Using data to make incremental improvements to performance in cycling

On the 9th & 10th September our partner Innovation Enterprise will be hosting the Sports Analytics Innovation Summit in San Francisco. Ahead of this they caught up with one of the speakers, Robby Ketchell, to learn more about changes taking place in cycling, his role at Team Sky and the datafication of sport in general.

Robby Ketchell’s role at Team Sky sees him working with their data to find the small incremental improvements that will hopefully bring about further improvements to the team. He has an impressive history of success with the Garmin-Sharp (now Cannondale-Garmin) team, where he worked with cyclists such as David Millar and Bradley Wiggins.

Do you think that cycling has now become a numbers based sport?


Numbers have always been a big part of sports, not just cycling. Endurance sports in general have recently become more and more data dependent with new sensors that measure aspects of physiology and physical performance. Cycling has grown to become more of a numbers aware sport with similar sensors, social media and using humans as sensors, on-board devices, and software dedicated to the analysis of all of the data collected.




Team Sky’s success has been based largely on the idea of marginal gains, where do you see marginal gains 2.0 taking us and how will powerful data gathering/analysis tools help with this?


Marginal gains is the concept of continuing to improve every aspect of performance a little bit at a time. Now that cycling has become a data rich environment, we're continuing to seek improvements in the way we collect and interpret data. We try to improve our performance by using data to make better informed decisions.

The Pro-Peloton is likely to change considerably in the next few years with new technologies, such as disc brakes, being introduced in 2016 - how important will data be in the integration of these to improve performance?


Every time new equipment is introduced into the sport, sponsors and teams spend a lot of time analysing the performance of these innovations by either going to labs like wind tunnels or testing in the field with devices like the BATbox [a box that sits at the front of a bike to calculate air resistance]. In addition, the athletes spend some time testing the equipment and giving feedback so that we can optimize performance. This is something that's important to the design of any innovation, whether it be a piece of software or a new aerodynamic wheel, getting the user's feedback helps drive the development. Using data in conjunction with some of these subjective measures is important to improve the performance as well as ensure the safety of the athletes.

With the proliferation of data being available in sports, do you think this has had an effect on the ability to identify potential doping cheats?


We now know so much more about the athletes due to increased data collection. Athletes now have a footprint that didn't exist in the past, which has allowed authorities to track performance gains and losses, health, and monitor events that weren't possible a few years ago. This puts authorities in a powerful position in regards to eliminating doping, but it also comes with a big responsibility. No matter how sophisticated technology gets, it is critical to take the results of any analysis within context of the sport and the environment.

Having worked within sports science, especially within cycling, for a number of years, how has the appreciation and understanding of data changed since you first began?


I think the biggest change is the understanding that data can be used to discover new possibilities. Previously, we used to do experiments with a hypothesis that something would occur, and if it did we would say we were on to something. Now we are finally getting to the point where people ask us to look at the numbers and see if we can learn something.

To read the original article from our partner Innovation Enterprise please click here.

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