In early 2025, Brighton made headlines after quietly cutting most of their senior scouts. Behind the scenes, AI tools had taken over much of the work—processing mountains of player data, cross-referencing stats with team needs, and even creating simulations to preview how a signing might perform in a real match. What once took a team of experts now takes a few clicks.
But Brighton isn’t alone. From Liverpool to Barcelona, and across several Premier League clubs, artificial intelligence has become a working piece of modern football.
Summary:
- Premier League clubs like Liverpool and Brighton are using AI to guide scouting, tactics, and even coaching changes.
- Barcelona’s Innovation Hub is testing AI-driven injury prevention using immune system data and genomics.
- Tools like PLAIER and MUD Analytics are simulating matches, predicting performance, and reshaping how decisions are made behind the scenes.
Premier League clubs use AI to improve transfers and sack decisions
PLAIER has quietly become a go-to name for clubs experimenting with AI. They’re already advising four Premier League sides, helping them crunch everything from injury history to player wages and match data. In some cases, clubs are even leaning on their models to decide whether to stick with a coach or move on.
“We tell you how strong your squad is, what you need to reach your goals, and what kind of impact each player has on goal difference,” says co-founder Jan Wendt. One tool, for example, ran 100,000 simulations to show that Harry Kane’s transfer to Bayern might hurt more than help. Bayern ended the season with a worse goal difference; Tottenham improved.
Even a coach’s performance is under the microscope. PLAIER’s models found that player quality determines roughly 90% of a team’s success. The remaining 10%? Down to coaching. That data has already changed how some clubs make decisions about sackings and extensions.
Liverpool and DeepMind, Barcelona and cell-level data
Liverpool had recently teamed up with Google DeepMind to rethink their corner-kick strategies using AI. But it’s Barcelona that may be pushing the boundaries furthest.
Their Innovation Hub has invested in Omniscope, a biotech startup using AI to map players’ immune systems cell-by-cell. The goal? Prevent injuries before they happen. Using just a blood sample, the system identifies inflammation or recovery problems long before they show up on a scan.
They’re even tailoring care to female players based on hormonal data and hope to one day use AI to reintroduce a player’s own healthy immune cells to accelerate healing. It sounds like science fiction, but it’s already being tested.
Zone7, another AI tool adopted by clubs like Napoli and Rangers, works similarly: monitor workloads, predict muscle injuries, and adjust training in real time.
AI-simulated matches are already here
Lee Mooney, who spent six years leading data at City Football Group, believes AI can simulate more football in 24 hours than has been played in the entire history of the game. His new company, MUD Analytics, builds simulations that let coaches run millions of matches against upcoming opponents, adjusting tactics virtually before testing them on real turf.
Coaches can ask: if we press harder in this zone, how does that affect the opponent’s build-up? If our striker drops deeper, does that pull their backline out? Then they can test it, virtually, thousands of times.
The goal isn’t just data overload. It’s to make coaching decisions easier.
Fans aren’t left out of the AI data revolution either. Sites like Doc’s Sports have built a following by digging into team form, matchups, and odds—offering a familiar kind of number crunching, just from a fan’s point of view.
Will AI replace scouts?
The rise of AI has unsettled many in football’s old guard, especially in recruitment. Brighton’s owner, Tony Bloom, is a data-savvy bettor who trusts the models more than people.
Some clubs now use large language models to summarize thousands of scouting reports. Others use AI to find “the nearest version” of a certain player profile, drawing from millions of appearances across two decades.
Still, even the most advanced users admit there are gaps. AI can’t always detect the softness of a player’s first touch or how they read space under pressure. There’s still a human side to talent that no algorithm can fully read.
Where it’s heading next
AI tools are getting faster—and bolder. Some clubs are already testing real-time data to guide substitutions or manage recovery timelines. Others are using virtual simulations to experiment with new formations during training, without the usual risks.
The ones who figure out how to mix data with gut instinct might stay ahead. Those who don’t could struggle to keep up.
For fans, it might mean fewer injuries, longer careers, and decisions that make a little more sense—even if you don’t always notice it from the stands.
