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American LeMans
ALMS: Chasing The Figures
A lot of data is collected at an ALMS event - from on-board computers to tire and team engineers. John Dagys breaks down how it helps everyone get faster and smarter.
John Dagys  |  Posted July 18, 2009   Lakeville, CT
Data from a race car is gathered from a variety of methods, such as taking tire temperatures. (John Dagys)

Since its inception in 1999, the American Le Mans Series has been one of the technological leaders in world of motorsports. Thanks to the Automobile Club de l’Ouest’s freedom in rules and regulations that govern the ALMS, a wide variety of manufacturers are able to build and develop cutting-edge prototypes and sexy-looking GT cars.

Every ALMS team has the same objective during each and every race weekend: to win on the racetrack. But teams won’t get to victory lane by just building the fastest car. Instead, entrants and manufacturers rely on vast amounts of information gathered at each event to help develop their cars into race winners.

It’s a familiar theme in modern sports. A batter might have the strongest arms in the major leagues, but without a coach to analyze and perfect his swing, home runs will be far and few between. In the world of sportscar racing, there are plenty of powerful engines, fast drivers and sticky tires, but without a host of computers capturing data and engineers to analyze the information, victory circle will be an elusive target to find.

From computer-generated data to hand-written notes and long-lasting debrief sessions, nothing is discarded in this fast-paced environment. It’s like a group of students studying everything possible, but in order to get an ‘A’, the homework must be done. And that involves hours of number crunching from hundreds of pages of raw data.

If you hate math as much as I do, then learning the intricate details of data could be quite a challenge. Instead, we’ll be showing you how teams use various types of data, the processes they’ve developed to turn digital information into real-world speed, and why it’s such an important element in the racing business.

Trust me, there’s no math involved here.

Data Acquisition

Since the mid-‘80s, computer-generated data has been one of the leading data collection methods. By collecting and processing data through computers, data acquisition paints a picture of what’s happening inside the race car. It helps teams diagnose possible problems or to develop a car to its full potential.

“Data is critical to everything we do in terms of on-track performance,” says Eric Ingraham, Flying Lizard Motorsports team manager. “Most everything is recorded for the purposes of having additional information to help guide the setup work on the car.”

In the prototype categories, data is collected through an on-board data logger, which then feeds a telemetry radio which transmits all of the car’s real-time data information to the pits wirelessly. Teams in the GT2 category, like Flying Lizard, aren’t so lucky, as telemetry isn’t allowed. Instead, data is manually downloaded during each pit stop. It still provides them with loads of invaluable information, just not as quickly as their prototype counterparts.

Flying Lizard’s data engineer Scott Jasmund points out the Motec data component inside the cockpit of the No. 45 Porsche. (John Dagys)
At Flying Lizard, two different types of electronic data is captured - one from Motec’s data logging unit that monitors the chassis and the other from Bosch’s engine management system. Data is collected through sensors, which measure the physical and electronic movement of a particular property, anything from cockpit temperature to suspension movement. No less than 50 sensors are equipped on each of its Porsche 911 GT3 RSRs, giving their data engineers plenty of numbers to crunch at the end of each session.

“The data engineers are really looking for anomalies, both positive and negative,” Ingraham explains. “These would be things they are not typically used to seeing. They’re looking for anything that stands out on the graphical interface.”

Scott Jasmund, data engineer for the Lizard’s No. 45 Porsche of Jorg Bergmeister and Patrick Long, says the most important sensors on the car are the ones that drivers have regular interaction with. Sensors that measure the throttle and brake positions, steering angle and gear changes are the most critical, but others, such as shock movement, can provide teams with crucial chassis information as well.

All sensor data is broken down into channels of information, which provides compiled data from multiple sensors. The Lizards have over 200 Motec channels to choose from, but drivers, for instance, only look at a handful of the performance-based channels.

“I think the biggest challenge is trying to understand what is driver-induced and what is car-related,” says Gil de Ferran, 2003 Indy 500 winner and owner/driver of the de Ferran Motorsports Acura in LMP1. “It could also be a mixture of both. We’re certainly developing a lot of mathematics and advanced analysis within the team to try to understand what’s actually the cause of a positive or negative performance result and what’s the consequence to make better decisions.”

De Ferran and his performance engineer, Scott Raymond, look at the most common sensors, such as throttle and brake traces, when trying to evaluate driver performance. But they also take it a step further by analyzing g-force levels and handling characteristics such as oversteer and understeer.

The Lizards have an ambient cockpit temperature sensor positioned inside the cockpit, a sensor which the ACO monitors in the European-based Le Mans Series and 24 Hours of Le Mans. (John Dagys)
“One of the mystics of data that comes out of the car is that it tells you a whole bunch of things. But it’s just information and nothing else,” de Ferran says. “The most difficult thing is interpreting that information to ensure that you’re making better decisions going forward. It really doesn’t tell you what to do. A human being still has that power.”

Processing raw data through formulas helps give engineers a better understanding of a car’s behavior, but that method generally takes hours to complete. When in the heat of the battle, a quick response to an issue could make or break one’s race. Seconds lost in the pits equates to positions lost on the track.

That’s why teams like the Lizards have action plans in place. If a sensor detects a mechanical problem with the car, that information would go directly to Tommy Sadler, the team’s crew chief. If it’s an electrical issue, the data engineer, Jasmund, would likely deal with the issue, and if a sensor detects a performance-related problem, it would go straight to the team engineer Stefan Pfeiffer. Other teams have similar methods of dividing responsibility up to ensure a quick solution to anything from a problem with the car to a simple setup change.

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