The Empty Spreadsheet in Transfer Season: A Football Data Writer's Discipline of Saying 'Insufficient Information'

There was a transfer-season evening when my phone buzzed nonstop. Three groups of friends, two colleagues, one man introduced as a player agent — all asked the same question: 'I heard this striker is coming to the V.League, is it true?' I opened my notebook, flipped past pages filled in blue ink to a blank one, and answered flatly: 'I don't have enough data to say anything.' It wasn't that I didn't want news. It was precisely because I wanted news that I sealed my own mouth. In this profession, the hardest sentence isn't 'I was wrong' — it's 'I don't know.'

I have spent my whole career writing about football through numbers. But there is a kind of emptiness outsiders never see: an analysis sheet with the right frame, the right sections, the right formatting — and absolutely no content. A beautifully ruled sheet of paper is still blank in the eyes of someone who knows how to read it. That is the biggest lesson I have taken from this transfer window, and it is what I want to tell with full seriousness.

Context: Transfer Season and the Thirst for Numbers

Transfer season is football season without football. There are no matches to watch, no points to fight for, yet the fans' demand for information peaks. News sites publish endlessly. Comment groups boil over. Every hour that passes without a fresh item feels like a failure to the reader.

That void produces what I call a thirst for numbers. When there is no real news, people still need a number to cling to. A transfer fee with no source. A wage figure 'leaked.' A completion probability 'as high as 90%.' These numbers share one trait: they cannot be verified, but they satisfy.

I have worked as a data journalist for more than twenty-eight years, most of it tied to Vietnamese football. I am used to opening my notebook every morning, recording every pass, every shot, every decisive ball. To me, a number is an oath. Once I write it down, I am responsible for it. Transfer season is when that oath is tested hardest, because the pressure to have big news exceeds the pressure to have true news.

The transfer market does not reward the writer who writes the most, but the one who writes the most accurately — and accuracy usually means saying the least.

In recent weeks I have received no fewer than twenty transfer rumors involving V.League clubs. I tried to run each one through a filter. The result made me write this piece: most of them, when peeled to the final layer, contained nothing — exactly like an analysis sheet ruled in a perfect frame with not a single data point inside.

The First xG Notebook and the Phan Van Duc Lesson

I wrote my first xG table by hand on a bus, back when nobody called it data. It was 2026. I sat on a bus from Saigon to the central region, notebook in hand, eyes on a phone replaying the match, recording the position of every shot and estimating its goal probability. Nobody hired me to do that. Nobody paid me for it. I did it because I believed the match held a story the naked eye could not read.

That season I built my own xG model for 14 V.League clubs. I collected every play. The work was so tedious I had to reward myself with a coffee after finishing each half. But within that tedium, a name emerged: Phan Van Duc, then a twenty-year-old winger at SLNA.

His xG per match reached 0.48 — higher than the average of foreign strikers in the league. He scored only five goals that season. Read with the eye, one sees a young player with modest goals. Read with a model, one sees an attacking outlet whose chance quality far exceeded the reward he actually received.

I wrote a prediction: Phan Van Duc would become a national-team pillar within three years. Many mocked me as deluded by numbers. One colleague texted me four words: 'Dreaming in daylight.' I did not argue. I noted the date and filed the article away.

In 2026, Phan Van Duc scored the decisive goal at the AFF Cup. That was not my victory. It was the victory of a way of seeing: when the eye had not yet caught up, the model had already seen ahead.

The lesson from Phan Van Duc is not that 'data is always right,' but that 'data thick enough earns the right to speak before the crowd.' That holds only when I genuinely have data. If my notebook is empty, I have no right to say anything — however attractive the name.

Croatia 2026: When PPDA Spoke Before the World

In 2026, during the World Cup in Russia, I applied the PPDA model — passes allowed per defensive action — to measure the pressing intensity of the big teams. The lower the figure, the more aggressive the press.

I was astonished to find Croatia under Zlatko Dalic had a PPDA of just 7.9 against Argentina. That was lower than even a team dubbed the 'king of possession,' Spain. Croatia did not control the ball much, but they pressed directly and extremely effectively against a loose Argentine defense, while controlling only about 40% of possession.

The world saw Croatia as an underdog; I saw them as a chain of coefficients no one had dared to mine. I wrote a long piece predicting Croatia would reach the final. I put my view on the scale publicly. A colleague laughed at me because 'nobody rates Croatia.' When they beat Argentina, Russia and England in turn, my article was shared wildly.

But I want to tell this story for another reason — not to praise myself. I tell it because I remember clearly how I felt before writing: I rechecked the PPDA three times. Three times. Not because I doubted the number, but because I knew that if it were wrong, I would drag thousands of people into believing something false.

The boldness of a prediction has value only when it rests on a foundation of data verified layer by layer. Boldness without a foundation is mere recklessness. And recklessness, in my profession, is a crime.

In Croatia 2026, I had real data to dare to speak against the crowd. In today's transfer window, most rumors do not give me even a third of that basis. Yet people still want my conclusion. I refuse. The gap in the data must be spoken aloud, not glossed over with dazzling speculation.

2026 and the Data Mine Nobody Dug

In March 2026, all major competitions were suspended due to COVID-19. There were no matches to analyze. Many sports journalists turned to entertainment writing to pass the days. I refused to sit idle.

In 2026 the stadiums were empty, yet every ball still fell into the model's cells, and I understood that data never befriends a pandemic. I spent six months digging through the V.League data mine from 2026 to 2026. I built a long-term study nobody asked me to build.

And I found a pattern that made me sit still for a few minutes: clubs that changed chairmen mid-season saw their win rate drop by 23% over the next five matches. The cause was not purely technical but a governance disruption — cash flow slowing, transfer plans upended, the coaching staff losing its backing.

I published a five-part retrospective series, analyzing each chairman transfer and how it affected on-pitch performance. After it ran, a club executive called to thank me for helping them avoid sacking their head coach at a sensitive moment.

That 23% figure is not a discovery to show off. It is evidence for a principle: when data is thick enough over time, it sees things a single match can never reveal.

Since then, I no longer write about instant data in the raw. I hang the long-term chart above every hot data table. If a team wins three in a row, I do not rush to call it form. I place that three-match run within a ten-year run, then ask myself: is this signal or just noise?

That question is exactly what I lack in the current transfer window. I have plenty of rumors, but no data history to place them in. And a rumor with no history is not data — it is noise.

Anatomy of a Rumor: A Hierarchy of Sources

When I sat down and arranged the twenty transfer rumors into a table, a source hierarchy emerged clearly. I divide it into four tiers.

Tier one is a source with an original document: a contract, an official club announcement, registration papers with the league authority. This tier is rare but almost never wrong. When I have it, I write immediately.

Tier two is a source with an accountable identity: a technical director speaking at a press conference, an agent confirming talks, a coach naming a player in an interview. This tier can be wrong, but it can be traced. I write with conditions: stating who said it, when, and where.

Tier three is an indirect insider source: a club staffer, a driver, an acquaintance in the coaching staff. This tier is right about half the time. I may use it to direct an investigation, but never to run a story on its own.

Tier four is an unsourced rumor — posts saying 'I heard,' 'likely,' 'almost certain.' This is the tier I refuse to use, unless it forces me to write a piece warning readers that information is being inflated.

A rumor with no source is not a piece of thin news; it is an emptiness decorated with words. And the data writer's job is to distinguish an honest emptiness from a painted one.

What worries me is that of the twenty rumors I collected, fourteen belonged to tier four. That means seven in ten rumors in today's transfer market contain nothing when peeled to the final layer. If I wrote about them as if they were data, I would turn myself into part of the noise-amplifying machine.

The V.League and the Problem of Baseline Data

There is a professional fact I must state plainly: Vietnamese football suffers a severe shortage of baseline data. Many European leagues publish data detailed down to every play, every position, every meter run. The V.League has no such privilege. We have to build it ourselves.

Building it yourself has pros and cons. The upside is that I understand the data to its roots, because I am the one who recorded it. The downside is that if I am not careful, I easily become overconfident in what I alone created. That is why I always ask myself: is this sample size large enough? Where is the confidence interval? Which variables does my model fail to capture?

In a V.League match, there are things the model cannot see. The state of the pitch after rain. A congested schedule making players run 5% slower in the second half. A night flight before a match. Trouble at home. All those qualitative variables can decide a match more than any coefficient.

My model does not cry, does not celebrate, but after every match it owes me a lesson. And most of that lesson lies in the variables I have not yet recorded.

The viewer sees the play; I see 22 numbers in motion — and I wait patiently for them to tell a different story. But I must admit: some afternoons, those 22 numbers tell nothing at all. Not because they are silent, but because I have not collected enough. In those moments, the right thing is to close the notebook and wait for the next match.

In transfer season, the temptation to do the opposite is enormous. People want the story immediately. But a story written on a thin data foundation is a story that will collapse when the season begins.

The 'Hollow Skeleton' Crisis

Let me describe precisely what I saw in this transfer window. It is not wrong numbers. Wrong numbers are easy to spot. What I saw is a subtler kind of defect: the hollow skeleton.

An analysis sheet has all its sections. A heading here. A summary line there. A cell for age, a cell for transfer value, a cell for the tactical conclusion. Everything looks professional. But when you examine each cell, each says one word: pending. No player name. No club name. No date. No source. A beautiful frame, a hollow core.

This is a lesson I learned bitterly. While building my analytical process, I once built very detailed frameworks for many leagues. I eagerly filled every cell with figures. But one time, preparing to analyze a team, I discovered I had not a single trustworthy data point to put in. My frame was ready, but the ingredients were not.

The temptation then was enormous: put something in anyway. A guess rewritten in a confident voice. A gut feeling dressed in terminology. I almost did it. But I remembered my handwritten xG notebook, remembered Phan Van Duc, remembered Croatia's PPDA, remembered the 23% figure that took six months to obtain. I closed the laptop.

An analysis sheet ruled in a perfect frame but containing not a single data point is, in information value, no better than a blank sheet. The only difference is that it deceives the lazy reader, while a blank sheet does not.

This is the real danger of transfer season. Not fake news — everyone is wary of that. But empty news: articles in the right form, with the right terms, right in everything except that they contain not a single fact.

The Counterintuitive Angle: The Trap of Certainty

Here I want to go against my own industry's habit. Everyone in the trade believes the biggest problem in sports journalism is inaccurate information. I believe the bigger problem lies in the expectation of complete information.

Public opinion always demands the right to know, immediately. But there are periods when the professionally correct answer is: not enough information. We have assumed that a good writer is one who always has a conclusion, who holds a decisive opinion. That is true for a finished match. But it is false in an ongoing transfer window, where a contract can snap at the ninetieth second.

I have predicted, written against the crowd, and won. But I have also stayed silent before the most tempting rumors, simply because I had nothing to stand on. That silence is not weakness. It is a form of discipline. It is the measure showing that I truly believe in my process, not in my fame.

In a transfer season where rumors are commodities, the data writer must choose: sell fake certainty, or keep real doubt.

Transfer season rewards the one who dares to speak decisively. But it pays durably only the one who dares to say: I don't have enough data yet.

A juicy rumor can give you a million views today. But when it is wrong, it takes away something more expensive: trust. And trust is the only capital a data writer can earn back — but only through years of patience.

A Few Rules I Bind Myself To

After peeling apart twenty rumors, I noted a compact set of rules for myself, so readers can check whether I follow them.

One, no piece without a specific source plus a date. If I cannot write who said it and when, I don't write.

Two, the sample size must be stated. Three matches are not long-term form. One season is not a cycle. Eight years begins to be enough for me to believe.

Three, qualitative variables must be named, not hidden. If the model cannot measure something, I state clearly that it cannot. There is no room for phrases like fighting spirit to fill a data gap.

Four, new data always has the right to beat old data. I was once right about Croatia, but if this week's metric says otherwise, I change. I do not stay conservative about old judgments just because they once helped me win.

Five, when there is not enough data, I say there is not enough data. I do not force an arithmetic formula onto a match with too many human variables.

I do not trust coaches; I trust the model. But I listen to coaches to fix the model. Because the model is mine, while the match is theirs. In transfer season, no model can replace a confirmation call from someone accountable.

A Yardstick for the Coming Transfer Window

The transfer market is a game for the far-sighted, not the well-informed — value always arrives after patience. I wrote that line years ago, and it still holds; this transfer window simply makes it hold more harshly.

Small V.League clubs are caught in a familiar trap: they are drawn into loan deals with mandatory purchase clauses, a contract structure that sounds attractive but wrecks long-term financial planning. They take a player, use him for a season or two, and when the purchase clause triggers, the wage budget is already drained. In the end they remain forever a place that nurtures semi-finished products for the big teams. This is a problem I consider more serious than any rumor, yet it is rarely discussed because it does not sell views.

I also keep an eye on the VAR story. There is a view I have held for years: VAR does not reduce controversy; it merely moves it from the pitch to the review room and into the grey zones of the law. Each time a new dispute arises, I find myself a little more right, but I am not happy about it. Because it means fans still endure ambiguity, only in a different location.

And on injuries, especially anterior cruciate ligament injuries, I have said it many times and will say it again: rushing back is destroying the second phase of many players' careers. The psychological fear after injury is far harder to repair than the body. In transfer season, clubs often ignore this variable when valuing a returning player. It is a mistake my model has recorded, but with too small a sample to quantify fully. And when the sample is too small, I do not conclude.

That is the whole spirit of this piece. Not a grand manifesto. Just a self-reminder: if I want to protect the honesty of every number, I must begin by protecting the honesty of the gaps between them.

Closing Words

When the new season begins, this transfer window's rumors will be tested by football itself. Who arrived rightly, who wrongly, who never came — all will surface on the pitch. The data writer need not win the speed race of rumors. He must win the race of trust.

My model is not wrong, it just needs an update. And the most important update this time is not a new metric, but learning to say three words correctly: not enough data. If I keep that discipline through this transfer window, then next season when I open my notebook, every number I write down will still weigh as heavily as it did ten years ago on that bus. That is the only thing I want to keep from this profession, amid a market that never stops demanding I know more than I actually can.