AI Revolutionizes NFL Coaching and Team Strategies

Published Date : 24/06/2025 

The NFL is on the brink of a technological revolution with the integration of artificial intelligence. From advanced analytics to real-time play calling, AI could transform how coaches and teams operate, potentially even replacing some human roles. 

In 1968, Stanley Kubrick released “2001: A Space Odyssey” and creeped out an entire country with the idea of a future controlled by artificial intelligence. In the summer of 2025, Zac Robinson is facing the idea of watching football and discussing strategy with a computer, and he’s a little creeped out, too.

The 38-year-old Atlanta Falcons offensive coordinator worked as an analyst for Pro Football Focus before starting his coaching career in 2019, a stint that convinced him of the value and potential of advanced analytics. But there’s a wide gulf between the math used to optimize fourth-down decisions and a voiced AI agent telling you to look out for the weakside linebacker while you’re sitting alone in your office on a Tuesday night.

“I don’t know,” Robinson said, considering the scenario. “I’m a little scared.”

He and other NFL coaches are going to have to get comfortable crossing that water soon. Instead of Hal 9000, think of it as the Bill Walsh 3000, which could be assigned to watch the rotations of the secondary while a human coach focuses on the front seven.

“I’d have to see what that looks like,” Robinson said. “(A computer) barking at me, I might get a little frustrated, but if it ends up being a cool tool, that’ll be interesting.”

Ryan Paganetti got his job in part because of artificial intelligence. He was hired by Las Vegas Raiders head coach Pete Carroll in March as the team’s “Head Coach Research Specialist,” but the job may be better understood as AI coordinator.

“I don’t think when I was hired the idea was, ‘This is our AI guy,’ but there is no doubt whatsoever that I am going to be using AI every single day,” he said. “And probably in increasingly larger amounts every month that goes by.”

In a league in which teams are constantly looking for an edge, the next big one won’t be coming through the draft or free agency, Paganetti believes, but from artificial intelligence tools that are on the verge of transforming how coaches think about the game and do their jobs—and maybe even which coaches still have those jobs in a decade.

“It almost might be a blockbuster moment where some coaches, their roles are replaced entirely,” Paganetti said. “That’s an issue in all sorts of industries where AI is just better and more accurate. I think that is going to happen with the football industry, to some degree.

“I feel pretty confident saying some team is going to win a Super Bowl in the next few years utilizing AI at a very high rate, significantly higher than it has ever been used before,” he said. “It’s really an opportunity to differentiate yourself from a team that might have a more talented roster or better coaches or whatnot. There is going to be more and more separation with teams that are bought in.”

Carroll is fully bought in. The NFL’s oldest head coach is maybe its biggest believer in its youngest technology. “Everything you can think of is possible right now,” the 73-year-old said. His early adopter status isn’t surprising considering his history, which includes head coaching stints with the New York Jets, New England Patriots, Seattle Seahawks, and at the University of Southern California, where last year he taught a class called “The Game of Life.” As part of that class, Carroll spoke with author and new-age guru Deepak Chopra.

“Check this out,” Carroll said, “he talked about AI giving him the opportunity to interview himself, talking to himself through AI so he was actually questioning his own person and being answered by his own person in return. Some of it does feel like science fiction, I get that, but AI is around the corner for us.”

Nearly three decades ago, IBM began developing the supercomputer Deep Blue to face off against world chess champion Garry Kasparov. Kasparov won his first match against the machine in 1996, but Deep Blue won the rematch the next year, and humans haven’t provided a chess challenge to computers since. Computers have since mastered the ancient Chinese board game Go, which involves exponentially more possible moves than chess. Football presents a much tougher computer problem than chess or Go for myriad reasons, but many experts agree that some of the analytical functions done by human coaches could be done better, or at least more efficiently, by artificial intelligence, and the current rate of improvement in the industry suggests that moment might not be far away.

While the world ponders a future where computers can generate their own decisions, the technology is still almost entirely machine learning and brute computing power rather than human-like intelligence. “Think of machine learning as a technique for achieving artificial intelligence,” said John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT.

The large language models that power most AI and machine learning “don’t know how to watch football yet, but I think with some work, they can be taught to watch football,” said Udit Ranasaria, a senior researcher at SumerSports, one of a handful of companies developing artificial intelligence tools with the potential to reshape professional football. “We can get to a place where we have something like ChatGPT that understands what’s happening in the NFL.”

It probably won’t take long, said Guttag, who leads the school’s Computer Science and Artificial Intelligence Laboratory Data Driven Inference Group and has co-presented several papers about the uses of machine learning in the NBA and Major League Baseball. In 2020, he was the thesis supervisor for a 55-page dissertation written by Udgam Goyal titled “Leveraging Machine Learning to Predict Playcalling Tendencies in the NFL.”

“A big branch of artificial intelligence from almost the beginning has been computer vision, trying to get computers to see things and figure out what is in the image,” Guttag said. But football is a more complex problem for computer vision than basketball, baseball, or soccer because of the proximity of players to the line of scrimmage and the variance in personnel.

“Fourth-and-1 with Mike Vick and Alge Crumpler looks a lot different than fourth-and-1 with Kirk Cousins and Kyle Pitts,” said Omar Ajmeri, the CEO and co-founder of Slants, which uses machine learning to pull scouting information from football film.

Current artificial intelligence is capable of “watching” game film from two teams, formulating a game plan, and printing out call sheets for offensive and defensive coordinators, said Vishakh Sandwar, one half of the winning team at this year’s Big Data Bowl, which is sponsored by the NFL. “It’s just a matter of the quality at this point,” he said.

Sandwar and fellow NYU alum Smit Bajaj’s winning project created an algorithm that can identify coverages based on the computer’s “visual” analysis of defenders. The model, which used technology developed by Sumer, achieved an accuracy level of 89 percent based only on pre-snap alignments. It adjusts in real time as defensive players move and can identify which ones are the worst offenders in giving away coverages before the snap. It also allows coaches to create custom looks by moving defenders on a digital whiteboard.

Artificial intelligence “is very good at piecing together relationships in very, very high-dimensional spaces,” Bajaj said. “With languages, it’s able to piece together and understand that based on the entire history of the internet, this is the word that is likely to come next. It’s increasingly being used, I would assume, in NFL buildings to piece together player-to-player relationships as well.”

“Over time, it will get better and better,” Guttag said. “And what you’ll do is say, ‘Here are all the series that led to first downs. Here are all the series that didn’t lead to first downs. What are the important differences?’—without hypothesizing before. You’ll just let the AI machine learning look at all that data and say, ‘Here are some interesting differences.’ One of the great things about machine learning is it finds things you didn’t know were there.”

Bajaj spoke to The Athletic for this article in March. By May, he had been hired by the Philadelphia Eagles (Sandwar was hired by Sumer this spring). Before joining the Eagles, Bajaj was interning in the Philadelphia Phillies’ analytics department, which has more than 35 employees. In the NFL, only three teams have more than six employees in their departments, according to research by ESPN’s Seth Walder. Fourteen have three or fewer, and none have more than the Cleveland Browns’ 10.

“I do know there are opportunities, but it requires a real commitment,” Guttag said. “If you’re going to do this, it’s going to take premier talent. We’re not going to be able to take an ex-player and say, ‘Go run this department.’ You look at what Google pays their top machine-learning people. It’s not NFL player salaries, but it’s not NFL office salaries, either.”

After Ajmeri presented at MIT’s Sloan Sports Analytics Conference in 2018, he was asked to meet with NFL teams in “really far corners of the conference center,” even across the street at a Starbucks. The upcoming arms race in artificial intelligence hiring will stay in the shadows, predicted Paganetti, who declined to discuss any specifics about how the Raiders will use the upcoming advancements.

An artificial intelligence agent could assist in play calling during games, but NFL rules ban that sort of assistance from kickoff until the clock hits zero. During the week, everything in the AI realm is in bounds, although the league continues to monitor developments, at least the ones it knows about.

“There’s still an extreme level of secrecy,” Paganetti said. “Even people who work in analytics have very little idea what people working in analytics for other teams do sometimes because it’s considered company secrets. We know what the scouts do on the other team: They scout. We know what the coaches do on the other teams: They coach. But when it comes to the actual contribution of the analytics department of another team, it’s really open-ended.”

Atlanta passing game coordinator T.J. Yates, like coaches in many buildings in the NFL, already works with Telemetry Sports for computer-generated coaching aids. The son of an engineer and the Falcons’ coaching staff’s biggest trumpeter of technological possibilities, Yates knows other advancements are looming.

“If you’re not using it, it’s dumb, because it’s there for us,” Yates said. “The days of sitting there grinding until two or three o’clock in the morning, there are way too many available opportunities to cut that out and be efficient and go home and get some sleep and have a sharper mind and have good energy for your players the next day.” 

Frequently Asked Questions (FAQS): 

Q: What is the current state of AI in the NFL?

A: AI is currently being explored and developed to assist with tasks such as analyzing game film, formulating game plans, and identifying player tendencies. While it is not yet widely used, several teams are investing in AI technology to gain a competitive edge.

Q: How might AI change the role of coaches in the NFL?

A: AI could take over some of the analytical functions currently performed by human coaches, potentially making their jobs more efficient. However, it could also lead to the replacement of some coaching roles, as AI becomes more accurate and reliable in certain areas.

Q: What are the potential benefits of using AI in the NFL?

A: AI can help coaches make more informed decisions by providing data-driven insights, reducing the time spent on repetitive tasks, and potentially improving player performance through advanced analytics and real-time feedback.

Q: What are the challenges in implementing AI in the NFL?

A: The main challenges include the complexity of football compared to other sports, the need for high-quality data, and the integration of AI into existing coaching and training processes. There is also the issue of maintaining secrecy and competitive advantage.

Q: How are NFL teams currently using AI?

A: Some NFL teams are using AI for tasks such as analyzing game film, identifying player tendencies, and formulating game plans. However, the use of AI is still in its early stages, and many teams are exploring its potential through research and development. 

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