The “peloton” – the pack of competing cyclists from rival teams working together to hunt down the leaders in a cycling race – is one of the most iconic and puzzling aspects of the Tour de France.
The peloton is a fast-moving contradiction, simultaneously representing co-operation and fierce competition, as rivals are forced to work together in order to have the best chance of individual and team glory. Over the course of a race, it can turn what looks like an unassailable lead from breakaway riders into an apparently foolish gamble, as it catches and consumes them.
And that’s the key to the peloton’s success. For all the risk and forced co-operation with the competition, hunting in a pack brings great reward. Riders in the middle of the peloton can enjoy a reduction in wind resistance of as much as 40%, meaning teams will often hide their prized sprinter away in the centre of the pack, letting them conserve energy until near the end of the race, before delivering them to frantically expend those reserves in a final burst of pace. Those riders who broke away and built a lead have had no such protection from the elements and, as a result, have none of the same energy reserves stored up in the peloton.
Our name for this complex balancing act is “pelotonomics”. And each year at the Tour de France, millions of data points are collected from the competing cyclists, from the second their wheels start turning to the final stage along the Champs-Élysées in Paris.
During this year’s Tour, we’ll be crunching over 150 million data points, meaning fans will have access to an incredible amount of information about their favourite riders, teams, and stages.
If the breakaway group during Stage 5 of the Tour is set to outpace the peloton, we’ll know about it, and so will Tour de France supporters across digital and broadcast, as data visuals flow seamlessly between all channels and platforms.
And most exciting of all, with the power of machine learning and predictive analytics, we’ll be able to understand even more about how those spectacular pelotons really work.
The tech behind the stories: How do we track the peloton?
Prior to the start of the Tour, state of the art sensors are placed on the back of every rider’s bike, which provides access to basic information such as cyclist speed and location. This is then combined with more unpredictable variables, such as wind strength and direction, along with the potential effects of gradient and altitude on the speed of the peloton.
Anything can happen in this high-octane race; in 2015, over 20 cyclists were involved in a terrifying peloton crash during Stage 3 of the Tour, with data revealing the average rider speed during the fall was a blistering 42 km/h.
This year, we’re translating these variables into a thrilling “rate of catch” prediction, where we’re able to calculate when the peloton is about to catch the breakaway group – and exactly how they’ve managed to accomplish it.
We’re also sharing detailed rider profiles on every cyclist. For example: it’s widely known that Mark Cavendish is one of the sport’s strongest sprinters, but we’ll now be able to delve in greater depth into his performance throughout every stage to understand more about why specific sprinters finish where they finish and how they perform throughout the stage.
Our increased insights will do nothing to detract from the wonder and spectacle of the peloton, but they will help us all understand more how it shapes and decides the outcome of the most unique sporting event in the world.
Peter Gray is the Senior Director of Technology, Sports Practice at Dimension Data.
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