>Currently, both the athletes' performance parameters and race course data are utilized to develop ideal pacing strategies for time trials or standard stages. Recently, at the Tour Auvergne-Rhône-Alpes, this kind of data modeling formed the basis for Luke Tuckwell's surprising second-place overall finish behind the Mexican standout Isaac Del Toro. 'We knew he wouldn't beat Del Toro on the climb. However, our goal was for him to finish on the podium,' Head Coach Wakefield explains, outlining the initial situation. 'So we analyzed the data from the previous two days, looking at how much time he had lost on specific climbs. We then fed in all the other information regarding temperature, humidity, course length, and gradient. From this, we deduced how quickly Del Toro would conquer the upcoming mountains, assuming a specific power output in watts per kilogram of body weight.'

This, in turn, allowed the team to calculate how much time Tuckwell would lose—and what performance he would need to deliver to ultimately secure a spot on the podium. That strategy successfully played out in the actual mountains as well.

Thank you Bora Bulls, my new GPU will cost as much as my road bike.

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