Machine Learning at Full Throttle: How Formula 1 Teams Use Machine Learning to Win Races
Engines don’t just power the fastest cars in the world; they’re powered by data. In Formula 1, milliseconds can decide the outcome of a race, so smart data analysis is just as important as the driver’s skill or the car’s design. Machine learning in Formula 1 has become the secret weapon that separates winning teams from the rest of the field.
In February 2022, Oracle Corporation and Red Bull Racing announced that they’d be growing their partnership beyond a simple sponsorship, but rather team up and use Oracle’s cloud technology known as Oracle Cloud Infrastructure (OCI), to help analyze millions of data points. In this post, we’ll take a look under the hood at how machine learning supports Formula 1 teams and how OCI is helping Team Red Bull stay ahead on and off the track.
Why is Data Important?
In Formula 1, data is just as, if not more important than speed. Since Oracle has teamed up with Red Bull Racing, the team can now run 25% more simulations during each race, enabling real-time strategy optimization. Unlike traditional sports where teams can practice scenarios repeatedly, F1’s limited track time and high costs make these virtual simulations essential for testing thousands of race situations that would be impossible to practice in real life.
Data and race engineers handle around 400 GB of data per race from the car alone. This data comes from sensors around the car that track aerodynamics, fuel usage, tire wear, and driver behavior. On top of this, engineers have to take into account data from race simulators and previous races. But all this data is useless if there isn’t a way to make actionable insights in real time, and with all that data, a human can’t analyze it all and make the best decision in real time.
Machine learning processes past and live data to find patterns and predict race outcomes that humans could never see. Real-time analysis allows engineers to make split-second strategic decisions during races. This ability to turn raw data into strategy has given Oracle Red Bull Racing a competitive edge in race strategy, driver development, and fan engagement.
How Red Bull Uses Machine Learning
Race Strategy
Machine learning has allowed the Red Bull team to make smarter race day decisions. By analyzing historical and live data, teams can simulate thousands of race scenarios and make strategic calls in real-time. Key applications include:
- Optimizing pit stop timing and tire selection
- Developing race strategy against specific competitors
- Converting data insights into split-second decisions
- Predicting optimal racing lines and fuel management
Driver Performance and Training
There’s also a need for machine learning in the development and training of drivers. Computer vision algorithms (technology that teaches computers to understand images) analyze video footage to identify driving patterns, comparing lap times, braking points, and racing lines. These tools benefit both F1 drivers and Esports drivers, providing clear and constructive feedback. These tools include:
- AI-powered video analysis using computer vision
- Identifying performance gaps from previous races or sims
- Data-backed insights to refine driving skills
- Pattern recognition to optimize racing lines
Rapid Regulatory Research
One of Red Bull’s newest AI applications addresses a unique F1 challenge: penalty protests. When teams believe a penalty decision is incorrect, they have just 30 minutes after race results are published to lodge an official protest with the FIA.
In this brief window, teams must review thousands of pages of historical regulatory rulings to build their case. Starting in 2025, Red Bull is piloting an innovative solution using Oracle’s artificial intelligence. The system employs retrieval augmented generation (RAG) – a technique that allows AI to search through vast amounts of documents and data to find specific information, then use that information to build detailed responses.
For Red Bull’s penalty research, this means the system can rapidly search through the entire F1 rulebook and historical precedents to help lawyers and team managers quickly identify relevant cases. While human experts still make the final decisions on whether to proceed with protests, the AI dramatically speeds up the research process. The tools used include:
- Using Oracle large language models to analyze the rulebook
- Automated research to quickly find relevant precedents
- Rapid evidence compilation to support potential appeals
- Helping teams make informed decisions within the 30-minute deadline
Power Unit Development
Formula 1 teams use machine learning to optimize their power units and engine performance. OCI’s computational power allows engineers to run complex simulations that evaluate thousands of design variables simultaneously, from fuel injection timing to hybrid energy recovery systems.
Machine learning algorithms analyze performance data from previous races and testing sessions to identify optimal engine configurations for different track conditions. This data-driven approach helps engineers:
- Apply machine learning to power unit design and testing simulations
- Use OCI to compute performance models across multiple variables
- Optimize hybrid energy systems based on track-specific data
- Predict component wear and maintenance needs
Fan Experience
The race team isn’t the only people being affected by machine learning; through products, such as Unity, CrowdTwist, and Responsys, are used to create personalized fan experiences, making the race even more enjoyable. These platforms collect fan data through website interactions, social media likes, live event participations, etc, to help Oracle Red Bull Racing create engaging marketing campaigns and enjoyable race day events.
With OCI collecting data during the race, they allow fans to check real-time stats during races, show strategy predictions, and highlight where drivers would have performed better. This data-focused approach has proven to bring fans a greater understanding of the sport, which is reflected by increased engagement.
Beyond Formula 1
F1 racing is the perfect testing ground for machine learning technology with its high stakes and split-second decisions. The ability to process vast datasets at speed and scale demonstrates what’s possible when businesses fully embrace ML for efficiency and competitive advantage.
Since machine learning has powered Red Bull Racing, we’ve seen championship wins and improvements in race strategy, driver development, and fan engagement. This data-driven approach proves that in any high-pressure environment, machine learning isn’t just helpful, it’s essential for staying ahead of the competition.
The lessons from F1 apply far beyond racing: whether you’re optimizing supply chains, personalizing customer experiences, or making strategic decisions under pressure, machine learning can transform how your business operates. The key is identifying where speed, accuracy, and pattern recognition can give you a competitive edge – just like on the racetrack.
If you found these F1 examples helpful for understanding how machine learning works in practice, we cover similar real-world applications in our newsletter – from healthcare to finance to retail. It’s designed for people who want to understand ML without getting lost in the technical weeds.
