2003
DOI: 10.2165/00007256-200333140-00003
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Systems Modelling of the Relationship Between Training and Performance

Abstract: Mathematical models may provide a method of describing and predicting the effect of training on performance. The current models attempt to describe the effects of single or multiple bouts of exercise on the performance of a specific task on a given day. These models suggest that any training session increases fitness and provokes a fatigue response. Various methods of quantifying the training stimulus (training impulse, absolute work, psychophysiological rating) and physical performance (criterion scale, arbit… Show more

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Cited by 79 publications
(60 citation statements)
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“…45,49,64 Moreover, these models do not account for other aspects that influence competitive performance (ie, environmental, psychological influences, pacing, etc) and may also limit their predictive capacity. Therefore from a practical point of view, these modeling techniques remain of little benefit for predicting future performance in highperformance athletes.…”
Section: Modeling Training Loads With a View To Enhance Or Predict Atmentioning
confidence: 99%
“…45,49,64 Moreover, these models do not account for other aspects that influence competitive performance (ie, environmental, psychological influences, pacing, etc) and may also limit their predictive capacity. Therefore from a practical point of view, these modeling techniques remain of little benefit for predicting future performance in highperformance athletes.…”
Section: Modeling Training Loads With a View To Enhance Or Predict Atmentioning
confidence: 99%
“…The model accounted for two primary physiological components: the positive effects of training, called fitness, and the negative effects of training, called fatigue. Several studies have built upon the Banister model with promising results [5,6,8,10,12,15]. However, these studies also recognized that the Banister model was based on linear systems theory, which limits its accuracy and applicability.…”
Section: Introductionmentioning
confidence: 99%
“…It is the incorporation of these well-known phenomena into a performance modeling framework that provides a more realistic and necessary counter balance; the inclusion of these phenomena also allows performance to be optimized. However, despite the potential concerns associated with the linear modeling approach, it has been used to successfully inform training strategies for many athletes by approximating optimal recovery times between workouts, predicting the success of training regimens, and determining how an athlete should taper before a competition [5,7,8,10,12,13,15].…”
Section: Introductionmentioning
confidence: 99%
“…Chiu, L. Z., & Barnes, J. L., 2003; Clarke, D. C., & Skiba, P. F., 2013; Hellard, P., Avalos, M., Lacoste, L., Barale, F., Chatard, J. C., & Millet, G. P., 2006;Jobson, S. A., Passfield, L., Atkinson, G., Barton, G., & Scarf, P., 2009;Taha, T., & Thomas, S. G., 2003) and to compare different training loads and psychological markers as input and output variables (e.g. Millet, G. P., Groslambert, A., Barbier, B., Rouillon, J. D., & Candau, R. B., 2005; Wallace, L. K., Slattery, K. M., & Coutts, A.…”
Section: Introductionmentioning
confidence: 99%