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Title: Modelling and optimising the sport and exercise training process
Author: Shrahili, M. M.
ISNI:       0000 0004 5357 8092
Awarding Body: University of Salford
Current Institution: University of Salford
Date of Award: 2014
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In elite sport, the fundamental aim of training is to improve performance in competition. It should develop the abilities of the athletes to achieve the highest level of performance. The fundamental aim of monitoring in training is to determine whether training is appropriate for an athlete and whether training should be modified. Broadly, the purpose is to control the training program of an athlete to ensure that the maximum level of performance by the athlete is reached at a known competition at a known time in the future. In this thesis, we aim to model the training process in cycling in particular. Our purpose is to find a quantitative model that coaches and athletes should follow to optimise training in advance of a major competition. To avoid under and over-training, training should be balanced and should support athletes to develop their capabilities. We develop a statistical model to optimise training. This model is based on the relationship between performance and the accumulation of training. To do this, both training and performance must be measured. We establish a new measure of performance based on the relationship between power output and heart-rate, with the appropriate time lag. The measure of the accumulation of training we use is the Banister model proposed in 1975. Then, we relate our performance measure to the accumulation of training. The parameter values of the Banister model are estimated using the method of maximum likelihood. This analysis is done using R statistical packages. Finally, we suggest some points of interest for developing this work in order to optimise a training schedule for an athlete to reach peak performance at a known competition at a given time.
Supervisor: Not available Sponsor: University of Salford
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available