You might have figured out from the title that this post detailshow datais being used to gain competitive advantage in F1 Racing. Formula 1 is an expensive sport and the cost associated with managing a racing team has gone up significantly. Unlike commercial cars which might take 2-5 years to go from the drawing board to production, an F1 racing car takes about 5 months to manufacture.However, owners of F1 teams are not able to keep up with the cost of manufacturing vehicles every year. Moreover, each teamspends around $400,000-$450,000to test cars on the track. Hence to tackle this expense, F1 teams have been employing analysis of massive amounts of data to designpredictive models to improve the performance of their existing vehicle.
Instead of manufacturing new cars every 5-6 months, F1 team owners are now embedding cars with more than 100sensors that are generating realtime data. They have also started recruiting large number ofdata scientists to generate insights from the collected data so as to tweak the features in existing cars. This process has proved to be very cost effective in gaining foothold in this risky and expensive sport.
Formula 1 Car
Teams are using real time data not only to ensure that the car runs smoothly, but also todevelop racing strategies. AT&T, whose technologyis used byInfiniti Red Bull Racing team
, reported that "more than 243 Terrabytes of data was collected by Grand Prix team"in October, 2014 alone. That data was used by data scientists to design models and simulate the actual race to predict future winning probability. It seems like engineers & data scientists have more responsibilities nowadays to win the race than the drivers themselves. Drivers merely use the insight reported by engineers and follow the instructions.
Can big data be used for commercial cars?
Since sensor prices are going down each year, we should soon see them embedded in our own vehicle. By analyzing data from these sensors, we will get in-depth internal diagnostics on our car which is currently limited only to basic onboard diagnostics.There are startups already attacking these problems from various angles. The future looks promising for a truly smart car, thanks to big-data technologies and of course data scientists.