How Big Data And Analytics Helped Deliver Olympic Top Performance

Across all sports, athletes and coaches are increasingly working with big data and analytics to squeeze every last insight out of every drop of data available. Nowhere is this more true than at the highest levels. The prospect of Olympic medals and success on the international stage mean at elite level, there is intense pressure to be on the cutting edge of analytics. 

I spent some time looking into how the British Olympic rowing team – the only GB team to have won gold in every Olympics since 1984 – have increasingly ramped up their data-driven analytics with one primary aim – to make their boats go faster. 

In some ways, rowing is intrinsically analytics-friendly. Most of what the athletes do can be measured – from on-water training time to sessions in the gym, in theory giving the team access to the data most likely to bear a relation to actual performance.

But it also poses particular challenges – in it’s competitive form, it will always take place outside, where  variables such as weather and water conditions are not only hard to predict but difficult to assess in terms of impact.

The GB rowing team had to approach their data strategy with these benefits and limitations in mind.  Sir David Tanner (see image), who has led the Olympic and Paralympic rowing programs since 1996, told me “The big attraction has been first to be more rigorous with the data we might have – which might be individual rowing performances in the boats, out of the boats, how much people lift in the gym, the progress an individual makes from 18 to when they are with us at 30 or older.”

Two of the most important big-picture use cases are in talent identification and talent tracking. Both are closely related, although talent tracking comes first.

The idea is that by collecting every bit of data about every athlete who enters the training program, new entrants can be matched against profiles of former entrants, to identify the approach most likely to turn each individual into a champion.

“Wouldn’t it be great if we could press a button now and see what Steve Redgrave was like at 16, 22, 28, 37 … that’s the concept we have – of what we can do. And we’re on the path to doing it.”

In common with businesses trying to get to grips with the potential that analytics can unlock, sports teams are finding that a holistic approach to data management and strategy is likely to pay off.

“We found ourselves working silos”, says Sir David, “so we held all our on water results in one place, all our medical stuff in another, all our bio mechanics some place else, and so on.

“For us, longitudinal profiling of athletes, biomechanics and the whole field of exercise physiology are the fields which have the most potential.”

The trick was to bring all of this information together, and in another parallel with the business world, sporting organizations are also increasingly finding that partnerships are of vital importance. The technical skills needed to build a Big Data-driven analytics system are not native to the world of elite sports scientists and physiologists.

To this end, GB Rowing has worked with a number of tech partners, including Siemens and, currently, SAS, to implement an analytical framework.

As well as collaboration between the technical staff of the two organizations, there is also collaboration at managerial level, where the data strategy is planned.

Sir David said “We found initially there was a very good synergy between our cultures, but we both had to do a good deal of work to add the value that was necessary. My mantra is always ‘does it make the boats go faster?’

Another specific challenge to rowing is that the sport involves a fine balance of strength and endurance. This means that conflicts can arise in training regimes, as strength training can work counter-actively to endurance training, and vice versa. Large amounts of past performance data can be very useful in overcoming this challenge, by highlighting precisely what gains a particular rower is likely to make, when subjected to a specific regime.

The team’s senior sports scientist, Mark Homer, told me “It’s about working out whether one weight session or two weight sessions is enough for this guy – and then he can plough on with the endurance training.”

This coordinated approach to analytics is also leading to athletes being injured less, and spending less time in recovery. Warning signs can be highlighted across all datasets – physiology, gym, medical, race performance – and matched with past data to show when an athlete is in danger of pushing themselves past breaking point.

“We want to have this athlete profile which is live,” Homer says, “Because a lot of the time when you notice these problems its too late. With a centralized system we can pick that up on the first day and are able to stop it getting any worse.”

With the Rio games now completed, the GB team has delivered an amazing performance and ended up on top of the medal standings. It’s clear that analytics and Big Data are playing an increasingly large role. Forward-thinking data strategies are now as important a part of the arsenal as cutting edge training and diet regimes, if teams want to be competitive at the very highest levels.

Thank you for reading my post. Here at LinkedIn and at Forbes I regularly write about management, technology and Big Data. If you would like to read my future posts then please click 'Follow' and feel free to also connect via Twitter, Facebook, Slideshare, and The Advanced Performance Institute.

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