Search

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

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

Johnston, RD, Murray, NB, Austin, DJ, and Duthie, G. Peak movement and technical demands of professional Australian football competition. J Strength Cond Res 35(10): 2818-2823, 2021-The aim of this study was to determine the average peak movement and technical demands of professional Australian football (AF) across a number of period durations using an observational cohort design. This information will be able to guide duration-specific intensities for training drills. Microtechnology and technical performance data were recorded across 22 games of the 2017 AF League season. The peak 1-, 3-, 5-, 7-, and 10-minute rolling periods were determined from each game for each player for each frequency of skill involvements. Average speed (m·min-1) and accelerometer load (PlayerLoad; PL·min-1) were used as measures of physical output, and any disposal of the football or tackle was used as a technical involvement. Linear mixed models and Cohen's effect size (ES) statistic were used to determine the impact technical involvements had on movement profiles. There were substantial reductions in average speed across each duration as the number of technical involvements increased, other than for the 10-minute period. The reductions in speed were greatest during the 1-minute period for 1 (ES = -0.59 ± 0.13), 2 (ES = -1.96 ± 0.17), and 3 (ES = -2.39 ± 0.27) involvements. Similarly, less pronounced reductions were seen for accelerometer load, other than during the 7- and 10-minute periods where there were small to moderate increases in load for periods with technical involvements. Players may have to perform as many as 3 technical involvements a minute while covering 150-160 m·min-1. This information provides coaches with the peak speed, accelerometer load, and technical demands of competition. There are reductions in movement profiles as the number of technical involvements increases.

This study aimed to observe whether there were substantial differences in acceleration during team-sport locomotion between GNSS manufacturers. Speed and acceleration were obtained from 7 professional rugby league athletes via 2 GNSS manufacturers (GPSports EVO, 10 Hz and STATSports Apex, 10 Hz) worn together during the same training sessions (n = 13). Raw GNSS data were exported from respective proprietary software and a 1 Hz, 4th-order Butterworth filter applied, with differences in speed and acceleration calculated between manufacturers. To determine the difference in acceleration and speed, a root mean square deviation (RMSD) was used. Linear mixed models were used and no substantial differences were found between manufacturers in raw and filtered speed variables. RMSD for average acceleration (m · s−2) decreased from raw (RMSD: 1.77 ± 0.37 m · s−2) to those seen at the filtered (RMSD: 0.27 ± 0.23 m · s−2) and twice filtered (0.24 ± 0.23 m · s−2) variables. Raw average acceleration (m · s−2) was substantially higher in Apex compared to EVO (Difference (Diff); CI: −0.82; −0.84 to −0.80). Following application of the common filter there was no substantial difference between GNSS models for average acceleration (Diff; CI: −0.04; −0.04 to −0.04). Acceleration variables derived from each manufacturer’s proprietary software were substantially different.

Thornton, HR, Armstrong, CR, Gamble, T, Rigby, A, Johnston, RD, and Duthie, GM. Quantifying the movement characteristics of Australian Football League Women's competition. J Strength Cond Res 36(12): 3415-3421, 2022-The purpose is to provide an overview of the externally measured movement characteristics of Australian Football League Women's (AFLW) competition, and the variability in this between matches. A range of movement variables were measured from 28 athletes who wore 10-Hz global positioning system devices during games and were summarized according to positional groups. The variance in each athlete's match loads for each round were expressed using standardized scores ( z -scores), and the change compared with the first game played was calculated and expressed as a standard deviation. Furthermore, using the raw export, moving means (1-10 minutes) of speed (m·min -1 ) and acceleration (m·s -2 ) were calculated. Following log transformation of the maximal means, intercept and slopes were calculated. Linear mixed models identified differences between positional groups for match loads, and intercept and slopes. Effects were described using standardized effect sizes (ESs) and magnitude-based decisions. There were no substantial and unclear differences between positional groups for match loads (ES range; ±confidence limits = 0.10-0.80; ±0.30-4.30) and for intercept and slopes (ES range; 0.04-0.44; ±0.52-2.11). Large within-athlete variation in match demands between rounds was observed ( z -score up to -1.8 SD for distance), and the maximal means for speed and acceleration demonstrate the extensive physical demands of AFLW competition. These data describe the intense and variable physical demands of AFLW competition, and further provide novel information regarding the maximal mean intensities and intercept and slopes, which should assist practitioners in planning and prescribing training in preparation for competition.

Johnston, RD, Hewitt, A, and Duthie, G. Validity of real-time ultra-wideband global navigation satellite system data generated by a wearable microtechnology unit. J Strength Cond Res 34(7): 2071–2075, 2020—This study aimed to determine the validity of real-time ultra-wideband data generated by a wearable microtechnology unit during rugby league training sessions using a repeated-measures crossover study. Twenty-four semiprofessional rugby league players wore a commercially available microtechnology device (StatSports Apex, Newry, Northern Ireland) during 10 training sessions. Total distance; moderate-speed running (3.6–4.9 m·s−1); high-speed running (5.0–6.9 m·s−1); very high-speed running (≥7 m·s−1); maximum velocity (m·s−1); the number of high-intensity accelerations (≥2.78 m·s−2) and decelerations (≥−2.78 m·s−2); dynamic stress load (AU); and high metabolic load distance (m) were recorded in real time through an Apex beacon over a secured wireless network before being exported to a csv file at the end of the session. The data were then downloaded to a computer after event. To determine the validity of the real-time data, they were compared with the postevent downloaded data using coefficient of variation and Pearson's correlation coefficient. There was almost perfect agreement between real-time and postevent downloaded data for all variables reported. The overall bias effect size scores were all trivial, ranging from 0.00 for total distance and high-speed running up to −0.12 for maximal velocity; Pearson's correlations were either perfect or nearly perfect (r = 0.98–1.00). Irrespective of the movement speed, the data collected by these devices in real time show excellent levels of agreement with postevent downloaded data.

Background The use of Global Positioning Systems (GPS) has provided Sports Scientists with the ability to investigate locomotive behavior in sport. With Lacrosse being the fastest growing sport in the USA, there is no GPS analysis available, in current literature, for female lacrosse competition. Aims This study aims to quantify the external outputs of women’s collegiate lacrosse competition and determine any positional differences. Methods Activity profiles were obtained, via GPS, from 14 players across 7 NCAA division-1 conference games. Total distance covered (metres), maximum velocity (m s−1), speed (m min−1), acceleration (m s−2), and metabolic power (PMET; W kg−1) were used to establish outputs throughout both periods of match-play. Peak values were obtained, by using a moving-average approach for durations lasting 1–10 min, for speed (m min−1), acceleration (m s−2), and metabolic power (PMET; W kg−1). Results Defenders covered more distance throughout a whole match play, compared to their positional counterparts (Effect size [ES] range = 0.69–1.17). All positions showed a decrease in the second half, compared to the first half, for speed, acceleration, and metabolic power (ES = 0.87, 0.64, and 0.86, respectively). The only positional difference between moving-average durations was at the 8, 9 and 10-min markers for average acceleration, where defenders were greater than attackers (ES range = 0.72 – 0.76). Conclusions This study presented a construction of the whole-period outputs as well as peak intensities for women’s collegiate lacrosse competition, that can provide coaches with the knowledge to assist with prescribing appropriate training drills to prepare for game-demands.

Three-dimensional motion capture systems such as Vicon have been used to validate commercial electronic performance and tracking systems. However, three-dimensional motion capture cannot be used for large capture areas such as a full football pitch due to the need for many fragile cameras to be placed around the capture volume and a lack of suitable depth of field of those cameras. There is a need, therefore, for a hybrid testing solution for commercial electronic performance and tracking systems using highly precise three-dimensional motion capture in a small test area and a computer vision system in other areas to test for full-pitch coverage by the commercial systems. This study aimed to establish the validity of VisionKit computer vision system against three-dimensional motion capture in a stadium environment. Ten participants undertook a series of football-specific movement tasks, including a circuit, small-sided games and a 20 m sprint. There was strong agreement between VisionKit and three-dimensional motion capture across each activity undertaken. The root mean square difference for speed was 0.04 m·s−1 and for position was 0.18 m. VisionKit had strong agreement with the criterion three-dimensional motion capture system three-dimensional motion capture for football-related movements tested in stadium environments. VisionKit can thus be used to establish the concurrent validity of other electronic performance and tracking systems in circumstances where three-dimensional motion capture cannot be used.

Johnston, RD, Thornton, HR, Wade, JA, Devlin, P, and Duthie, GM. The distribution of match activities relative to the maximal mean intensities in professional rugby league and Australian football. J Strength Cond Res XX(X): 000–000, 2020—This study determined the distribution of distance, impulse, and accelerometer load accumulated at intensities relative to the maximal mean 1-minute peak intensity within professional rugby league and Australian football. Within 26 rugby league (n = 24 athletes) and 18 Australian football (n = 38 athletes) games, athletes wore global navigation satellite system devices (n = 608 match files). One-minute maximal mean values were calculated for each athlete per game for speed (m·minP−1P), accelerometer load (AU·minP−1P), and acceleration (m·sP−2P). Volumes for each parameter were calculated by multiplying by time, specifying total distance, accelerometer load, and impulse. The distribution of intensity of which these variables were performed relative to the maximal mean was calculated, with percentages ranging from 0–110%, separated into 10% thresholds. Linear mixed models determined whether the distribution of activities within each threshold varied, and positional differences. Effects were described using standardized effect sizes (ESs), and magnitude-based decisions. Across both sports, the distribution of activity (%) largely reduced the closer to the maximal mean 1-minute peak and was highest at ∼60% of the maximal mean peak. When compared with Australian football, a higher percentage of total distance was accumulated at higher intensities (70–80% and 100–110%) for rugby league (ES range = 0.82–0.87), with similar, yet larger differences for accelerometer load >80% (0.78–1.07) and impulse >60% (1.00–2.26). These findings provide information of the volume of activities performed relative to the mean maximal 1-minute peak period, which may assist in the prescription of training.

Duthie, GM, Thomas, EJ, Bahnisch, J, Thornton, HR, and Ball, K. Using small-sided games in field hockey: Can they be used to reach match intensity? J Strength Cond Res 36(2): 498-502, 2022-Small-sided games (SSGs) are commonly used in field hockey to replicate or overload physical and tactical competition demands; however, little evidence is available regarding if specific SSG formats achieve this. This research investigated how speed and acceleration differed between 5-minute SSG formats (2v2, 3v3, and 4v4) and also compared with competition. Elite male field hockey athletes (n = 10) participated in the SSGs with standard rules, on pitches of 30 × 20, 35 × 25, and 40 × 30 m, respectively (∼150 m2 per athlete excluding goal keepers). Global Positioning System devices measured speed (m·min-1) and acceleration (m·s-2). Differences between mean speed and acceleration during SSGs and 5-minute maximal mean values from competition (calculated using moving averages) were quantified using linear mixed models and described using standardized effect sizes (ES) and 90% confidence intervals. Between SSG formats, there were nonsubstantial differences for mean speed and acceleration (ES range -0.4 to 0.8). Compared with competition, mean speed was substantially lower for all SSG formats (ES range -1.4 to -1.8), whereas mean acceleration was higher (ES range 3.9-4.8). The SSG formats examined had a lower mean speed and higher mean acceleration than the maximal mean observed in competition. Therefore, these SSGs are an effective training drill for the development and overload of acceleration. However, manipulating SSG constraints to expose athletes to the maximal mean competition speed is required.

This study aimed to quantify the validity and reliability of load-velocity (LV) relationship of hill sprinting using a range of different hill gradients and to describe the effect of hill gradient on sprint performance. Twenty-four collegiate-level athletes performed a series of maximal sprints on either flat terrain or hills of gradients 5.2, 8.8 and 17.6%. Velocity-time curves were recorded using a radar device. LV relationships were established using the maximal velocity achieved in each sprinting condition, whilst force-velocity-power (FVP) profiles were established using only the flat terrain sprint. LV profiles were shown to be valid (R2 = 0.99) and reliable (TE < 4.4%). For every 1-degree increase in slope, subjects' velocity decreased by 1.7 ± 0.1% on average. All the slopes used represented low resistance relative to the entire LV spectrum (

Background Wearable tracking devices are commonly utilised to quantify the external acceleration load of team sport athletes during training and competition. The ability to accelerate is an important attribute for athletes in many team sports. However, there are many different acceleration metrics that exist in team sport research. This review aimed to provide researchers and practitioners with a clear reporting framework on acceleration variables by outlining the different metrics and calculation processes that have been adopted to quantify acceleration loads in team sport research. Methods A systematic review of three electronic databases (CINAHL, MEDLINE, SPORTDiscus), was performed to identify peer-reviewed studies that published external acceleration load in elite team sports during training and/or competition. Articles published between January 2010 and April 2020 were identified using Boolean search phrases in relation to team sports (population), acceleration/deceleration (comparators), and competition and/or training (outcome). The included studies were required to present external acceleration and/or deceleration load (of any magnitude) from able-bodied athletes (mean age ≥ 18 years) via wearable technologies. Results A total of 124 research articles qualified for inclusion. In total, 113/124 studies utilised GPS/GNSS technology to outline the external acceleration load of athletes. Count-based metrics of acceleration were predominant of all metrics in this review (72%). There was a lack of information surrounding the calculation process of acceleration with 13% of studies specifying the filter used in the processing of athlete data, whilst 32% outlined the minimum effort duration (MED). Markers of GPS/GNSS data quality, including horizontal dilution of precision (HDOP) and the average number of satellites connected, were outlined in 24% and 27% of studies respectively. Conclusions Team sport research has predominantly quantified external acceleration load in training and competition with count-based metrics. Despite the influence of data filtering processes and MEDs upon acceleration, this information is largely omitted from team sport research. Future research that outlines acceleration load should present filtering processes, MEDs, HDOP, and the number of connected satellites. For GPS/GNSS systems, satellite planning tools should document evidence of available satellites for data collection to analyse tracking device performance. The development of a consistent acceleration filtering method should be established to promote consistency in the research of external athlete acceleration loads.

In professional team sports, the collection and analysis of athlete-monitoring data are common practice, with the aim of assessing fatigue and subsequent adaptation responses, examining performance potential, and minimizing the risk of injury and/or illness. Athlete-monitoring systems should be underpinned by appropriate data analysis and interpretation, to enable the rapid reporting of simple and scientifically valid feedback. Using the correct scientific and statistical approaches can improve the confidence of decisions made from athlete-monitoring data. However, little research has discussed and proposed an outline of the process involved in the planning, development, analysis, and interpretation of athlete-monitoring systems. This review discusses a range of methods often employed to analyze athlete-monitoring data to facilitate and inform decision-making processes. There is a wide range of analytical methods and tools that practitioners may employ in athlete-monitoring systems, as well as several factors that should be considered when collecting these data, methods of determining meaningful changes, and various data-visualization approaches. Underpinning a successful athlete-monitoring system is the ability of practitioners to communicate and present important information to coaches, ultimately resulting in enhanced athletic performance.

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.

This study presented a method of quantifying the manoeuvrability of two field-based team sport athletes and investigated its relationship with running velocity during competition. Across a season, 10 Hz Global navigation satellite system (GNSS) devices were worn during matches by 62 athletes (Australian Football League [AFL]; n = 36, 17 matches, National Rugby League [NRL]; n = 26, 21 matches). To quantify manoeuvrability, tortuosity was calculated from the X and Y coordinates from match GNSS files (converted from latitude and longitude). Tortuosity was calculated as 100 x natural logarithm of the chord distance (distance travelled between X and Y coordinates), divided by the straight-line distance. The maximal tortuosity was then quantified for each 0.5 m∙s-1 speed increment, ranging from 0 to the highest value for each game file. A quadratic model was fitted for each match file, controlling for the curvilinear relationship between tortuosity and velocity. A comparison of the quadratic coefficients between sports, and within sport between positions was investigated using linear mixed models. Resulting standard deviations (SDs) and mean differences were then assessed to establish standardized effect sizes (ES) and 90% confidence intervals (CI). A curvilinear relationship exists between maximal tortuosity and running speed, reflecting that as speed increases, athletes’ ability to deviate from a linear path is compromised (i.e., run in a more linear path). Compared to AFL, NRL had a greater negative quadratic coefficient (a) (ES = 0.70; 0.47 to 0.93) for the 5 second analysis, meaning that as speed increased, NRL athletes’ manoeuvrability reduced at a faster rate than when compared to AFL. There were no positional differences within each sport. GNSS derived information can be used to provide a measure of manoeuvrability tortuosity during NRL and AFL matches. The curvilinear relationship between tortuosity and speed demonstrated that as speed increased, manoeuvrability was compromised.

Background Technology has long been used to track player movements in team sports, with initial tracking via manual coding of video footage. Since then, wearable microtechnology in the form of global and local positioning systems has provided a less labour-intensive way of monitoring movements. As such, there has been a proliferation in research pertaining to these devices. Objective A systematic review of studies that investigate the validity and/or reliability of wearable microtechnology to quantify movement and specific actions common to intermittent team sports. Methods A systematic search of CINAHL, MEDLINE, and SPORTDiscus was performed; studies included must have been (1) original research investigations; (2) full-text articles written in English; (3) published in a peer-reviewed academic journal; and (4) assessed the validity and/or reliability of wearable microtechnology to quantify movements or specific actions common to intermittent team sports. Results A total of 384 studies were retrieved and 187 were duplicates. The titles and abstracts of 197 studies were screened and the full texts of 88 manuscripts were assessed. A total of 62 studies met the inclusion criteria. Additional 10 studies, identified via reference list assessment, were included. Therefore, a total of 72 studies were included in this review. Conclusion There are many studies investigating the validity and reliability of wearable microtechnology to track movement and detect sport-specific actions. It is evident that for the majority of metrics, validity and reliability are multi-factorial, in that it is dependent upon a wide variety of factors including wearable technology brand and model, sampling rate, type of movement performed (e.g., straight line, change of direction) and intensity of movement (e.g., walk, sprint). Practitioners should be mindful of the accuracy and repeatability of the devices they are using when making decisions on player training loads.

The aims were to investigate the externally measured weekly loads, and the distribution intensity relative to the 1-min maximal mean (MM) intensity of matches. Athletes (n = 28) wore 10 Hz GNSS devices during training and matches. For the descriptive analysis, a range of movement variables were collected, including total distance, high-speed distance, very high-speed distance, acceleration, and acceleration load. Using raw GNSS files, 1-min moving averages were calculated for speed (m·min−1) and acceleration (m·s−2), and were multiplied by time, specifying total distance (m), and by body mass to quantify impulse (kN·s−1). The distribution of distance and impulse accumulated at varied intensities relative to MMs was calculated, with percentages ranging from zero to 110%. Drills were categorized as either; warm-ups, skill drills, games (i.e., small-sided games), conditioning and matches. Linear mixed models determined if the distribution of intensity within each threshold (>50%) varied between drill types and matches, and if the distribution within drill types varied across the season. Effects were described using standardized effect sizes (ES) and 90% confidence limits (CL). Compared to matches, a higher proportion of distance was accumulated at 50% of the MM within warm-ups and conditioning (ES range 0.86–1.14). During matches a higher proportion of distance was accumulated at 60% of MM when compared to warms ups, skill drills and conditioning (0.73–1.87). Similarly, greater proportion of distance was accumulated between 70 and 100% MM in matches compared to skill drills and warm-ups (1.05–3.93). For impulse, matches had a higher proportion between 60 and 80% of the MM compared to conditioning drills (0.91–3.23). There were no other substantial differences in the proportion of impulse between matches and drill types. When comparing phases, during competition there was a higher proportion of distance accumulated at 50% MM than general preparation (1.08). A higher proportion of distance was covered at higher intensities within matches compared to drills. The proportion of impulse was higher between 60 and 80% MM within matches compared to conditioning. Practitioners can therefore ensure athletes are not only exposed to the intensities common within competition, but also the volume accumulated is comparable, which may have positive performance outcomes, but is also extremely important in the return to play process.

Goal-kicking is an important skill in Australian Football (AF). This study examined whether kinematic differences exist between accurate and inaccurate goal-kicks and determined the relationships between technical factors and accuracy. Eighteen elite to sub-elite AF players performed 15 x 30 m goal-kicks on an AF training ground, with three-dimensional kinematics collected using the Xsens inertial measurement system (Xsens Technologies B.V., Enschede, the Netherlands). A general linear mixed modelling approach and regression-based statistics were employed to quantify differences between accurate and inaccurate goal kicks and the relationships between technical factors and accuracy. Accurate goal-kicks were characterised by a straighter approach line, with less kick-leg joint range of motion (knee and hip), lower linear velocity (centre of mass, foot speed), angular velocity (knee and shank), and less support-leg knee flexion during the kicking phase compared to inaccurate goal-kicks. At the end of the follow through, players produced greater ankle plantarflexion and a straighter-leg line in accurate goal-kicks. Findings in this research indicated that many factors interact with goal-kicking accuracy in AF, ranging from the players’ approach line path, their support-leg mechanics, the kick-leg swing motion, to the final position of the kicker during their follow through.

Graham, S, Duthie, G, Aughey, R, and Zois, J. Comparison of physical profiles of state-level netball players by position. J Strength Cond Res 34(9): 2654-2662, 2020-Understanding the physical profiles of state-level netball athletes provides conditioning professionals with testing norms and an understanding of important capacities within particular positions. The purpose was to examine positional differences in physical capacities of state-level netball athletes. Forty-six state-level netball athletes completed physical capacity assessments in the second week of their preseason, over 2 seasons. Tests included stature, 20-m sprint (with 5 and 10 m splits), 505 change-of-direction, countermovement jump, single-leg bounding, and the Yo-Yo intermittent recovery test level one. A linear mixed-model with fixed and random effects was used in conjunction with magnitude-based inferences to determine meaningful differences with 90% confidence limits (CLs). Midcourt athletes were shorter in stature than shooters (effect size [ES]: 1.26, CL 0.88-1.63) and defenders (-2.58, -3.35 to -1.80). Midcourt athletes were faster than shooters and defenders in linear acceleration over 5 and 10 m distances and 505 change-of-direction (ES range: -0.34 to -0.59). Shooters presented poorer intermittent endurance than midcourt (-0.82, -1.34 to -0.31) and defenders (-0.56, -1.19 to 0.07). Defenders jumped higher than midcourt athletes (-0.56, -1.30 to 0.19) and shooters (-0.61, -1.18 to -0.04), and possessed greater bounding ability compared with shooters (ES range: -0.23 to -0.57). Midcourt athletes require a more holistic set of physical capacities including acceleration, change-of-direction, jumping, and intermittent endurance. For defender and shooter positions, results indicate physical requirements that may be more specific to their positions. Conditioning professionals can use these findings to target specific physical qualities to maximize position-specific performance.

Duthie, GM, Thornton, HR, Delaney, JA, Connolly, DR, and Serpiello, FR. Running intensities in elite youth soccer by age and position. J Strength Cond Res 32(10): 2918-2924, 2018-The purpose of this investigation was to examine differences between the peak running speed, acceleration, and metabolic power of elite youth soccer across a range of age levels by position. Ninety-six elite junior soccer players were assessed between 2015 and 2017. Ninety-six elite junior soccer players (at time of match: age, 15.8 ± 0.9 years; body mass, 69.1 ± 8.0 kg) were assessed during 61 games within the 2015, 2016, and 2017 season, for a total of 441 individual match observations (4.8 ± 3.3 matches per player, range 1-13). Participants were classified by age group: under 15 (U15, n = 121, 14.7 ± 0.3 years), under 16 (U16, n = 176, 15.8 ± 0.3 years), or under 17 (U17, n = 144, 16.7 ± 0.4 years), and according to their playing position: Attacker (ATT), Defender (DEF), Mid-Fielder (MID), or Wide (WIDE). Participants wore global positioning system units during each match, where speed (m·min), acceleration/deceleration (m·s), and metabolic power (Pmet) were established. A 1- to 10-minute moving average was applied to establish the intercept (c) and slope (n) of running intensity variables as a power law y = cx relationship. Linear mixed models were used to examine differences in the intercept and slope between age group and player position. There were no substantial differences in peak (intercept) or decline (slope) in running intensity between playing levels. Several differences were observed in the peak running speeds (m·min), particularly peak running speeds of ATT and DEF being substantially lower than the MID. Despite variability between positions, we suggest that the magnitude of these differences would not warrant the prescription of different running intensities across positions at the elite junior level. These findings describe the peak running intensities of elite junior soccer, useful in the monitoring and prescription of training to ensure that players are prepared for the most demanding periods of competition.

Purpose: To determine the relationship between drill type and accelerometer-derived loads during various team-sport activities and examine the influence of unit fitting on these loads. Methods: Sixteen rugby league players were fitted with microtechnology devices in either manufacturer vests or playing jerseys before completing standardized running, agility, and tackling drills. Two-dimensional (2D) and 3-dimensional (3D) accelerometer loads (BodyLoad™) per kilometer were compared across drills and fittings (ie, vest and jersey). Results: When fitted in a vest, 2D BodyLoad was higher during tackling (21.5 [14.8] AU/km) than during running (9.5 [2.5] AU/km) and agility (10.3 [2.7] AU/km). Jersey fitting resulted in more than 2-fold higher BodyLoad during running (2D = 9.5 [2.7] vs 29.3 [14.8] AU/km, 3D = 48.5 [14.8] vs 111.5 [45.4] AU/km) and agility (2D = 10.3 [2.7] vs 21.0 [8.1] AU/km, 3D = 40.4 [13.6] vs 77.7 [26.8] AU/km) compared with a vest fitting. Jersey fitting also produced higher BodyLoad during tackling drills (2D = 21.5 [14.8] vs 27.8 [18.6] AU/km, 3D = 42.0 [21.4] vs 63.2 [33.1] AU/km). Conclusions: This study provides evidence supporting the construct validity of 2D BodyLoad for assessing collision/tackling load in rugby league training drills. Conversely, the large values obtained from 3D BodyLoad (which includes the vertical load vector) appear to mask small increases in load during tackling drills, rendering 3D BodyLoad insensitive to changes in contact load. Unit fitting has a large influence on accumulated accelerometer loads during all drills, which is likely related to greater incidental unit movement when units are fitted in jerseys. Therefore, it is recommended that athletes wear microtechnology units in manufacturer-provided vests to provide valid and reliable information.

Objective: To establish whether a simple integration of selected internal and external training load (TL) metrics is useful for tracking and assessing training outcomes during team-sport training. Methods: Internal [heart rate training impulse (HR-TRIMP), session rating of perceived exertion (sRPE-TL)] and selected external (global positioning systems; GPS) metrics were monitored over seven weeks in 38 professional male rugby league players. Relationships between internal and external measures of TL were determined, and an integrated novel training efficiency index (TEI) was established. Changes in TEI were compared to changes in both running performance (1.2 km shuttle test) and external TL completed. Results: Moderate to almost perfect correlations (r = 0.35–0.96; ±~0.02; range ± 90% confidence limits) were observed between external TL and each measure of internal TL. The integration of HR-TRIMP and external TL measures incorporating both body mass and acceleration/deceleration were the most appropriate variables for calculating TEI, exhibiting moderate (ES= 0.87–0.89; ±~0.15) and small (ES = 0.29–0.33; ±~0.07) relationships with changes in running performance and completed external TL respectively. Conclusions: Combination of the TEI and an athlete monitoring system should reveal useful information for continuous monitoring of team-sport athletes over several weeks.

Knowledge of the most intense periods of competitive soccer may assist in the development of specific training methodologies. Objectives: To quantify the peak running intensity of professional soccer and to establish the rate of decline in this intensity as a function of time. Methods: Activity profiles were obtained from 24 players across 40 professional matches. Peak values were calculated for moving averages 1–10 minutes in duration for relative distance (m∙min−1), high-speed relative distance (HS m∙min−1), average acceleration/deceleration (m∙s2) and metabolic power (Pmet). To quantify the decrease in running intensity for longer moving average durations, each measure was evaluated relative to the moving average duration, as a power law relationship. Results: Peak relative distance and Pmet were lowest for central defenders (effect size [ES] = 0.79–1.84), whilst acceleration/deceleration intensity was highest for wide defenders (ES = 0.67–1.42). Differences in the rate of decline in running intensity between positions were considered trivial to small, indicating a similar rate of decline in running intensity across positions. Conclusions: Using power law, the peak running intensities of professional soccer can now be predicted as a function of time, providing coaches with a useful tool for the prescription and monitoring of specific training drills.

Purpose:To investigate the effects of a training camp on the sleep characteristics of professional rugby league players compared with a home period.Methods:During a 7-d home and 13-d camp period, time in bed (TIB), total sleep time (TST), sleep efficiency (SE), and wake after sleep onset were measured using wristwatch actigraphy. Subjective wellness and training loads (TL) were also collected. Differences in sleep and TL between the 2 periods and the effect of daytime naps on nighttime sleep were examined using linear mixed models. Pearson correlations assessed the relationship of changes in TL on individuals’ TST.Results:During the training camp, TST (–85 min), TIB (–53 min), and SE (–8%) were reduced compared with home. Those who undertook daytime naps showed increased TIB (+33 min), TST (+30 min), and SE (+0.9%). Increases in daily total distance and training duration above individual baseline means during the training camp shared moderate (r = –.31) and trivial (r = –.04) negative relationships with TST.Conclusions:Sleep quality and quantity may be compromised during training camps; however, daytime naps may be beneficial for athletes due to their known benefits, without being detrimental to nighttime sleep.

Purpose:To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.Methods:TL and injury data were collected across 3 seasons (2013–2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes’ corresponding injury status was marked as “available” or “unavailable.” Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.Results:Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.Conclusions:Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

Purpose: This study determined differences in prolonged high-intensity running (PHIR) performance and running momentum (pIFT) between competition levels and positional groups in rugby league. Methods: Elite Australian National Rugby League (NRL), sub-elite [state-based competition (SRL); National Youth Competition (NYC); local league (LL)] and junior-elite (U18; U16) rugby league players completed the 30–15 Intermittent Fitness Test (30–15IFT) to quantify PHIR performance. Final running momentum (pIFT; kg·m∙s−1) was calculated as the product of body mass and final running velocity (VIFT; m∙s−1). Effect sizes (ESs) were used to examine between-group differences. Results: 30–15IFT performance was possibly to likely higher in NRL players (19.5 ± 1.0 km·h−1; mean ± SD) when compared with SRL (ES = 0.6 ± 0.5; ES ± CI), NYC (ES = 0.6 ± 0.5) and U18 (ES = 0.8 ± 0.5) players. NRL players (537 ± 41 kg·m·s−1) possessed possibly to very likely greater pIFT than SRL (ES = 0.7 ± 0.5), NYC (ES = 1.2 ± 0.5), U18 (ES = 2.3 ± 0.6), U16 (ES = 3.0 ± 0.7) and LL players (ES = 2.0 ± 0.7). Middle forwards attained a likely superior pIFT (ES = 0.5 − 1.8) to all other positional groups. Conclusions: This study demonstrated that elite rugby league players possess superior PHIR capacities, whilst highlighting that pIFT can account for the disparities in body mass between groups.

Previous research has quantified the peak movement demands of elite rugby league match-play, but the peak accelerometer load or the semi-professional peak demands remain unknown. The aim of this research was to determine the peak movement demands of professional and semi-professional rugby league competition. Wearable microtechnology devices tracked the physical activity profiles of players during 26 professional (n = 351 files) and 22 semi-professional (n = 267 files) matches. Following each match, data were exported in raw form to extract the peak 1- to 10-min periods for speed, average acceleration, and accelerometer load of each player, using a rolling average method. To determine the difference between playing levels (professional vs. semi-professional) and position (forwards vs. backs), linear mixed models were used. The intercept and the slope were calculated based on the power law relationship to provide the peak, and rate of decay, of each dependent variable. Cohen’s effect size (ES) statistic was used to determine the magnitude of differences between positions and playing level. There was little difference between playing standards, with only small differences in running speed, with a greater intercept and slope for the professional forwards compared with semi-professional forwards (intercept ES: 0.37; 90%CL: 0.19 to 0.55; slope ES: 0.35; 0.15 to 0.55). For positional comparisons (forwards vs. backs), there was no difference in running speeds at the professional level, but there was substantially greater running speed for backs compared to forwards in semi-professional competition, with small to moderate differences (ES range: 0.60–0.39). Both professional and semi-professional forwards showed small to moderately higher accelerometer load compared to backs, which increased with period duration (ES range: 0.22–0.79). Similarly, acceleration demands were greater for forwards compared to backs across both playing standards, with moderate to large differences (ES range: 0.52–0.96). Overall, the results of this study show that there is a small difference in the peak running speed for forwards in professional competition, but otherwise there are no meaningful differences in movement demands of professional and semi-professional rugby league match-play. Forwards display greater acceleration and accelerometer load across a number of rolling average durations compared to backs.

Delves, RIM, Bahnisch, J, Ball, K, and Duthie, GM. Quantifying mean peak running intensities in elite field hockey. J Strength Cond Res 35(9): 2604–2610, 2021—To replicate match demands in training, field hockey (FH) coaches typically prescribe intensities based on whole-match data. Such data may underestimate peak competition periods, potentially underpreparing athletes for competition. This study then aimed to quantify maximal mean running intensities during elite FH competition to facilitate enhanced training prescription. Ten-Hertz Global Positioning System data were collected from 17 male and 11 female FH athletes who competed in the 2016 and 2017 Australian Hockey League tournaments. Maximal mean values for speed, acceleration, and metabolic power (Pmet) were calculated over a 1- to 10-minute moving average by position. Summary match statistics were also analyzed. Linear mixed models were constructed to determine the effect of position on moving average and summary variables. Pairwise comparisons between groups were made using magnitude-based inferences. In female competition, speed and Pmet intensities were greater in midfielders, whereas defenders were lowest in acceleration demands over the 10-minute window and in corresponding intercepts. In male competition, acceleration was greater in defenders during the 10-minute window and in subsequent intercepts compared with midfielders, whereas defenders were lowest in speed intercepts. In comparison with previously reported summary match variables, intensities from the 1-minute moving average interval were 50–65% greater in male competition and 30–50% greater in female competition. The 10-minute moving average framework has identified FH running intensities that are greater than previously reported whole-match averages. This information enhances the understanding of the demands of FH, assisting practitioners to prepare their athletes for the most demanding instances of play.

Delaney, JA, Wileman, TM, Perry, NJ, Thornton, HR, Moresi, MP, and Duthie, GM. The validity of a global navigation satellite system for quantifying small-area team-sport movements. J Strength Cond Res 33(6): 1463–1466, 2019—The recent development of global navigation satellite systems (GNSS) has improved the availability and signal strength of surrounding satellites compared with traditional global positioning systems, although their ability to quantify rapid changes in speed may still be limited. This study aimed to evaluate the validity of GNSS to quantify the mean speed (m·s−1) and acceleration (m·s−2) of movements typical to team sports. One participant completed 9 periods of 4 minutes of activity, separated by 2-minute rest periods, which involved walking, jogging, and running in a variety of directions and patterns, aimed to simulate a team-sport movement profile. Speed and acceleration were quantified from a 10-Hz GNSS unit and compared with a 10-camera, 3-dimensional motion capture system (VICON), from which the movement of both the participant's center of mass (COM) and the location of the GNSS unit (e.g., C7 vertebrae) were calculated. Practical estimates of speed were associated with small differences from both the criterion COM (effect size; ±90% confidence limits = 0.19–0.25; ± ∼0.21) and criterion C7 (0.14–0.22; ± ∼0.13). The corresponding estimates of acceleration derived from raw data were classified as small (0.16–0.22; ± ∼0.15) and small to moderate (0.25–0.35; ± ∼0.24) for the COM and C7, respectively. Software-exported acceleration values exhibited very large mean bias compared with both criterion measures (−3.81 to −3.77; ± ∼0.24). This study demonstrates that 10-Hz GNSS possess acceptable validity for assessing the average demands of movements typical of team-sports training and competition, although caution is recommended when using software-exported measures of acceleration.

Objectives To investigate the peak accelerometer-derived intensity of state-level netball matches and compare differences between positional groupings. Findings will provide guidance for sport science professionals on how to best replicate the most intense passages of play in training settings. Design Longitudinal (one season). Method Twenty-eight netball athletes across three teams from the same club wore an accelerometer (S5 Optimeye, Catapult sports) for all matches, in one season. Raw acceleration data were downloaded and converted into a vector magnitude (Player Load™) we then quantified the peak intensity over 30-s and one to ten-minute time periods. Positional groupings were created based on the number of thirds on a netball court that a particular position can enter, as this was deemed more appropriate for the current study than the traditional combinations based on tactical requirements. A linear mixed-model with fixed and random effects was utilised along with magnitude-based inferences to determine meaningful differences with 90 % confidence limits (CL). Results Across all time periods post 30-s, only one comparison was not meaningfully different i.e. three-thirds v two-thirds at the one-minute timepoint (effect size: 0.27, CL −0.05 to 0.60). Conclusions Findings justify that netball athletes, depending on positional group defined by this study, should train at different intensities dependent on a specified duration. Conditioning professionals and coaches should design training drills that best replicate the peak intensity of match play. This may improve an athlete’s physical performance capacity during highly exertive periods of competition, which regularly occur at critical moments in play.

Purpose: To establish the interunit reliability of a range of global positioning system (GPS)-derived movement indicators, to determine the variation between manufacturers, and to investigate the difference between software-derived and raw data. Methods: A range of movement variables were obtained from 27 GPS units from 3 manufacturers (GPSports EVO, 10 Hz, n = 10; STATSports Apex, 10 Hz, n = 10; and Catapult S5, 10 Hz, n = 7) that measured the same team-sport simulation session while positioned on a sled. The interunit reliability was determined using the coefficient of variation (%) and 90% confidence limits, whereas between-manufacturers comparisons and comparisons of software versus raw processed data were established using standardized effect sizes and 90% confidence limits. Results: The interunit reliability for both software and raw processed data ranged from good to poor (coefficient of variation = 0.2%; ±1.5% to 78.2%; ±1.5%), with distance, speed, and maximal speed exhibiting the best reliability. There were substantial differences between manufacturers, particularly for threshold-based acceleration and deceleration variables (effect sizes; ±90% confidence limits: −2.0; ±0.1 to 1.9; ±0.1), and there were substantial differences between data-processing methods for a range of movement indicators. Conclusions: The interunit reliability of most movement indicators was deemed as good regardless of processing method, suggesting that practitioners can have confidence within systems. Standardized data-processing methods are recommended, due to the large differences between data outputs from various manufacturer-derived software.

The primary purpose of this study was to determine if a difference existed between peak speed attained when performing a sprint with maximal acceleration versus from a gradual build-up. Additionally, this investigation sought to compare the actual peak speed achieved when instructed to reach 75% and 90% of maximum speed. Field sport athletes (n = 21) performed sprints over 60 m under the experimental conditions, and the peak speed was assessed with a radar gun. The gradual build-up to maximum speed (8.30 &plusmn, 0.40 m∙s&minus, 1) produced the greater peak speed (effect size = 0.3, small) than the maximum acceleration run (8.18 &plusmn, 1), and the majority of participants (62%) followed this pattern. For the sub-maximum runs, the actual mean percentage of maximum speed reached was 78 &plusmn, 6% for the 75% prescribed run and 89 &plusmn, 5% for the 90% prescription. The errors in attaining the prescribed peak speeds were large (~15%) for certain individuals, especially for the 75% trial. Sprint training for maximum speed should be performed with a gradual build-up of speed rather than a maximum acceleration. For sub-maximum interval training, the ability to attain the prescribed target peak speed can be challenging for field sport athletes, and therefore where possible, feedback on peak speeds reached should be provided after each repetition.

Wearable inertial measurement systems (IMS) allow for three-dimensional analysis of human movements in a sport-specific setting. This study examined the concurrent validity of a IMS (Xsens MVN system) for measuring lower extremity and pelvis kinematics in comparison to a Vicon motion analysis system (MAS) during kicking. Thirty footballers from Australian football (n = 10), soccer (n = 10), rugby league and rugby union (n = 10) clubs completed 20 kicks across four conditions. Concurrent validity was assessed using a linear mixed-modelling approach, which allowed the partition of between and within-subject variance from the device measurement error. Results were expressed in raw and standardised units for assessments of differences in means and measurement error, and interpreted via non-clinical magnitude-based inferences. Trivial to small differences were found in linear velocities (foot and pelvis), angular velocities (knee, shank and thigh), sagittal joint (knee and hip) and segment angle (shank and pelvis) means (mean difference: 0.2–5.8%) between the IMS and MAS in Australian football, soccer and the rugby codes. Trivial to small measurement errors (from 0.1 to 5.8%) were found between the IMS and MAS in all kinematic parameters. The IMS demonstrated acceptable levels of concurrent validity compared to a MAS when measuring kicking biomechanics across the four football codes. Wearable IMS offers various benefits over MAS, such as, out-of-laboratory testing, larger measurement range and quick data output, to help improve the ecological validity of biomechanical testing and the timing of feedback. The results advocate the use of IMS to quantify biomechanics of high-velocity movements in sport-specific settings.

The validity of an Ultra-wideband (UWB) positioning system was investigated during linear and change-of-direction (COD) running drills. Six recreationally-active men performed ten repetitions of four activities (walking, jogging, maximal acceleration, and 45º COD) on an indoor court. Activities were repeated twice, in the centre of the court and on the side. Participants wore a receiver tag (Clearsky T6, Catapult Sports) and two reflective markers placed on the tag to allow for comparisons with the criterion system (Vicon). Distance, mean and peak velocity, acceleration, and deceleration were assessed. Validity was assessed via percentage least-square means difference (Clearsky-Vicon) with 90% confidence interval and magnitude-based inference; typical error was expressed as within-subject standard deviation. The mean differences for distance, mean/peak speed, and mean/peak accelerations in the linear drills were in the range of 0.2–12%, with typical errors between 1.2 and 9.3%. Mean and peak deceleration had larger differences and errors between systems. In the COD drill, moderate-to-large differences were detected for the activity performed in the centre of the court, increasing to large/very large on the side. When filtered and smoothed following a similar process, the UWB-based positioning system had acceptable validity, compared to Vicon, to assess movements representative of indoor sports.

Delaney, JA, Scott, TJ, Ballard, DA, Duthie, GM, Hickmans, JA, Lockie, RG, and Dascombe, BJ. Contributing factors to change-of-direction ability in professional rugby league players. J Strength Cond Res 29(10): 2688–2696, 2015—Rugby league is an intermittent team sport in which players are regularly required to accelerate, decelerate, and change direction rapidly. This study aimed to determine the contributing factors to change-of-direction (COD) ability in professional rugby league players and to validate the physical and physiological components of a previously proposed COD ability predictor model. Thirty-one male professional rugby league players (age: 24.3 6 4.4 years; height: 1.83 6 0.06 m; body mass: 98.1 6 9.8 kg) were assessed for anthropometry, linear speed, various leg muscle qualities, and COD ability. Change-of-direction ability was assessed for both the dominant (D) and nondominant (ND) legs using the 505 test. Stepwise multiple regression analyses determined the combined effect of the physical and physiological variables on COD ability. Maximal linear speed (SpMax) and relative squat strength (squat:BM) explained 61% of the variance in 505- D performance, whereas measures of mass, unilateral, and bilateral power contributed 67% to 505-ND performance. These results suggest that the 505-ND task was heavily dependent on relative strength and power, whereas the 505-D task was best predicted by linear sprint speed. Second, the physical component of the COD predictor model demonstrated poor correlations (r = 20.1 to 20.5) between absolute strength and power measures and COD ability. When made relative to body mass, strength and power measures and COD ability shared stronger relationships (r = 20.3 to 20.7). Change-of-direction ability in professional rugby league players would be best improved through increases in an athlete’s strength and power while maintaining lean muscle mass.

Duthie, GM, Thornton, HR, Delaney, JA, McMahon, JT, and Benton, DT. Relationship between physical performance testing results and peak running intensity during professional rugby league match play. J Strength Cond Res 34(12): 3506-3513, 2020-The purpose of this study was to examine the relationship between individual athletes' physical characteristics and both the peak running intensities and the decline in peak running intensities during competition. Twenty-two professional rugby league athletes (age; 24.1 ± 4.0 years, body mass; 101.4 ± 9.5 kg) underwent a series of physical testing procedures. Peak running intensity was determined using a moving average technique, applied to the speed (m·min), acceleration/deceleration (m·s), and metabolic power (W·kg) during competition, across 10 different durations. The power law relationship was then established, yielding an intercept and slope for the movement variables. Mixed linear models were then used to determine the relationship between physical characteristics and intercept and slope values. There were large, positive relationships between a player's maximal speed and both peak running speeds (effect size = 0.56, 90% confidence interval: 0.20-0.78) and metabolic power (0.57, 0.21-0.79) during competition. By contrast, there were large, negative associations between maximal speed and the rate of decline in running speed (-0.60, -0.81 to -0.27) and metabolic power (-0.65, -0.83 to -0.32) during competition. Similarly, there were negative associations between relative squat strength and the rate of decline in running speed (moderate: -0.41, -0.69 to -0.04) and metabolic power (large: -0.53, -0.77 to -0.17) during competition. The findings of this study demonstrate that a players running intensity during competition is underpinned by the individual athletes physiological qualities. Athletes demonstrating higher maximal speeds in testing were able to maintain higher running intensities over short durations but had a greater decrease in running intensity as duration increased.

Purpose: To compare relative and absolute speed and metabolic thresholds for quantifying match output in elite rugby league. Methods: Twenty-six professional players competing in the National Rugby League were monitored with global positioning systems (GPS) across a rugby-league season. Absolute speed (moderate-intensity running [MIRTh > 3.6 m/s] and high-intensity running [HIRTh > 5.2 m/s]) and metabolic (>20 W/kg) thresholds were compared with individualized ventilatory (first [VT1IFT] and second [VT2IFT]) thresholds estimated from the 30-15 Intermittent Fitness Test (30-15IFT), as well as the metabolic threshold associated with VT2IFT (HPmetVT2), to examine difference in match-play demands. Results: VT2IFT mean values represent 146%, 138%, 167%, and 144% increases in the HIR dose across adjustables, edge forwards, middle forwards, and outside backs, respectively. Distance covered above VT2IFT was almost certainly greater (ES range = 0.79–1.03) than absolute thresholds across all positions. Trivial to small differences were observed between VT1IFT and MIRTh, while small to moderate differences were reported between HPmetVT2 and HPmetTh. Conclusions: These results reveal that the speed at which players begin to run at higher intensities depends on individual capacities and attributes. As such, using absolute HIR speed thresholds underestimates the physical HIR load. Moreover, absolute MIR and high metabolic thresholds may over- or underestimate the work undertaken above these thresholds depending on the respective fitness of the individual. Therefore, using relative thresholds enables better prescription and monitoring of external training loads based on measured individual physical capacities.

Purpose: To investigate the influence of daily and exponentially weighted moving training loads on subsequent nighttime sleep. Methods: Sleep of 14 professional rugby league athletes competing in the National Rugby League was recorded using wristwatch actigraphy. Physical demands were quantified using GPS technology, including total distance, high-speed distance, acceleration/ deceleration load (SumAccDec; AU), and session rating of perceived exertion (AU). Linear mixed models determined effects of acute (daily) and subacute (3- and 7-d) exponentially weighted moving averages (EWMA) on sleep. Results: Higher daily SumAccDec was associated with increased sleep efficiency (effect-size correlation; ES = 0.15; ±0.09) and sleep duration (ES = 0.12; ±0.09). Greater 3-d EWMA SumAccDec was associated with increased sleep efficiency (ES = 0.14; ±0.09) and an earlier bedtime (ES = 0.14; ±0.09). An increase in 7-d EWMA SumAccDec was associated with heightened sleep efficiency (ES = 0.15; ±0.09) and earlier bedtimes (ES = 0.15; ±0.09). Conclusions: The direction of the associations between training loads and sleep varied, but the strongest relationships showed that higher training loads increased various measures of sleep. Practitioners should be aware of the increased requirement for sleep during intensified training periods, using this information in the planning and implementation of training and individualized recovery modalities

Delaney, JA, Cummins, CJ, Thornton, HR, and Duthie, GM. Importance, reliability and usefulness of acceleration measures in team sports. J Strength Cond Res 32(12): 3494–3502, 2018—The ability to accelerate, decelerate, and change direction efficiently is imperative to successful team sports performance. Traditional intensity-based thresholds for acceleration and deceleration may be inappropriate for time-series data and have been shown to exhibit poor reliability, suggesting other techniques may be preferable. This study assessed movement data from one professional rugby league team throughout 2 full seasons and 1 preseason period. Using both 5 and 10 Hz global positioning systems (GPS) units, a range of acceleration-based variables were evaluated for their interunit reliability, ability to discriminate between positions, and associations with perceived muscle soreness. The reliability of 5 Hz global positioning systems for measuring acceleration and deceleration ranged from good to poor (CV = 3.7–27.1%), with the exception of high-intensity deceleration efforts (CV = 11.1–11.8%), the 10 Hz units exhibited moderate-to-good interunit reliability (CV = 1.2–6.9%). Reliability of average metrics (average acceleration/deceleration, average acceleration, and average deceleration) ranged from good to moderate (CV = 1.2–6.5%). Substantial differences were detected between positions using time spent accelerating and decelerating for all magnitudes, but these differences were less clear when considering the count or distance above acceleration/deceleration thresholds. All average metrics detected substantial differences between positions. All measures were similarly related to perceived muscle soreness, with the exception of high-intensity acceleration and deceleration counts. This study has proposed that averaging the acceleration/deceleration demands over an activity may be a more appropriate method compared with threshold-based methods, because a greater reliability between units, while not sacrificing sensitivity to within-subject and between-subject changes.

Objectives To establish the position and duration-specific running demands of Australian Football (AF) competition for the prescription and monitoring of specific training drills. Design An observational time-motion analysis was performed on 40 professional AF players during 30 games throughout the 2014–15 competitive seasons. Methods Player movements were collected and peak values were calculated for moving averages of between 1–10min in duration for relative distance (mmin −1 ), high-speed relative distance (HSmmin −1 ), average acceleration/deceleration (ms 2 ) and metabolic power (P met ). A mixed-model analysis was used to detect positional differences, and differences were described using a magnitude-based network. Results Relative distance was likely greater for midfielders (MID), and mobile forwards (MF) compared to tall backs (TB) across all moving average durations assessed, with MF peaking at 223±35mmin −1 for a 1-min window. High-speed relative distance was at least likely to be greater for MF compared to all other positions, across all moving average durations (ES=0.27–0.94). Acceleration/deceleration demands were similar across positions. Conclusions The present study demonstrated that the peak running intensities of AF are well above previously reported peak intensities when considering the distance-based running requirements of match-play. Whilst the acceleration-based metric was unable to detect large differences between positions, it is important to note their contribution to the overall competition demands. This study presents a useful framework for the prescription and monitoring of drills specific to AF competition requirements.

Purpose:To quantify the duration and position-specific peak running intensities of international rugby union for the prescription and monitoring of specific training methodologies.Methods:Global positioning systems (GPS) were used to assess the activity profile of 67 elite-level rugby union players from 2 nations across 33 international matches. A moving-average approach was used to identify the peak relative distance (m/min), average acceleration/deceleration (AveAcc; m/s2), and average metabolic power (Pmet) for a range of durations (1–10 min). Differences between positions and durations were described using a magnitude-based network.Results:Peak running intensity increased as the length of the moving average decreased. There were likely small to moderate increases in relative distance and AveAcc for outside backs, halfbacks, and loose forwards compared with the tight 5 group across all moving-average durations (effect size [ES] = 0.27–1.00). Pmet demands were at least likely greater for outside backs and halfbacks than for the tight 5 (ES = 0.86–0.99). Halfbacks demonstrated the greatest relative distance and Pmet outputs but were similar to outside backs and loose forwards in AveAcc demands.Conclusions:The current study has presented a framework to describe the peak running intensities achieved during international rugby competition by position, which are considerably higher than previously reported whole-period averages. These data provide further knowledge of the peak activity profiles of international rugby competition, and this information can be used to assist coaches and practitioners in adequately preparing athletes for the most demanding periods of play.

Scott, TJ, Duthie, GM, Delaney, JA, Sanctuary, CE, Ballard, DA, Hickmans, JA, and Dascombe, BJ. The validity and contributing physiological factors to 30-15 intermittent fitness test performance in rugby league. J Strength Cond Res 31(9): 2409-2416, 2017-This study examined the validity of the 30-15 Intermittent Fitness Test (30-15IFT) within rugby league. Sixty-three Australian elite and junior-elite rugby league players (22.5 +/- 4.5 years, 96.1 +/- 9.5 kg, [SIGMA]7 skinfolds: 71.0 +/- 18.7 mm) from a professional club participated in this study. Players were assessed for anthropometry (body mass, [SIGMA]7 skinfolds, lean mass index), prolonged high-intensity intermittent running (PHIR; measured by 30-15IFT), predicted aerobic capacity (MSFT) and power (AAS), speed (40 m sprint), repeated sprint, and change of direction (COD-505 agility test) ability before and after an 11-week preseason training period. Validity of the 30-15IFT was established using Pearson's coefficient correlations. Forward stepwise regression model identified the fewest variables that could predict individual final velocity (VIFT) and change within 30-15IFT performance. Significant correlations between VIFT and [SIGMA]7 skinfolds, repeated sprint decrement, V[Combining Dot Above]O2maxMSFT, and average aerobic speed were observed. A total of 71.8% of the adjusted variance in 30-15IFT performance was explained using a 4-step best fit model (V[Combining Dot Above]O2maxMSFT, 61.4%; average aerobic speed, 4.7%; maximal velocity, 4.1%; lean mass index, 1.6%). Across the training period, 25% of the variance was accounted by [DELTA]V[Combining Dot Above]O2maxMSFT (R2 = 0.25). These relationships suggest that the 30-15IFT is a valid test of PHIR within rugby league. Poor correlations were observed with measures of acceleration, speed, and COD. These findings demonstrate that although the 30-15IFT is a valid measure of PHIR, it also simultaneously examines various physiological capacities that differ between sporting cohorts.

High levels of lean mass are important in collision-based sports for the development of strength and power, which may also assist during contact situations. While skinfold-based measures have been shown to be appropriate for cross-sectional assessments of body composition, their utility in tracking changes in lean mass is less clear.Purpose:To determine the most effective method of quantifying changes in lean mass in rugby league athletes.Methods:Body composition of 21 professional rugby league players was assessed on 2 or 3 occasions separated by ≥6 wk, including bioelectrical impedance analysis (BIA), leanmass index (LMI), and a skinfold-based prediction equation (SkF). Dual-X-ray absorptiometry provided a criterion measure of fat-free mass (FFM). Correlation coefficients (r) and standard errors of the estimate (SEE) were used as measures of validity for the estimates.Results:All 3 practical estimates exhibited strong validity for cross-sectional assessments of FFM (r > .9, P < .001). The correlation between change scores was stronger for the LMI (r = .69, SEE 1.3 kg) and the SkF method (r = .66, SEE = 1.4 kg) than for BIA (r = .50, SEE = 1.6 kg).Conclusions:The LMI is probably as accurate in predicting changes in FFM as SkF and very likely to be more appropriate than BIA. The LMI offers an adequate, practical alternative for assessing in FFM among rugby league athletes.

Resistance exercise is difficult to quantify owing to its inherent complexity with numerous training variables contributing to the training dose ( type of exercise, load lifted, training volume, inter-set rest periods, and repetition velocity ). In addition, the intensity of resistance training is often inadequately determined as the relative load lifted ( % 1-repetition maximum ), which does not account for the effects of inter-set recovery periods, repetition velocity, or the number of repetitions performed in each set at a given load. Methods to calculate the volume load associated with resistance training, as well as the perceived intensity of individual sets and entire training sessions have been shown to provide useful information regarding the actual training stimulus. In addition, questionnaires to subjectively assess how athletes are coping with the stressors of training and portable technologies to quantify performance variables such as concentric velocity may also be valuable. However, while several methods have been proposed to quantify resistance training, there is not yet a consensus regarding how these methods can be best implemented and integrated to complement each other. Therefore, the purpose of this review is to provide practical information for strength coaches to highlight effective methods to assess resistance training, and how they can be integrated into a comprehensive monitoring program.

Purpose:To identify contributing factors to the incidence of illness for professional team-sport athletes, using training load (TL), self-reported illness, and well-being data.Methods:Thirty-two professional rugby league players (26.0 ± 4.8 y, 99.1 ± 9.6 kg, 1.84 ± 0.06 m) were recruited from the same club. Players participated in prescribed training and responded to a series of questionnaires to determine the presence of self-reported illness and markers of well-being. Internal TL was determined using the session rating of perceived exertion. These data were collected over 29 wk, across the preparatory and competition macrocycles.Results:The predictive models developed recognized increases in internal TL (strain values of >2282 AU, weekly TL >2786 AU, and monotony >0.78 AU) to best predict when athletes are at increased risk of self-reported illness. In addition, a reduction in overall well-being (Conclusions:These results indicate that self-report data can be successfully used to provide a novel understanding of the interactions between competition-associated stressors experienced by professional team-sport athletes and their susceptibility to illness. This may help coaching staff more effectively monitor players during the season and potentially implement preventive measures to reduce the likelihood of illnesses occurring.

Rugby league coaches often prescribe training to replicate the demands of competition. The intensities of running drills are often monitored in comparison with absolute match-play measures. Such measures may not be sensitive enough to detect fluctuations in intensity across a match or to differentiate between positions.Purpose:To determine the position- and duration-specific running intensities of rugby league competition, using a moving-average method, for the prescription and monitoring of training.Methods:Data from a 15-Hz global positioning system (GPS) were collected from 32 professional rugby league players across a season. The velocity–time curve was analyzed using a rolling-average method, where maximum values were calculated for 10 different durations, 1, 2, 3, 4, 5, 6, 7, 8, 9, and 10 min, for each player across each match.Results:There were large differences between the 1- and 2-min rolling averages and all other rolling-average durations. Smaller differences were observed for rolling averages of greater duration. Fullbacks maintained a greater velocity than outside backs and middle and edge forwards over the 1- and 2-min rolling averages (ES 0.8−1.2, P < .05). For rolling averages 3 min and greater, the running demands of the fullbacks were greater than those of the middle forwards and outside backs (ES 1.1−1.4, P < .05).Conclusions:These findings suggest that the running demands of rugby league fluctuate vastly across a match. Fullbacks were the only position to exhibit a greater running intensity than any other position, and therefore training prescription should reflect this.

Scott, TJ, Delaney, JA, Duthie, GM, Sanctuary, CE, Ballard, DA, Hickmans, JA, and Dascombe, BJ. Reliability and usefulness of the 30-15 Intermittent Fitness Test in rugby league. J Strength Cond Res 29(7): 1985–1990, 2015—This study examined the reliability and usefulness of the 30-15 Intermittent Fitness Test (30-15IFT) within rugby league. Fifty-five young rugby league players participated in the study. These included representative players from Under 16s (n = 19; 15.6 6 0.3 years; 78.1 6 10.9 kg), Under 18s (n = 21; 17.4 6 0.5 years; 86.9 6 11.2 kg), and Under 20s (n = 15; 19.4 6 0.5 years; 95.9 6 8.7 kg) squads within a professional rugby league club. Players performed the 30-15IFT twice within 9 days of each other. Maximal intermittent running velocity (VIFT) and heart rate at exhaustion (HRpeak) were collected for both tests. Intraclass correlation coefficients (ICCs) for the “Combined” and Under 20s were very large (r . 0.7), whereas the ICCs for Under 16s and Under 18s were almost perfect (r . 0.9). Coefficients of variation were 1.9% (95% confidence interval, 1.6–2.4) for the combined test-retest of the 30-15IFT and 0.6% (0.5–1.0) for HRpeak. As the typical error of measurement (TE) (0.36 km$h21) was greater than the smallest worthwhile change (SWC) (0.21 km$h21) value, the usefulness of the VIFT was rated as “marginal.” The TE for HRpeak was similar to the SWC, rating the usefulness of this variable as “OK.” Despite the usefulness of the 30-15IFT being deemed Marginal, a change as small as 0.5 km$h21 (1 stage) in VIFT could be considered substantial or “real.” As a consequence, the 30-15IFT presents as both a reliable and useful field test in the assessment of intermittent fitness for rugby league players.