Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players.

Youth Sport Participation Trends Across Europe: Implications for Policy and Practice

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.

Optimising classification in sport: a replication study using physical and technical-tactical performance indicators to classify competitive levels in rugby league match-play

  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.

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

PURPOSE

There has been a proliferation in technologies in the sport performance environment that collect increasingly larger quantities of athlete data. These data have the potential to be personal, sensitive, and revealing and raise privacy and confidentiality concerns. A solution may be the use of synthetic data, which mimic the properties of the original data. The aim of this study was to provide examples of synthetic data generation to demonstrate its practical use and to deploy a freely available web-based R Shiny application 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 were generated using the synthpop package in R Studio software, and comparisons between the original and synthetic data sets were made through Welch t tests and the distributional similarity standardized propensity mean squared error statistic.

RESULTS

There were no significant differences between the original and more synthetic data sets across all variables examined in both data sets (P > .05). Further, there was distributional similarity (ie, low standardized propensity mean squared error) between the original observed and synthetic data sets.

CONCLUSIONS

These findings highlight the potential use of synthetic data as a practical solution to privacy and confidentiality issues. Synthetic data can unlock previously inaccessible data sets for exploratory analysis and facilitate multiteam or multicenter collaborations. Interested sport scientists, practitioners, and researchers should consider utilizing the shiny web application (SYNTHETIC DATA-available at https://assetlab.shinyapps.io/SyntheticData/).

The speed and acceleration of the ball carrier and tackler into contact during front-on tackles in rugby league

The aim was to use a combination of video analysis and microtechnology (10 Hz global positioning system [GPS]) to quantify and compare the speed and acceleration of ball-carriers and tacklers during the pre-contact phase (contact - 0.5s) of the tackle event during rugby league match-play. Data were collected from 44 professional male rugby league players from two Super League clubs across two competitive matches. Tackle events were coded and subject to three stages of inclusion criteria to identify front-on tackles. 10 Hz GPS data was synchronised with video to extract the speed and acceleration of the ball-carrier and tackler into each front-on tackle (n = 214). Linear mixed effects models (effect size [ES], confidence intervals, p-values) compared differences. Overall, ball-carriers (4.73 ± 1.12 m∙s-1) had greater speed into front-on tackles than tacklers (2.82 ± 1.07 m∙s-1; ES = 1.69). Ball-carriers accelerated (0.67 ± 1.01 m∙s-2) into contact whilst tacklers decelerated (-1.26 ± 1.36 m∙s-2; ES = 1.74). Positional comparisons showed speed was greater during back vs. back (ES = 0.66) and back vs. forward (ES = 0.40) than forward vs. forward tackle events. Findings can be used to inform strategies to improve performance and player welfare.

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.

External and internal maximal intensity periods of elite youth male soccer matches

Understanding the maximal intensity periods (MIP) of soccer matches can optimise training prescription. The aim was to establish differences between positions and other contextual factors (match location, match outcome, playing formation and score line) for both external and internal MIP variables and to investigate the differences in the match start time between MIP variables. Maximal moving averages (1 to 10 min) for average speed, high-speed running (5.5-7 m·s^-1), sprinting (>7 m·s^-1; all m·min^-1), average acceleration/deceleration (m·s^-2) and heart rate (bpm, % maximal) were calculated from 24 professional youth players across 31 matches. Linear mixed models determined differences in MIP variables between positions, contextual factors and in the match start time of MIPs. Trivial to large positional differences existed in maximal external intensities while central defenders presented the lowest heart rate. It was unclear whether maximal intensities were influenced by contextual factors. MIPs for average speed, acceleration/deceleration and heart rate tend to occur concurrently (ES = trivial) within the first 30 min, while high-speed running and sprinting are likely to occur concurrently (ES = trivial) throughout a whole match. Practitioners could target maximising average speed and average acceleration/deceleration in technical-tactical based training to maximise heart rate responses.

A global perspective on collision and non-collision match characteristics in male rugby union: Comparisons by age and playing standard

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.

Moving beyond velocity derivatives; using global positioning system data to extract sequential movement patterns at different levels of rugby league match-play

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

Defining and quantifying fatigue in the rugby codes

The rugby codes (i.e., rugby union, rugby league, rugby sevens [termed 'rugby']) are team-sports 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.

The contributing external load factors to internal load during small-sided games in professional rugby union players

Introduction

This study aimed to investigate which external load variables were associated with internal load during three small-sided games (SSG) in professional rugby union players.

Methods

Forty professional rugby union players (22 forwards, 18 backs) competing in the English Gallagher Premiership were recruited. Three different SSGs were designed: one for backs, one for forwards, and one for both backs and forwards. General linear mixed-effects models were implemented with internal load as dependent variable quantified using Stagno's training impulse, and external load as independent variables quantified using total distance, high-speed (>61% top speed) running distance, average acceleration-deceleration, PlayerLoad™, PlayerLoad™ slow (<2 m·s^-1), number of get-ups, number of first-man-to-ruck.

Results

Internal load was associated with different external load variables dependent on SSG design. When backs and forwards were included in the same SSG, internal load differed between positional groups (MLE = -121.94, SE = 29.03, t = -4.20).

Discussion

Based on the SSGs investigated, practitioners should manipulate different constraints to elicit a certain internal load in their players based on the specific SSG design. Furthermore, the potential effect of playing position on internal load should be taken into account in the process of SSG design when both backs and forwards are included.

A multi-club analysis of the locomotor training characteristics of elite female soccer players

OBJECTIVE

Quantifying differences in locomotor characteristics of training between two competition levels and between training days within elite female soccer players.

METHODS

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.

RESULTS

WSL players performed more high-speed running distance (HSR; >5.29 m∙s^-1), sprint distance (SpD; >6.26 m∙s^-1), acceleration (ACC; >3 m∙s^-2) and deceleration (DEC; <-3 m∙s^-2) distance than WC players. The largest difference between WSL and WC in HSR and HSR per minute occurred on MD-4, (354.7 vs. 190.29 m and 2.8 vs. 1.7 m∙min^-1). On MD-2, WSL players also covered greater SpD (44.66 vs. 12.42 m), SpD per minute (0.38 vs. 0.11 m∙min^-1) and HSR per minute (1.67 vs. 0.93 m∙min^-1). Between training days both WSL and WC teams reduced HSR and SpD but not ACC and DEC distance from MD-4 to MD-2, with MD-4 the highest training day of the week.

CONCLUSION

MD-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.

The Maximal Intensity Period: Rationalising its Use in Team Sports Practice

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.

The effect of acute sleep extension vs active recovery on post exercise recovery kinetics in rugby union players

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.

Ready for impact? A validity and feasibility study of instrumented mouthguards (iMGs)

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.

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.

Locomotor and technical characteristics of female soccer players training: exploration of differences between competition standards

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.

Digitally Optimizing the Information Flows Necessary to Manage Professional Athletes: A Case Study in Rugby Union

Practical case studies elaborating end-to-end attempts to improve the quality of information flows associated with athlete management processes are scarce in the current sport literature. Therefore, guided by a Business Process Management (BPM) approach, the current study presents the outcomes from a case study to optimize the quality of strength and conditioning (S&C) information flow in the performance department of a professional rugby union club. Initially, the S&C information flow was redesigned using integral technology, activity elimination and activity automation redesign heuristics. Utilizing the Lean Startup framework, the redesigned information flow was digitally transformed by designing data collection, management and visualization systems. Statistical tests used to assess the usability of the data collection systems against industry benchmarks using the System Usability Scale (SUS) administered to 55 players highlighted that its usability (mean SUS score of 87.6 ± 10.76) was well above average industry benchmarks of similar systems (Grade A from SUS scale). In the data visualization system, 14 minor usability problems were identified from 9 cognitive walkthroughs conducted with the High-Performance Unit (HPU) staff. Pre-post optimization information quality was subjectively assessed by administering a standardized questionnaire to the HPU members. The results indicated positive improvements in all of the information quality dimensions (with major improvements to the accessibility) relating to the S&C information flow. Additionally, the methods utilized in the study would be especially beneficial for sporting environments requiring cost effective and easily adoptable information flow digitization initiatives which need to be implemented by its internal staff members.

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 (> 7 m∙s-1) alongside combined high acceleration and deceleration distance (> 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.

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 (ΔPAV) 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 ΔPAV than secondary contact and play the ball tackle stages (p < 0.001). Indirect HAE accounted for 29.8% of HAE and resulted in significantly greater ΔPAV (p < 0.001) than direct HAE, but significantly lower PLA (p < 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.

Longitudinal changes in Super League match locomotor and event characteristics: A league-wide investigation over three seasons in rugby league

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.

Quantifying Fatigue in the Rugby Codes: The Interplay Between Collision Characteristics and Neuromuscular Performance, Biochemical Measures, and Self-Reported Assessments of Fatigue

Locomotor and collision actions that rugby players complete during match-play often lead to substantial fatigue, and in turn, delays in recovery. The methods used to quantify post-match fatigue and recovery can be categorised as subjective and objective, with match-related collision characteristics thought to have a primary role in modulating these recovery measures. The aim of this review was to (1) evaluate how post-match recovery has been quantified in the rugby football codes (i.e., rugby league, rugby union, and rugby sevens), (2) to explore the time-course of commonly used measures of fatigue post-match, and (3) to investigate the relationships between game-related collisions and fatigue metrics. The available evidence suggests that upper-, and lower-body neuromuscular performance are negatively affected, and biomarkers of muscular damage and inflammation increase in the hours and days following match-play, with the largest differences being at 12–36 h post-match. The magnitude of such responses varies within and between neuromuscular performance (Δ ≤ 36%, n = 13 studies) and tissue biomarker (Δ ≤ 585%, n = 18 studies) measures, but nevertheless appears strongly related to collision frequency and intensity. Likewise, the increase in perceived soreness in the hours and days post-match strongly correlate to collision characteristics across the rugby football codes. Within these findings, there are specific differences in positional groups and recovery trajectories between the codes which relate to athlete characteristics, and/or locomotor and collision characteristics. Finally, based on these findings, we offer a conceptual model of fatigue which details the multidimensional latent structure of the load to fatigue relationship contextualised to rugby. Research to date has been limited to univariate associations to explore relationships between collision characteristics and recovery, and multivariate methods are necessary and recommended to account for the latent structures of match-play external load and post-match fatigue constructs. Practitioners should be aware of the typical time windows of fatigue recovery and utilise both subjective and objective metrics to holistically quantify post-match recovery in rugby.

Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?

Athlete external load is typically quantified as volumes or discretised threshold values using distance, speed and time. A framework accounting 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 its stability. Thirteen rugby league players during one match were analysed to demonstrate the application of the framework. The framework (Sequential Movement Pattern-mining [SMP]) applies techniques to analyse i) geospatial data (i.e., decimal degree latitude and longitude), ii) determine players turning angles, iii) improve movement descriptor assignment, thus improving movement unit formation and iv) improve 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 dis-similarities between players and playing positions. The SMP framework provides a robust and stable method that 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.

Misuse of the term 'load' in sport and exercise science

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.

The Duration-specific Peak Average Running Speeds of European Super League Academy Rugby League Match Play

ABSTRACT

Whitehead, S, Till, K, Weaving, D, Dalton-Barron, N, Ireton, M, and Jones, B. Duration-specific peak average running speeds of European Super League Academy rugby league match play. J Strength Cond Res 35(7): 1964-1971, 2021-This study aimed to quantify the duration-specific peak average running speeds of Academy-level rugby league match play, and compare between playing positions. Global positioning system data were collected from 149 players competing across 9 teams during 21 professional Academy (under-19) matches. Players were split into 6 positions: hookers (n = 40), fullbacks (n = 24), halves (n = 47), outside backs (n = 104), middles (n = 118), and backrow forwards (n = 104). Data were extracted and the 10-Hz raw velocity files exported to determine the peak average running speeds, via moving averages of speed (m·min-1), for 10- and 30-second, and 1- to 5- and 10-minute durations. The data were log transformed and analyzed using linear mixed-effect models followed by magnitude-based inferences, to determine differences between positions. Differences in the peak average running speeds are present between positions, indicating the need for position-specific prescription of velocity-based training. Fullbacks perform possibly to most likely greater average running speeds than all other positions, at each duration, except at 10 seconds vs. outside backs. Other differences are duration dependent. For 10 seconds, the average running speed is most likely greater for outside backs vs. the hookers, middles, and backrow forwards, but likely to most likely lower for 10 minutes. Hookers have possibly trivial or lower average speed for 10 seconds vs. middles and backrow forwards, but very likely greater average running speed for 10 minutes. The identified peak average running speeds of Academy-level match play seem similar to previously reported values of senior professional level.

Does perceived wellness influence technical-tactical match performance? A study in youth international rugby using partial least squares correlation analysis

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

Latent variable dose-response modelling of external training load measures and musculoskeletal responses in elite rugby league players

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.

A systematic review of small sided games within rugby: Acute and chronic effects of constraints manipulation

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.

Quantifying the External and Internal Loads of Professional Rugby League Training Modes: Consideration for Concurrent Field-Based Training Prescription

Weaving, D, Jones, B, Till, K, Marshall, P, Earle, K, and Abt, G. Quantifying the external and internal loads of professional rugby league training modes: consideration for concurrent field-based training prescription. J Strength Cond Res 34(12): 3514-3522, 2020-Practitioners prescribe numerous training modes to develop the varied physical qualities that professional rugby league players must express during competition. The aim of this study was to determine how the magnitude of external and internal training load per minute of time differs between modes in professional rugby league players. These data were collected from 17 players across 716 individual sessions (mean [SD] sessions: 42 [13] per player) which were categorized by mode (conditioning [CON], small-sided games, skills, and sprint training). Derived from global positioning systems (5 Hz with 15 Hz interpolation), the distances covered within arbitrary speed and metabolic power thresholds were determined to represent the external load. Session rating of perceived exertion and individualized training impulse represented the internal load. All data were made relative to the session duration. The differences in time-relative load methods between each mode were assessed using magnitude-based inferences. Small-sided games and CON very likely to almost certainly produced the greatest relative internal and external loads. Sprint training provided players with the greatest sprinting and maximal-power distances without a concomitant increase in the internal load. The metabolic power method complements speed-based quantification of the external load, particularly during small-sided games and skills training. In practice, establishing normative loads per minute of time for each mode can be useful to plan future training by multiplying this value by the planned session duration.

Rugby league ball carrier injuries: The relative importance of tackle characteristics during the European Super League

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.

Locomotor characteristics of the women's inaugural super league competition and the rugby league world cup

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.

The relative contribution of training intensity and duration to daily measures of training load in professional rugby league and union

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, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60-70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.

Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Objective sleep patterns and validity of self-reported sleep monitoring across different playing levels in rugby union

Background

Growing evidence highlights that elite rugby union players experience poor sleep quality and quantity which can be detrimental for performance.

Objectives

This study aimed to i) compare objective sleep measures of rugby union players between age categories over a one week period, and ii) compare self-reported measures of sleep to wristwatch actigraphy as the criterion.

Methods

Two hundred and fifty-three nights of sleep were recorded from 38 players representing four different age groups (i.e. under 16, under 18, senior academy, elite senior) in a professional rugby union club in the United Kingdom (UK). Linear mixed models and magnitude-based decisions were used for analysis.

Results

The analysis of sleep schedules showed that U16 players went to bed and woke up later than their older counterparts (small differences). In general, players obtained seven hours of sleep per night, with trivial or unclear differences between age groups. The validity analysis highlighted a large relationship between objective and subjective sleep measures for bedtime (r = 0.56 [0.48 to 0.63]), and get up time (r = 0.70 [0.63 to 0.75]). A large standardised typical error (1.50 [1.23 to 1.88]) was observed for total sleep time.

Conclusion

This study highlights that differences exist in sleep schedules between rugby union players in different age categories that should be considered when planning training. Additionally, self-reported measures overestimated sleep parameters. Coaches should consider these results to optimise sleep habits of their players and should be careful with self-reported sleep measures.

Application of velocity loss thresholds during free-weight resistance training: Responses and reproducibility of perceptual, metabolic, and neuromuscular outcomes

The aim of this study was to investigate the differences and long-term reliability in perceptual, metabolic, and neuromuscular responses to velocity loss resistance training protocols. Using a repeated, counterbalanced, crossover design, twelve team-sport athletes completed 5-sets of barbell back-squats at a load corresponding to a mean concentric velocity of ~0.70 m·s^-1. On different days, repetitions were performed until a 10%, 20% or 30% velocity loss was attained, with outcome measures collected after each set. Sessions were repeated after four-weeks. There were substantial between-protocol differences in post-set differential ratings of perceived exertion (dRPE, i.e., breathlessness and leg muscles, AU) and blood lactate concentration (B[La], mmol·L^-1), such that 30%>20%>10% by small to large magnitudes. Differences in post-set countermovement jump (CMJ) variables were small for most variables, such that 30%<20%<10%. Standard deviations representing four-week variability of post-set responses to each protocol were: dRPE, 8-11; B[La], 0.8-1.0; CMJ height, 1.6-2.0; CMJ PPO, 1.0-1.8; CMJ PCV, 0.04-0.06; CMJ 100ms-Impulse, 5.7-11.9. Velocity loss thresholds control the magnitude of perceptual, metabolic, and neuromuscular responses to resistance training. For practitioners wanting to reliably prescribe training that can induce a given perceptual, metabolic, or neuromuscular response, it is strongly advised that velocity-based thresholds are implemented.

Visualizing the Complexity of the Athlete-Monitoring Cycle Through Principal-Component Analysis

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.

Peak movement and collision demands of professional rugby league competition

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.

Overcoming the problem of multicollinearity in sports performance data: A novel application of partial least squares correlation analysis

Objectives

Professional sporting organisations invest considerable resources collecting and analysing data in order to better understand the factors that influence performance. Recent advances in non-invasive technologies, such as global positioning systems (GPS), mean that large volumes of data are now readily available to coaches and sport scientists. However analysing such data can be challenging, particularly when sample sizes are small and data sets contain multiple highly correlated variables, as is often the case in a sporting context. Multicollinearity in particular, if not treated appropriately, can be problematic and might lead to erroneous conclusions. In this paper we present a novel ‘leave one variable out’ (LOVO) partial least squares correlation analysis (PLSCA) methodology, designed to overcome the problem of multicollinearity, and show how this can be used to identify the training load (TL) variables that influence most ‘end fitness’ in young rugby league players.

Methods

The accumulated TL of sixteen male professional youth rugby league players (17.7 ± 0.9 years) was quantified via GPS, a micro-electrical-mechanical-system (MEMS), and players’ session-rating-of-perceived-exertion (sRPE) over a 6-week pre-season training period. Immediately prior to and following this training period, participants undertook a 30–15 intermittent fitness test (30-15_IFT), which was used to determine a players ‘starting fitness’ and ‘end fitness’. In total twelve TL variables were collected, and these along with ‘starting fitness’ as a covariate were regressed against ‘end fitness’. However, considerable multicollinearity in the data (VIF >1000 for nine variables) meant that the multiple linear regression (MLR) process was unstable and so we developed a novel LOVO PLSCA adaptation to quantify the relative importance of the predictor variables and thus minimise multicollinearity issues. As such, the LOVO PLSCA was used as a tool to inform and refine the MLR process.

Results

The LOVO PLSCA identified the distance accumulated at very-high speed (>7 m·s^-1) as being the most important TL variable to influence improvement in player fitness, with this variable causing the largest decrease in singular value inertia (5.93). When included in a refined linear regression model, this variable, along with ‘starting fitness’ as a covariate, explained 73% of the variance in v30-15_IFT ‘end fitness’ (p<0.001) and eliminated completely any multicollinearity issues.

Conclusions

The LOVO PLSCA technique appears to be a useful tool for evaluating the relative importance of predictor variables in data sets that exhibit considerable multicollinearity. When used as a filtering tool, LOVO PLSCA produced a MLR model that demonstrated a significant relationship between ‘end fitness’ and the predictor variable ‘accumulated distance at very-high speed’ when ‘starting fitness’ was included as a covariate. As such, LOVO PLSCA may be a useful tool for sport scientists and coaches seeking to analyse data sets obtained using GPS and MEMS technologies.

Modelling the relationships between volume, intensity and injury-risk in professional rugby league players

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 (mmin^-1), high speed distance ([m]>20kmh^-1), very-high speed distance ([m]>25kmh^-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: >60mmin^-1) demonstrated a positive association with injury-risk, whilst high two-weekly loads (high: >82 mmin^-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.

The peak duration-specific locomotor demands and concurrent collision frequencies of European Super League rugby

Understanding the most demanding passages of European Super League competition can optimise training prescription. We established positional and match half differences in peak relative distances (m·min^-1) across durations, and the number of collisions, high-speed- and very-high-speed-distance completed in the peak 10 min period. Moving-averages (10 s, 30 s, 1 min, 5 min, 10 min) of instantaneous speed (m·s^-1) were calculated from 25 professional rugby league players during 25 matches via microtechnology. Maximal m·min^-1 was taken for each duration for each half. Concurrently, collisions (n), high-speed- (5 to 7 m·s^-1; m) and very-high-speed-distance (> 7 m·s^-1; m) were coded during each peak 10 min. Mixed-effects models determined differences between positions and halves. Aside from peak 10 s, trivial differences were observed in peak m·min^-1 between positions or halves across durations. During peak 10 min periods, adjustables, full- and outside-backs ran more at high-speed and very-high-speed whilst middle- and edge-forwards completed more collisions. Peak m·min^-1 is similar between positional groups across a range of durations and are maintained between halves of the match. Practitioners should consider that whilst the overall peak locomotor "intensity" is similar, how they achieve this differs between positions with forwards also exposed to additional collision bouts.

Validity of Real-Time Data Generated by a Wearable Microtechnology Device

The purpose of this study was to investigate the validity of global positioning system (GPS) and micro-electrical-mechanical-system (MEMS) data generated in real time through a dedicated receiver. Postsession data acted as the criterion as it is used to plan the volume and intensity of future training and is downloaded directly from the device. Twenty-five professional rugby league players completed 2 training sessions wearing an MEMS device (Catapult S5, firmware version: 5.27). During sessions, real-time data were collected through the manufacturer receiver and dedicated software (Openfield v1.14), which was positioned outdoors at the same location for every session. The GPS variables included total-, low- (0-3 m·s), moderate- (3.1-5 m·s), high- (5.1-7 m·s), and very high-speed (>7.1 m·s) distances. Micro-electrical-mechanical-system data included total session PlayerLoad. When compared to postsession data, mean bias for total-, low-, moderate-, high-, and very high-speed distances were all trivial, with the typical error of the estimate (TEE) small, small, trivial, trivial and small, respectively. Pearson correlation coefficients for total-, low-, moderate-, high- and very-high-speed distances were nearly perfect, nearly perfect, perfect, perfect, and nearly perfect, respectively. For PlayerLoad, mean bias was trivial, whereas TEE was moderate and correlation nearly perfect. Practitioners should be confident that when interpreting real-time speed-derived metrics, the data generated in real-time are comparable with those downloaded directly from the device postsession. However, practitioners should refrain from interpreting accelerometer-derived data (i.e., PlayerLoad) or acknowledge the moderate error associated with this real-time measure.