The Empty File: When Vietnamese Football Data Isn't Enough to Conclude

Last month, I opened a scouting report file sent from a V.League club. The file had every field: player name, year of birth, position, height, weight, matches, minutes, goals. But when I scrolled down to the technical assessment section, the data cell was empty. The opponents-faced field held a dash. The injury context field was left at default. And on the last line, where a conclusion should have been, the report's author had written: “Promising young player, recommend further monitoring.”

That is not a report. It is an empty shell presented as a conclusion.

I sat looking at that file for a long while. It reminded me of something this profession rarely admits: most decisions about young players in Vietnam are made on a foundation of missing data, and most of the loudest praise is built on exactly that missing foundation.

Numbers are the surface layer; I always dig three more layers. But when the surface layer does not exist, the right question is not “how good is he” but “what do we actually know about him”. In many cases, my answer is: almost nothing.

A football nation that reads with its eyes more than its numbers

In Europe, a U17 player in a national youth league may be captured by two or three camera systems, with per-second positional data, ball-touch data, pressure data. In Vietnam, most youth matches are recorded by a single wide-angle camera, sometimes by an assistant's phone, and the data is entered by hand into a spreadsheet afterwards.

The gap is not merely technological. It is a gap in the nature of the question. When data is dense, people ask “how much did he run, how much did he shoot, how many chances did he create”. When data is thin, people ask “does he look good”. And the second question, humble as it sounds, is far more dangerous, because it opens the door to bias, to the feeling of one afternoon, to one beautiful move remembered and a hundred ordinary moves forgotten.

I once sat in a scouting meeting at a northern academy. On the table were files for eight U16 players. Six of the eight contained not one minute of video, only verbal descriptions sent up by local coaches. The meeting lasted three hours. The final decision selected two players. I asked for the basis of the decision. The answer was so honest I wrote it down verbatim: “Because that one runs fast — you look at him and you like him.”

That is an honest answer. But it is not a professional answer. It is an aesthetic reflex.

What is striking is that the aesthetic reflex is not wrong emotionally. It is only wrong methodologically. On an empty data foundation, emotion fills every gap, and it fills it confidently. That is why the biggest praise tends to go to the players we know least about.

Null result: the hardest skill of an analyst

In statistics there is a concept called the “null result” — an experiment that yields no signal. Statistics students are taught to present results with a signal; few are taught to present a result with no signal. In this profession, I believe the most important skill is being able to say: “I do not know yet.”

A scouting file missing data is not a bad file. It is an incomplete file. The two are entirely different, and our response to them must differ too. With a bad file, we reject. With an incomplete file, we fill it in. But to fill it in, we must walk outside the spreadsheet.

In 2026, I did the opposite. When I was a senior specialist at the Viettel youth training centre, I underrated a 16-year-old midfielder named Nguyen Duc Nam. My reasoning was very “scientific”: BMI below the national U17 standard, 30-metre sprint below threshold, muscle mass undeveloped. I concluded he lacked the physical foundation.

I ignored one important data field: Nam had just returned from a ligament injury, and he was in a growth-spurt phase. Neither was in my spreadsheet, because my spreadsheet had no column for them. Three months later, Nam debuted for the first team in the V.League and recorded four assists in five matches.

I do not tell that story to flagellate myself attractively. I tell it because it shows what I believe is the core of this profession: a data table always contains an empty field you do not yet know is empty. That empty field is where mistakes are born.

After that episode, I added a column to my table called “biomedical context”. It records recovering injuries, biological age, growth cycles, time out of play. From then on, whenever a physical metric fell below standard, the first thing I did was not to conclude but to check that column.

The three layers of a number

I do not excavate stars, I excavate context. A number in my work never stands alone. It must be held up by two further layers. For example, when I say a striker scores 0.8 goals per 90 minutes, I cannot stop there. I must ask: where did those goals come from, against which defences, in what match state, and on what foundation of load tolerance.

In 2026, when global football paused because of the pandemic, I accepted an invitation from Song Lam Nghe An to review their academy. Old data showed an 18-year-old striker named Tran Van Cong with a strike rate of 0.8 goals per 90 minutes, the highest in the academy. But he rarely played and often cramped. On the efficiency number alone, he deserved a professional contract. On the cramping alone, he did not deserve to start.

Because the training ground was closed, I interviewed his family online and analysed archived GPS data. The result showed the problem was not fitness but minute distribution: he was thrown on in the second half at maximum intensity after a cold warm-up. I recommended signing him professionally before the league resumed. When the 2026 V.League kicked off, Cong scored six goals.

What I learned was not that “goals per 90 matters”. What I learned was that goals per 90 only means something when we know where on the pitch and in what state the player was placed. A goal only means something when we know what he had just been through.

The trap of filling the void

The greatest danger of an empty file is not that it stops us acting. The danger is that it makes us act confidently. Because when there is no data, we use the templates already in our heads to fill in. And the templates in our heads are mostly stories, not evidence.

That is the mechanism that produces what I call the “star from below”. A young player produces one beautiful move in a televised match. The move is clipped, shared, and suddenly becomes a file. No one asks what he did in the previous ten matches, because no one watched the previous ten matches. And because no one watched, they become missing data. And because of the missing data, the beautiful move becomes the whole story.

In 2026, I followed the mid-season transfer window of Hai Phong FC. There was talk of a defender named Le Van Son being offered a long-term deal. The surface numbers were beautiful: 12 successful tackles across three AFC Cup matches. Stopping there, it was a contract worth signing.

But when I watched each match, I saw the other side of the number. In those same three matches, Son made three direct errors leading to goals, and all three occurred under away pressure. The successful-tackle count shows he defended a lot; it does not show he read situations well. A high tackle count in a defender sometimes only means he was repeatedly put in positions where he had to tackle.

I advised the club not to sign long-term. Two weeks later, Son was injured and the contract was cancelled. I do not tell this to praise myself. I tell it because it shows a principle: a beautiful defensive metric can be the result of defending a lot, and defending a lot can be the result of reading the game poorly. Without digging the second layer, we pay for the consequence and mistake it for the cause.

With the same reading, I once analysed Kylian Mbappe's four goals at the 2026 World Cup. Instead of stopping at the number, I measured 11 successful dribbles in the match against Argentina. But I also noted clearly: that effectiveness came from playing on the left and rarely being tightly marked. Remove those two conditions from the picture and the number loses its meaning. That report was later used by PVF as teaching material, but its value did not lie in predicting France's title. It lay in separating the number from tactical position and opponent quality.

When data is not just missing but contradictory

There is a more awkward kind of empty file: a file that has data, but data that contradicts itself. That is when we realise the risk is not in the missing number but in a number that does not match its context.

In 2026, working with a group of young journalists at the Euros and the Paris Olympics, I tracked Spain's Pedri. His data looked fine in aggregate metrics. But when split by time, one detail stood out: his distance covered dropped by about 18 per cent after the 75th minute. The full-match average hid the final 15 minutes.

I warned in the report that if he was pushed into extra time, the risk of decline and injury would rise. The team did not rotate, and he left the tournament injured. That time I recognised I had been slow to adapt to the high-intensity trend, so I began studying machine-learning algorithms to supplement my method. I still do not treat it as a solution. I treat it as one more excavation layer, to be verified before it is trusted.

There is something league tables do not show here. Distance covered and sprint counts are often packaged as effort metrics. But ineffective running also produces beautiful numbers. A player who runs 11 km may simply be chasing the ball in the wrong position, while a player who runs 9.5 km may have stood in the right place and intervened at the right moment. If we read the number without reading the terrain, we will celebrate exhaustion and forget effectiveness.

A data map can point you the wrong way if you do not read the terrain. That is why every conclusion of mine carries its own limit. Not as a shield for safety, but to say clearly what I believe and what I doubt.

Back to the empty file

When I received that report with the empty shell, I had two options. The first was to fill the blanks with whatever I knew from elsewhere — rumours, reputation, a match I once watched. The second was to return the file with one line: “Insufficient data to conclude; please provide video of the last three matches and injury information.”

I chose the second. Not because it is easy. Because it is right.

In a football nation where data is still thin, the greatest value an analyst can create is not producing more conclusions. It is producing fewer conclusions that are more trustworthy. A report that says “I do not know yet” may cost us a meeting. A report that fills the blanks with feeling may cost us a player — or worse, may make us sign a three-year contract with the wrong person.

Compensatory growth is the most beautiful thing the league table cannot measure. And the emptiness of data is another thing the league table cannot measure. Both are invisible to the viewer, and both decide the career of a child.

The counter-view: praise can also be a form of failure

We tend to think the biggest error in youth development is missing a talent. I used to think so, and I wrote a lot about the fear of missing out. But looking back over several years of data, I see a more common and less discussed error: praising a young player too early, on an empty data foundation, and then placing on his shoulders an expectation he has no basis to carry.

Praise built on full data is a hypothesis. Praise built on missing data is a debt. And that debt is usually repaid in injury, in lost form, in a 19-year-old being turned into a symbol before he has finished learning to play at high speed.

It took me three years to understand that data also needs compensatory growth. That is, a number at time A cannot be compared with a number at time B if we ignore what happened in between. A player who scores two goals at 17 and none at 18 may be improving, if the two goals at 17 came from penalties while the 18-year-old's non-scoring came from harder positions blocked by excellent goalkeepers. Without a column for “chance quality” and “opposing finishing quality”, those two numbers are meaningless.

This is where I must repeat what I believe: I do not excavate stars, I excavate context. Context is the only thing that lets two identical numbers tell two different stories.

Signals to track for the rest of the season

From the viewpoint of someone tracking the V.League and its academies closely, I see three signals worth continuing to observe, and I present them as hypotheses, not conclusions.

First, the “biomedical context” column needs to be institutionalised. Academies should record injury, time out, and biological age in the same table as minutes played. If this is done, the number of unfairly underrated cases will fall. If it is not, we will keep making judgments about the physicality of players just back from injury, and keep being wrong.

Second, data must come with sample size. A metric over three matches is not a metric; it is an anecdote with a unit of measurement. I want reports to state the sample size, the opponent level, and the match state. A player performing well against a bottom-of-the-table side says nothing about his ability against a title contender.

Third, and perhaps most important, academies should have a process for the “null result”. A form that permits entering “insufficient data”, and a person responsible for fetching the missing data. Today, many places have no such form, so the report writer is forced to write a conclusion without a basis. That pressure to conclude is the origin of most distortion.

If those three things are sustained across two consecutive seasons, I believe the quality of scouting decisions at Vietnam's leading academies will improve measurably, and the number of Nguyen Duc Nam-type errors will fall. If only one of the three is done, I am not sure. If none is done, the data columns will keep looking beautiful on screen and staying empty in reality.

Injury does not erase a talent's name; it only moves that talent down into the sediment. And that sediment, if we refuse to dig, will remain forever outside the view of hastily compiled reports.

A thought to leave with

I still keep that empty report file on my machine. I have not deleted it. I leave it there as a reminder of my own limits.

There is one thing I have not yet answered, and I want to raise it rather than hide it: given how thin Vietnamese data still is, is waiting for complete data causing us to miss players the market will take before we can measure them? That is a question that can be tested, and it can be tested by comparing the list of young players who moved abroad against the time from when they first appeared in a report to when they left. If that interval is shorter than the average time for an academy to complete a file, then waiting for complete data is not caution — it is lateness.

I do not yet have the data to conclude that question. But I know where the excavation begins.