During the Rio Olympics, the British cycling team had huge success. On the road, they picked up a bronze in the Mens Time Trial, with Chris Froome finishing behind Thomas Demoulin and Fabian Cancellara, however, it was in track cycling where they really dominated, with the team taking home 11 medals from a possible 30. They managed to win 6 golds out of a possible 10, and of the 4 where they didn't win gold, one they didn't enter a team and the other three they came second.
It was one of the most dominant displays in Olympic history, causing some teams to question the success and insinuating they had some kind of secret advantage over the others. The truth is that they did - they used big data and analytics far better than any other team.
At a summit in April 2016, we heard from Andy Harrison, interim head of performance (at the time programmes director) who discussed the use of data in the British cycling team.
The key to what Andy was discussing was that British Cycling have adopted data for 'multi time scales'. By this, it means how can you use data for a performance during your next competition and also use it to build success over time.
Part of the way this has come about is the British Cycling missions statement:
'We are developing a sporting dynasty. Our succession of senior riders that are achieving international podium results makes Great Britain the dominant cycling nation.'
Data has had a considerable impact on this, with the recruitment of new riders especially important for the process, due to the magnitude of the long-term planning involved. According to Andy, who was speaking in April 2016, 'Over 6 to 7 months, we have put together the next 4 to 6 years of our strategy and how we will operationalize that post-Rio.' The level of detail within this recruitment is down to the granular level, Andy even made the claim that 'We think we can pretty much name who will medal in 2020.'
Ian Yates, Performance Pathway Manager, who presented alongside Andy, discussed the importance of this long-term planning. He talked about a situation that they had identified and changed 18 months before - that they may have a smaller pool in 8 years time than they needed. To remedy this they changed their programme from having 6 under 10's to 60.
Having the ability to look that far into the future comes from a historical use of data that is now paying dividends and is something at the forefront of thinking within the team. Ian pointed out the importance of this in terms of tracking progression, 'What should our riders be able to do, driven by what it takes to win at the top end but then how does that track back in terms of what does a 14 year old need to look like if they are offered an apprentice status.'
This use of historical data is imperative to tracking who has the potential to be the best at specific ages, by looking at the top performing cyclists throughout their progression and then comparing new athletes to their data at that point, Ian pointed out that 'we looked at an Ed Clancy or a Laura Trott and backtracked the results that they got all the way back through.' This allows for the identification of true podium potential at earlier and earlier stages, helping to develop rider across decades, rather than just when they start achieving enough results in local races to be noticed.
However, it is not only about creating the riders of tomorrow, it's also about WITTW - what it takes to win. Here the team collect the data the shows exactly what is needed to win, which could be a certain power output, times in a race or simply their tactical skill. Some of these are simple to find, for instance using a power meter to measure power output and a stopwatch to calculate times, but the tactical skill is not something that can be displayed as simply. Often it is something that is seen as subjective, so is assessed by 4 separate coaches who all watches performances to ascertain skills. This according to Ian allows for a more 'informed, tangible decision'.
This isn't simply about pride and sporting prowess for coaches, though, although it certainly plays a part. They have a huge pressure on them as the only reason they receive money is because they win Olympic medals and, as Andy pointed out, 'If we get it wrong we cannot buy talent, we fail, the program shuts and I can't pay my mortgage.' It means they need to perform every time they are on the track, but also peak at Olympic games. It is a difficult job, but one that data is having a huge impact on.