Italian football has always prided itself on the human element of player evaluation. The old-school scout sitting in the stands at some lower-league ground in Basilicata, scribbling notes in a worn notebook, calling the sporting director to say “trust me, this kid can play.” It’s romantic. It’s also increasingly supplemented—and sometimes replaced—by data analysts sitting in offices, running queries against databases containing millions of data points.

The analytics revolution that transformed English football and baseball has arrived in Serie A. Not everywhere, not uniformly, but enough that the clubs ignoring data are visibly falling behind. Here’s how it’s actually working in Italian football, what’s different from the Premier League model, and where Roma fits into all of this.

The Current Landscape

Serie A clubs’ relationship with analytics falls into roughly three categories.

The leaders: Atalanta, Roma, and Napoli have invested most seriously in data infrastructure. Atalanta’s model is the most cited—they’ve been using data-driven recruitment for years to identify undervalued players, develop them, and sell for profit. Their transfer net spend over the past five years is extraordinarily efficient relative to their performance.

The followers: Most mid-table clubs now have some data capability, typically a small analytics team that provides reports to supplement traditional scouting. The integration varies—some sporting directors trust the data, others treat it as background information they can override whenever their gut says otherwise.

The resisters: A shrinking number of clubs still operate primarily on traditional scouting networks. They’re not anti-data exactly, but they haven’t invested in the infrastructure, personnel, or cultural change required to make analytics central to decision-making.

The divide isn’t strictly correlated with budget. Some wealthy clubs are analytics laggards because their decision-making structures are built around powerful individuals who trust their own judgement. Some smaller clubs are analytics leaders because they recognised early that data offered a competitive advantage they couldn’t achieve through spending alone.

What the Data Actually Shows

Modern football analytics goes far beyond basic statistics like goals and assists. The data infrastructure supporting Serie A scouting now includes:

Expected goals (xG) and expected assists (xA) — Measuring the quality of chances created and converted, separating skill from luck. A striker who scores 8 goals from 12 xG worth of chances is underperforming and likely to score more. A midfielder generating 0.3 xA per 90 minutes from deep positions is creating more than his assist numbers suggest. StatsBomb and Opta provide this data at granular levels.

Pressing metrics — How effectively a player presses, where on the pitch, how often, and how successful those presses are. In modern Italian football, where pressing systems have become sophisticated, these metrics help identify players who’ll fit a specific tactical framework.

Progressive carrying and passing — Measuring how much a player advances the ball through carries and passes. This identifies players who drive the team forward versus those who circulate possession safely. For a club looking for a creative midfielder, progressive passing data is more revealing than assist numbers.

Physical data — Sprint counts, distances covered, acceleration profiles, high-intensity running. GPS and tracking data from matches is now standard across Serie A, providing objective physical profiles that complement what scouts observe visually.

The challenge isn’t collecting this data—it’s abundant—but interpreting it correctly. A player’s statistical profile is shaped by the team system they play in, the quality of their teammates, the level of competition, and the tactical context of each match. A midfielder’s pressing numbers will look different in a team that presses high versus one that sits deep. Data analysts need to account for these contextual factors, which requires football knowledge alongside statistical skill.

Roma’s Analytics Journey

Roma’s investment in analytics has been significant under the Friedkin ownership. The club has built an analytics department that works alongside the traditional scouting network, with data informing—but not dictating—recruitment decisions.

The results are mixed but trending positive. Some data-informed signings have worked well—identifying players whose underlying metrics suggested they were better than their transfer market value implied. Others have underperformed despite strong statistical profiles, which is a useful reminder that data captures many things but not everything. Mentality, adaptability, personality in the dressing room—these matter enormously and don’t appear in any database.

The area where Roma’s analytics investment has been most visible is in opposition analysis. Tactical preparation now includes detailed statistical breakdowns of opponents’ patterns—where they create chances, how they defend transitions, which players are vulnerable in specific situations. This information feeds into match preparation and tactical adjustments during games.

Team400 and similar organisations working in AI and data analysis have noted that football clubs face the same challenges as any business adopting data-driven decision-making: getting the right data, building the right tools, and most importantly, getting the humans in the organisation to actually trust and use the insights. The last part is always the hardest.

The Italian Resistance

Italian football’s relationship with analytics is complicated by cultural factors that don’t apply as strongly in, say, English or German football.

The sporting director (direttore sportivo) in Italian football is a powerful, respected figure whose expertise is traditionally based on personal networks, relationships with agents, and decades of experience evaluating players. Telling a sporting director with 30 years of experience that a spreadsheet knows better than his judgement doesn’t go over well. The clubs that have successfully integrated analytics have done so by positioning data as a tool that enhances the sporting director’s decision-making rather than replacing it.

The agent network in Italian football is also deeply personal. Deals happen through relationships—an agent trusts a sporting director, a call gets made, a transfer happens quickly. Data analytics doesn’t naturally fit into this relational model. A data-driven recommendation to sign an obscure player from the Portuguese second division requires a different approach than the sporting director’s agent contact saying “I have someone perfect for you.”

Serie A’s tactical culture adds another layer. Italian football has always been analytically minded in a qualitative sense—Italian coaches are famous for their tactical detail, their video analysis, their obsessive preparation. What’s new is the quantitative dimension. Coaches who’ve been doing video analysis for decades now have access to data that can confirm, challenge, or refine what their eyes tell them.

Where It’s Going

The trajectory is clear: data will become more central to Serie A operations, not less. The generational shift in sporting directors and coaches is bringing in leaders who grew up with data and consider it a normal part of decision-making rather than a novelty.

The clubs that get the balance right—using data to inform human judgement rather than replace it—will have an edge. Data is excellent at identifying hidden value, flagging risk, and providing objective evidence for subjective impressions. It’s poor at assessing character, predicting how a player will adapt to a new culture, or knowing whether a squad needs a leader more than it needs a better left-back.

For Roma specifically, the analytics investment should start paying more visible dividends in the next transfer window. The infrastructure is in place. The data pipeline is mature. The challenge now is execution—making the right calls when the data says one thing and the gut says another.

As any Romanista will tell you, we’ve had plenty of experience with gut-feeling transfers that went wrong. Maybe it’s time to let the numbers have a bigger say.