Formula E: Asphalt to Analytics

Formula E Debuts in Hong Kong

The roar of the crowd occasionally was louder than the roar of the Formula E cars — racing past. Yet the speed and skill required by the drivers to pilot is not necessarily less than a normal Formula One Championship race.

What is the biggest difference is the men and women who make up the pit crew: data scientists.

Every part of a Formula E car is a sensor-steering, hydraulics, aerodynamics, torque, and the reaction times of the driver. Combine this with drivers hot swapping their entire cars instead of the mechanical parts when they head to pit — now you have Big Data Problem.

Notice that the pit crews hovered around digital read out screens to analyse, theorize on how to win. Remember the forty times laps and the track are constants — but how the other cars behave — crashes, bumps, and erratic moves plus how your own driver reacts — is not. Taking these constant streams of input to calculate optimal speed for finite energy is the key strategy to winning.

Other strategies include hugging curves or going wide to use inertia and other forces to sling shot you ahead without wasting precious electricity.

Notice that the top three winners of Hong Kong’s Formula E championship raced into lap forty-five at average energy consumption percentage of six percent. Hadfield was third having 5% energy left. De-Grassi of Brazil was at 6%; Buemi from Renault had the highest energy left at 7% and won.

So you could say the loudest roar came from the zeros and ones.

One clap, two clap, three clap, forty?

By clapping more or less, you can signal to us which stories really stand out.