Can data turn the wheel clean for pro-cycling?

17th January, doomsday in the History of Competitive Sports. Lance Armstrong’s confession raised a question mark on WADA (World Anti  Doping Agency) and all the sports personalities in the world. Lance Armstrong won the Tour de France a record 7 consecutive times from 1999-2005 after successfully fighting cancer and his story went on to become an inspiration for many around the world. But, he had some skeletons hidden in his closet.  During the interview with Oprah Winfrey, he confessed that he won all seven of his record Tour de France championships with the help of performance-enhancing drugs and that he acted like a bully who needed to “win at all costs” .

Lance Armstrong confesses to doping on Oprah Winfrey show
Lance Armstrong confesses to doping on Oprah Winfrey show

What I’d like to do with this blog is establish a connection between what happened with Lance and Analytics and Big Data. How? Read on.

Analytics is revolutionizing every sphere of life  and competitive sports is no different.  Analytics is being used to improve ticket and merchandise sales, draw better labour agreements and player contracts and land lucrative TV and digital media deals. We have all heard about and probably seen the movie “ Moneyball”. It is actually based on a book written by  Michael Lewis about the statistical approach called Sabermetrics used to assemble a competitive baseball team. Its not just baseball, major sports like football, cricket and tennis have also resorted to using analytics for the betterment of the game.  IBM has recently developed a dashboard named ‘SlamTracker’ that will support all four grand slam events (the Australian Open, French Open, Wimbledon, and the U.S. Open) serving match by match analysis based upon seven years grand slam data. Football coaches use several analytics toosl to take data driven decision to get better results. One of them is Statszone. It analyses a player’s past performance and tries to forecast the future  based upon several dimensions of the performance. You can see it in action here.

With the emerging popularity of T20 Cricket, Analytics has also proved its significance in the game. Almost every IPL team today, uses analytics to pick the best team based on fanbase and player performance. A Bangalore based analytics company  is developing  a “Real-time cricket analysis tool” which will analyse structured and unstructured data using mathematical and statistical modelling to help teams get suggestions on field placing and other aspects of the game.

In spite of all such advancements in sports analytics, Professional Cycling seems a few steps behind, given that so much data is available. The lack of data driven decisions and the confession of Lance Armstrong has created an environment of suspicion around top performing cyclists. In the 100th edition of Tour de France , Chris Froome entered the final after a dominating performance during 19 days across 2000 miles.

Froome wins Tour De France
Froome wins Tour De France

He had to end his tiring days with reporters probing Froome’s  performance and reliance on performance enhancing drugs. To end speculation and vent out their frustration, his team released two years Froome’s physical power output data to the French newspaper L’Equipe and French physiologist Frederic Grappe, who then analyzed whether Froome is capable of cycling up the hills, where a normal person would struggle to walk. Froome came out clean, you can read the summary of findings  here. Use of analytics also helped the US Women’s cycling team to grab an Olympic silver medal.

In my opinion, this is just the beginning.The decision of Team Sky to release Froome’s power data has raised created new opportunities for the sport of cycling to use data in a better way.

Power data of the cyclists can be used to detect doping which is the most concern in cycling after Armstrong’s debacle. According to Christopherson, a member of the U.S. Cycling Team and alternate on the 1996 and 2000 U.S. Olympic teams; “Doping is a huge issue right now in sports, And the reason it’s been abused by athletes is that it accelerates recovery. At this level of athletics, everyone is training hard enough. The key is: How do you recover faster?”

According to Dr.Frederic Grappe, who analysed the power data of Froome, the 4 key points to look at from the power data of a cyclist are:

i)              Power drop offs of the cyclist

ii)             Aerobic potential

iii)            Distribution of body weights during the career

iv)            Recovery

These can easily detect whether a cyclist is using performance enhancing drugs.

The other key point that came out from Christopherson’s analysis (the analyst for the US Women Cycling team) was that the early morning sun exposure impacts not only on the skin for vitamin D synthesis but also the eyes. It was anchoring the biorhythms and that was related to sleep latency and quality, which improved recovery.

Like all other competitive sports, cycling has a doping issues, but it also has the problem of getting more people to get a grip of the sport. Such constructive analyses can go a long way in bringing back people trust and increase awareness for the game. With apps like Strava and MapMyRide cyclists now have access to important data like heart rate and power monitoring. So, it is the time for Pro cycling to embrace this growing data culture by getting more data about its riders and finding new ways to market their sport with it.

Being an amateur cyclist this was my point of view. What’s yours? Do you think making the performance data public like Froome’s Team Sky, will help the sport? Leave me a comment.

This blog is authored by  Rahul Dutta, Analytics consultant at BRIDGEi2i

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