A friend recently introduced me to Formula One racing. I’ve never followed racing motorsports in the past, but F1 might have converted me.

There’s natural appeal to seeing F1 cars scream down a track at over 200 miles per hour, but there’s also something incredible about the cars themselves. Each machine is the output of hundreds of millions of dollars in engineering, and each team employs hundreds of people. An F1 team is an enterprise dedicated to squeezing ever more performance out of their machines. F1 cars bristle with sensors. During a race, live telemetry is scrutinized, analyzed, and fed into simulations in real time to enable teams to make the best decisions.

As an engineer, I was surprised and delighted to learn that F1 teams regularly use statistical simulations to make tactical decisions before and during a race. How many pit stops should a team target in a race, and when? What tire compounds should be used? Should our driver attempt to overtake, or hang back? Multiple decisions, made in the moment, interact in complex ways to affect the race.

In the past, teams made intuitive judgment calls with inconsistent results. Now, analysts look at existing race conditions, run simulations, and produce recommendations that are accurate and specific. They might determine that a single pit stop enables a first-place finish, but it also increases the chances of a finish in seventh place or worse if something goes wrong. Meanwhile, two pit stops might make a first-place finish impossible, a third- or fourth-place finish likely, and a seventh-place finish or worse unlikely. Decisions can then be made based on how many points the team needs to earn compared to competitors, among other things.

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