The ‘platoon’ – a group of cyclists from rival teams that work together in pursuit of the leading group. It is one of the most iconic and at the same time confusing aspects of the Tour de France.
The peloton is a contradiction in movement, in which both cooperation and ruthless rivalry predominate. The peloton forces rivals to cooperate in order to have the greatest chance of an individual or team victory. During the race, the peloton can turn an apparently unassailable group of escaped riders into a seemingly foolish action after swallowing them up.
That is exactly where the strength of the pack lies. Despite the risks and forced cooperation with the competition, there are many advantages to operating as one team. Riders in the middle of the peloton experience a wind resistance reduction of up to forty percent. It is therefore not surprising that teams keep their best sprinter safely in the middle so that they can save their energy until the end of the race – to release those accumulated reserves in a true speed explosion. Escaped riders on lead miss that protection and are already losing a lot of their energy reserves.
We call this complicated balancing act ‘pelotonomics’. Every year we collect millions of data points from Tour de France participants; from the moment they click their feet in the pedals for the first time until the final stage in Paris.
This year we process more than 150 million data points, giving fans access to a wealth of data about their favorite riders, teams and stages. If, for example, during stage five it starts to look like the leading group will stay ahead of the peloton, we know that right away. Just like the viewers, who are kept up-to-date with data visualizations via every platform. And not only that, because machine learning and predictive analytics give us even more insight into how these spectacular platoons function exactly.
Hundreds of drivers crash through the sleepy French village of Mouilleron-Saint Germain during the 185 kilometer long second stage of the Tour de France 2018. The stage in Western France seems to have been made for a mass sprint.
Live GPS coordinates show that a small group of drivers were early in the race and now feverishly trying to make a gap between them and the peloton. The peloton makes speed during the first part of the race. Subtle changes in the speed and position of riders show how the different teams are making plans to get the leading group. The GPS sensors record an almost imperceptible acceleration in the team that is leading.
In the last few minutes alone thousands of data points have been collected. If we can believe the predictive analytics the peloton will reach the leading group just before the explosive mass sprint. With just one kilometer to go and just past the red bow the peloton actually flies past the exhausted leading group. From the comfortable location in the race centers, data specialists quickly process this data and provide riders with detailed information in their team. About their exact speed and acceleration needed to reach the head of the peloton and the final sprint.
How do we follow the platoon?
Stories like this we hear during the Tour de France. But few people realize how much technology and science is involved to make it a coherent story. For the Tour de France, each bike is first equipped with a sensor that measures speed and location. This data is combined with environmental factors such as wind force and direction, slope and altitude.
Everything is possible during this titanic battle. In the third stage of the Tour de France in 2015, twenty drivers got entangled in a huge crash. Data revealed the average speed at the time of the crash: no less than 42 kilometers per hour.
Rate of catch
This year we are using our data to produce a ‘rate of catch’ prediction, where we can predict when the peloton will reach the leading group – and how they have achieved that.
In addition, we publish detailed profiles of each driver. For example, fans of Tom Dumoulin know not only where he is in the standings, but also how he has performed at every moment of the race.
These detailed insights do not detract from the spectacle, but help us understand how special situations arise and how they determine the final ranking of perhaps the most unique sporting event in the world.
This blog post was written by Peter Gray, Senior Director of the Sports Practice – Technology at Dimension Data.