• Helen Pritchett

Big Talent meets Big Data; The Secret of Formula One’s Success

Updated: Feb 2

Many sports now rely on data to improve player strategy, inform team tactics and develop winning potential. Formula One is one of the sports which gains most from big data analytics. Racing is a complex and technical sport; the cars are incredibly sophisticated and the gap between success and failure can be measured in milliseconds. The number of team members that can be present in the pits is very limited, so a lot of race analysis work is performed by team members back at the team HQ. They analyse data streamed back to the HQ in real time, combining it with GPS, weather information, and what the competition is doing, to give the team an analytical overview of every race. All data is then fed to trackside analysts who are in constant communication with the driver.

When one considers the vast sums of money involved in running an F1 team it’s not hard to understand why data is so important.

What Impact does Data have on Formula One?

Millions of dollars are poured into F1 teams as they compete to develop the best car and the best strategy. Each car is an intelligent and connected data system; some have even called them an Internet of Things in themselves. The cars contain an incredibly sophisticated set of data collection devices which measure every possible environmental and behavioural factor. According to Intel, every aspect of an F1 car is monitored by hundreds of sensors, measuring lap times, tyre and brake temperatures, air flow and engine performance. Data analytics is integral to F1. It dictates how the cars are built, how they are driven, and how the race strategy is decided. Real time data drives the sport with multiple sensors, across car and driver, constantly monitoring and transmitting information. These streams of data give teams hidden insights that would be otherwise invisible to the human eye. Data is also used in development and testing, to optimise pit stop procedures and to re-engineer vehicles for the next race.

Can Smaller F1 Teams Keep Up in The Data Race?

Smaller teams also use data to maximise their potential against the bigger budget teams. Rich Energy Haas F1, which started racing in Formula 1 in 2016, has a considerably smaller budget than manufacturer-backed rivals such as Mercedes and Ferrari, but looks to embrace digital systems that might lead to higher race finishes. In a recent interview Gary Foote, CIO at Rich Energy Haas F1, stated that “The management team here really respects the power of technology and I’m incredibly lucky to have that support”

Formula One switches from on-premise to machine learning on AWS

Computer Weekly reported, in 2018, that the Formula One Group plans to use advanced analytics to improve the data it can show to motor racing fans. Using Amazon SageMaker, Formula 1’s data scientists are training deep learning models with 65 years of historical race data, stored in both Amazon DynamoDB and Amazon Glacier, said AWS. The tool enables the data scientists to obtain race performance statistics for making race predictions and give fans insight into the split-second decisions and strategies adopted by teams and drivers.

How data analytics helped Lewis Hamilton win his fifth Formula One drivers’ championship

In 2018, Lewis Hamilton won the Formula One drivers’ championship for the fifth time. He was the fastest driver, in the best car and his team, Mercedes-AMG Petronas Motorsport team, also won the constructors’ title.

While Hamilton’s speed and driving style were clearly significant aspects of his victory, a less heralded aspect was the data analytics used to give him the best possible chance of winning. Hamilton’s Silver Arrow contains over 200 sensors, some of which collect data points up to 1000 times a second. The car generates millions of data points per race weekend – about 300GB of data. Add in data from the rest of the business and the total reaches 45TB of data a week.

Data matters, and F1 is a prime example of this. As you watch the race on Sunday spare a thought for the data scientists crunching the data behind the scenes to provide the drivers with the very best possible opportunity to win.