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AI Everyday

Drivers, Data and Engineers: A Winning Formula for Formula 1

Ari Kaplan
Ari Kaplan
June 13, 2022
A man kneeling in front of an orange formula 1 car

The author, Ari Kaplan, and the MCL36 Car

In Formula 1, there are 10 teams on the Grid, each competing against each other to secure the maximum points total to go towards the Constructors’ Championship, but with teams consisting of two drivers, despite sharing the same resources and data, they are also competing with one another. They must draw from their individual strengths and experiences to succeed – but optimal performance goes beyond drivers alone.

Race and performance engineers in particular are pivotal in maximizing performance and results. They are the bridge between drivers and insights from data analysts, mechanics and other team members.

Through DataRobot’s partnership with McLaren Formula 1 Team, we have been able to work with people across the team, physically attend many races to observe and participate, be in the Paddock during races to see how insights are generated and consumed in real-time, and listen on headsets to hear how engineers communicate with the two McLaren drivers. Having been a part of the action, one key learning is that each driver is unique. Data is critical for the success of each race, but it is crucial to give actionable insights that are tailored to the drivers as individuals. With this approach, human intelligence is augmented with artificial intelligence with the best outcomes in mind with trust and teamwork.

I sat down with McLaren’s Adrian Goodwin (Performance Engineer, Daniel Ricciardo) and Jose Manuel López (Performance Engineer, Lando Norris) and they shared their knowledge on team relationships within Formula 1, how strategies impact performance, the importance of data, and how to deal with the immense pressure the sport brings.

Two men standing next to each other, smiling.

The author, Ari Kaplan, and CEO of McLaren Racing Zak Brown

Individual strengths

Do driver personalities affect how you work?

Adrian: Daniel is easy going and happy to voice his opinion and debate race options. That adds to the engineering information available to strategists. In the car, he’s incredibly focused, so it’s a case of reminding him to provide us with input on his pace and tyre condition, or he’ll slip into the zone of just focusing on driving.

Jose: Lando has the hunger and ambition of a young driver with a long, successful career ahead. He’s very focused when working with us, absorbing information, highlighting car limitations and strengths, and digging into details to find ways to go faster.

What data types show the differences between drivers?

Adrian: Braking style is the clearest – especially with the characteristics of last year’s car. Daniel’s natural style is to not brake quite as hard as Lando, but carry that brake pressure much deeper into a corner. The difference is subtler this year.

Do Daniel and Lando have different strengths?

Jose: Lando is particularly strong through high-speed corners, where he’s able to carry more speed than others. On low-speed corners, he has to work harder to find the time Daniel finds right away. But thanks to Lando’s hard work on this topic, tyre management has become one of his strengths.

Adrian: Before Daniel started at McLaren, we identified how in low-speed chicanes he’d carry a higher speed through the first corner without compromising the exit line. Where we have to work to match Lando is in higher-speed corners, where you have to hustle the car a bit more to get the rotation. Daniel’s natural approach is more towards being super smooth so as to not destabilize the car.

Two men standing next to each other, smiling.

The author, Ari Kaplan, with McLaren Formula 1 driver Lando Norris

Working together

Do drivers share data?

Adrian: The data from both cars is available to all engineers, and input from drivers is reviewed across the team after each session. With limited track time, we often adopt a setup based on data and driver feedback from the other car that tested it. Both drivers want the quickest car with which to demonstrate their capabilities – and that requires collaboration.

So how can drivers help each other to perform better?

Adrian: Collaborate (again)! Even without car technical issues, the amount of track time to prepare for a race is very limited. Often, only one driver will test a certain upgrade or setup, or long-run a tyre compound, so sharing that knowledge is vital.

Do drivers become more conservative when near each other during a race?

Adrian: I’d say they’re equally aware they have a responsibility to the team to make sure they don’t compromise each other’s race result. We’ve had recent examples of close side-by-side first-lap racing – without incident!

Jose: Both drivers are sensible and we should give them credit for that. We’ve in the past seen teammates make contact and cost their teams points, wasting countless hours of hard work from factory and trackside personnel. So: hats off to our drivers!

Team players

What is the relationship between engineers and drivers like?

Jose: The performance engineer and race engineer are the team members who work closest with drivers. Honesty and trust between the three is fundamental. We know each other so well that when operating during a race weekend, we can read each other without talking.

Adrian: It’s definitely a relationship of close collaboration – although because the driver’s job is to drive as fast as possible, that means they’re experts at hiding car problems! So we must understand what the driver’s trying to do with the car, to make both quicker. This is only possible when you combine their viewpoint with engineering data – which requires a lot of review and communication.

How much influence do drivers have on when to pit?

Adrian: Driver feedback is one of many sources of data that feed into the decision-making process. The level of influence varies depending on race circumstances. When the driver’s really pushing to generate a given lap time, trying to stay out until the end of tyre life, the decision to pit is theirs. But, mostly, the decision is a strategy team call – with the team having been supplied with as much relevant information and data as possible from the engineers and driver.

Finally, how do you deal with the pressure in Formula 1 of having to always perform?

Jose: Preparation, reviewing, learning and a constant search for opportunities to improve. Those are key factors – along with support from the great people in our trackside and factory teams. Time to relax and disconnect from the track is a must too.

Adrian: Also, a lot of pressure ultimately comes from dealing with fast-paced situations, and so we must ensure those are as routine and familiar as possible. A good coffee in the morning also helps!

If you want to learn more about how AI and machine learning can build success in Formula 1,
watch our on-demand keynote with Andrew McHutchon, Senior Data Scientist at McLaren Formula 1. 

Ari Kaplan
Ari Kaplan
Global AI Evangelist
McLaren Formula 1
Innovation Keynote with Andrew McHutchon of McLaren Formula 1 Team
Read More
Tags: AI Adventures AI Everyday article Artificial Intelligence data analytics data capture data science data visualization DataRobot machine learning McLaren Formula 1 Team Motor Racing

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