F1 Data Analysis: How Big Data Drives Formula 1 Racing

If you’re interested in the power of big data in sport, F1 data analysis is the ideal example. Throughout the year, the F1 teams are continuously strategically planning to maximise their points, with a seemingly infinite range of factors to consider for each race.

What will the weather be like?
How hot is the track and the air?
Does the track suit a higher or lower downforce setup on the car?
How quickly will each of the different tyre compounds degrade over a lap?
Are there certain corners that require a wider or narrower apex approach to prevent the backend from sliding out?

These plus countless other data points are analysed in real-time throughout entire race weekends to support decisions that need to be made on the next lap.

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So, although the media attention is usually on the feuding drivers, Formula 1 is truly a team sport that requires everyone (not least the data analysts) to play their part. You could say it’s like a well-oiled machine.

Formula 1 is powered by AWS

Data-driven sport - Formula 1 is powered by AWS

By partnering with AWS, Formula 1 is using innovative technologies like machine learning and high performance computing, which is taking the sport to the next level by making it data-driven.

“We needed a technology partner that would help us innovate faster and push our organisation into the future,” said Ross Brawn, Managing Director of Motor Sports, F1. “By tapping into the breadth and depth of AWS and its innovative cloud technologies, we’ve been able to bring fans closer to the split-second decisions on the track, redesign our future F1 cars, better understand the wealth of F1 data, and run analytics and machine learning.”

Rich insights for data-driven decisions

Each Formula 1 car is equipped with 300 sensors, generating more than 1.1 million telemetry data points per second transmitted from the cars to the pit. By combining this real-time data with over 70 years of historical race data stored on Amazon S3, AWS provide rich insights to support the F1 teams’ strategic decisions.

High-performance computing

With AWS high-performance computing, F1 ran aerodynamic simulations to develop its next generation car 70% faster. This led to the creation of a car that reduces downforce loss from 50% to 15%, giving the chasing driver a higher chance of overtaking. F1 is also exploring the use of machine learning in its simulation process to provide new insights into more than 550 million data points collected through more than 5,000 single and multicar simulations.

Increasing the excitement for F1 fans

A range of F1 Insights – such as Battle Forecast, Driver Performance and Tyre Performance – are powered by AWS to optimise the fans’ experience before, during, and after each race. Distinct data points make it easy for fans to understand why drivers make split-second decisions and how teams devise and implement race strategies in real-time.

Who’s the fastest F1 driver ever?

For the first time in history, data scientists from F1 and the Amazon Machine Learning (ML) Solutions Lab have created a data-driven ranking of driver speed. AWS machine learning has been applied to remove the car differential and produce an objective ranking of all F1 drivers from 1983 to the present day.

Here are top 3:

Ayrton Senna (Brazil)
Michael Schumacher (Germany)
Lewis Hamilton (UK)

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