The Pro Peloton ascending the Col du Galibier in the 2022 Tour de France. Photo c. Red Bull Content Pool/KramonOver the 21 stages of the Tour de France, riders of the Pro Peloton may ride a number of different models from their frame sponsor. They will switch between the brand’s dedicated aero road bike, time-trial bike, and lightweight climbing bike, making a decision based on what that stage will bring on the day; elevation profile, wind conditions, mileage, terrain, etc.
It stands to reason then, there will be some cross-over point along this gradient spectrum at which the rider expends an equal amount of power overcoming both drag and climbing power. However, this still isn’t the full picture. The question of interest isn’t about the magnitude of power, but rather differences in performance. What we are really interested in is how changes in drag or mass affect performance. A simple example might be a choice of wheels.
How does rider power affect the gradient transition? There is no one answer to this; it comes down to those three key variables: change in drag, change in mass, and rider power to weight. For a given configuration you can model a curve for tipping point versus rider power to weight. This will show how your performance with a given setup compares to that of a pro who might have significantly higher power to weight. But that will only apply to that specific equipment configuration.
The power distribution as a function of gradient for this rider is plotted below. This model effectively has the rider maintaining a constant power output and redistributes that power based on the requirements at each gradient. From experience, we know that the increase in gradient is coupled with a decrease in speed. At 0% the rider is moving very quickly , compared to very slowly at 10%. This plot highlights how the rider’s power distribution changes as the road becomes steeper.
Wout van Aert racing stage 4 of the 58th Tirreno-Adriatico 2023 from Greccio to Tortoreto. Credit: Red Bull Content Pool / Kristof Ramon Orange: Gradient at which the 2021 Emonda is faster than the 2023 Madone; Red: Gradient at which the 2018 Emonda is faster than the 2023 Madone. It’s interesting to see how different rider speeds play a big role in when the Aethos versus the Tarmac choice delivers an advantage.
In a racing context this could mean adjusting to different gradient pitches, accelerating out of corners, launching attacks, following crucial attacks, descending safely, and avoiding crashes.
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