The Plan, the Forecast, and the Result

In 2016, Como went bankrupt and restarted in Serie D, the fourth tier of Italian football. In October 2026 they beat Manchester United 4-1 in the Champions League. Two weeks later they won 9-1 away at Lens.

Between those two sentences, the club did not discover a secret metric. It did something harder: it got a president and a coach to disagree in a useful way.

The Analyst's Real Job

I manage data analysts, and the part of the job nobody warns you about is that the analysis is the easy half. The hard half is the room. A finding that nobody acts on is a spreadsheet.

Football makes this obvious. The manager has spent twenty years learning to trust what he sees. The analyst arrives with a number that says otherwise. If the number wins by default, you get a club that signs spreadsheets. If the manager wins by default, you get a club that never needed an analyst. Both are failures. The interesting question is what a club does in between.

What Como Does in Between

Como's president, Mirwan Suwarso, came from advertising and broadcasting, not football. He has described a process that treats the disagreement as information rather than a problem to settle.

Before each match, Cesc Fàbregas writes a game plan and submits it to the board, roughly a day ahead. The data team separately builds its own prediction of how the match should go. After the game, the club compares three things: the plan, the prediction and what happened.

That changes what a result means. Suwarso gave an example on a podcast: against a team sitting in a low block, the data might expect Como to create around 0.8 to 0.9 expected goals. If they reach 1.3 and still draw 0-0, "we did better than we were supposed to do." He has also described the win over Juventus as a ten-out-of-ten performance against the intended plan.

The score is only one input. The question the club asks is: did we do what we said we would do?

When the Data Said No

The more revealing story is recruitment. Suwarso has said that for some players the club's data said the player wasn't good enough, and Fàbregas disagreed. His argument was that the numbers reflected the role the player had been given, not his ability.

The example Suwarso gives is Lucas Da Cunha, a winger whom Fàbregas moved into central midfield. Como had signed him from Nice for a modest fee, reportedly under half a million euros. Suwarso calls him "Fabregas's invention," and says many clubs are now interested.

Read that carefully, because the lesson is not "the coach beats the data." Fàbregas did not ignore the numbers. He explained them. The data described what the player had done in one role. Fàbregas was making a claim about what he would do in another. The model was answering a question about the past, and the coach was answering a question about the future.

That is the analyst's blind spot, and mine. A model only knows what its data has shown it. The person on the touchline can imagine a version of the player the data has never seen.

Da Cunha is now Como's captain, at a club that was in Serie D ten years ago.

It cuts the other way too. Suwarso says signings need majority agreement and that Fàbregas's voice is the most influential, but not the only one. He has described a winger, Jayden Addai, whom Fàbregas was unsure about before trusting the group's view. The coach is not always right, and the structure allows for that.

The Part That Isn't Data

Suwarso has said Como's player valuations rose from €60 million to €389 million in two seasons. A club that finishes fourth in Serie A and reaches the Champions League for the first time is clearly doing something right.

But the useful thing to notice is how the club is built, not the headline. Suwarso says that signing a player the coach won't use has little value. So the president, the data team and the coach are not three departments sending reports to each other. By Suwarso's account, they share the data with each other and argue about it.

That alignment is the whole story, and the president is the one holding it together.

A Caution

Almost everything we know about this process comes from Suwarso himself, in interviews and podcasts, and none of it has been independently checked. Como is also funded by wealthy owners, so a good process is only part of the story.

So I would not say Como proved that data plus intuition wins. I would say they built a process where neither is allowed to win by default, and that is rarer than any single metric.

The Number Never Decides

When I think about what makes a good analyst, it isn't the model. It is the willingness to say "here is what the data shows, here is what it cannot see, and here is who has to decide."

Como's version of that is a coach who writes down his plan before the match, a data team that writes down its prediction, and a president who reads both.

The number never decides. Someone still has to. The analyst's job is to make that someone better at it.