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Big data analytics accelerates Williams' Formula One performance

7/07/2015 by Sharon Shahzad


Formula One cars are essentially big data factories on wheels. They have hundreds of sensors across many components collecting data on everything from fuel levels and engine performance, to oil temperature and tyre pressure.

Turning that data into useful information to tweak a car's performance is nothing new in Formula 1. But driving efficiency in the analysis process so that data can shave race-deciding tenths of seconds from lap times could be the difference between winning and losing.

V3 visited the Silverstone racetrack to talk to the Williams F1 team ahead of the British Grand Prix to hear how, having joined with IT services and consulting firm Avanade, it's aiming to improve the way it uses big data in the quest for glory.

Big data collected from F1 cars is vital for maximising performance. It starts at the design stage of a new car, by using previous race data to see where improvements can be made. Further analytics can then be used to maximise component performance as the car is built.

Data collected and analysed during wind tunnel testing is also used to fine tune the aerodynamics of the car to extract the best performance within F1 regulations.

F1 teams will use the data gleaned from the process to configure the cars for the course during the day of track testing ahead of a Grand Prix weekend.

When the race starts, a team of engineers monitors the deluge of data transmitted from the car, and uses it to inform the driver about tactics and strategies.

Graeme Hackland, Williams' chief information officer, said that his race team generates around 120GB of data from sensors, telemetry and video feeds over the course of a race.

"During the race we're making decisions on when we should tell the driver to push, when we should tell them to back off," said the former Lotus F1 CIO.

"There's a lot around the strategy during the race that relies on multiple data sources."

However, Hackland explained that preparing this data for analysis was a time- and resources-sapping process for the Williams engineers, who were spending 70 percent of their time during races working on data preparation and just 30 percent actually analysing the data.

"[Williams] has really good engineers and they were spending some of their time doing software development and some of their time on their main job," he said.

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