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Thomas Kempton and Aaron J. Coutts

Purpose:

To describe the physical and technical demands of rugby league 9s (RL9s) match play for positional groups.

Methods:

Global positioning system data were collected during 4 games from 16 players from a team competing in the Auckland RL9s tournament. Players were classified into positional groups (pivots, outside backs, and forwards). Absolute and relative physical-performance data were classified as total high-speed running (HSR; >14.4 km/h), very-high-speed running (VHSR; >19.0 km/h), and sprint (>23.0 km/h) distances. Technical-performance data were obtained from a commercial statistics provider. Activity cycles were coded by an experienced video analyst.

Results:

Forwards (1088 m, 264 m) most likely completed less overall and high-speed distances than pivots (1529 m, 371 m) and outside backs (1328 m, 312 m). The number of sprint efforts likely varied between positions, although differences in accelerations were unclear. There were no clear differences in relative total (115.6−121.3 m/min) and HSR (27.8−29.8 m/min) intensities, but forwards likely performed less VHSR (7.7 m/min) and sprint distance (1.3 m/min) per minute than other positions (10.2−11.8 m/min, 3.7−4.8 m/min). The average activity and recovery cycle lengths were ~50 and ~27 s, respectively. The average longest activity cycle was ~133 s, while the average minimum recovery time was ~5 s. Technical involvements including tackles missed, runs, tackles received, total collisions, errors, off-loads, line breaks, and involvements differed between positions.

Conclusions:

Positional differences exist for both physical and technical measures, and preparation for RL9s play should incorporate these differences.

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Patrick Ward, Johann Windt and Thomas Kempton

The application of scientific principles to inform practice has become increasingly common in professional sports, with increasing numbers of sport scientists operating in this area. The authors believe that in addition to domain-specific expertise, effective sport scientists working in professional sport should be able to develop systematic analysis frameworks to enhance performance in their organization. Although statistical analysis is critical to this process, it depends on proper data collection, integration, and storage. The purpose of this commentary is to discuss the opportunity for sport-science professionals to contribute beyond their domain-specific expertise and apply these principles in a business-intelligence function to support decision makers across the organization. The decision-support model aims to improve both the efficiency and the effectiveness of decisions and comprises 3 areas: data collection and organization, analytic models to drive insight, and interface and communication of information. In addition to developing frameworks for managing data systems, the authors suggest that sport scientists’ grounding in scientific thinking and statistics positions them to assist in the development of robust decision-making processes across the organization. Furthermore, sport scientists can audit the outcomes of decisions made by the organization. By tracking outcomes, a feedback loop can be established to identify the types of decisions that are being made well and the situations where poor decisions persist. The authors have proposed that sport scientists can contribute to the broader success of professional sporting organizations by promoting decision-support services that incorporate data collection, analysis, and communication.

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Samuel Ryan, Aaron J. Coutts, Joel Hocking and Thomas Kempton

Purpose:

To examine the influence of a range of individual player characteristics and match-related factors on activity profiles during professional Australian football matches.

Methods:

Global positioning system (GPS) profiles were collected from 34 professional Australian football players from the same club over 15 competition matches. GPS data were classified into relative total and high-speed running (HSR; >20 km/h) distances. Individual player aerobic fitness was determined from a 2-km time trial conducted during the preseason. Each match was classified according to match location, season phase, recovery length, opposition strength, and match outcome. The total number of stoppages during the match was obtained from a commercial statistics provider. A linear mixed model was constructed to examine the influence of player characteristics and match-related factors on both relative total and HSR outputs.

Results:

Player aerobic fitness had a large effect on relative total and HSR distances. Away matches and matches lost produced only small reductions in relative HSR distances, while the number of rotations also had a small positive effect. Matches won, more player rotations, and playing against strong opposition all resulted in small to moderate increases in relative total distance, while early season phase, increased number of stoppages, and away matches resulted in small to moderate reductions in relative total distance.

Conclusions:

There is a likely interplay of factors that influence running performance during Australian football matches. The results highlight the need to consider a variety of contextual factors when interpreting physical output from matches.

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Thomas Kempton, Anita C. Sirotic and Aaron J. Coutts

Purpose:

To examine differences in physical and technical performance profiles using a large sample of match observations drawn from successful and less-successful professional rugby league teams.

Methods:

Match activity profiles were collected using global positioning satellite (GPS) technology from 29 players from a successful rugby league team during 24 games and 25 players from a less-successful team during 18 games throughout 2 separate competition seasons. Technical performance data were obtained from a commercial statistics provider. A progressive magnitude-based statistical approach was used to compare differences in physical and technical performance variables between the reference teams.

Results:

There were no clear differences in playing time, absolute and relative total distances, or low-speed running distances between successful and less-successful teams. The successful team possibly to very likely had lower higher-speed running demands and likely had fewer physical collisions than the less-successful team, although they likely to most likely demonstrated more accelerations and decelerations and likely had higher average metabolic power. The successful team very likely gained more territory in attack, very likely had more possessions, and likely committed fewer errors. In contrast, the less-successful team was likely required to attempt more tackles, most likely missed more tackles, and very likely had a lower effective tackle percentage.

Conclusions:

In the current study, successful match performance was not contingent on higher match running outputs or more physical collisions; rather, proficiency in technical performance components better differentiated successful and less-successful teams.

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Samuel Ryan, Thomas Kempton, Emidio Pacecca and Aaron J. Coutts

Purpose: To examine the measurement properties of an adductor strength-assessment system in professional Australian footballers. Methods: Observational, longitudinal design. Test–retest reliability data were collected from 18 professional Australian footballers from 1 club on the same day during the 2017 Australian Football League season. Week-to-week variation data were collected on 45 professional Australian footballers from 1 club during the same season at 48, 72, and 120 h postmatch (rounds 1–23). Players lay beneath a GroinBar hip-strength testing system in supine position with their knee joints at an angle of 60°. Force (in newtons) was extracted for the left and right limbs of each player and a pain score from 0 to 10 (0 = no pain, 10 = maximum pain) was provided. Coefficient of variation (CV) and smallest worthwhile change were calculated on test–retest data. Signal-to-noise ratio was calculated for each major time point. Mean difference between force scores in a subgroup of players with and without groin pain (n = 18) was collected as evidence of construct validity for the system. Results: Test CV was 6.3% (4.9–9.0%). CV exceeded the smallest worthwhile change on both limbs. Intraclass correlation coefficient was .94. Signal-to-noise ratio ranged from 1.6 to 2.6 on average for 48, 72, and 120 h postmatch. Groin pain had a very likely moderate negative effect on adductor strength (effect size: 0.41). Conclusions: The system possesses greater measurement precision than dynamometry and sphygmomanometer adductor strength-assessment methods in professional Australian footballers. Increased groin pain reduced groin squeeze force production. Practitioners may interpret changes exceeding 6.3% in adductor strength as real.

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Johann C. Bilsborough, Thomas Kempton, Kate Greenway, Justin Cordy and Aaron J. Coutts

Purpose:

To compare development and variations in body composition of early-, mid-, and late-career professional Australian Football (AF) players over 3 successive seasons.

Methods:

Regional and total-body composition (body mass [BM], fat mass [FM], fat-free soft-tissue mass [FFSTM], and bone mineral content [BMC]) were assessed 4 times, at the same time of each season—start preseason (SP), end preseason (EP), midseason (MS), and end season (ES)—from 22 professional AF players using pencil-beam dual-energy X-ray absorptiometry. Nutritional intake for each player was evaluated concomitantly using 3-d food diaries. Players were classified according to their age at the beginning of the observational period as either early- (<21 y, n = 8), mid- (21 to 25 y, n = 9), or late- (>25 y, n = 5) career athletes.

Results:

Early-career players had lower FFSTM, BMC, and BM than mid- and late-career throughout. FM and %FM had greatest variability, particularly in the early-career players. FM reduced and FFSTM increased from SP to EP, while FM and FFSTM decreased from EP to MS. FM increased and FFSTM decreased from MS to ES, while FM and FFSTM increased during the off-season.

Conclusions:

Early-career players may benefit from greater emphasis on specific nutrition and resistance-training strategies aimed at increasing FFSTM, while all players should balance training and diet toward the end of season to minimize increases in FM.

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Samuel Ryan, Emidio Pacecca, Jye Tebble, Joel Hocking, Thomas Kempton and Aaron J. Coutts

Purpose: To examine the measurement reliability and sensitivity of common athlete monitoring tools in professional Australian Football players. Methods: Test–retest reliability (noise) and weekly variation (signal) data were collected from 42 professional Australian footballers from 1 club during a competition season. Perceptual wellness was measured via questionnaires completed before main training sessions (48, 72, and 96 h postmatch), with players providing a rating (1–5 Likert scale) regarding their muscle soreness, sleep quality, fatigue level, stress, and motivation. Eccentric hamstring force and countermovement jumps were assessed via proprietary systems once per week. Heart rate recovery was assessed via a standard submaximal run test on a grass-covered field with players wearing a heart rate monitor. The heart rate recovery was calculated by subtracting average heart rate during final 10 seconds of rest from average heart rate during final 30 seconds of exercise. Typical test error was reported as coefficient of variation percentage (CV%) and intraclass coefficients. Sensitivity was calculated by dividing weekly CV% by test CV% to produce a signal to noise ratio. Results: All measures displayed acceptable sensitivity. Signal to noise ratio ranged from 1.3 to 11.1. Intraclass coefficients ranged from .30 to .97 for all measures. Conclusions: The heart rate recovery test, countermovement jump test, eccentric hamstring force test, and perceptual wellness all possess acceptable measurement sensitivity. Signal to noise ratio analysis is a novel method of assessing measurement characteristics of monitoring tools. These data can be used by coaches and scientists to identify meaningful changes in common measures of fitness and fatigue in professional Australian football.

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Samuel Ryan, Aaron J. Coutts, Joel Hocking, Patrick A. Dillon, Anthony Whitty and Thomas Kempton

Objectives: To examine the collective influence of a range of physical preparation elements on selected performance measures during Australian football match play. Design: Prospective and longitudinal. Methods: Data were collected from 34 professional Australian football players from the same club during the 2016 Australian Football League competition season. Match activity profiles and acute (7-d) and chronic (3-wk) training loads were collected using global positioning system devices. Training response was measured by well-being questionnaires completed prior to the main training session each week. Maximal aerobic running speed (MAS) was estimated by a 2-km time trial conducted during preseason. Coach ratings were collected from the senior coach and 4 assistants after each match on a 5-point Likert scale. Player ratings were obtained from a commercial statistics provider. Fifteen matches were analyzed. Linear mixed models were constructed to examine the collective influence of training-related factors on 4 performance measures. Results: Muscle soreness had a small positive effect (ES: 0.12) on Champion Data rating points. Three-week average high-speed running distance had a small negative effect (ES: 0.14) on coach ratings. MAS had large to moderate positive effects (ES: 0.55 to 0.47) on relative total and high-speed running distances. Acute total and chronic average total running distance had small positive (ES: 0.13) and negative (ES: 0.14) effects on relative total and high-speed running distance performed during matches, respectively. Conclusions: MAS should be developed to enhance players’ running performance during competition. Monitoring of physical preparation data may assist in reducing injury and illness and increasing player availability but not enhance football performance.