Sports Medicine

, Volume 38, Issue 3, pp 239–252 | Cite as

Describing and Understanding Pacing Strategies during Athletic Competition

Review Article

Abstract

It is widely recognized that an athlete’s ‘pacing strategy’, or how an athlete distributes work and energy throughout an exercise task, can have a significant impact on performance. By applying mathematical modelling (i.e. power/velocity and force/time relationships) to athletic performances, coaches and researchers have observed a variety of pacing strategies. These include the negative, all-out, positive, even, parabolic-shaped and variable pacing strategies. Research suggests that extremely short-duration events (≤30 seconds) may benefit from an explosive ‘all—out’ strategy, whereas during prolonged events (>2 minutes), performance times may be improved if athletes distribute their pace more evenly. Knowledge pertaining to optimal pacing strategies during middle—distance (1.5–2 minutes) and ultra-endurance (>4 hours) events is currently lacking. However, evidence suggests that during these events well trained athletes tend to adopt a positive pacing strategy, whereby after peak speed is reached, the athlete progressively slows. The underlying mechanisms influencing the regulation of pace during exercise are currently unclear. It has been suggested, however, that self-selected exercise intensity is regulated within the brain based on a complex algorithm involving peripheral sensory feedback and the anticipated workload remaining. Furthermore, it seems that the rate and capacity limitations of anaerobic and aerobic energy supply/utilization are particularly influential in dictating the optimal pacing strategy during exercise. This article outlines the various pacing profiles that have previously been observed and discusses possible factors influencing the self-selection of such strategies.

Keywords

Power Output Exercise Intensity Time Trial Pace Strategy Exercise Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

Chris Abbiss is supported by an Australian Postgraduate Award (Department of Education, Science and Training, Australia) and an Edith Cowan University Excellence Award (ECU Postgraduate Scholarship Office, Edith Cowan University, Australia). There are no conflicts of interest that relate to the contents of this manuscript.

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© Adis Data Information BV 2008

Authors and Affiliations

  1. 1.School of Exercise, Biomedical and Health SciencesEdith Cowan UniversityJoondalupAustralia

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