*Hello, geek.*

Hello, you science nerd, you technology aficionado, you analytical thinker, you.

Do you like watching sports?

I ask because there is a sport that will appeal to every aforementioned aspect of your personality, although judging from American TV viewing figures, you are probably not paying attention to it—even though its competitors are geeks, just like you. It is the pinnacle of automobile racing, the league known as Formula 1.

A Ferarri and Red Bull scream around the streets of Singapore in 2011. Photo: Chuljae Lee / CC

When it comes to adrenaline, these cars have no match. They’re screaming, winged rockets of carbon fiber cradling a driver with no roof over his head at top speeds exceeding 200 mph. There are no fenders to protect the wheels and suspension as they strain under the 5 Gs of stress that these cars exert as they scream around corners.

But despite that, forget the notion that modern racing is an exercise in pure sensation and blind bravery. Nor is it the gentlemanly pastime of European princes, hobbyist mechanics, and thrill-seeking rascals that it once was many decades ago. Today, more than any other sport, F1 is driven by design and data. It’s engineering. It’s technology. It’s physics soup for the scientific soul.

It’s no wonder that when Ron Howard began production on his 1970s-era F1 pic *Rush*, he described the world he found as a “combo of engineering brilliance and fearless courage [that] reminded me of people I met at NASA while directing Apollo 13.”

The workings of an F1 team are relentless, iterative, like a computer algorithm designed to obtain a minimum value: for a race distance of 305 km, solve for the shortest time possible.

Watching a race on TV, it’s almost startling to hear the quantitative way in which the most competent commentators analyze the race as it unfolds—the cars are going over 200 mph and the guys on TV are calculating fuel loads and tire wear. It’s a bit like that epic moment in Apollo 13 when astronaut Jim Lovell is struggling to convert the gimbal angles from the stricken command module to the lifeboat lunar module and everyone in Mission Control whips out their slide rule.

To see a bit of this strategy and how F1’s geeks solve it, consider the quandary teams face when planning pit stops to change tires. A typical race might last between 50–80 laps, but the tires on an F1 car wear quickly, and each successive lap takes a tenth of a second longer on average, or more. Changing to fresh rubber means the drivers regain their speed, but a total of about 20 seconds is lost as the team swaps tires and the driver obeys a 100 km/hr speed limit on pit lane. (This is called the “bogey time” and is measured by the teams at each track.) So how often should a driver sacrifice those 20 seconds to gain back the most time on fresh rubber?

The math works out to be 1 to 3 times during a race, depending on the rate of wear, trading 20 to 60 seconds in the pits for the consistently quicker lap times on fresh tires.

But when? Imagine you’re the leader of the race. If you time it too early, you may emerge from the pits in the middle of the swarming peloton of cars, fighting with them for position. That would cost you precious time. Perhaps you should wait a handful of laps and let the cars behind you pit first.

But wait. If they pit first, they will have fresh tires while you are running around on worn rubber, bleeding time each lap. By the time you pit, the other cars may have leapfrogged you as you sit in pit lane. (This tactic is called the “undercut”.)

Now perhaps, my geeky race strategist, you have determined the perfect laps on which to pit to minimize your time (and made sure that your team is free of moles who might leak your strategy—a very real danger). But here’s the thing: the other teams can calculate their numbers just as well as you can. What are they likely to do? Well, it depends. Does that change what should you do? Maybe.

No computer could find a single perfect solution for this kind of problem. It’s mathematically impossible; there are simply too many variables. Instead, the best method is to simulate *tens of thousands* of races, randomly trying as many different strategies as you can to see which ones result in you winning the race the most times.

This kind of technique is called a Monte Carlo method, named since every simulation is like a gambler’s roll of the dice. It was enabled by the rise of computers and pioneered on the primitive ENIAC. Today, it’s ubiquitous. It’s the same probabilistic math that Nate Silver uses to predict elections and that scientists use to forecast the paths of hurricanes—the rolling of multitudes of virtual dice to see which outcomes are most likely to come true, down which branches of reality the river of time will meet the least resistance. And it’s why the top F1 teams have squads of statisticians and data analysts working in Mission Control-style computer rooms back in their factories during a race, conducting their simulations, feeding their teams the latest model runs and dictating race strategy.

So what does this mean for you, dear geek? For one, the raw timing data is available to view at Formula1.com during races. Observing the lap times and the gaps between cars will allow you to see strategies unfold faster than the TV announcers can comment on them. If you want to go even further, there is an open source API project to intercept the data, allowing you to write your own code and make your own predictions.

F1 isn’t just about watching a competition—it also gives fans the chance to experience the joy of watching an outcome emerge from a sea of data. That’s something every geek can appreciate.