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<p>Finding the optimal pacing is a&#xa0;major challenge in bicycle races. For the pacing in time trials, three advices should be considered: (i)&#xa0;speed variation over time should be minimised, since for given length a&#xa0;leg with low speed counts more strongly than a&#xa0;leg with high speed, (ii)&#xa0;air resistance increases quadratically with air speed, calling for an equalisation of the rider’s speed relative to the air, and (iii)&#xa0;physiological stress increases more strongly than linear with increasing power output, which calls for a&#xa0;constant power output over time. Under variable environmental conditions it is impossible to consider all these three advices simultaneously. A&#xa0;model that considers the important environmental and physiological factors, such as slope, wind conditions, air density and body weight, is systematically analysed to optimise the pacing on a&#xa0;simple fictitious race course. The optimal pacing strategy is identified as an even balance between the three outlined advices. The result is robust against variation in the considered environmental and physiological factors.</p>

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Balancing trade-offs to optimise pacing in cycling time trials under variable conditions

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  • 发表日期：

    2026年3月24日

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  • Martin Drechsler

摘要

Finding the optimal pacing is a major challenge in bicycle races. For the pacing in time trials, three advices should be considered: (i) speed variation over time should be minimised, since for given length a leg with low speed counts more strongly than a leg with high speed, (ii) air resistance increases quadratically with air speed, calling for an equalisation of the rider’s speed relative to the air, and (iii) physiological stress increases more strongly than linear with increasing power output, which calls for a constant power output over time. Under variable environmental conditions it is impossible to consider all these three advices simultaneously. A model that considers the important environmental and physiological factors, such as slope, wind conditions, air density and body weight, is systematically analysed to optimise the pacing on a simple fictitious race course. The optimal pacing strategy is identified as an even balance between the three outlined advices. The result is robust against variation in the considered environmental and physiological factors.