super League regular (regular-SL), three Super League (semi-)Finals (final-SL) and four international rugby league (international) matches. The SMP framework extracted movement pattern data for each athlete within the dataset. Between competition levels, differences were analysed using linear discriminant analysis (LDA). Movement patterns were decomposed into their composite movement units; then Kruskal-Wallis rank-sum and Dunn post-hoc were used to show differences. The SMP algorithm found 121 movement patterns comprised mainly of "walk" and "jog" based movement units. The LDA had an accuracy score of 0.81, showing good separation between competition levels. Linear discriminant 1 and 2 explained 86% and 14% of the variance. The Kruskal-Wallis found differences between competition levels for 9 of 17 movement units. Differences were primarily present between regular-SL and international with other combinations showing less differences. Movement units which showed significant differences between competition levels were mainly composed of low velocities with mixed acceleration and turning angles. The SMP algorithm found 121 movement patterns across all levels of rugby league match-play, of which, 9 were found to show significant differences between competition levels. Of these nine, all showed significant differences present between international and domestic, whereas only four found differences present within the domestic levels. This study shows the SMP algorithm can be used to differentiate between levels of rugby league and that higher levels of competition may have greater velocity demands.

The 2019 and 2020 Super League (SL) seasons included several competition rule changes. This study aimed to quantify the difference between the 2018, 2019 and 2020 SL seasons for duration, locomotor and event characteristics of matches. Microtechnology and match event data were analysed from 11 SL teams, comprising 124 players, from 416 competitive matches across a three-year data collection period. Due to an enforced suspension of league competition as a consequence of COVID-19 restrictions, and subsequent rule changes upon return to play, season 2020 was divided into season 2020a (i.e. Pre-COVID suspension) and season 2020b (i.e. Post-COVID suspension). Duration, locomotor variables, and match events were analysed per whole-match and ball-in-play (BIP) periods with differences between seasons determined using mixed-effects models. There were significant (ρ ≤ 0.05) reductions in whole-match and BIP durations for adjustables and backs in 2019 when compared to 2018; albeit the magnitude of reduction was less during BIP analyses. Despite reduced duration, adjustables reported an increased average speed suggesting reduced recovery time between bouts. Both forwards and adjustables also experienced an increase in missed tackles between 2018 and 2019 seasons. When comparing 2019 to 2020a, adjustables and backs increased their average speed and distance whilst all positional groups increased average acceleration both for whole-match and BIP analyses. When comparing 2020a to 2020b, all positional groups experienced reduced average speed and average acceleration for both whole-match and BIP analyses. Forwards experienced an increased number of tackles and carries, adjustables experienced an increased number of carries, and backs experienced an increased number of missed tackles when comparing these variables between season 2020a and 2020b. Rule changes have a greater effect on whole-match duration and locomotor characteristics than those reported during BIP periods which suggests the implemented rule changes have removed stagnant time from matches. Amendments to tackle related rules within matches (e.g., introduction of the ‘six-again’ rule) increases the number of collision related events such as carries and tackles.

Typical player management processes focus on managing an athlete’s physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.

Understanding differences in locomotor and collision characteristics between phases of play can help rugby league coaches develop training prescription. There are no data currently available describing these differences at the elite international level. The aim of our study was to determine the differences in average speed (m∙min−1), high-speed running (>5.5 m∙s−1) per minute and collision frequencies per minute (n∙min−1) between attack and defence during the 2017 Rugby League World Cup (RLWC). Methods: Microtechnology data were collected from 24 male professional rugby league players from the same international squad across six matches of the RLWC. Data were then subject to exclusion criteria and stratified into forwards (n = 9) and backs (n = 7) before being analysed with linear mixed-effects models. Results: When comparing attack with defence, forwards and backs had substantially slower average speeds (effect size [ES]; ±90% confidence limits: −2.31; ±0.31 and −1.17; ±0.25) and substantially greater high-speed distance per minute (1.61; ±0.59 and 4.41; ±1.19). Forwards completed substantially more collisions per minute when defending (2.75; ±0.32) whilst backs completed substantially more when attacking (0.63; ±0.70). There was greater within- and between-player variability for collision frequency (coefficient of variation [CV] range; 25–28%) and high-speed distance (18–33%) per minute when compared to average speed (6–12%). Conclusions: There are distinct differences in locomotor and collision characteristics when attacking and defending during international rugby league match-play, yet the variability of high-speed running and collisions per minute is large. These data may be useful to plan or evaluate training practices.

Purpose: To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing complex multivariate data sets associated with the training of athletes. Conclusions: Using PCA, it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PCs), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (ie, variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA, it is possible to visualize complex data sets containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete-monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.

Objective This study aimed to: (a) identify the association between external-workloads and injury-risk in the subsequent week; and (b) understand the effectiveness of workload variables in establishing injury-risk. Design Retrospective cohort study. Methods Workload and injury data (soft-tissue) were collected from forty-eight professional male rugby league players. Load variables included duration (min), total distance (m), relative distance (m min−1), high speed distance ([m]>20 km h−1), very-high speed distance ([m]>25 km h−1), acceleration and deceleration efforts (count) and PlayerLoad (Arbitrary Unit: AU). Cumulative two-, three- and four-weekly loads; Acute:Chronic Workload Ratio (ACWR); Mean-Standard Deviation Workload Ratio (MSWR) and strain values were calculated and divided into three equally-sized bins (low, moderate and high). Generalised Estimating Equations analysed relationships between workload variables and injury probability in the subsequent week. Results Injury-risk increased alongside increases in the ACWR for duration, total distance and PlayerLoad. Conversely, injury-risk decreased (Area Under Curve: 0.569–0.585) with increases in the four-weekly duration, total distance, accelerations, decelerations and PlayerLoad. For relative distance, high four-weekly workloads (high: >60 m min−1) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 m min−1) were negatively associated. Conclusions A range of external workload metrics and summary statistics demonstrate either positive or negative associations with injury-risk status. Such findings provide the framework for the development of decision-support systems in which external workload metrics (e.g. total or high speed distance) can be uniquely and routinely monitored across a range of summary statistics (i.e. cumulative weekly loads and ACWR) in order to optimise player performance and welfare.

To quantify the peak movement and contact demands of National Rugby League (NRL) and European Super League (ESL) competition players were tracked during 10 NRL (166 files) and 10 ESL (143 files) matches using microtechnology devices. The peak 1- to 5-min periods were then calculated for average match speed (m·min−1), and acceleration (m·s−2) when 0, 1, 2, and ≥3 collisions per min occurred. Linear mixed effect models and Cohen’s effect size statistic (± 90%CI) were used to determine the differences in movement profiles when collisions occurred. Compared to no collision periods, as frequency of collisions per minute increased, there were progressive reductions in running speed for most positional groups. The addition of 1 or more collisions per min resulted in average effect size reductions in match speed of −0.14 for NRL forwards, −0.89 for NRL backs, −0.48 for ESL forwards, and −2.41 for ESL backs. ESL forwards had the highest frequency of peak periods involving 3 or more collisions per min, 22% of all periods, followed by NRL forwards (14%), NRL backs (10%) and ESL backs (8%). This study highlights the peak movement and collision demands of professional rugby league competition and allows practitioners to develop training drills that reflect worst case scenarios.

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.

Establishing dose-response relationships between training load and fatigue can help the planning of training. The aim was to establish the relative importance of external training load measurements to relate to the musculoskeletal response on a group and individual player level. Sixteen elite male rugby league players were monitored across three seasons. Two to seven day exponential weighted averages (EWMA) were calculated for total distance, and individualised speed thresholds (via 30-15 Intermittent Fitness Test) derived from global positioning systems. The sit and reach, dorsiflexion lunge, and adductor squeeze tests represented the musculoskeletal response. Partial least squares and repeated measures correlation analyses established the relative importance of training load measures and then investigated their relationship to the collective musculoskeletal response for individual players through the construction of latent variables. On a group level, 2 and 3 day EWMA total distance had the highest relative importance to the collective musculoskeletal response (p < 0.0001). However, the magnitude of relationships on a group (r value = 0.20) and individual (r value = 0.06) level were trivial to small. The lack of variability in the musculoskeletal response over time suggest practitioners adopting such measures to understand acute musculoskeletal fatigue responses should do so with caution.

The purpose of this study was to determine the relationship between matchday wellness status and a technical-tactical performance construct during rugby match-play. One hundred and thirty-three male rugby union players (73 forwards and 60 backs) from five under-18 national squads who participated in the under-18 Six Nations competition completed a subjective wellness questionnaire on each matchday morning. Players subjectively rated each item (sleep quality, fatigue, muscle soreness, stress and mood) on a five-point Likert scale to calculate their daily wellness status (i.e., difference between matchday and baseline perceived wellness). Technical-tactical performance during match-play was quantified by coding individual key performance indicators (e.g., number of carries, number of tackles). Partial least squares correlation analysis (PLSCA) was employed to compute the latent variables of perceived wellness status (X matrix) and technical-tactical performance (Y matrix) for each player observation (n=271). The latent variables are a construct of each variable group, enabling higher dimensional data to be visualised more simply. Linear mixed-effect models were later conducted to assess the relationships between the latent variables. The effect of perceived wellness status on technical-tactical performance was statistically significant in forwards (p=0.042), not statistically significant in backs (p=0.120) and accounted for 4.9% and 1.9% variance in the technical-tactical performance construct, respectively. The findings of this study suggest that perceived wellness status can influence technical-tactical match performance, but the practical significance of these findings should be interpreted with caution given the amount of variance in technical-tactical performance accounted by the models.

Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework that accounts for the movement sequences of athletes has previously been proposed using radio frequency data. This study developed a framework to identify sequential movement sequences using GPS-derived spatiotemporal data in team-sports and establish it’s stability. 13 rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies analysis techniques to handle i) geospatial data (i.e., decimal degree latitude and longitude), ii) determined players turning angles, iii) improved movement descriptor assignment thus, improving movement unit formation and iv) improved the classification and identification of players frequent SMP. The SMP framework allows for sub-sequences of movement units to be condensed, removing repeated elements, which offers a novel technique for the quantification of similarities or dissimilarities between players and playing positions. The SMP framework provides a robust and stable method which allows, for the first time the analysis of GPS-derived data and identifies the frequent SMP of field-based team-sport athletes. The application of the SMP framework in practice could optimise the outcomes of training of field-based team-sport athletes by improving training specificity.

Small-sided games is a commonly used training method to develop technical, tactical and physical qualities concurrently. However, a review of small-sided games in rugby football codes (e.g. rugby union, rugby league) is not available. This systematic review aims to investigate the acute responses and chronic adaptations of small-sided games within rugby football codes considering the constraints applied. Four electronical databases were systematically searched until August 2020. Acute and chronic studies investigating rugby football codes small-sided games, with healthy amateur and professional athletes were included. Twenty studies were eventually included: 4 acute and 1 chronic in rugby union, 13 acute and 2 chronic in rugby league. Acute studies investigated task and individual constraints. Chronic studies showed that small-sided games would be an effective training method to improve physical performance. Current research in rugby football codes is heavily biased towards investigating how manipulating constraints can affect the physical characteristics of small-sided games, with limited literature investigating the effect on technical skills, and no studies investigating tactical behaviour. Future research is needed to evidence the effects of constraint manipulation on technical and tactical behaviour of rugby football players in small-sided games, in addition to physical characteristics.

Instrumented mouthguards (iMGs) have the potential to quantify head acceleration exposures in sport. The Rugby Football League is looking to deploy iMGs to quantify head acceleration exposures as part of the Tackle and Contact Kinematics, Loads and Exposure (TaCKLE) project. iMGs and associated software platforms are novel, thus limited validation studies exist. The aim of this paper is to describe the methods that will determine the validity (ie, laboratory validation of kinematic measures and on-field validity) and feasibility (ie, player comfort and wearability and practitioner considerations) of available iMGs for quantifying head acceleration events in rugby league. Phase 1 will determine the reliability and validity of iMG kinematic measures (peak linear acceleration, peak rotational velocity, peak rotational acceleration), based on laboratory criterion standards. Players will have three-dimensional dental scans and be provided with available iMGs for phase 2 and phase 3. Phase 2 will determine the on-field validity of iMGs (ie, identifying true positive head acceleration events during a match). Phase 3 will evaluate player perceptions of fit (too loose, too tight, bulky, small/thin, held mouth open, held teeth apart, pain in jaw muscles, uneven bite), comfort (on lips, gum, tongue, teeth) and function (speech, swallowing, dry mouth). Phase 4 will evaluate the practical feasibility of iMGs, as determined by practitioners using the system usability scale (preparing iMG system and managing iMG data). The outcome will provide a systematic and robust assessment of a range of iMGs, which will help inform the suitability of each iMG system for the TaCKLE project.

This study aimed to identify which physical and technical-tactical performance indicators (PI) can classify between levels of rugby league match-play. Data were collected from 46 European Super League (ESL) and 36 under-19 Academy (Academy) level matches over two seasons. Thirty-one ESL players and 41 Academy players participated. Microtechnology units were used to analyse the physical PI and matches were videoed and coded for individual technical-tactical PI, resulting in 157 predictor variables. Data were split into training and testing datasets. Random forests (RF) were built to reduce the dimensionality of the data, identify variables of importance and build classification models. To aid practical interpretation, conditional inference (CI) trees were built. Nine variables were identified as most important for backs, classifying between levels with 83% (RF) and 78% (CI tree) accuracy. The combination of variables with the highest classification rate was PlayerLoad2D, PlayerLoadSLOW per Kg body mass and high-speed running distance. Four variables were identified as most important for forwards, classifying with 68% (RF) and 64% (CI tree) accuracy. Defensive play-the-ball losses alone had the highest classification rate for forwards. The identified PI and their unique combinations can be developed during training to aid in progression through the rugby league playing pathway.

The contact nature of American football has made head acceleration exposure a concern. We aimed to quantify the head kinematics associated with direct helmet contact and inertial head loading events in collegiate-level American football. A cohort of collegiate-level players were equipped with instrumented mouthguards synchronised with time-stamped multiple camera-view video footage of matches and practice. Video-verified contact events were identified as direct helmet contact or inertial head loading events and categorised as blocking, blocked, tackling, tackled or ground contact. Linear mixed-effects models were utilised to compare peak head kinematics between contact event categories. The timestamp-based cross-verification of the video analysis and instrumented mouthguard approach resulted in 200 and 328 direct helmet contact and inertial head loading cases, respectively. Median linear acceleration, angular acceleration and angular velocity for inertial head loading cases was greater than direct helmet contact events by 8% (p = 0.007), 55% (p < 0.001) and 4% (p = 0.007), respectively. Median head kinematics for all contact event categories appeared similar with no pairwise comparison resulting in statistical significance (p > 0.05). The study highlights the potential of combining qualitative video analysis with in-vivo head kinematics measurements. The findings suggest that a number of direct helmet contact events sustained in American football are of lower magnitude to what is sustained during regular play (i.e. from inertial head loading). Additionally, the findings illustrate the importance of including all contact events, including direct helmet contact and inertial head loading cases, when assessing head acceleration exposure and player load during a season of American football.

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 (

Rugby union is a late specialisation sport. As a consequence, youth players may still be engaged in other activities and sports throughout the year as they transition to rugby specialisation. Limited research exists quantifying rugby union training and matches as well as engagement in other activities and sports. Therefore, the aim of this study was to quantify and compare rugby union training, matches and other activities of elite youth U15 and U16 rugby union players at different stages of the season. Four-hundred and ninety-two youth (Under-15 and 16-year-old) rugby union players self-reported the frequency, intensity and duration of their participation in rugby union matches, rugby union training, gym, physical education and other sports during three different stages – September to December (Sept–Dec), January to April (Jan–Apr), May to August (May–Aug) – of the year. When all activities were combined, the frequency and volume of rugby matches and training was the greatest during the Sept–Dec stage. The frequency and volume of participating in other sports increased in the May–Aug stage of the season. Gym training frequency, intensity, and duration were stable across the year for both U15 and U16 players. Coaches and practitioners working with elite youth rugby union players should be aware that players are still participating in other activities outside of rugby training and competition. Coaches and practitioners should put structures in place to plan and monitor players' activities in order to optimise the positive outcomes of specialisation (e.g., expertise) while reducing the potential negative outcomes (e.g., injury, burnout).