Stats Matter

The world of Formula 1 may seem like a bit of an unexpected departure for a community otherwise devoted to discussions of accelerating scientific innovations with applied statistics. But scientists and engineers have hobbies, too. And sometimes hobbies and applied statistics aren’t so different after all.

Earlier this year, JMP announced an exciting new partnership with mechanical engineer and racing telemetry expert Mirco Bartolozzi , PhD. On his channel Formula Data Analysis , Mirco gives Formula 1 fans a peak behind the scenes at how data and statistics inform everything from a racing team’s development program to race-day tire strategy.

JMP’s partnership with Mirco, writes JMP Customer Alliance Manager Meg (Eberle) Hermes , is not just about Formula 1. It’s about the democratization of analytics and giving the sport’s increasingly data-literate audience a new kind of fan experience. It’s also about inspiring non-fans – or not-yet-fans – to see their own data, their own favorite JMP platform, in a new light.

If you like data, you’ll love Formula 1. Let me explain.

By Meg Hermes

I may be motorsport’s most unlikely fan.

For one thing, I’m not into cars. I don’t even have one. After my last car, the 1998 El Dorado I borrowed from my grandfather, died a long and slow coolant-leaking death, I resolved to forego a replacement. ?

But not to worry. You see, driving is not the allure of Formula 1. At least not for me.

For those who haven’t yet seen Drive to Survive: Formula 1 is the pinnacle of international motorsport. It is also a proving ground for engineering and analytics that, to my knowledge, has no parallel in any sport, anywhere.

Data-driven decisions at 220 mph (355 km/h)

It was on a work trip to Singapore in 2017 that I heard the words “Formula 1” for the first time. In a cab coming from the airport, I’d marveled aloud at the traffic. Learning that roads closures were due to an upcoming street race (and for want of anything better to do with my weekend), I bought a last-minute general admission ticket. That Sunday, I was off to the races.

Besides the obvious allure of the Singapore Grand Prix – the undeniably spectacular view of the iconic Marina Bay Sands towering over cars rocketing past at 170 mph – I quickly learned that Formula 1 Grand Prix are more than what meets the eye.

Formula 1 is a sport where even the world’s best driver, in the world’s best car, can be beaten by an inferior car with an inferior driver... but a better data analyst.

Of course, extreme athleticism is part it; watching F1 drivers train their bodies to sustain 2 hours of up to 230 mph (355 km/h) and cornering at 5Gs of force, it’s impossible not to be impressed. Pit crews, too, are remarkable in their ability to change 4 tires in under 2 seconds. And then to, in the lingo of the sport, double-stack – or to pit both cars one immediately after the other – is testament to a highly disciplined practice.

But, data nerds, hear this: Formula 1 is a sport where even the world’s best driver, in the world’s best car, can be beaten by an inferior car with an inferior driver... but a better data analyst. A few recent examples come to mind.

Among the most electrifying was the 2019 Brazilian Grand Prix, a race in which Red Bull Racing strategist and mechanical engineer Hannah Schmitz made what at the time seemed to fans an unthinkable call to pit driver Max Verstappen from the lead in the dwindling laps of the race. Reacting swiftly to an opportunity she saw in predictive models and real-time telemetry, what some called a “brave” strategy decision earned Verstappen the win – and Schmitz a place on the podium.

Part athleticism, part engineering, part analytics

Unlike other sports where the lion’s share of the analysis takes place off the pitch, Formula 1 is as close as you come to a simultaneous blend of athleticism, engineering and analytics. Grand Prix are won – and lost – on all three.

This season’s Formula 1 cars collect around 1.5 terabytes of data over the course of a single race, meaning that 1.1 million data points are transmitted back to the pit each second. Teams have extensive knowledge of the car – steering angle, g-force, car speed, engine speed, fuel usage, DRS effect, gear, and ERS usage and storage, to name a few – and use these measures to diagnose problems, understand how arisings might derail pre-race predictions or carve out previously unforeseen paths to a win.

And it’s not just telemetry; there are also vast quantities of external data from track temperature and tire degradation to weather conditions. Competitor metrics also provide crucial contextual performance insight. As any fan of Ferrari knows all too well, the way a strategy unfolds – and whether the team proceeds with “Plan A” or “Plan E” – is a decision that responds to how the story of the race is playing out.

This season’s Formula 1 cars collect around 1.5 terabytes of data over the course of a single race, meaning that 1.1 million data points are transmitted back to the pit each second.

Data from race weekends also of course influence engineering both prior to and during the Formula 1 season as teams design and optimize their car within specifications laid out by the sport’s regulatory body, the?Fédération Internationale de l'Automobile?(FIA). Car performance, reliability and efficiency actually range quite a lot across all 10 teams on the grid, and teams are allowed to bring a set number of engineering upgrades to the car throughout the season.

Dynamics, aerodynamics and materials science all play a key role in teams’ development programs, as even micro-efficiencies and performance improvements provide an edge to a car often described as a high-performance upside-down airplane. Not to mention that data optimization is crucial to the teams’ ability to meet the sport’s most ambitious sustainability goal: Net Zero by 2030. In fact, as early as 2026, Formula 1 cars – which have run a hybrid power unit since 2014 – will run on 100% sustainable fuel derived from some combination of biomass and carbon capture.

Mirco Bartolozzi and Christian Bille discuss the effects of tire age, compound, and fuel load on the performance of a Formula 1 car.

These advances in sustainable technology are illustrative of how the big budget behind Formula 1 has helped to accelerate climate-critical innovations in industry that have life far beyond the world of motorsport. There are numerous examples of past Formula 1 innovations making their way not just into the consumer automotive industry (e.g., hybrid powertrain, traction control, carbon fiber) but to all areas of manufacturing (e.g., aerofoil technology developed by Williams Racing is now used in making refrigeration more energy efficient). Just as we so often hear in industry about digitalization as the path to working lean and more sustainably, every Formula 1 factory is a digital factory. But so is every Formula 1 garage and pit wall. Data is at the heart of this sport.

And where there is data, there is JMP! But you don’t have to take my word for it.

JMP is a natural fit for Formula 1

Of course with Formula 1 IP some of the most tightly protected on the planet, I can’t say which Formula 1 teams are using JMP or what they’re using the software for. But, since Formula 1 telemetry data is publicly available, we can do one step better and show you. Because trust me, if you like Formula 1, you will love seeing what goes on in the data behind the scenes. And we’ve got an expert here to guide you!

JMP has partnered with mechanical engineer and Formula 1 telemetry expert Mirco Bartolozzi, PhD. Mirco publishes Formula 1 data insights on his channel @FDataAnalysis each week and recently appeared on Stats Like Jazz .

“Formula 1 teams use cutting-edge technology to gain tenths of a seconds to prevail over competitors,” Mirco says. “The difference in performance might not be clear when looking at the cars from the grandstands, but it’s evident when looking at the data. Not only do telemetry and lap-time data tell us how much quicker one car is than the others, they also tell us why that car is quicker!’

Not only is Mirco giving us a whole new way to enjoy Formula 1 as a sport; his posts are inspiration for anyone and everyone who does cool s*** with data. ?

To close, allow me one short anecdote. I was in Palm Springs last month for JMP Discovery Summit Americas when I had a chance conversation with an expert in semiconductor processing. He was thrilled to see how the very same JMP platform used in wafer screening and defect reduction can also surface insights into Formula 1.

That is precisely, I think, what makes Formula 1 such an exciting sport to follow as a fan. Exploratory data analysis and visualization (that we can DIY!) make the fan experience even more exciting for us data nerds.

Watch Mirco Bartolozzi and Christian Bille discuss the effects of tire age, compound, and fuel load on the performance of a Formula 1 car. You can download the data , used in the video and follow along or try to create your own analysis. Download a free 30-days trial!