Abstract
This study examines optimal pacing strategies for cycling time trials by considering both the peak power–time curve and acceleration. We developed a computational model to simulate exertion and speed based on each cyclist's individual power characteristics. Two amateur cyclists (YF and AT) tested the proposed strategies during the Rumoi Hill Climb Time Trial (2.3 km), and their performances were compared with those from previous races using constant-power or free-ride strategies. The optimized two-level pacing reduced simulated finish times by 6.8 s (2.1%) and 8.2 s (2.5%), while actual improvements of 2.6 s (0.8%) and 7.9 s (2.4%) were observed. The model further suggested a potential 2.3% reduction in finish time for the 2025 UCI World Championships Women's Individual Time Trial (31.2 km) using a six-level pacing strategy based on YF's power profile. In addition, the proposed pacing strategy appeared potentially effective even in a mass-start hill climb: a solo effort using a constant-power strategy achieved an 8.8% improvement over the previous year, although this was based on a single case (n = 1). These results indicate that simplified and practical pacing strategies derived from power–time relationships can meaningfully enhance performance, including under real race conditions.
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