Machine learning and physical modelling: optimizing the performance and strategy for time trials
Abstract
Despite well-established equations and modern computational capabilities, physical modelling is only beginning to be used in cycling. Such theoretical approach allows to estimate the effect of an equipment or a strategy on the overall performance based on the physiological capabilities of the rider and external parameters. In this paper, we present results obtained from the recent realistic models for time trials developed in collaboration with a World Tour team in order to support sport directors and coaches in making the right decision in terms of strategy and equipment.
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