Formula E has gained popularity as a sustainability-conscious sport that originates innovations to improve electric vehicles. The premise behind Formula E, is not only that the cars are fully electric, but that the 11 teams, each with two drivers, compete in identically set-up, electric battery-powered race cars. Rich within each season, performance data on racing dynamics, the driver and the car itself across the past seven seasons are excellent sources to provide a reliable basis for any forecasting/simulation using state of the art optimization and data science algorithms.
With rich sources of available data ranging from past driver performances, their lap times, standings in past races, weather, and car technical information such as battery, tires, engine; data scientists can predict the number of laps a car can complete by quantifying behavioural attributes such as driver’s risk-taking appetite, and other traits such as track information, weather which can affect the races. The purpose of this exercise is to define the approach to use historical data to predict the number of laps a car would finish in 45min for an upcoming race. We built an ensemble model with combination of an intuitive mathematical model and an instinctive deep learning model to predict the number of laps at the end of every race.