Six laps into the 2008 Monaco Grand Prix, Lewis Hamilton’s McLaren race car skidded on the rain-slicked pavement, bumped against a barrier, and blew out its right rear tire. At the time, Hamilton, a gifted, impatient driver and one of auto racing’s biggest stars, was in second place. A puncture is a serious setback in any Formula One competition. In Monaco, the most prestigious title on the schedule, it’s a disaster: The course is laid out on the principality’s twisting, hilly streets, rather than a purpose-built racetrack, so passing is nearly impossible, and ground lost is particularly hard to regain. The three-time Formula One champion Nelson Piquet once likened the race to “riding a bicycle around your living room.” Rain only compounds the challenge.


When Hamilton clipped the barrier, 13 members of the McLaren race team were sitting in a windowless control room in the English town of Woking, 900 miles away. Outside, herons stood in the manmade lake that laps at the curving glass facade of the McLaren Technology Centre. The men and women at the banks of monitors, dressed in the same black and white uniforms as their teammates at the track, included strategists, systems engineers, performance engineers, mechanical engineers, and IT specialists; dozens of others in the building were patched in as well. Many of the decisions about the car’s setup and management over the course of the race are made here, not at the track. The team now had less than 30 seconds, the time it would take Hamilton to ease his car into the pit area, to make a very important call.


In the weeks and days leading up to the race, McLaren engineers had been running thousands of simulations, testing components, configurations, settings, and strategies. After the race started, the simulations continued to run, their predictive power improving lap by lap as information from the track was fed in. That meant there was a recommendation in the system for exactly what happened—Hamilton needing a pit stop in the sixth lap in a drizzle expected to soon taper off. Just six seconds after Hamilton called in his flat, a note of panic in his voice, the race engineer got on the radio and calmly told the pit crew to ready a set of tires—not deep-treaded “full wets,” but intermediate tires that could grip drier pavement as well. At almost the same instant the team manager told the crew to pump in extra fuel.


Both decisions were calculated gambles: The extra gas would weigh the car down, and the intermediates wouldn’t perform quite as well in the rain, but Hamilton would be able to stay out on the track past the point when his competitors would need to refuel and change their tires, gaining ground on them when they did. Within 10 laps, Hamilton had climbed back to third place, and when the two drivers ahead of him had to pit, Hamilton took over the lead. He held it until the checkered flag. It was one of the most dramatic Monacos in memory, and Hamilton would go on to win that season’s Drivers’ Championship.


“It’s all probabilistic,” says Mark Williams, McLaren’s head of vehicle engineering. “Because the system is running races live in the background, you can say to it, ‘How am I going to beat the guy in front?’ It goes off and specifically looks at all the options that he could do and you could do and comes up with the best solution based on probabilistic analysis. You may or may not beat him, but the closest you’ll get to him is by doing this strategy.”


McLaren has long had a reputation as a data-obsessed racing operation. It makes the telemetry systems for all its Formula One competitors, along with the computerized engine control units for Formula One, IndyCar, and Nascar. When a McLaren car is on the track, more than 120 sensors transmit a torrent of information on tire pressure, torque, temperature, and downforce (the vertical pressure, vitally important in cornering, created by airflow over a moving object).

The leader of the effort was Caroline Hargrove, a mechanical engineer with more than a decade of experience in racing. Hargrove was a lecturer at Cambridge University, just beginning to question her suitability to a life in academia, when she spotted an ad for a job at McLaren Racing in an engineering journal. The Montreal native had little interest in cars, but she liked interesting problems, and she applied.

She started at McLaren in 1997 as part of a small team developing a racing simulator. Then as now, women were a rarity among Formula One engineers. Simulators were rarer still: None of the teams had one. Even at McLaren there was little enthusiasm for the project. The drivers complained about the poor graphics, and the team’s head designer at the time, Adrian Newey, let it be known that he found the technology useless. Gradually, Hargrove and the other engineers improved the software and the mechanics. Then Formula One imposed limits on the amount of time cars could be tested on the track, and suddenly every team needed a simulator.

Today, McLaren has two of the machines: full-size car bodies mounted on hydraulics surrounded by curving video screens, with robotic arms that jerk the driver’s helmet back and forth to imitate the violent G-forces of high-speed turns. One sits in the basement of the Woking headquarters, near the wind tunnel; the other is in the laboratory-like area above, where McLaren’s race cars are built and the ultrahigh-end consumer cars it recently began selling are designed. An encyclopedic range of courses and conditions can be programmed into the simulators, but they’re more than just training tools. They’re able to calculate how different components affect a car’s handling, even if those components exist only as a set of specs. That means the team can test parts on the simulator before it actually builds them, so only the promising ones would be fabricated and tried out at the track. Before McLaren started using a simulator, just 10 percent of the parts it made ended up being used in its race cars; now, 90 percent of them do.

Over three Olympics, the athletes McLaren worked with won 17 gold medals, and 32 medals in all. McLaren couldn’t take all the credit—these were sports in which Britain had done well before, and its pursuit cycling team has dominated the event for a decade—but it was an auspicious place to begin diversifying from cars. As it grew toward its current head count of 250, MAT hired engineers and designers of its own, many from outside the racing world. Another early client was Specialized. The Northern California bicycle maker had long claimed that “smoother is faster,” meaning that the ability of its high-performance road and mountain bikes to absorb bumps not only made them more comfortable to ride but also quicker than the competition. Smoother, however, isn’t always faster: A too-flexible bike steals power from the rider’s pedaling. So how smooth was too smooth? The testing methods that even industry-leading bikemakers like Specialized used—putting people on prototypes to ride them and report back—were highly subjective and of limited use.

Hired in early 2010, the MAT team decided to build a bicycle version of its race car simulator. They quickly realized that the project raised its own set of challenges. Unlike in a car, the rider’s body was a major part of the calculation. “The human’s a big, fat, blobby mass on a very stiff, light structure,” says Duncan Bradley, the engineer and product designer who led the team. To work, the simulator had to re-
For three years, the McLaren team gathered data on how different parts of different Specialized bikes performed, individually and in combination, with and without the rider. A serious cyclist himself, Bradley often served as the test rider, perching in spandex and helmet on a deconstructed bit of bicycle mounted on hydraulics. The rig would send calibrated jolts up the handlebars or the seat, into his hands or hindquarters, with sensors measuring the diffusion of force. It was slow work, but by mid-2013 the team succeeded in creating, essentially, a virtual bicycle: “We turned the whole system, including the rider, into a mathematical formula,” says Bradley. Now Specialized’s designers and engineers could, with a few keystrokes, change the shape or weight or stiffness of various parts, and try out the resulting bike over different road surfaces.

“We’d been taking the measurements that were used to build this model for years, but we could not put it together,” says Mark Cote, Specialized’s manager for aerodynamics research and development. “There’s a kind of analysis paralysis in the world today, with every single piece of your world being measured in some way.” McLaren, he says, laughing, “has way too many sensors on their cars, and they know how and when to interpret that data.”