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Tour de France 2015: road racing and big data

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Photographs: BrakeThrough Media

Traditionally, the first week of the Tour de France had been the realm of the sprinters and 1s, revealing little about the potential of the General Classification contenders as expectation accumulated, leading into the mountains.
In contrast, over the first 1344 kilometres of racing in the 102nd edition of La Grande Boucle, we’ve witnessed the mixed fortunes of the ‘cuatro galácticos’ – Chris Froome, Alberto Contador, Nairo Quintana, Vincenzo Nibali – as well as the emergence of Tejay van Garderen as a GC challenger. This year’s event is proving to be a truly modern affair, offering time-gaps, bonuses, classic climbs, and cobblestones.
And as the story of the race unfolds, an unparalleled barrage of data has spewed from a range of new technology. GoPro, ‘official camera’ supplier to the Tour, has been experimenting with live-feeds from units mounted to stems and seat posts. As part of a five-year deal with Dimension Data, a global IT solutions and services provider, every bike in the peloton has been equipped with a GPS transponder, streaming live top and average speeds, revealing the fastest competitors on key climbs, and even the riders’ proximity to each other during the race.
New Insights
These telecommunications have provided interesting insights into peloton performance. The continuous transmission of speed and location means that we are no longer reliant on intermediate time-checks and post-processed average speeds.

While Strava pros who upload their race data have facilitated the granular examination of individual performance, the mass conscription of the peloton in the services of Dimension Data’s analytical army afford a new level of inter-rider and team comparison.
For example, we were able to see that Trek’s Fabian Cancellara achieved an incredible top speed of 69.16 km/h in the Stage two finishing sprint, higher than that of the day’s winner André Greipel. Live speed data was broadcast during the climb of the Mur de Huy on Stage three, conceding that the leader’s speeds dropped to 11km/h as the gradients reached 19 per cent. In the finale, we were able to quantify Nibali’s attempts to close the gap to Froome while the Sky leader drove the lead group. In the final 500 metres, while Froome pressed forward at 19km/h with Katusha’s Joaquim Rodríguez on his wheel, Nibali continued to push at 20.1km/h, but still lost 11 seconds by the stage finish.
A Hunger For Data
“We are drowning in information and starving for knowledge” said John Naisbitt in his 1982 book “Megatrends”, which describes 10 key shifts in society. These patterns may be mirrored in cycling, as we witness the transition of the sport from it’s blue-collar roots to a landscape in which information is mass-produced and instantly available, but distributed, for the most part, without selectivity or values. However, some riders and staff are clearly aware of the potential to cherry pick, correlate and draw conclusions.

Whilst Lotto-Soudal’s Andre Greipel uploaded his stage five-winning ride to Strava displaying his speed and cadence, power data was conspicuous in its absence, despite an SRM power meter being fitted to his bike along with the new PC8 head-unit. Access to this knowledge could provide a competitive advantage to his rivals, perhaps offering the opportunity to analyse the characteristics of the German’s finishing sprint and devise a precise strategy to counter it.
Such is the hunger for data that Sky principle Sir Dave Brailsford has expressed fears that hackers have targeted Chris Froome’s performance stats.
Mapping Meaning
In the face of this statistical mountain, how can we map meaning and derive value? Has this fresh information provided any knowledge, or is it simply a distraction?
Naisbitt goes on to describe a second pattern in ‘Megatrends’: one in which we transition from technology being forced upon us to a world in which we balance high-tech with “high-touch” human response. Perhaps we are at this watershed in the world of professional cycling?
With careful consideration, we now have the potential to paint a rich picture of road racing which describes its demands and drama in greater depth and clarity than ever. What better microcosm to explore the potential of big data for cycling than the Tour de France?
202 is a performance coach at HINTSA Performance, Geneva

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