Formula 1 has been working with Amazon Web Services (AWS) to compare driver speeds throughout the ages and define an ultimate ranking of the fastest drivers ever.
Fastest Driver, the latest F1 Insight powered by AWS, uses machine learning technology to provide an objective, data-driven ranking of all drivers from 1983 through present day, by removing the F1 car differential from the equation.
Ranked by qualifying speed – the fastest that all drivers traverse the course during a Grand Prix weekend – three-time World Champion Ayrton Senna came out on top. Seven-time World Champion, Michael Schumacher is second with a time differential of +0.114 seconds to Senna. Current World Champion Lewis Hamilton is third at +0.275 seconds.
By comparing teammates in qualifying sessions, the machine learning-based tool focuses on a driver’s performance output, building a network of teammates across the time-range, all interlinked, and therefore comparable. By comparing laptimes between teammates only, the Fastest Driver algorithm effectively normalises for car and the team performance. Overall, this builds up a picture of how drivers from different generations compare, by analysing the purest indication of raw speed – the qualifying lap.
As a part of F1 Insights, it also provides a unique understanding into a similar exercise F1 teams undergo to define their target drivers for upcoming seasons, but is here applied over a 37-year period of F1 history, despite the differences in rules and machinery.
Completing the Top 10 are Max Verstappen, Charles Leclerc, and Sebastian Vettel, Fernando Alonso and Nico Rosberg, Heikki Kovalainen and Jarno Trulli. Further drivers will be announced on F1.com in the coming weeks as the season continues and more data is analysed.
“As drivers are more often than not the most expensive asset of the team it is important that the selection process is as robust as possible,” said Rob Smedley, director of data systems at F1. “A process such as this therefore would be deployed by the F1 team’s strategists in order to present the most objective and evidence-based selection possible.”
Priya Ponnapalli, principal scientist and senior manager of Amazon ML Solutions Lab, said the project showcases the use of ML in a way to which many can relate.
“There are a number of opportunities to apply the technology to answer complex problems, and in this case, we hope to help settle age-old disputes with fans by using data to inform decisions,” she said.
