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Data Science Cycling Echelons

The Drag Side of the Moon

At PickleTech we thrill when two -apparently- separate projects end up connected in some way. And when the reason is related to Physics, writing about it becomes an imperative. Do you know what connects a Tour de France stage with the management of a constellation of satellites?

Aerodynamic drag heavily impacts both cycling and low Earth orbit satellites. Estimating drag with precision and integrating it on complex operational strategies are key to their success. Data Science is contributing to optimize such complex challenges with the use of Artificial Intelligence and Machine Learning techniques.

Individualization matters: the gradient adjusted pace model of Kilian Jornet

Gradient Adjusted Pace (GAP) models are integrated as a common feature in many running sports devices and companion apps. Recently, Kilian Jornet announced his movement from Suunto to Coros, and one of the anecdotal quotes to motivate the change was Coros interest on Kilian’s adjusted pace model. How useful is it to look at a GAP model not derived from your own individual data?

Kilian-Coros’ example highlights the importance of individualized performance modeling for athletes. In this post, we derive the individual GAP model for Kilian. Thanks to his collaboration, we have computed the model using only his own recorded data from last year’s activities. We compare his individualized GAP version with the public version by Strava, focusing on what makes Kilian the ultimate mountain athlete.