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Data Science

How Data is transforming Formula 1 and revolutionising the sport

Discover how data is transforming Formula 1, from crafting cutting-edge cars to revolutionising race strategy and fan engagement. Explore the tech behind the world's fastest sport and learn how you can launch a career in data.

11 min read

It’s no secret that the popularity of Formula 1 (F1) has skyrocketed in recent years. Where you may have previously seen countless Instagram stories about football, you now see (yet another) victory photo of Max Verstappen.

While a few years ago Verstappen's endless victories might have seemed like a miracle, the average viewer is now able to see the data and patterns behind his driving and success.  This increased emphasis on data means that Formula 1 is almost unrecognisable from its birth a hundred years ago. The first F1 car was the Alfa Romeo 158, which was introduced in 1938 and has a 296HP engine. In this year's season, Red Bull launched a car with 900HP.

Written by

Lyanna is a Marketing Executive at Learning People who holds a passion for the intersection between culture and tech.

Lyanna HindleyMarketing Executive

Data has not only allowed engineers and teams to craft faster, leaner, meaner vehicles - it’s also led to the sport exploding in popularity. This boom in popularity is no accident - F1 has made a conscious effort to increase its profile and become a more accessible sport. While tickets to see races are still exponentially more expensive than seeing a local football match, watching the sport online or on TV has become easier and arguably - more exciting than ever.

This isn’t to say that F1 hasn’t always been a tech and data-driven sport. Each year countless improvements are made to the vehicles, to the extent that the sports governing bodies have to implement new rules each year to make things more difficult for engineers. Behind every 250mph car is a huge team of engineers, designers, strategists and scientists. We’re exploring how data has changed the sport and sharing some industry insights ahead of this season's final race on Sunday.

 

Strategy and competitive edge

Strategy has always been intrinsic to Formula 1, but over the last couple of decades, it’s become more vital than ever. Contemporary teams use a sophisticated blend of algorithms and simulations to plan out their moves to the almost cerebral levels of world-champion chess players. The real-time decision-making by drivers and teams can mean the difference between a podium win or a dangerous crash.

“Data is in our team’s blood. From developing cars to improving performance, selecting and analysing drivers is done through data analytics.” - CEO of Oracle Red Bull Racing, Christian Horner

Race strategy encompasses a wide range of decisions, from overall car design to selecting the optimal lap for a pit stop. For years, teams have focused on achieving the ideal fuel level during pit stops, as this can significantly impact the car's weight, stability, and overall positioning in the race. The record for the fastest pit stop in Formula 1 belongs to McLaren Racing Limited, with a pit time of just 1.80 seconds!

But Formula 1 isn’t just about fast cars and laps; it’s about the deluge of data flowing at over 3,000 data points per second. With 300 sensors on each car, the amount of data generated each second is comparable to streaming an HD film. Over the course of a race, 1.5 terabytes of data is collected to analyse and optimise up to 4000 parameters, producing 100,000 data points — all of which are analysed to inform everything from car design to racing strategy. This near unfathomable amount of data is sent to team pit walls in real-time via a dedicated radio frequency communication system in order to support team decision-making during the heat of a race.

Telemetry data specifically concerns speed, braking patterns and balancing power- the full skill set of a Formula 1 driver. Engineers and data scientists are also able to monitor tyre degradation, track temperature, aerodynamics, and many more factors in order to make fast, informed decisions. These adjustments improve the stability and integrity of vehicles. As we previously mentioned, this allows (among many other things) pit stops to be timed perfectly and cause the least impact on position.

 

Data Acquisition 

To manage the deluge of data received from all 20 cars on the track, Formula 1 teams rely on advanced data acquisition and processing software. The software, often custom-built by teams or provided by partners, allows for real-time visualisation, analysis and interpretation of the telemetry data.

Data acquisition reaches beyond just a driver's vehicle. Historical racing data, which includes track records, weather conditions, and more, is gathered and analysed. This additional information enhances a team's dataset, enabling them to create a clearer picture of how their car will perform on the track.

This data, however, isn’t evergreen and dates rapidly, meaning that analysis can quickly lose relevance. Due to this, teams have moved to the cloud to store masses of historical data. This data is then easily accessible to all members of a team who require access to it and is easily updated with every race and simulation.

 

Partnerships

All of this data collection and analysis takes enormous amounts of time and money and building partnerships with tech providers has been an essential component of strategy for both Formula 1 teams and the institution itself. In 2018, F1 famously teamed with AWS, a partnership that has changed the entire sport by driving innovation.

This huge partnership has allowed F1 to respond to a changing media landscape and to offload its team's activities to free up time to work on cutting-edge developmental projects. By finding and making the right partnerships, teams can benefit from technical expertise and free up time and resources to focus on their strategy. For example, McLaren also partners with Dell, who provide computing solutions to power the team's simulations.

But partnerships don’t just allow teams to build strategy and analytics, they also allow the data collected to be kept safe. Technology partners can provide robust encryption protocols and other security measures to safeguard data from rival teams. In a field as competitive as F1, stolen data and insights can be the difference between a win and a crash.

Another massively successful partnership is the one between last year's drivers AND constructors winning team Red Bull Racing and tech giant Oracle. Red Bull utilised Oracle's technical expertise to run its racing simulations as well as support their engineering development and fan engagement. This partnership is so significant that Red Bull has been renamed to ‘Oracle Red Bull Racing’. CEO Christian Horner has even stated, “Oracle Cloud is playing a key role in the outcome of every single Grand Prix that we've won this year and every Grand Prix where we've achieved significant results."

 

Simulations and Digital Twins

In what feels like something straight out of a sci-fi film, AI-powered simulations are used by F1 teams to study countless parameters during a race and determine what variables could lead to favourable or unsatisfying outcomes. Prior to every single race, Oracle Redbull Racing run 1 billion simulations featuring every possible scenario.

Data provided by partnerships with AWS, Dell, Oracle and more allow teams to simulate, predict and ease the impact of everything from weather, pit-stop strategies, track conditions and competitors' race history. It can also allow engineers to test the durability of cars - assessing how each season's updated designs will stand up to the pressures of the race. This allows the entire team to identify weak points and potential failure points during simulation phases - which is a lot less costly and safer than on the track!

Money saving is a huge concern as teams are given strict limits on how much money and time can be spent developing and designing their cars for each new season. AI has also allowed teams to further prioritise the safety of their drivers who are now able to learn tracks and practice without risking injury or damage to highly expensive vehicles. Teams are permitted to keep the data generated during simulations confidential, with certain exceptions. Some data must be shared with Formula 1 and rival teams, including the GPS data that tracks the car's path around the circuit on race days. This real-world information allows drivers to train by racing against simulated versions of their opponents.



Fan Engagement

As we mentioned, it’s no secret that F1 has catapulted in popularity in recent years. In 2023, Formula received an average viewership of 1.11 million, a 100% increase from numbers in 2018! The biggest change? The sport has begun to directly communicate with its audience using social media analytics, they’ve examined and responded to fans' likes and dislikes.

Another hugely successful fan-oriented aspect of data in F1 is the on-screen insights provided for viewers that encourage deeper engagement and interaction. F1 is incredibly fast-paced, making it near impossible for the average viewer to keep up with everything happening amongst 20 cars on the 5km track.

Throughout its partnership with AWS, F1 has been able to leverage data including live car positioning in order to display insight on screen for viewers at home. Alongside camera coverage and commentary, this creates an engaging and more well-rounded experience for any viewer. After all, a camera can only focus on one or a handful of cars at a time. Imagine not knowing or seeing if your favourite driver dropped to the bottom!

AWS has also employed a number of new features on the F1 insights app and site, one being Battle Forecast, which uses track history and projected vehicle pace to predict how many laps until cars are within striking distance. Close to the Wall also provides extremely tight views of how close F1 cars get to the track walls in some of the most exciting track corners of the championship.

Robert Smedley, an F1 Automotive Engineer, has said that placing data insights on screen has been a huge success. “We’re finding that fans are really leaning into that level of insight.”

 

Careers in Data in Formula 1

With high salaries and races to look forward to, it’s no surprise that data-related roles in Formula 1 have been met with increased interest. A typical day as an F1 Data Engineer might involve:

  • Establishing processes for data channels, ensuring their accuracy and allowing for the detection and correction of errors.

  • Developing and maintaining models that improve acquired data.

  • Working closely with stakeholders throughout a team to ensure that data flows work efficiently.

  • Enhancing and providing support for existing systems.

Launching a data career in the F1 industry can be an exciting and lucrative path, and plenty of insiders have shared their insight on what makes a great Data Engineer. Pippa Treacy, Aston Martin Aramco Team Data Engineer, has said the following:

"To sustain (...) focus you need to be passionate about the role, about what you're working on. A career in F1 is hugely rewarding, but it will challenge you: it's high pressure, there are tight deadlines, long hours – and it's fiercely competitive. Passion is what will fuel your determination to overcome the challenges.” 

 

The future of Data in Formula 1

While data has undoubtedly changed Formula 1 forever, the sport remains at its core about great racing. Driver instincts and skill can’t be replaced entirely by machine learning, algorithms and engineering - the greatness of F1 is down to the unique blend of drivers' talent and the incredibly skilful teams and tech that support them.

Interested in a career in Data? Arrange a call with one of our expert Career Consultants to find out more about our training today.

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