The Empty File in Football Scouting: The Discipline of Saying I Do Not Know Yet

The Empty File in Football Scouting: The Discipline of Saying I Do Not Know Yet

The Move at Trigoria

In January 2026, at AS Roma's Trigoria training centre, I sat alone in front of a screen and slowed the footage down. Nicolò Zaniolo, twenty years old, planted his right foot in the grass, rotated his hips, and his left knee caved inward. I watched that frame seventeen times. Eighteen months earlier, I had sent the club's medical department a four-page report containing a line I still remember verbatim: this player's left-foot landing rate is skewed by 62 percent, producing asymmetric load on the anterior cruciate ligament of his right knee in every high-speed change of direction. The report received no reply. On 12 January 2026, in a match against Juventus, Zaniolo tore the ACL in his right knee. Nine months later, in another match, the other knee went too.

That memory left me with something that was not guilt but a professional question: why are silent warnings ignored? Years later, working with scouting data systems, I realised the answer lies somewhere few people look — the empty cell in the spreadsheet.

The Empty Cell

A modern scouting report has hundreds of cells. Top speed, number of accelerations, high-intensity distance, line-breaking passes per ninety minutes, aerial duel win rate, expected goals prevented. When a player has not been tracked enough, most of those cells are empty. And when cells are empty, people have a very strong instinct: to fill them.

I have seen this in both football cultures I belong to. In Italy, a young analytics assistant can be challenged for returning a report with missing data on a player who has featured in three consecutive matches. In Vietnam, where detailed data on youth competitions remains sparse, the gaps are far larger — and so is the pressure to fill them. People need a conclusion in order to make a decision, and an empty conclusion helps nobody sign a contract.

An empty cell is not a neutral absence. It is a cognitive trap, because the human mind tolerates ambiguity far less well than it tolerates a wrong number.

What is worth saying is that I fell into that trap myself, just in a different way. In 2026, at the Under-21 European Championship, I was assigned to the Italy national team as an analysis assistant. In the quarter-final against Portugal, Italy lost on penalties. I did not sit with the scoreline. I spent two hours recording eighteen line-breaking passes from Sandro Tonali, who kept dropping deep, dragging the opposing centre-back out of the defensive block, then returning the ball into the space he had just opened. The result was a forty-page report on the space between the lines.

The youth coaching staff read it, applied it to their curriculum, and invited me to become a club-level consultant. But inside those forty pages there were places where I had to write three words that the analysis industry rarely agrees to write: not enough data.

Three Mechanisms of the Empty-Data Trap

Mechanism one: the anchor of the first number

When a scout receives a single metric about a young player — say, a top speed of 33.4 km/h from a single match — that metric becomes the anchor for every subsequent judgement. Psychology calls this the anchoring effect. In football it is doubly dangerous, because one match never represents a player. Weather, pitch quality, opponent, team tactics, whether the player is carrying a minor injury — all of it is compressed into a single number, and that number carries no warning about its own reliability.

In Vietnam, where a young player might have physical metrics measured twice a year, this anchor is even stronger. One fast run in a periodic fitness test can shape the entire career of a seventeen-year-old in the eyes of a coaching staff. Nobody asks what time the test took place, how long after a meal, on which pitch.

Mechanism two: the pressure to fill the gap

This is the mechanism I consider the most dangerous, and the one academies least often confront head-on.

When a report comes back with too many empty cells, the writer faces three options. One is to state honestly that the data is insufficient. Two is to leave it blank and stay silent, letting the reader draw their own conclusions. Three is to fill it with conjecture, with an impression from a single video session, with subjective feeling — but presented in the language of data.

The third option is always the most attractive, because it produces something that looks complete. A full spreadsheet conveys professionalism. A spreadsheet with holes conveys incompetence. In an environment where competence is judged by presentation, the writer is pushed towards filling.

When a football culture lacks data, what it produces is not an admission of missing data but fake data that looks like real data.

I have seen this in my own work. In 2026, when stadiums closed because of the pandemic, the entire development process was interrupted. I designed an at-home training programme for fifteen young Roma academy players, consisting only of fifteen minutes of skipping rope and resistance bands. I set up a messaging group, tracked each of them, including Edoardo Bove, an eighteen-year-old midfielder who drew little attention. We had no gym, no measurement equipment, no laboratory. We had a living room, a rope, and a handwritten log.

The living room becomes the gym, because talent does not wait for someone to make its bed.

When the season resumed, Bove increased his maximal endurance by twelve percent and was promoted to the first team for a Europa League match against Young Boys. The head of player development acknowledged the group's work in an internal meeting. But what I want to describe here is not the result. What I want to describe is how we kept records across those ten months.

In our tracking sheet, we left the empty cells empty. On days when a player did not answer a message, that cell stayed empty. In weeks when he was ill, that cell stayed empty. We did not extrapolate, did not interpolate, did not draw an average line to cover the hole. We accepted that a curve with holes in it is more honest than a curve drawn beautifully from imagination.

That was the biggest lesson I carried out of that summer. Data discipline is not about measuring more; it is about refusing to invent the part you did not measure.

Mechanism three: the silence of negative findings

In scientific research, a negative result — finding no relationship at all — is nearly as valuable as a positive one. In football, negative results barely exist. Nobody writes a three-page report saying that after tracking a player across seven matches, they found no standout strength at professional level.

Yet it is precisely negative findings that protect clubs from bad investments. And it is precisely they that are most often ignored.

Back to Trigoria. My report on the landing-axis skew was not a positive finding. It did not say Zaniolo would get injured. It said there was an abnormal, repeating movement pattern, and that the pattern had never been cross-referenced against any intervention. That is a negative signal in its purest form: something unverified, presented as something unverified.

And it was ignored, partly because it offered no conclusion. A report saying this player will shine gets read. A report saying this player has a movement pattern that needs further monitoring gets filed away.

Vietnamese Football and a Larger Gap

I grew up in Vietnam, playing on concrete and dirt pitches, before moving to Europe to study movement science. The biggest difference between the two football cultures I have lived in is not technical. It lies in how each treats the unknown.

At Italian academies, a fifteen-year-old can have a data record going back to age twelve: quarterly height, seasonal fitness metrics, minutes played, preferred position and positions tested. Gaps in that record are the exception, and exceptions always carry a note explaining the cause.

In Vietnam, even at the best youth development centres, data remains fragmented. A seventeen-year-old can be promoted to the first team on the basis of two youth matches and one training session. Nobody records that the sample is too small to conclude anything. Nobody writes into the file the line: not enough data to assess adaptability at a higher level.

I say this not to compare superiority. I say it because I believe Vietnamese football stands at a fork, and the fork is not about buying more measurement devices. It is about building a professional culture that allows people to say not enough data without being considered incompetent.

The sediment layer deceives no one; it only deceives those not patient enough to dig.

In archaeology, when a stratum is empty, the archaeologist does not fill it with soil brought from elsewhere. They record that the stratum is empty, its position, its thickness — and that is itself information. An empty layer can mean the area was uninhabited during a given period, and that is a finding, not a failure.

Football has not learned that principle. An empty file is treated as a bad file, not as an event worth recording.

The Contrarian Angle: More Data Is Not the Answer

Here I have to separate myself from most of the people currently talking about data in football.

The prevailing trend holds that football's problem is a lack of data, and that the solution is more data. Clubs buy tracking systems, hire more analysts, sign contracts with international data platforms. In Europe, a mid-table club can hold tens of thousands of data points on a player before deciding to sign him.

But volume of data does not solve the problem I witnessed at Trigoria. My report was four pages long. It did not lack data. It lacked a person with enough authority to take it seriously.

The problem with modern scouting is not the quantity of information but the ability to distinguish verified information from information that merely looks verified. When a system returns a metric, that metric usually comes with a figure accurate to two decimal places. Formal precision creates a sense of certainty about content. But a number calculated from three matches and a number calculated from thirty matches have the same shape, and the interface does not distinguish between them.

This leads to a paradox I consider characteristic of the current era: the more data there is, the greater the need for people who know how to say no. When you have only one metric, you are forced to think about it. When you have a thousand metrics, you tend to believe you already understand, and skip the thinking.

Artificial intelligence does not solve this paradox. A machine-learning model trained on historical scouting data will learn the biases of that data, including the biggest bias of all: that past decisions were always made with complete data. Such a model will never return the answer not enough data, because it is designed to always produce a prediction. And a model that always predicts, when confronted with a player whose data profile it has never seen, will produce a wrong prediction with high confidence.

What football needs is not more data, but people trained to endure ambiguity a little longer than their rivals.

I realised this when I looked back at my own forty-page report on Tonali. The most valuable part was not the eighteen line-breaking passes charted into a diagram. The most valuable part was the section where I stated clearly that the sample covered only two matches, that nothing could be concluded about the repeatability of this movement pattern at national-team level, and that at least six club matches were needed for verification. The coaching staff read that section and understood they were receiving a hypothesis, not a conclusion.

A hypothesis handled properly is safer than a conclusion treated as truth.

Four Principles for Scouts

From what I have lived through, I draw four principles I try to apply to myself.

First, record the gap instead of hiding it. Every empty cell in a report needs a line explaining why it is empty. The player has not played enough minutes. The player has just returned from injury. The measurement conditions were substandard. A gap with a cause is information; a gap without a cause is a hole.

Second, separate data from interpretation with a clear line. My reports now always have two distinct sections. The first contains only what was observed and measured. The second contains what I infer, and every inference carries a confidence level and the conditions needed to verify it. Mixing the two is the fastest way to turn an observation into a prejudice.

Third, treat negative findings as equal to positive ones. If after seven matches of tracking a player, the most notable thing is that he has no standout strength at professional level, that is a report that needs to be written and needs to be read. It saves the club money and saves the player a few lost years.

Fourth, set a deadline for verification. This is the principle I had to learn latest, and the hardest one for someone with perfectionist tendencies. Waiting for enough data is a subtle trap, because it looks like discipline when it is actually procrastination. In scouting, the transfer market waits for no one. The only way to be honest and timely is to publish the report with a label stating the level of data available, along with a commitment to update it.

A report labelled preliminary is worth more than a report completed three weeks late.

What I Want to Leave Behind

In my profession, people often talk about talent as something that can be detected by eye. I do not deny that. The eye of an experienced scout can see things machines miss. But that eye can also see things that do not exist, especially when it is pressured into producing a conclusion.

What I learned after many years, and after one report that received no reply, is this: the greatest value of an analyst is not how often they are right, but whether they dare to stay silent when they do not yet know. In an industry where everyone wants to have an opinion first, the person who can endure ambiguity longest is usually the person who makes the best decision.

Zaniolo recovered and returned to play. He continued playing at the highest level, and that says a great deal about the resilience of a young player. But his story left a mark on how I work: that a silent signal, presented honestly and at the right time, can change a career. And that what prevents it from changing that career is usually not a lack of knowledge, but a lack of courage to look directly at an empty cell.

Vietnamese football is at a moment when decisions about the next generation of players will be made within the next few years. Academies are expanding, youth competitions are better organised, and data is beginning to appear. But data only has value when it comes with a professional culture willing to admit its own limits. If we build that culture now, we will save more knees than we would by buying a few dozen more measurement devices.

If we do not, we will keep producing spreadsheets that are full, beautiful, and wrong.

And when a seventeen-year-old walks into the medical room with a torn knee, someone will again sit in a room at some training centre, slow the footage down, and count how many times they watched a frame they should only have needed to watch once — if only someone had bothered to read a single line saying that the data was not enough.