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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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Enrico Perri, Carlo Simonelli, Alessio Rossi, Athos Trecroci, Giampietro Alberti, and F. Marcello Iaia

. Such an approach may aid the enhancement of training-induced adaptations, maximizing the playersreadiness on MD, as well as potentially reducing the drops in performance and the likelihood of injury occurrence that may be associated with poor wellness scores. Practical Application The current results

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Samuel Ryan, Thomas Kempton, and Aaron J. Coutts

excessive physical impairment, mental fatigue, or psychological distress. 1 Player readiness can be informed by objective and subjective information including external training load measures, 2 , 3 internal load measures, 4 exposure to maximum speed, 5 and perceptual wellness assessments. 6 These data

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Markus N.C. Williams, Jordan L. Fox, Cody J. O’Grady, Samuel Gardner, Vincent J. Dalbo, and Aaron T. Scanlan

competitive games against other teams in the league. Therefore, the goal during the regular season is to retain developed performance capacities from the preseason and optimize player readiness to compete considering the game schedule faced. Given that game scheduling varies across the regular season with

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Kellyanne J. Redman, Logan Wade, Vincent G. Kelly, Mark J. Connick, and Emma M. Beckman

season leading into, and following, the extended break). A final aim was to evaluate the type and volume of training players self-selected during the off-season, and to determine whether this had any relationship to player readiness in returning to regular team training. Methods Subjects Twenty

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

.1080/02640410903045337 19813137 2. Lacome M , Buchheit M , Broad N , Simpson B . Monitoring playersreadiness using predicted heart-rate responses to soccer drills . Int J Sports Physiol Perform . 2018 ; 13 ( 10 ): 1273 – 1280 . PubMed ID: 29688115 doi: 10.1123/ijspp.2018-0026 10.1123/ijspp.2018-0026 29688115 3

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Matthew Springham, Robert U. Newton, Anthony J. Strudwick, and Mark Waldron

approach to determining player readiness is likely to consider the overall hormonal balance (T:C) in football players. Practically, immunoendocrine measures can be used to inform player load planning. Current evidence indicates that postmatch immunoendocrine responses necessitate ∼48 hours and ∼72 hours to

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Cédric Leduc, Jason Tee, Mathieu Lacome, Jonathon Weakley, Jeremy Cheradame, Carlos Ramirez, and Ben Jones

Team-sport practitioners are required to assess playersreadiness for training and matches using valid and reliable tests. 1 The constraints of a high-level sport environment (eg, access to players, competition focus, time pressures) make fatigue monitoring challenging. 2 Neuromuscular function