How AI may be helping Tadej Pogacar win races - "We gain insights that you cannot gain by simply watching the race on television"

In the modern world, it is almost impossible to escape the term 'AI' and the cycling world is no exception. Not only that, but seemingly the use of Artificial Intelligence is quickly growing in the peloton and UAE Team Emirates - XRG may have in it's own system 'Anna' a weapon to better prepare it's riders in the ideal way.

Already in the team's December training camp, team coached Javier Sola and head of performance Jeroen Swart have highly praised the use of 'Anna', an AI system developed and used specifically with the Emirati team. In it, the team store data regarding training, nutrition, race data and more.

"We let Anna watch a stage of the Tour and asked how the race unfolded. Anna then told us the 'key stories' of that race, focusing on Tadej and all the data he produced," Swart shared in a lengthy interview with GCN. "In this way, we gain insights that you cannot gain by simply watching the race on television."

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UAE use it as means to better understand their riders and everything related to their performances. If it has successfully helped the team or not is an ambiguous question, however the team's success (specially in 2024) can't be denied. The team puts the AI to use analyzing races together with the riders' physiological information and gathers results immediately. "Anna produces all that in just one minute. If you had to examine all that data yourself, you would need a person with an infinite amount of time."

In recent years the sport has evolved tremendously when it comes to nutrition mainly, but technology also plays an immense role - specially when it comes to the improvement of aerodynamics, both in the riders' equipment and also bikes. The attention to detail is extreme in top teams, and it became hard to imagine where else there could be evolution, but AI may be the field where teams may be able to obtain an extra step in improvement.

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Specially with the team sponsors' long-term commitment, the team is going to ultimately gather years of data, and this can prove even more valuable in the future. "For example, we ask Anna the question: rider X will ride the Flèche Wallonne. Tell us what training he did in the past in the run-up to his best two-minute value ever. So then AI goes through the training data of the last five years of that rider to see how hard that rider had trained in the period before he pedaled his best value ever," the South-African explains.

Specially for the coaches who work with the riders, this can prove to be a key weapon, that may perhaps start having widespread use throughout the whole peloton. "And then AI tells us: that rider must start the race with a CTL of, for example, 115. For a Grand Tour, that answer will be, for example, 130."

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