Purpose: To assess the impact of microcycle (MC) structures on physical and technical performances in rugby league training and matches. Methods: Thirty-four professional rugby league players were monitored during all training sessions and matches across a single season wherein 2 different competition-phase MC structures were implemented. The first MC structure involved the first session on match day (MD) + 2 and the main stimulus delivered MD − 3, and the second structure delayed all sessions by 1 day (first session on MD + 3 and main session MD − 2; MC structure in the second half of the season). Physical output was quantified via relative total speed (in meters per minute), high-speed running (per minute; ≥4.0 m·s−1), and very-high-speed running (per minute; ≥5.5 m·s−1), measured using a global positioning system (10 Hz) in addition to accelerometer (100 Hz) metrics (PlayerLoad per minute and PlayerLoadslow per minute]) during training and matches. Technical performance (number of runs, meters gained, tackles made and missed) was recorded during matches. Generalized linear mixed models and equivalence tests were used to identify the impact of MC structure on physical and technical output. Results: Nonequivalent increases in meters per minute, high-speed running per minute, and PlayerLoad per minute were observed for the first training stimulus in MC structure in the second half of the season with no practical difference in midcycle sessions observed. The MC structure in the second half of the season structure resulted in increased high-speed running per minute and decreased PlayerLoadslow per minute during MD with no differences observed in technical performance. Conclusions: Delaying the first training stimulus of the MC allowed for greater training load accumulation without negative consequences in selected match running and technical performance measures. This increased MC load may support the maintenance of physical capacities across the in-season.
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Tahleya Eggers, Rebecca Cross, Dean Norris, Lachlan Wilmot, and Ric Lovell
James J. Malone, Ric Lovell, Matthew C. Varley, and Aaron J. Coutts
Athlete-tracking devices that include global positioning system (GPS) and microelectrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete-tracking devices and to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal, and data-filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision, and software/firmware versions in any published research. In addition, details of inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance.