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Mathieu Lacome, Ben Simpson, Nick Broad and Martin Buchheit

Purpose: To examine the ability of multivariate models to predict the heart-rate (HR) responses to some specific training drills from various global positioning system (GPS) variables and to examine the usefulness of the difference in predicted vs actual HR responses as an index of fitness or readiness to perform. Method: All data were collected during 1 season (2016–17) with players’ soccer activity recorded using 5-Hz GPS and internal load monitored using HR. GPS and HR data were analyzed during typical small-sided games and a 4-min standardized submaximal run (12 km·h−1). A multiple stepwise regression analysis was used to identify which combinations of GPS variables showed the largest correlations with HR responses at the individual level (HRACT, 149 [46] GPS/HR pairs per player) and was further used to predict HR during individual drills (HRPRED). Then, HR predicted was compared with actual HR to compute an index of fitness or readiness to perform (HRΔ, %). The validity of HRΔ was examined while comparing changes in HRΔ with the changes in HR responses to a submaximal run (HRRUN, fitness criterion) and as a function of the different phases of the season (with fitness being expected to increase after the preseason). Results: HRPRED was very largely correlated with HRACT (r = .78 [.04]). Within-player changes in HRΔ were largely correlated with within-player changes in HRRUN (r = .66, .50–.82). HRΔ very likely decreased from July (3.1% [2.0%]) to August (0.8% [2.2%]) and most likely decreased further in September (−1.5% [2.1%]). Conclusions: HRΔ is a valid variable to monitor elite soccer players’ fitness and allows fitness monitoring on a daily basis during normal practice, decreasing the need for formal testing.

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Darren J. Burgess

Research describing load-monitoring techniques for team sport is plentiful. Much of this research is conducted retrospectively and typically involves recreational or semielite teams. Load-monitoring research conducted on professional team sports is largely observational. Challenges exist for the practitioner in implementing peer-reviewed research into the applied setting. These challenges include match scheduling, player adherence, manager/coach buy-in, sport traditions, and staff availability. External-load monitoring often attracts questions surrounding technology reliability and validity, while internal-load monitoring makes some assumptions about player adherence, as well as having some uncertainty around the impact these measures have on player performance This commentary outlines examples of load-monitoring research, discusses the issues associated with the application of this research in an elite team-sport setting, and suggests practical adjustments to the existing research where necessary.

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Gareth N. Sandford, Simon A. Rogers, Avish P. Sharma, Andrew E. Kilding, Angus Ross and Paul B. Laursen

Olympic athlete . J Sports Sci . 2009 ; 27 ( 13 ): 1433 – 1442 . PubMed ID: 19813137 doi:10.1080/02640410903045337 19813137 10.1080/02640410903045337 2. Lacome M , Buchheit M , Broad N , Simpson B . Monitoring playersreadiness using predicted heart-rate responses to soccer drills . Int J

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Daniele Conte, Nicholas Kolb, Aaron T. Scanlan and Fabrizio Santolamazza

-performance sport. 3 , 13 Typically, well-being questionnaires are used to determine player readiness; players report several parameters, such as their mood, stress level, and sleep quality, on 5- to 10-point Likert scales, and the sum of scores across questions indicates well-being status. 3 , 13 , 14 This

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Mitchell J. Henderson, Job Fransen, Jed J. McGrath, Simon K. Harries, Nick Poulos and Aaron J. Coutts

aerobic fitness improves the physical performance of players competing within the same level of competition. To assist players in preparing for training and competition, sports scientists monitor various factors relating to individual playersreadiness. 1 Typical tools for monitoring readiness include