Whole, half and peak running demands during club and international youth rugby league match-play

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.

What Tests are Used to Assess the Physical Qualities of Male, Adolescent Rugby League Players? A Systematic Review of Testing Protocols and Reported Data Across Adolescent Age Groups

BACKGROUND

Understanding the physical qualities of male, adolescent rugby league players across age groups is essential for practitioners to manage long-term player development. However, there are many testing options available to assess these qualities, and differences in tests and testing protocols can profoundly influence the data obtained.

OBJECTIVES

The aims of this systematic review were to: (1) identify the most frequently used tests to assess key physical qualities in male, adolescent rugby league players (12-19 years of age); (2) examine the testing protocols adopted in studies using these tests; and (3) synthesise the available data from studies using the most frequently used tests according to age group.

METHODS

A systematic search of five databases was conducted. For inclusion, studies were required to: (1) be original research that contained original data published in a peer-reviewed journal; (2) report data specifically for male, adolescent rugby league players; (3) report the age for the recruited participants to be between 12 and 19 years; (4) report data for any anthropometric quality and one other physical quality and identify the test(s) used to assess these qualities; and (5) be published in English with full-text availability. Weighted means and standard deviations were calculated for each physical quality for each age group arranged in 1-year intervals (i.e., 12, 13, 14, 15, 16, 17 and 18 years) across studies.

RESULTS

37 studies were included in this systematic review. The most frequently used tests to assess anthropometric qualities were body mass, standing height, and sum of four skinfold sites. The most frequently used tests to assess other physical qualities were the 10-m sprint (linear speed), 505 Agility Test (change-of-direction speed), Multistage Fitness Test (aerobic capacity), bench press and back squat one-repetition maximum tests (muscular strength), and medicine ball throw (muscular power). Weighted means calculated across studies generally demonstrated improvements in player qualities across subsequent age groups, except for skinfold thickness and aerobic capacity. However, weighted means could not be calculated for the countermovement jump.

CONCLUSION

Our review identifies the most frequently used tests, but highlights variability in the testing protocols adopted. If these tests are used in future practice, we provide recommended protocols in accordance with industry standards for most tests. Finally, we provide age-specific references for frequently used tests that were implemented with consistent protocols. Clinical Trial Registration This study was conducted in accordance with the Preferred Reporting Items of Systematic Review and Meta-analysis guidelines and was registered with PROSPERO (ID: CRD42021267795).

The introduction of the six-again rule has increased acceleration intensity across all positions in the National Rugby League competition

The impact of the six-again rule change on the movement of National Rugby League (NRL) athletes was examined. Player Global Navigation Satellite System (GNSS) data (10 Hz) was collected from 42 athletes who competed in 56 matches across the 2019 to 2021 NRL seasons. Maximal mean speed (m·min^-1) and acceleration (m·s^-2) were established across a 10 s to 10-min duration via raw GNSS files, with subsequent intercept (mean estimates) and slope values determined via power law analysis. The distributions of match distance (m) and impulse (kN·s^-1) were established during ball-in-play time. To determine the significance between positions and seasons under different rules, linear mixed models were used. Effects were described using standardised effect sizes (ES) with 90% confidence limits (CL). Acceleration intercepts (power law-derived) across all positions were substantially greater (>0.6 SD) following the introduction of the six-again rule in the 2020 (mean ± SD; 1.02 ± 0.10 m·s^-2) and 2021 seasons (1.05 ± 0.08 m·s^-2) compared to the 2019 season (0.91 ± 0.07 m·s^-2). Mean acceleration during ball-in-play time was greater in 2020 (ES; 90% CL = 0.75; ± 0.32) compared to 2019. The acceleration requirements of rugby league increased across all positional groups following the modification in NRL competition rules. Practitioners should tailor training programs for athletes to reflect the increased acceleration intensity found under the revised competition format.

The field and resistance training loads of academy rugby league players during a pre-season: Comparisons across playing positions

Male academy rugby league players are required to undertake field and resistance training to develop the technical, tactical and physical qualities important for success in the sport. However, limited research is available exploring the training load of academy rugby league players. Therefore, the purpose of this study was to quantify the field and resistance training loads of academy rugby league players during a pre-season period and compare training loads between playing positions (i.e., forwards vs. backs). Field and resistance training load data from 28 adolescent male (age 17 ± 1 years) rugby league players were retrospectively analysed following a 13-week pre-season training period (85 total training observations; 45 field sessions and 40 resistance training sessions). Global positioning system microtechnology, and estimated repetition volume was used to quantify external training load, and session rating of perceived exertion (sRPE) was used to quantify internal training load. Positional differences (forwards n = 13 and backs n = 15) in training load were established using a linear mixed effect model. Mean weekly training frequency was 7 ± 2 with duration totaling 324 ± 137 minutes, and a mean sRPE of 1562 ± 678 arbitrary units (AU). Backs covered more high-speed distance than forwards in weeks two (p = 0.024), and 11 (p = 0.028). Compared to the forwards, backs completed more lower body resistance training volume in week one (p = 0.02), more upper body volume in week three (p< 0.001) and week 12 (p = 0.005). The findings provide novel data on the field and resistance-based training load undertaken by academy rugby league players across a pre-season period, highlighting relative uniformity between playing positions. Quantifying training load can support objective decision making for the prescription and manipulation of future training, ultimately aiming to maximise training within development pathways.

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis

Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes.

The inter-device reliability of global navigation satellite systems during team sport movement across multiple days

OBJECTIVES

(1) Determine the inter-device and inter-manufacturer reliability; and (2) investigate the variation in reliability over time for common global navigation satellite systems.

DESIGN

Repeated measures.

METHODS

A total of twenty 10-Hz devices manufactured by StatSports (n = 10, Apex Pro; StatSports, Newry, Ireland) and Catapult Sports (n = 10, Vector S7; Catapult Sports, Melbourne, Australia) were towed on a sprint sled during 8 × 40-minute team sport movement protocol over a 4-week period. The coefficient of variations for distance, velocity and acceleration/deceleration metrics were calculated to show dispersion of the data relative to the mean or median for each manufacturer and interpreted as good, ≤5%; moderate, <10%; and poor, coefficient of variation ≥10%. The coefficient of variation range described the variation in reliability and was interpreted as small, ≤5%; moderate, <10% and large, ≥10%. Inter-manufacturer agreement was represented as a Cohen d (±95% confidence interval) standardised effect size.

RESULTS

Inter-device reliability for distance, peak velocity and average acceleration was good (coefficient of variation = 0.1 to 3.9%) for both manufacturers, with small variation across sessions. For most threshold-based acceleration and deceleration counts, StatSports devices showed good to moderate reliability, with moderate variation across sessions; Catapult showed good to poor reliability, with large variation across sessions. Inter-manufacturer agreement demonstrated moderate to very large effect sizes reported for most metrics.

CONCLUSIONS

Reliability was suitable and consistent for measures of distance, velocity, and average acceleration. StatSports devices generally possessed suitable reliability and consistency for threshold-based accelerations and decelerations, though Catapult devices did not. Most metrics should not be compared between manufacturers.

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.

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.

Using Principal Component Analysis to Compare the Physical Qualities Between Academy and International Youth Rugby League Players

PURPOSE

To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis.

METHODS

Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups.

RESULTS

Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = -1.83) were identified between U18 academy and international backs within PC1.

CONCLUSION

Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.

A Comparison of Match Demands Using Ball-in-Play versus Whole Match Data in Professional Soccer Players of the English Championship

This is the first study to report the whole match, ball-in-play (BiP), ball-out-of-play (BoP), and Max BiP (worst case scenario phases of play) demands of professional soccer players competing in the English Championship. Effective playing time per soccer game is typically <60 min. When the ball is out of play, players spend time repositioning themselves, which is likely less physically demanding. Consequently, reporting whole match demands may under-report the physical requirements of soccer players. Twenty professional soccer players, categorized by position (defenders, midfielders, and forwards), participated in this study. A repeated measures design was used to collect Global Positioning System (GPS) data over eight professional soccer matches in the English Championship. Data were divided into whole match and BiP data, and BiP data were further sub-divided into different time points (30-60 s, 60-90 s, and >90 s), providing peak match demands. Whole match demands recorded were compared to BiP and Max BiP, with BiP data excluding all match stoppages, providing a more precise analysis of match demands. Whole match metrics were significantly lower than BiP metrics (p < 0.05), and Max BiP for 30-60 s was significantly higher than periods between 60-90 s and >90 s. No significant differences were found between positions. BiP analysis allows for a more accurate representation of the game and physical demands imposed on professional soccer players. Through having a clearer understanding of maximum game demands in professional soccer, practitioners can design more specific training methods to better prepare players for worst case scenario passages of play.

Evaluating Methods for Imputing Missing Data from Longitudinal Monitoring of Athlete Workload

Missing data can influence calculations of accumulated athlete workload. The objectives were to identify the best single imputation methods and examine workload trends using multiple imputation. External (jumps per hour) and internal (rating of perceived exertion; RPE) workload were recorded for 93 (45 females, 48 males) high school basketball players throughout a season. Recorded data were simulated as missing and imputed using ten imputation methods based on the context of the individual, team and session. Both single imputation and machine learning methods were used to impute the simulated missing data. The difference between the imputed data and the actual workload values was computed as root mean squared error (RMSE). A generalized estimating equation determined the effect of imputation method on RMSE. Multiple imputation of the original dataset, with all known and actual missing workload data, was used to examine trends in longitudinal workload data. Following multiple imputation, a Pearson correlation evaluated the longitudinal association between jump count and sRPE over the season. A single imputation method based on the specific context of the session for which data are missing (team mean) was only outperformed by methods that combine information about the session and the individual (machine learning models). There was a significant and strong association between jump count and sRPE in the original data and imputed datasets using multiple imputation. The amount and nature of the missing data should be considered when choosing a method for single imputation of workload data in youth basketball. Multiple imputation using several predictor variables in a regression model can be used for analyses where workload is accumulated across an entire season.

Whole and peak physical characteristics of elite youth female soccer match-play

This study quantified whole and peak physical characteristics of Under (U)14 and U16 elite youth female soccer, and compared by position and age-group. Data was collected using 10 Hz GPS units from 431 match observations, during 50 matches involving 201 players (U14 n = 93; U16 n = 108) representing Regional Talent Centres in The Football Association's Girl's England Talent Pathway League. Whole match data were reported as absolute and relative; total (TD), high-speed running (HSR; ≥3.46 m·s^-1), very high-speed running (VHSR; ≥5.29 m·s^-1), and sprinting (SPR; ≥6.26 m·s^-1) distance, and maximum velocity. Moving average analysis determined peak data (1-10 minute durations). Linear mixed models established position-specific differences. U16s covered greater; absolute distance at all speeds (small-moderate ESs; p < 0.001); relative VHSR and SPR m·min^-1 (small-moderate ESs; p < 0.001); peak TD and HSR m·min^-1 (small ESs) across several peak-durations, and VHSR m·min^-1 (small ESs; p < 0.001) across all peak-durations compared to U14s. Position-specific differences were observed across all positions between and within both age-groups, identifying whole and peak physical characteristics are age- and position-dependent within elite youth female soccer match-play. Findings may facilitate informed coaching practices and training programme design, talent identification and development processes.

On-field movements, heart rate responses and perceived exertion of lead referees in Rugby World Cup matches, 2019

OBJECTIVES

Quantify the on-field movements, heart rate and perceived exertion (RPE) of lead referees during Rugby World Cup matches.

DESIGN

Descriptive, observational.

METHODS

On-field movements (distance, average speed, high-speed running [>5ms^-1]), heart rate and RPE of 11 lead referees were assessed during 29 Rugby World Cup (Knockout and Pool) matches. Average speed and heart rate were assessed using rolling average methods (1-10min epochs). Linear mixed models and Cohen's effect size (d) were used to compare match variables between Pool and Knockout matches.

RESULTS

Referees covered on average 6674±566m (65.8±6.3mmin^-1), with 586±290m in high-speed running. Mean heart rate was 146±9 beatsmin^-1, summated-heart-rate-zones was 235±36AU, and sRPE load was 577±205AU. A large reduction (d=1.40) in high-speed running distance and moderate reductions in average speed over 1 (d=0.81) and 2min (d=0.83) epochs were found during Knockout, compared to Pool matches. Differences between Pool and Knockout matches on other measures of referee movement, heart rate and RPE were trivial.

CONCLUSIONS

This is the first investigation to examine the effect of competition stage on rugby union referees on-field performance. Individual and match contextual factors may explain the reduction in high-speed running during Knockout matches, however more detailed examination of the factors influencing referee performance is required for greater insight into the key performance indicators in rugby union. Nonetheless, these data provide practitioners with knowledge to assist in preparation of rugby union referees for future Rugby World Cup competitions.

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

BACKGROUND

Quantifying the peak match demands within the football codes is useful for the appropriate prescription of external training load. Wearable microtechnology devices can be used to identify the peak match demands, although various methodologies exist at present.

OBJECTIVES

This systematic review aimed to identify the methodologies and microtechnology-derived variables used to determine the peak match demands, and to summarise current data on the peak match demands in the football codes.

METHODS

A systematic search of electronic databases was performed from earliest record to May 2018; keywords relating to microtechnology, peak match demands and football codes were used.

RESULTS

Twenty-seven studies met the eligibility criteria. Six football codes were reported: rugby league (n = 7), rugby union (n = 5), rugby sevens (n = 4), soccer (n = 6), Australian Football (n = 2) and Gaelic Football (n = 3). Three methodologies were identified: moving averages, segmental and 'ball in play'. The moving averages is the most commonly used (63%) and superior method, identifying higher peak demands than other methods. The most commonly used variables were relative distance covered (63%) and external load in specified speed zones (57%).

CONCLUSION

This systematic review has identified moving averages to be the most appropriate method for identifying the peak match demands in the football codes. Practitioners and researchers should choose the most relevant duration-specific period and microtechnology-derived variable for their specific needs. The code specific peak match demands revealed can be used for the prescription of conditioning drills and training intensity.