Incorporating the Maximal Mean Power Profile in Time Trial Simulations for More Efficient Optimal Pacing Strategy Calculations

Abstract

Mathematical modelling in cycling enables retrospective analysis and predictive simulations, crucial for optimizing performance. This study introduces a numerically efficient notation for incorporating a rider’s maximal mean power profile, enhancing computational times for pacing strategy calculations while maintaining physiological relevance. Using exponentially weighted rolling averages (EWM) expedites MMP computation compared to classic averages. Applied to the 21st stage of the 2024 Tour de France, the methodology is adopted here in an optimal pacing strategy calculation with state-of-the-art complexity. The proposed solution offers a streamlined alternative to existing models, promising reduced computational costs and enhanced optimization algorithms, thus advancing cycling performance analysis.