Sports and data analytics are becoming fast friends, and their relationship is a topic I've explored before. Another example I recently came across is how the U.S. women’s cycling
The team was struggling when it turned to Sky Christopherson for help. Christopherson was a former Olympian cyclist and broke a world record in the over 35s 200m velodrome sprint a decade after retiring as a professional athlete. He had done this using a training regime he designed himself, based on data analytics and originally inspired by the work of cardiologist Dr. Eric Topol.
Christopherson developed the Optimized Athlete program after becoming disillusioned with doping in the sport, putting the phrase “data not drugs” at the core of the philosophy. He put together a set of sophisticated data-capture and monitoring techniques to record every aspect affecting the women athletes’ performance, including diet, sleep patterns, environment and training intensity. However, he soon realized the data was growing at an unmanageable rate.
This prompted him to contact San Francisco's data analytics and visualization specialists Datameer, which helped to implement the program. Datameer’s CEO, Stefan Groschupf, himself a former competitive swimmer at a national level in Germany, immediately saw the potential of the project. Christopherson said “They came back with some really exciting results – some connections that we hadn’t seen before. How diet, training and environment all influence each other – everything is interconnected and you can really see that in the data.”
The depth of the analytics meant that tailored programs could be tweaked for each athlete to get the best out of every team member. One insight which came up was that one cyclist – Jenny Reed – performed much better in training if she had slept at a lower temperature the night before. So she was provided with a temperature water-cooled mattress to keep her body at an exact temperature throughout the night. “This had the effect of giving her better deep sleep, which is when the body releases human growth hormone and testosterone naturally,” says Christopherson.
Big Data enables high performance sports teams to quantify the many factors that influence performance, such as training load, recovery, and how the human body regenerates. Teams can finally measure all these elements and establish early warning signals that, for example, stop them from pushing athletes into overtraining, which often results in injury and illness. The need to train hard while avoiding the dangers of injury and illness is, in Christopherson’s opinion, the leading temptation for athletes to use the performance-enhancing drugs (PEDs) which have blighted cycling and other sports for so long.
Christopherson’s system has not been put through rigorous scientific testing but it seems to work fairly well based his personal success as well as the success of the team he coached. The key is finding balance during training. “It’s manipulating the training based on the data you have recorded so that you are never pushing into that danger zone, but also never backing off and under-utilizing your talent. It’s a very fine line and that’s what Big Data is enabling us to finally do.”
When used accurately and efficiently, it is thought that Big Data could vastly extend the careers of professional athletes and sportsmen well beyond the typical retirement age of 30, with the right balance of diet and exercise, and avoiding injury through over-exertion. Christopherson spoke to me from Hollywood, where he is trying to finalize a distribution deal for a new documentary called ‘Personal Gold,’ that tells this amazing story in much more detail. The Optimized Athlete program has also been turned into an app (OAthlete), which will be made available to early adopters from June 18th.
