In the summer of 2014, Brazil hosted the World Cup on home soil. They were the obvious favorites. The crowd in Belo Horizonte was there to watch them win. Instead, Germany scored four goals in six minutes and walked off the pitch with a 7-1 result that still doesn’t make sense twelve years later. Unless you know what Germany knew.
Germany had spent years building one of the most sophisticated data analysis operations in international soccer, tracking player movement, modeling opponent tendencies, and identifying tactical vulnerabilities across the teams they faced. Brazil was a step behind, on the pitch and off it.
Germany was ahead of its time, but the sport as a whole was not. Soccer was slow to take machine learning seriously, and the FIFA World Cup 2026 is the first tournament where it’s impossible to ignore. If you’re curious about how AI and machine learning are being used in soccer, you’re in the right place. This is the first post in a series that walks through where it fits in the game, how it works, and what it’s changing.
The Two Ways AI Enhances Sports Teams
Machine learning is a subset of AI, and the distinction is worth keeping in mind throughout this series. Machine learning is the pattern-recognition side of things, where systems improve by training on data. In soccer, that looks like injury prediction models trained on years of player load data, or scouting systems that surface overlooked talent by learning from thousands of match performances. AI, in the broader sense, also includes things like the generative assistant inside Football AI Pro or the decision-support tools powering VAR. In practice the two overlap constantly, and the distinction matters less than the marketing would have you believe, but it’s useful shorthand for what’s actually happening under the hood.
Every AI and machine learning application in professional soccer falls into one of two categories, and understanding the difference makes everything else in this series easier to follow.
The first is what happens on the pitch: tools that help teams win. This includes scouting systems that surface players coaches would have overlooked, injury prediction models that flag which players are at risk before they pull something in training, and tactical analysis that tells a coach how an opponent’s striker moves in the buildup to a shot. Most of this work happens before the match starts, invisible to anyone watching from the stands or a bar.
The second is what happens off the pitch: tools that change the experience for fans. The VAR system reviewing whether a goal should stand, the broadcast graphics showing expected goals (xG)and pass completion probabilities in real time, and the pricing model that decided your group stage ticket cost three times what you expected all fall into this category. Fans encounter these technologies constantly, whether they recognize them as AI or not.
On the pitch and off the pitch. That’s the organizing principle for this whole series, and it maps pretty cleanly onto the two questions most soccer fans have about AI: is it making my team better, or is it making the game worse.
The AI Tool Every World Cup 2026 Team Uses
FIFA and Lenovo built Football AI Pro to give all 48 national teams access to the same AI-driven analysis system in this tournament. Under the hood, it’s a generative AI knowledge assistant built on what FIFA calls its Football Language Model, pulling from hundreds of millions of data points across more than 2,000 performance metrics to deliver tactical insights in text, video, and 3D visualizations. Coaches can use it to simulate tactical changes against opponents, analyze individual player performances, and help players understand exactly what they’re seeing on the pitch, while players can pull personalized match analyses and strengthen communication between players and staff. It’s a pre- and post-match tool, not something teams can run during live play.
Whether that works in practice is more complicated. I talked to Finn McCallum, a soccer player and coach, about this, and he cut to it quickly. “It definitely helps even the playing field,” he said, “but at the end of the day, someone who has never touched AI in their life versus someone who works with it daily is going to get a different outcome.” A tool is not expertise. The wealthier federations have analysts and data scientists who know what questions to ask, engineers who know how to build on top of a shared platform, and years of proprietary match data to feed into their models. Cape Verde gets access to the same starting point as France, but they are still constrained by the budget, staff, and experience needed to get the most out of it.
There’s also a security angle to consider. If all 48 teams are running tactical planning through the same shared platform, a single vulnerability exposes every team’s strategy simultaneously, not just one federation’s. That’s a different kind of risk than anything soccer has dealt with before, and FIFA hasn’t said much publicly about how it’s being managed. For a tournament this size, the silence is notable.
Why Soccer Resisted AI for So Long
Soccer is one of the last major sports to take AI technology seriously, lagging behind baseball, basketball, and American football, all of which have been building machine learning infrastructure for years. If you follow any of those sports, you’ve seen the transformation: the Statcast graphics in baseball, the player tracking dashboards in the NBA, the Next Gen Stats overlays in the NFL. Soccer fans got something too, but it wasn’t a dashboard they could appreciate. It was VAR, a technology that was supposed to make the game fairer and mostly just made fans angrier.
The structural reason is that those sports are built around interruption. Football has huddles, timeouts, and a play clock. Basketball has shot clocks, fouls, and substitution windows. Baseball got so slow they had to legislate a pitch clock. All of that stopping creates natural moments for coaches to process information and for the underlying technology to catch up.
Soccer is designed to do the opposite, and increasingly, technology is being used to protect that. Starting in 2024-25, the Premier League began calculating more precisely how much time is lost to free kicks, goal kicks, and injuries, then adding it back at the end of each half. Newcastle’s win over Leeds this season ran past the 101st minute, with the decisive goal timed at 101:48, the latest winner in Premier League history.
Even in a 90-minute match, an outfield player is in possession of the ball for roughly three minutes. The other 87 are positioning, pressing, making runs, tracking runners, and trying to pull defenders out of shape without ever touching it. Any model trying to evaluate a player’s contribution or predict how a match unfolds has to account for what 21 players are doing when none of them has the ball. That’s a harder problem than tracking what happens when a receiver catches a pass, and it’s a big part of why soccer was late to embrace machine learning.
Why the FIFA World Cup 2026 Is Different
The 2026 tournament is the first to expand to 48 teams, up from 32. It’s the first co-hosted by three countries, the United States, Canada and Mexico. And it’s running in June and July in North America, which means fans in these countries are watching a World Cup in their own summer for the first time since 1994. For American fans, hosting it locally in summer changes things in ways that previous tournaments couldn’t. People can actually attend matches, and host cities have real economic and civic stakes in it. Mexico went as far as trying to end its school year six weeks early so students could watch, then reversed course within days after parents, teachers, and state officials revolted. The impulse alone says something about the stakes. And unlike previous tournaments, teams play in a different stadium each game across three countries, creating travel demands that have no precedent at this level, for players managing recovery between matches and for fans trying to follow their country across borders.
For players, the calendar shift has created something most of them rarely get: a real break. European leagues run from late August through late May, with preseason typically starting again in early July. In a normal year, that leaves most players roughly a month off, but this summer some are getting closer to six weeks. Because the World Cup is in North America and MLS clubs paused their seasons to accommodate it, several players will reach the tournament off a longer-than-usual gap from club play. For players grinding through 50-plus matches a season, sometimes multiple games a week, across nine months, that kind of rest is rare enough that teams notice the difference. Hugo Ekitike, a Liverpool striker, ruptured his Achilles in April and faces a roughly nine-month recovery, ruling him out of the tournament. Whether real rest changes injury rates is something machine learning is only now getting enough data to answer.
What’s Coming in This Series
The rest of this series goes deeper on each application, starting with the question most AI-curious soccer fans are asking right now: can a model actually predict who wins? How do national teams find their players, and where does AI fit into scouting at the international level? How are teams using computer vision and biometric data to keep players on the pitch across a tournament that has now grown to 104 matches? How does dynamic pricing work, and who decided what you paid for that ticket? How does VAR function under the hood, and why do fans who understand the technology still have such complicated feelings about it? And finally, a post written after the tournament about what all of this looked like once the games were actually played.
Germany beat Brazil 7-1 without most of what this series is going to describe. The sport was already capable of producing outcomes that defied every model long before machine learning or AI entered the picture. What’s changed is how teams prepare for those moments, how referees get checked, how broadcasters explain what you just watched, and how much the whole experience costs. That’s a narrower claim than the hype usually makes, and it’s also a more honest one.
Next up: the question every fan and analyst is already arguing about. Can a model actually predict who wins the 2026 World Cup? And if it can, does that make watching it more or less interesting?
