Football is no longer just about instinct and tradition. It’s now a test of who can use data more effectively. From Brighton’s clever transfer strategy to Luis Campos’s work at PSG, clubs are turning to advanced analytics to get ahead. That means using tools powered by artificial intelligence, machine learning, and football data to find talent earlier, reduce transfer risk, and unlock competitive advantages.
Scouting used to be all about watching games and trusting a gut feeling. Today, clubs use football scouting platforms and AI models to assess hundreds of players at once. These systems break down actions like tackles, progressive passes, and off-ball movement, the kinds of things that are hard to track with the naked eye.
- A recent study introduced a multi-layered neural network that predicts player potential more accurately than traditional methods.
- The PlayeRank framework evaluates players based on tactical role and match context, helping scouts spot players that truly fit their system.
- xG-based scouting helps quantify the quality of chances a player creates or finishes, rather than just counting goals.
What Is xG and Why It Matters in Scouting
xG, or expected goals, is a metric that estimates how likely a shot is to result in a goal based on several factors such as shot angle, distance from goal, body part used, and the amount of defensive pressure.
- A shot from close range with no defenders nearby might have an xG of 0.75, meaning a 75 percent chance of scoring.
- A long-range effort from outside the box might have an xG of 0.05.
Clubs use xG to look beyond traditional stats. For example, a striker with only five goals but an xG of twelve might be seen as underperforming their chances or simply unlucky. Meanwhile, a player consistently outperforming xG could be an elite finisher.
xG has become central to recruitment because it:
- Highlights players who regularly get into dangerous positions, even if they haven’t scored yet.
- Allows comparisons between players across leagues and systems.
- Identifies tactical fits based on shot types and chance creation.
It’s now one of the most important metrics in modern scouting, giving clubs deeper insight into a player’s decision-making and goal threat.
Real-World Wonderkids and Bargains Discovered by Data
Brighton & Hove Albion
Brighton have built a reputation as one of the smartest recruitment teams in Europe.
- Moisés Caicedo: Bought for £4.5M from Ecuador’s Independiente del Valle, sold to Chelsea for £115M
- Alexis MacAllister: Picked up for £7M and later moved to Liverpool after winning the World Cup.
- Marc Cucurella: Cost £15M, sold to Chelsea for £60M.
- Kaoru Mitoma: Signed from Kawasaki Frontale in 2021 for £2.5 million. As of 2025, valued at £50–70 million, representing a 20x+ return on investment. Brighton’s Asia scouting team had been monitoring Mitoma’s performances in the J-League, where he stood out for his explosive dribbling, one-on-one ability, and tactical intelligence.
Karou Mitoma helped develop his elite dribbling ability through academic research, he wrote a university thesis on dribbling biomechanics, which deepened his technical understanding and helped shape his playing style.
They rely on StatsBomb, SkillCorner, and internal data models to track things like progressive passing, ball recoveries, and expected goal contribution from different positions.
Ajax
Ajax combine world-class development with data-powered scouting.
- Antony: Signed for €15M, sold for €95M.
- Lisandro Martínez: Bought for €7M, sold for €57M.
- Frenkie de Jong: Spotted while at Willem II, sold to Barcelona for €86M.
Ajax track spatial awareness, passing tempo, and pressing intelligence using in-house analytics tools and role-based recruitment frameworks.
Brentford
Brentford shut down their traditional academy in 2016 and launched a B-team model based on analytics. Since then, they’ve used data to find value in overlooked markets.
- Ollie Watkins: £1.8M signing, sold for over £30M.
- Said Benrahma: £2.7M from Nice, later sold for £25M to West Ham.
Brentford were among the first English clubs to adopt expected goals as a key metric. Their recruitment model has helped them climb from League One (3rd Division In England) to the Premier League (1st Division).
Luis Campos and PSG’s Data-Driven Reinvention
When Paris Saint-Germain lost Kylian Mbappé, Lionel Messi, and Neymar, most assumed a decline was inevitable. But instead of rebuilding through marquee names, PSG changed direction, and did it under the guidance of Luis Campos.
Campos, known for transforming Monaco and Lille through smart recruitment, brought the same model to PSG. He focused on building a balanced, cohesive squad. Using a private scouting platform, advanced performance data, and psychological profiling, he targeted players who fit the system, not just the spotlight.
Rather than rely on individual stars, PSG built a team that worked as a unit. The payoff was immediate. They won the UEFA Champions League, the one trophy they had been chasing for over a decade.
It was a clear statement that data, structure, and intelligence can succeed at the highest level. Campos turned PSG from a superstar brand into a true football team.
AI in Matchday and Tactical Planning
Clubs are also applying artificial intelligence to their in-game preparation. Teams now use real-time data from GPS trackers and video tools to analyse how players press, move in defensive blocks, and transition in different scenarios.
RB Leipzig and Club Brugge are working with Leuven’s Sports Analytics Lab to apply predictive models that suggest tactical changes during matches, not just in post-match reviews.
Scouting Players Beyond the Stats
Modern recruitment goes beyond just physical and technical attributes. It’s about mindset, consistency, and availability too.
Together, these approaches give clubs a full picture of how a player might perform on and off the pitch.
Making Scouting More Accessible
Platforms like aiScout let players upload videos of skill tests and match footage to receive AI-generated evaluations. It’s helping smaller clubs and academies find talent without spending huge sums on scouting networks.
Clubs now combine tools like Wyscout, SkillCorner, and Opta to review video, generate heatmaps, and identify players that fit specific tactical profiles. It’s a powerful way to level the playing field across budgets and continents.
The idea that data can uncover hidden value is often called the Moneyball approach. It became popular after the 2011 film, which told the story of the Oakland A’s baseball team using stats to build a competitive squad on a limited budget.
Football has adopted the same logic. Brentford and Brighton have built their squads this way, and fans now mirror this behaviour in games like Football Manager, where finding a £1M player with superstar potential is part of the fun.
What once was a backroom secret is now a global obsession, for clubs and fans alike.
Final Thoughts
Data is now at the centre of modern football. Whether you’re building a title-winning side or trying to survive relegation, the ability to understand and apply analytics can determine success.
The clubs that embrace expected goals, AI, and smart scouting are finding value that others miss. And in a world where a £20M signing might turn into a £100M star, that edge can make all the difference.
