NASCAR uses AI to speed race analysis & fan feedback

NASCAR is using artificial intelligence to analyse race data and fan feedback across its operations. Executives say the effort has moved from limited testing to broader use in day-to-day decision-making.

The technology is helping staff cut analysis time from hours to minutes in some cases, while also shaping how NASCAR reviews race quality, internal workflows and employee adoption.

Derek Thomas, NASCAR's vice-president of analytics and racing operations, said race-day decisions already rely on large volumes of information, including speed, engine revolutions, throttle and brake use, lap times and tyre wear. That data is now being reviewed alongside fan feedback, giving officials and leadership a broader picture of race performance.

One focus has been unstructured comments from fans. NASCAR has collected race-by-race feedback since 2009, but manually reviewing written responses created time pressure between a Sunday race and a Tuesday post-race debrief.

"It was taking me hours, literally hours, Natalie, when I would go through these fan comments. I don't have a whole lot of time between a check and flag flying on a Sunday evening and having a Tuesday morning post-race debrief. There's not a whole lot of time to put together analysis. So it was taking a lot of time. And just think about the human. I mean, as much as I was trying to do a good job, I mean, there's always the human factor that could be skewed one way or the other. So not just the amount of time, but seeing that this was going to be a way for us to come through all of the data quickly and get a good representative look at what fans were telling us and sorting it by what were the most common themes that people were saying. That was changing. I'm now down to minutes on a Monday afternoon. And so I'm prepared, certainly, when I get on Tuesday to talk about that," Thomas said.

Race review

Thomas said NASCAR holds a weekly post-race review with engineers, officials and other staff. The group discusses inspection issues, race incidents and performance measures, using metrics to examine what happened during an event and how fans responded.

Initially, NASCAR identified three key performance indicators to measure race quality, including the gap between leading cars and the extent to which one driver dominated. After combining race data with fan feedback over several years and using AI tools to review the results, it added two more indicators.

One notable finding, Thomas said, was that close competition at the front of the field was not enough on its own. Actual passes between the leaders mattered more than passing deeper in the order.

That analysis has informed discussions with partners including tyre supplier Goodyear and engine builders working with Chevrolet, Ford and Toyota teams. The data has given NASCAR more confidence in assessing whether changes such as different tyre compounds or higher horsepower could improve racing.

NASCAR also tries to avoid reacting too quickly to a single race, instead looking for patterns that persist over time before making changes that could affect teams and costs.

Trust and access

Rich Bowman, NASCAR's vice-president of corporate innovation and AI strategy, said adoption has depended less on access to tools than on staff confidence in how they are used. Employees needed clear guidance on what data could be entered, which tools were approved and where to go for support.

"And so it's on the company to really say, hey, this is how this is the approved tool. This is what good use looks like with the tool. This is what's appropriate to put in the tool. And if you hit a roadblock, where do you go to get help? All of those things to me create, they're the ingredients to the trust cake, so to speak. And what I realize is that adoption isn't happening at the point of access. It's happening at the point of trust. And so when people trust the tool, trust the company they work for, and if we're really honest, trust themselves, right? Because they need to feel like this is something that's empowering for them, not something that's bearing down on them, right? And so once we have those ingredients in place, that's really the red carpet for adoption to strut right into the organization," Bowman said.

Bowman said AI use at NASCAR is strongest in foundational work such as finding information, drafting first versions of documents, summarising material and helping staff prepare for meetings. He argued that these repeatable tasks are often more important for uptake than attention-grabbing experimental projects.

He also said NASCAR has built a structured process for AI projects, including review of safety, governance and business need before tools are introduced more widely. The process is intended to avoid piecemeal innovation led only by technically confident staff.

Workforce debate

NASCAR is also trying to frame AI internally as a support tool rather than a replacement for workers. Bowman said language mattered when introducing the systems, particularly in response to concerns about job losses.

"So let me say it is super crystal clear at NASCAR. We see AI not as a robot in your chair, but as a jetpack on your back and I take the time to make that clear because I am NOT interested in a battle between AI and human beings. I'm seeing the value of what happens when the AI and the humans collaborate. That to me is the strongest force. It beats AI and it beats the human, right? The combination of the two. We are greater than just the sum of the parts. The work that I've just been seeing throughout the organization, when human ingenuity meets an acceleration pedal like AI, it's incredible what we can produce," Bowman said.

Thomas said NASCAR is also discussing AI in classrooms and with visiting students at its research and development centre, presenting it as a tool for people interested in data and analysis as well as those drawn to more visible roles in motorsport.