The right choice of a pacing strategy for a time trial race is important and often difficult to establish. Methods are now available to generate pacing strategies that are optimal, however, only in a mathematical sense. Until now, they were tested in practice only under laboratory conditions [1]. Pacing strategies are generally based on two mathematical models: (1) to describe the relation between power output and speed [2], and (2) to describe the fatigue of the rider related to the power output [3]. The quality and validity of these pacing strategies relies on the accuracy of the predictions made by those models.Our goal is to leave laboratory conditions and move on to the field. One problem there is that deviations within the physical model parameters like wind, road surface, or slope may lead to premature exhaustion if the cyclist follows precomputed speed, power or time. Thus, we need a way to guide the cyclist on a pacing strategy following the precomputed exhaustion (remaining anaerobic energy (ean)) and adapting precomputed power and speed. Besides proposing a suitable real-time adaptation of the strategy we show that the cyclist can successfully complete the prescribed ride with only small overall deviations from the strategy, using one of three kinds of feedback modes.
© Copyright 2018 Sportinformatik XII. 12. Symposium der dvs-Sektion "Sportinformatik und Sporttechnologie" vom 5.-7. September 2018 in Garching. Abstracts.. Published by Feldhaus, Ed. Czwalina. All rights reserved.