When it comes to leading a hyper competitive organization, whether it’s a trillion dollar company. a cycling team, or even your own body during a marathon, optimization is one of the most crucial steps for improving your performance. I recently came across a very interesting article regarding F1 racers that has surprisingly useful principles for any leader. F1, like professional cycling, employs the leading technologies in the world of engineering and coordination. The mission control room of a F1 team looks impressive to say the least, with engineers sitting quietly in front of a mosaic of screens, each detailing different telemetry and data feeds. McLaren’s mission control specifically had maps of the course, and plotted exactly where every other car was in addition to their own, the splits, and more.
It’s hard to imagine the engineers keeping up with the influx of data, to make split-second decisions. That’s because they didn’t. All this data was being fed into simulation engines that took into account number of pit stops, pit stop duration and timing, different tire interactions with the ground, and many other variables. This all output to something McLaren calls a decision-support system, which details the optimal course of action for any scenario. That’s why during the 2008 Monaco Grand Prix, when McLaren driver Lewis Hamilton hit a barrier and punctured one of the rear tires, he was forced to do a pit stop. Typically, a unexpected pit stop usually meant automatic defeat but McLaren saw this as an opportunity, one that they’ve spent countless man hours of optimization engineering preparing for.
Based on a number of changing environmental factors such as the drying of the roads, the thirteen members of the pit crew knew exactly how they would service the vehicle before the it even came to a stop, all thanks to the decision-support system churning away at millions of different simulations. They also decided to do a refuel, giving Hamilton just enough fuel to finish the race. There wouldn’t be any more pit stops. Better equipped than the rest of the racers, he went on to win the rest of the race. This translates surprisingly well to any leadership circumstance as it demonstrates the power in adaptive preparation and fully utilizing data in real time. Humans aren’t fully equipped to make decisions under this much pressure in such a short time frame. An effective leader can look to offload some of this burden to machines that don’t feel stress. At the very least, a leader can find ways to prepare the infrastructure for leveraging data in real time, in whatever domain-specific form that might take.
In the following years, McLaren suffered slow pit stops compared to the rest of the competition, 4.5 seconds on average, and they realized that strong pit stops had just as much power as the actual driving when it came to winning races. After all, any marginal gain made a huge difference. By extension, the pit crew performed duties that were nothing short of professional athleticism. Why not treat them as professional athletes? The pit team went on to overhaul their pit routine analysis, and aimed to reduce the gap between the fastest man and slowest man. In 2011 McLaren’s chief engineer Dave Redding reached out to Stafford Murray, who lead a team of performance analysts and biomechanists. At the time, Murray’s team was working with Britain’s Olympic athletes for the 2012 London Olympics. By analyzing other teams, they found that the pit engineers were anticipating exactly where the car would be before it came to a stop, allowing them to begin their motions earlier. This meant that the car needed hit the mark exactly, something that drivers began working on immediately. Murray’s previous work with other professional sports teams has shown that objective data is king, and even coaches, who are often regarded as having strong expertise, could only recall a small portion of the critical factors that went into a winning performance. In 1992, Joan Vickers, a kinesiology professor, performed gaze research on a NBA team and found that the players with the best free throw percentages had certain gaze patterns and timings. By teaching the other players this gaze pattern, Vickers was able increase their free throw accuracy to 76.6% after a couple of seasons, much higher than the NBA average. A 20% increase at this caliber of basketball is almost unheard of.
Murray performed the same gaze analysis on the McLaren pit team, finding that the front left gunman had the fastest individual performance. Eye tracking cameras revealed that he had a dead focus on where the lug nut would be, while others often had unsteady gazed that darted to anywhere from the sky to their own feet. After training the other pit crew to the front left gunman’s level of visual discipline, and making the hilarious realization that McLaren needed to “PAINT NUTS ORANGE!!!” to help pit crew easily see the lug nut locations, Murray helped the team achieve sub 2.5 second pit stops.
For a leader, the application here is clear. When starting an optimization process, nothing can replace the robustness of objective data, and you should look to the best performing individuals or teams in your organization and figure out what they’re doing differently. You can make serious improvements across your leadership domain by transferring what works and minimizing what doesn’t. At the core of this principle is the idea that when leaders encourage learning and meta-analysis within a team, the results are astounding.
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