HomeMotorsportsF1 Will Use AI To Plan Trackside Sponsorship Placement

F1 Will Use AI To Plan Trackside Sponsorship Placement

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F1 Will Use AI To Plan Trackside Sponsorship Placement

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ormula One (F1) will use Artificial Intelligence (AI) to try and predict camera coverage and plan where to place trackside sponsors to ensure maximum exposure.

The system, designed by Flamingo AI, will analyse the camera placement of past races to build a database from which they can plan where to place trackside sponsor material.

Speaking with SportBusiness, Formula One senior analytics manager, Max Métral, said the sport faces many difficulties in providing all its sponsors with consistent levels of trackside visibility.

“With more partners, less races, more uncertainty, we need to have a better tool in order to better forecast and [help] our analysts and the guys that put together the signage maps around the track,” Métral said.

“We needed some tools based on previous data, previous years, in order to better forecast; if we want to deliver ‘X’ amount of exposure for a partner, this is roughly where we should be positioning the signage.

“I wouldn’t say this was about squeezing [more branding into the coverage], it was about optimising exposure.

“It’s more that we treat each partner as being independent and we have targets for each of them based on their needs, and their objectives,” he said.

SportBusiness report Flamingo AI built up a database of “types and quantities of coverage devoted to each sponsor across the 2019 season” which was then used to plan the signage placement for 2020.

The database now contains content from both the 2019 and 2020 seasons which will both be used to plan where the 2021 signage will be placed.

Flamingo head of applied data science, Lucas Galan, explained to SportBusiness the company built a bespoke “machine learning tool” for the analysis of the camera footage and developed a dedicated platform for the Formula One team to organise and interpret the data.

“Every element of this project is tailored to those specific track-building questions where we’re being very surgical about every element of a corner, including the makeup of cameras,” Galan said.

“Is this a camera that is looking towards the inside of a corner?

“What does the signage look like?

“And then we round it all up and are able to create macro views of how attractive it is going to look.

“And then [that] obviously adds predictability for the future to say, ‘this is what’s likely to happen if you change these cameras’,” he said.

Galan also noted the difficulty of the project and said he believes the technology can be adapted to other sports.

“It’s also a unique challenge, because obviously logos are not static, we’re talking about a sport that plays at an incredibly fast pace and to be able to train a machine to recognise the Rolex logo, as its zooming by at 200 miles per hour, had its own unique challenges,” Galan said.

“This [technology] is applicable to everything from wrestling to Nascar.

“We’re looking for those marginal gains, for technology to give you those analytics that help you project into the future,” he said.

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