The aim of this study was to quantify movements of Super 12 rugby players in competition because information on elite rugby players' movements is unavailable. Players were categorized into forwards [front (n = 16) and back row (n = 15)] and backs [inside (n = 9) and outside backs (n = 7)] and their movements analysed by video-based time motion analysis. Movements were classified as rest (standing, walking and jogging) and work (striding, sprinting, static exertion, jumping, lifting or tackling). The total time, number and duration of individual activities were assessed, with differences between groups evaluated using independent sample t-tests (unequal variances), while differences between halves were assessed with paired sample t-tests. Forwards had 7:47 min:s (95% confidence limits: 6:39 to 8:55 min:s, P<0.01) more time in static exertion than backs, but backs spent 0:52 (0:34 to 1:09, P = 0.01) min:s more time sprinting than forwards, and had a 0.7 (0.3 to 1.2, P = 0.01) s longer duration of each sprint. Forwards spent 7:31 (5:55 to 9:08) min:s more time in work activities (P = 0.01) and had 2.1 (1.3 to 2.8) s longer work durations (P<0.01) than backs. The results indicate frequent short duration (<4 s) work efforts followed by moderate duration (100 s) rest duration for backs. High-intensity efforts involved static exertion for forwards (mean ± standard deviation frequency = 80 ± 17) and sprinting for backs (27 ± 9). In conclusion, after nearly a decade since becoming professional, elite rugby union is still characterized by highly intense, intermittent movement patterns and marked differences in the competition demands of forwards and backs.

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.

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.

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.