Insights & Data Blog

Insights & Data Blog

Meningen op deze blog weerspiegelen de opvattingen van de schrijver en niet per definitie die van de Capgemini Group

What can we learn from the way motorsports does analytics?

In motorsports doing a lap a few thousands of a second faster than your competition can mean the difference between winning and not winning. It also means the difference in earning a lot more money from prices, sponsoring and merchandising. Because everyone wants to identify themselves with winners.

Racing cars that compete in the top classes of motorsports like for instance WEC, Formula 1 and Indycar are stuffed with sensors that capture Terabytes of telemetry data which is send real time to the pit. During the race weekend teams in the WEC and Formula 1 also have a complete team of analysts in their factory location who also receive all telemetry data.

This telemetry data is used for a multiple of things. The most logical that most people are aware of is improving the car setup to make the car faster. Small setup changes can make a huge difference on how a car handles on the track.

Race engineers use it to discuss with the driver to see where time can be won with improving the drivers track knowledge. Because data does not lie an race engineer can see if a driver lifts their throttle on a place where he is supposed to be full on the throttle. Or that the drivers does not brake as hard as he could or to early or too late. Anything that causes time to be lost will be identified. And because it is the truth drivers accept the information and try to learn from it. The latest generation of drivers are fully using the data and often spend hours and hours after practice or races immersing themselves into the data in order to get a better understanding.

But it is also used to monitor the health of the engine, gear box, brakes and tires during practice and races. It is used for maintenance purposes so a team knows when to change parts in order to make sure they do not fail during a race weekend. Which also means that teams know when to need to create or order more spare parts.

Before a race all the acquired knowledge (of the practice sessions and previous years races) and weather data is used to create and simulate race strategies. Based on these simulations the best ones will be used ad primary and alternative race strategies. But that does not mean that race strategies are set in stone. During a race all the race data the teams have available is continuously used to simulate the race and adapt the race strategy of when to do pit stops, when to save fuel / tires or when to push really hard.

So what can we learn from this as a business?

Data is crucial for a lot of decisions that can and need to be made in order to be successful. A good team of data analysts can make the difference between coming over the finish line as a winner or as a loser. Developing new ways to use data or capture data previously not captured can give a team a winning edge. Data acquisition and making the data available for this is the starting point. From there analysts can try anything in order to learn things from the data.

From a business point of view it can make the difference between making founded decisions that can result in saving money or making more profit. It can make the difference between going bankrupt by not being adaptive enough to changes markets or being the one that destroys the competitors in the market because you continuously outperform them.

Over de auteur

Richard Hogenberg
Richard Hogenberg

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