Insights from Formula 1: How data can drive (the best) performance

I like to think of Formula One as a gladiatorial sport amid the drivers and virtual combat between the engineers and specialists to deliver the best car on the planet. Because it's a complete team competition, neither can win without the other. And Formula 1 cars are data-driven, intelligent systems that can attain speeds of over 200 mph.?

But what's really impressive is that the drivers experience 5g in cornering and braking. To the uninitiated -- At 5 Gs, a driver experiences a force equal to five times his weight. For instance, throughout a 5-G turn, there are 60 to 70 pounds of force straining the driver's head to the side.

A Formula One racing car is the pinnacle of automobile technology. Every component of each vehicle gets monitored by hundreds of sensors, including lap times, tire and brake temperatures, airflow, and engine performance.

In a modern F1 car, the following are the typical electrical items that communicate jointly, relying on data transferred from one unit to another.?

1. Engine control unit (ECU)
2. Gearbox control unit (GCU)
3. Power control module (PCM)
4. Data logger master control unit (MCU)

Here is an example of a watch screen populated when the car server is interacting with the car.?Source -- https://www.racecar-engineering.com/tech-explained/sensors-f1-technology/

Few sports employ data analytics as extensively as Formula One – it affects car design, driver behavior, and race broadcasting.?

The outcome of a race solely depended on the driver's split-second judgments on the track before data analytics was introduced. It wasn’t until the 1970s when advancements in electronic components and microprocessors allowed -- for the initiation -- of what we’d realize today as a microcomputer.?

It was in 1975 when McLaren first stationed telemetry – amassing data about the car. And it wasn’t in F1 -- it was on the company’s IndyCar effort, obtaining 14 different pieces of data about the car that could be downloaded back at the garage.

When telemetry technology came into being in the F1 world in the late 80s, things changed drastically. In the current era of motorsport racing, race analysis is all about telemetry — or a means of data acquisition that gets examined to know even the most micro subtlety of the race car.

It was during this time that electronic systems became commonplace on F1 cars. Drivers were given a signal to turn on the telemetry when the team needed to accumulate data, and storage got initially limited to a single lap's worth of data. After that, the data would be retrieved from the car and uploaded to garage computer systems for extra processing.

At its nascent stage, telemetry started transmitting radio signals from the car to the garage during a race, rendering pit crews information on the car's physical state. Streaming data gets used to reinstate the bursts, which were piped back to the garage and eventually to the factory. It has afterward emerged as a vital element of the F1 Grand Prix's operation and planning.

Image source -- https://forums.codemasters.com/topic/84225-pxg-f1-telemetry-for-f1-2021/

Real-time data streams now affect pre-race simulations, real-time decision-making by analysts and pit crew, post-race analysis, and the broadcast experience. Numerous sensors are continually discovering and transporting data throughout the vehicle and the driver. These data streams give teams entree to hidden aspects that aren't noticeable to the human eye.

E.g., the Mercedes AMG F1 W08 EQ Power+ vehicles have 200 sensors that send millions of data points throughout a race weekend. According to reports, the automobile communicates around 300GB of data. At any given time during a race, Williams claims to have over a thousand channels of data getting captured. The Red Bull RB12 car, on the other hand, is furnished with roughly 100 sensors that gather data on 10,000 components.

Cloud computing has a role to play in this. Some of this crucial data get conveyed to the garage via specialized feeds for mechanics and engineers. Big data sets get sent to safe servers. This gets retrieved by team members in operations rooms and factories.

Because each team is limited to a singular number of persons trackside, much of the pre-and post-race dissection is completed by team members at the team headquarters. They investigate the data in real-time, integrating it with GPS, meteorological data, and information about the competition to present an analytical prospect of each race.

After that, all of the data is combined, examined, and a race craft gets devised. This data is finally sent on to trackside analysts who are in touch with the driver.

Mercedes (used to) create a virtual team of data analysts with Tibco Software, a data analytics and integration firm, to work with race data. Every detail can be studied to identify where a problem happened, for example, a car's trajectory, tire pressure, weight, track conditions, and several other variables are gathered into a data reservoir for analysts to comb through and discover what went wrong (or right).

This analysis shows data from a car driving along a race track. Source -- https://www.tibco.com/products/tibco-spotfire/learn/demos/streaming-car-data

These brand associations are becoming increasingly familiar -- Aston Martin and Cognizant, Red Bull and Oracle, Alpine and Microsoft, etc.

Data is also used to better pitstop methods. Pitstops have traditionally been a crucial aspect of winning or losing an F1 race, with crews prepared to handle any tire or nose changes with military precision.

A driver can and will lose a race as a result of a delay. After practice runs, teams can save precious seconds via the camera footage and data from the car and pit equipment. In 2016, the Williams team gave four pit crew members biometric monitors that assessed heart rate variability, recovery times, breathing rate, and predicted core temperature.

"VISM (Vital Signs Monitor) is a tool for professional drivers to monitor biometric data," said Riccardo De Filippi, CEO of Marelli Motorsport.

"It is designed with a direct interface to the data acquisition and telemetry systems of a race car and includes end-to-end protection of sensitive data, giving the user full control of its use. We believe this experience is a major step forward in the development of safety systems as well as active driver aids, for passenger cars too."

Source -- https://www.autosport.com/f1/news/f1-news-biometric-underwear-to-measure-drivers-vitals-gets-fia-homologation-4981898/4981898/

The extensive amount of data gathered during a race gets used to reengineer vehicles. After a race, the data is used in simulations to enhance the car's systems until the next competitive cycle. Teams claim that by doing so, they will be able to achieve more in one day than it could in a week of on-track testing.

Data is also used substantially in development and testing, whether through computational fluid dynamics (CFD) to simulate airflow and its interplay with surfaces or driver 'in-loop' simulators that shape actual car behavior based on previously accumulated data.

The F1 data revolution helps broadcasters as well. Viewers can view pit stop times, sector times, DRS charge, heat maps (pointing which parts of the car gets exposed to the highest temperatures), listen in on select live radio feeds between the race engineer and the driver, and be privy to some streams of live data sent to teams, proffering them an insight into the race.?

Here, more than anywhere else, big data drives sizeable decisions. As the reach and utilization of data analytics grow in the broader world, F1 is bound to take benefit of whatever the tech industry has to offer -- with cloud computing, predictive analytics, predictive intelligence, machine learning, and prescriptive intelligence playing enormous roles in the sport's future.

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Author: Rahul Kumar Srivastava (Asst. Mgr. Marketing -- upGrad North America)