The coming weeks are exciting for cycling fans. Many of us will spend our Sunday on our couch watching the Tour of Flanders or Paris-Roubaix, recreating the victorious sprint of Mathieu Van der Poel or Wout Van Aert with our cycling friends, or just enjoying the atmosphere of these celebrations of cycling.
Is there a way AI could create impact for cycling and cycling teams? Yes. Explore how AI could make a difference in this blog.
Winning by attacking in the right place, at the right moment
Every cyclist has a limited maximal power, which can be deployed for a limited time. Getting insights into the maximal values of the competitors and how much is already ‘used’ during a race, gives a great advantage. AI can predict these maximal values for every cyclist and link them to the power already used during a race. The impact of AI? Based on this information, team management can give their cyclist a final go for the race-winning attack. The outcome of the Brabantse Pijl 2021 could have been totally different: AI had delivered Jumbo-Visma insights in the data of Tom Pidcock to give Wout Van Aert the edge to win instead of losing by less than 1 centimeter.
Winning by assembling the best team
Cycling is a team sport, and to win major races, a cycling hero like Mathieu van der Poel needs to rely on his team. AI could help compose the best team, considering several factors like budget, availability, personality, willingness to help, and contract terms. Machine Learning is the best fit to find patterns in the data and to suggest the best riders for a team. The impact of AI? Creating the theoretically ideal team, which can be a starting point for recruiting. Combined with their experience and know-how, the management of for example Alpecin-Fenix can create the best team to win major cycling races.
Winning by optimizing training
Cyclists must start a race extremely well-prepared. Remember the Amstel Gold Race 2021 sprint between Wout Van Aert and Tom Pidcock? The difference between winning or losing can literally be 1 millimeter. AI can help to find the marginal gains needed to win by predicting the perfect level of training, nutrition & hydration levels based on the individual data of each cyclist in the team. The impact of AI? Increase the performance and recovery, so cyclists are better prepared for important races.
Winning via the best aerodynamics
Even as amateur cyclists, we all know the impact of the wind on our cycling performance. It is the first thing we highlight on Strava: that it was a windy day. For pro-riders such as Tadej Pogacar, beating the wind by being as aerodynamically efficient as possible is crucial in winning races. AI could once again help to improve their performance. Computer Vision allows coaches and riders to quickly understand their posture and position throughout training and races, analyzing thousands of hours of video or using sensors on the bike. The impact of AI? Offering teams a new tool to improve their riders’ position on their bike to go faster while pushing the same watts.
To sum up, the impact of AI could be major for professional cycling teams.
Some of them have already experimented with data and AI, e.g., the NTT Pro Cycling team. We believe that AI can help INEOS Grenadiers, Quick-Step - Alpha Vinyl, Trek-Segafredo, or other teams win the Tour of Flanders, Paris-Roubaix, or the Tour de France in 2022.
Our team will be happy to help!
