Formula 1 and Predictive Analytics
A couple weeks ago, I discussed the use of predictive analytics in the transportation industry, specifically the use of acoustic bearing monitors to predict bearing failures on Union Pacific freight trains. Today the conversation turns to predictive analytics in a very different type of transportation, Formula One (F1) racing. Download our Case Study on Mechanical Failure and Predictive Analytics.
F1 is to many the pinnacle of motor sports. F1 has the most technologically advanced cars, the most skilled drivers (some compensated $50+ million per year), the most exotic race locations, and yes, the most beautiful paddock girls. Formula One is a closely sanctioned “space race” creating and refining innovative and ground breaking technologies including traction control, anti-lock brakes, direct injection, synthetic oil, kinetic energy recovery systems (KERS), carbon fiber, and computational fluid dynamics (CFD). Many advancements made in technology by F1 teams have contributed to the efficiency and safety of everyday vehicles.
With team budgets around half a billion dollars, Formula One racing is big business. Similar to most businesses, teams are scratching and clawing for any advantage. F1 is a ruthless take no prisoners game where at seasons end, 10 of the top 20 drivers can all be separated by less than five Championship points. Each championship point can be worth millions. Teams are always looking for any competitive advantage to get as much of the $700+ million season purse as possible.
Race strategy is THE key to success. Formula One race strategy is equivalent to solving an impossible puzzle. Variables include weather conditions, tire degradation, position and speed of others on track, safety car deployment, fuel conservation, opponent pit forecasting, and aerodynamic design. In real time, team engineers must take an impossible number of inputs and design the most efficient pit stop schedule. F1 engineers are expected to do what most (except for a few crazies) believe is impossible, predict the future.
In the past few years, F1 teams have been working with companies including QuantumBlack to push the envelope and create predictive algorithms to optimize race pit strategies. QuantumBlack has produced predictive algorithms which they claim have assisted in accruing more than 300 championship points since 2009. These algorithms take into account current values of the variables listed in the previous paragraph and couple them with the real-time telemetry readouts from a team car to give an optimized race strategy. The process is so advanced, it allows these predictive algorithms to be adjusted and updated less than 4 seconds off real-time.
These algorithms help team engineers make the most informed decisions for pitting. Formula One is notorious for its difficulty and lack of overtaking. If an engineer is able to adjust a scheduled pit for a driver currently stuck in traffic and release that driver into a clean section of track, the payout can be valuable seconds = points = $ millions.
As I previously stated, Formula One is big business. If predictive analytics can be used to increase revenue of F1 teams by hundreds of millions of dollars, imagine the rewards your business could reap from developing your own predictive algorithms. Explore our case studies.
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Grant, does a demo involve getting a few laps in a F1 car?
Grant, does a demo involve getting to take a few laps in a F1 car?