Beginning at the season opening Grand Prix in Spielberg, Austria July 3-5, the "Car Performance Scores" statistics will isolate an individual car's performance and allow race fans to compare its performance with that of different vehicles head to head. The new set of statistics, say the organizations, will use a range of AWS services, including machine learning, to give fans the ability to compare their favorite drivers and cars and better predict race outcomes.
"Formula 1 racing mixes physics and human performance, yielding powerful, but complex data that AWS is helping them to harness." says Mike Clayville, Vice President, Worldwide Commercial Sales at AWS. "Our existing relationship with F1 has already produced statistics that have brought fans into the race paddocks, and our study of race car aerodynamics is influencing vehicle designs for the 2022 season,. This year, we’re thrilled to extend the power of F1 data in the cloud and unlock new insights that help fans understand more of F1's rich complexity."
Each F1 race car has 300 sensors generating more than 1.1 million data points per second transmitted from the cars to the pit. F1 relies on AWS services to stream, process, and analyze that flood of data in real time, and then present it in a meaningful way for F1 global TV viewers.
The new "Car Performance Scores" insight will display as an on-screen graphic that provides fans with a complete breakdown of a car's total performance using four core metrics: Low-Speed Cornering, High-Speed Cornering, Straight Line, and Car Handling. The new graphic will illustrate how those metrics compare from one car to another, enabling race fans to gauge a given car's relative performance in those different areas and see where each team and driver is leading the pack or losing crucial time to their rivals.
F1 and AWS had previously announced six F1 Insights, including Exit Speed, Predicted Pit Stop Strategy, Pit Window, Battle Forecast, Pit