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The world of sport has become a numbers dominated space.

Today data is permeating through every part of sport, from the ways in which fans interact with their favorite teams, through to how bars in stadiums are stocked.

Throughout this transformation, some sports have moved from focussing on tradition and what has worked for decades, into powerhouses of data. The best example of this is cycling, where traditional training techniques were still being used and unfortunately improvements tended to come from the use of illegal substances rather than pure athletic athleticism.

The rise of data use has seen this doping culture more or less obliterated as teams like Team Sky and Giant Alpecin have seen huge successes whilst openly avoiding performance enhancing drugs. In fact, the policy of marginal gains implemented by Sir Dave Brailsford at Team Sky would not be possible without the extensive use of data and data gathering techniques. This same policy was responsible for Sir Bradley Wiggins and Chris Froome winning the 2012 and 2013 Tour de France races.


Ahead of his presentation at the Sports Analytics Innovation Summit in San Francisco, we spoke to Robby about the change in cycling, his role at Team Sky and the datafication of sport in general.

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

Robby Ketchell: 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, onboard 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.


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.

You can hear from Robby about his work in sports data at the Sports Analytics Innovation Summit in San Francisco on September 9 & 10.


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