Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals
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Purpose: The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the “ergodicity issue.” Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field. Methods: The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level. Results: Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery. Conclusions: The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery.
Neumann, Van Yperen, and Den Hartigh are with the Dept of Psychology, and Brauers, Frencken, Brink, and Lemmink, the Center for Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands. Frencken is also with the Football Club Groningen, Groningen, the Netherlands. Meerhoff is with the Leiden Inst of Advanced Computer Science (LIACS), Leiden University, Leiden, the Netherlands.
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