When Data Is Empty: The Courage of N/A in Football Analysis

On the night of March 15, 2026, I sat in front of a screen with a 96-minute V-League recording, a notebook filled to its seventh page, and a post due before 6 a.m. In that file, the player-position data feed cut out from minute 34 to minute 58 — nearly half an hour of spatial data lost. I looked at the terminal line: 24 minutes with no coordinates. Then I did something many in the industry would consider career suicide: I wrote two words into the middle of the article — N/A, insufficient data to conclude — and submitted it as it was.

No one shouted. But no one praised it either. That says almost everything about how the football analysis industry operates.

Context: an industry that lives by always having an answer

Modern football runs on an enormous data ecosystem. Every match in the top European leagues generates millions of data points: player coordinates every ten seconds, touches, distance covered by spatial zone, xG, xA, PPDA, duel win rates by pitch zone. Those numbers flow into platforms such as Opta, StatsBomb, and Wyscout, and from there into articles, television programmes, and live streams.

But the downstream side of that flow has a strange feature: it almost never permits a blank. When a match ends, the audience expects an immediate analysis explaining why team A won, why team B lost, why striker C hit the post. The writer is placed in a frame where answers are rewarded and silence is punished. If you say "I don't have enough data", the platform algorithms push your article below those with assertive headlines: "Three reasons why X collapsed". In the attention game, modesty always loses to assertion.

I understand that pressure from my own experience. In 2026, at sixteen, I wrote a 900-word blog post about SHB Da Nang losing 0-3 at home to Hanoi. I had data then. I showed that all three goals originated from the left flank, and that Hanoi completed 134 more passes but produced only four shots on target. One account commented: "What does a girl know about football to lecture us." I did not reply. I added a chart of each player's average position. The data spoke for itself.

But the 2026 lesson taught me one thing, and the March 2026 V-League lesson taught me something deeper. When you have data, numbers are your shield. When you do not, the only thing you can honestly present is N/A. And in this industry, presenting N/A is harder than presenting an entire data table.

A hand-drawn diagram from the 2026 World Cup can still read tonight's match — but only if you know that diagram does not apply to everything. I still draw by hand, still record data-collection dates at the end of each article, still cross-check every number against at least two sources. Precisely because I respect data that much, I must also respect its absence.

Core: three cases, three different levels of evidence

To understand why N/A matters, we must distinguish three fundamentally different analytical situations. I call them three levels of evidence: full evidence, partial evidence, and zero evidence. The football industry routinely blends all three into a single voice of equal confidence — and that is the root of most professional error.

Level one: full evidence — Japan 2-1 Colombia, 2026 World Cup

On June 19, 2026, I was seventeen, in eleventh grade, watching Japan beat Colombia 2-1 in Saransk. Carlos Sanchez was sent off in the third minute. That match had good data, clear footage, and enough context to draw conclusions.

What I remember most is not the scoreline. After the red card, Colombia dropped into a 4-4-1 block. A natural reflex for a team with a man advantage is to surge forward fast, push the defensive line to midfield, squeeze the opponent into their box. Japan did not do that. I drew six hand diagrams in my notebook that night. The average distance between Japan's midfield and forward lines was 22 metres. Colombia's was 35 metres. That 13-metre gap was the whole story.

When you keep the distance between lines at 22 metres, you create a structure that can move as a single block. The ball circulates from flank to flank without midfielders sprinting to recover position. The shape stretches like a spring, and that spring snaps into exactly the gap the 4-4-1 leaves open. Japan did not push high in a frenzy; they pushed high with control. That is why, technically, they broke through a packed defence — not luck.

A former Vietnam international shared that article. Two days later, it had 4,200 reads. A football fan page offered me regular collaboration. This is level one: you have data, you have conclusions, and your conclusions can be verified by eye on the footage.

Level two: partial evidence — Morocco 1-0 Portugal, 2026 World Cup

In December 2026, I was twenty-one, writing my graduation thesis. Morocco eliminated Portugal 1-0 to reach the semi-finals — one of the greatest stories in World Cup history. I wrote about their 4-1-4-1 defence. And I made a mistake.

I wrote that they pressed only 31% of the time but succeeded 87% of the time, and won 100% of 14 aerial duels. In reality they won 13 of 14. An account specialised in data errors accused me of lacking professionalism. I did not argue. I deleted the post, reviewed the entire footage, and reposted a corrected version within two hours, opening with: "The numbers have been re-verified; the error is here." Reads doubled.

But there was something I realised later: part of that error was an error of evidence level. The 13-out-of-14 aerial figure was accurate, but it told only part of the story. No data in my hands was sufficient to measure why Morocco chose a 4-1-4-1 with a narrow midfield, relying on deep-lying full-backs. That was a tactical choice built over years under Walid Regragui, and the footage of one match cannot reconstruct that entire process. I had partial evidence, and had I written as if I had full evidence, I would have exaggerated.

At level two, the analyst must state clearly what they see and what they infer. That is the thinnest line in the profession. Data does not lie, but it is skilled at hiding surprises — and the writer's job is to mark clearly what is a number and what is an interpretation.

Level three: zero evidence — and why you must not invent

Now the case I want to name plainly. There are situations where you have no data. Not little data — none: a broken feed, a match not fully recorded, an unverifiable source, a topic supplied only as an empty description.

In that situation, the only honest move is to state clearly: N/A — insufficient data for a professional conclusion. And that honest move is opposed by the industry at every level. Editors need copy. Algorithms need keywords. Audiences need answers. No one in that chain is rewarded for modest honesty.

I have been in the position of being forced to fill a gap with speculation. In 2026, when the Bundesliga returned behind closed doors, I was twenty, a second-year statistics student, stuck at home for ten weeks. I wrote a Python script to filter data for the first twelve matches after the restart. I found average goals rising from 2.8 to 3.2 per match, and passes into the final third rising 9% with no crowd-mind pressure. I published it, got 500 reads. A first-division coach messaged to ask for the raw data.

But there was one thing I was careful not to write in that piece: I did not claim to understand why those numbers changed. No physiological data, no player-psychology data, no internal interviews. I had evidence of a phenomenon and a correlation. Causation, not yet. Had I written "empty stadiums make players pass more recklessly", I would have sold a guess as a fact. I did not. That was the most correct decision of that article.

At level three, N/A is not a sign of weakness. It is a product of discipline. An analyst who knows what he does not know is more trustworthy than one who is confidently wrong.

Structure produces results: when the ball is elsewhere, who stands where

When the pitch is empty, the sound of the ball becomes data. I listen and I note it down. But there is one professional principle I have kept for nine years: the most important thing in a match rarely lies where the ball is. It lies where the ball is not — the gap between two lines, the space behind a full-back when he advances, the space a midfielder leaves when he presses.

That is why I read matches by geometry first and scoreline later. The crowd watches the stars; I watch the space behind them. Japan's 22 metres in 2026 was a geometrical number. Colombia leaving a 35-metre gap between midfield and attack was a structure. The goal was merely a consequence of that structure, not its cause.

But this principle also has a trap, and I must name it. If you believe every match can be read with the same set of diagrams, you will impose the 2026 World Cup template on every V-League match, every K-League match, every youth match. And you will be wrong. Colombia's 4-4-1 of 2026 differs from the defensive structures of Vietnamese football, where the tempo is slower, space is compressed, and coaches' decisions are shaped by variables absent from European data.

My method of checking whether an old diagram fits a new match is not whether the diagram feels familiar. It is first-half data: line distances, ball circulation speed, number of times the opponent's structure is broken. If those three numbers do not match the diagram's premise, I discard the diagram. I do not keep it just because it was once right.

This is where football analysis often confuses the model with reality. A model is a set of assumptions. A match is a specific event. The gap between them is where real expertise lives — and where N/A appears, when reality does not supply enough data for the model to speak.

Contrarian angle: this industry rewards organised fabrication

Here is what I want to say that many in the industry know but do not voice. The modern football-content ecosystem rewards a form of structured fabrication. Not crude fabrication of the "player X signed for club Y" kind when nothing happened. But subtler fabrication: presenting a confident analysis of a phenomenon you lack data to analyse.

Example: a team loses three in a row. Immediately ten articles explain that team's "tactical problem", with diagrams, arrows, terminology. But ask the writer: do you have that team's pressing data across those three matches? Do you have training-ground footage? Have you interviewed the coaching staff? The answer is usually no. So what is that analysis? It is a guess packaged in professional language to appear grounded.

I call it decorative analysis. It is not factually wrong — the team did lose — but it is wrong in evidentiary terms. It presents a hypothesis as a conclusion.

The contrarian point is this: data honesty is not rewarded in the short term. In the long term, it is. The Morocco 2026 lesson taught me that. When I publicly corrected my error, reads doubled, and transparency became my brand. Readers trusted me more than pages with larger followings. But to reach that point, I had to accept short-term loss first.

There is an execution blind spot I want to point out. Many analysts have good data but cannot present the boundaries of that data. They draw strong conclusions from weak data because they were never taught to say "I don't know". The skill of saying N/A is not an analytical skill. It is a professional-communication skill — and it is severely lacking in current training.

This is why I raise the issue of grassroots coach education. A young coach in a province is taught to draw diagrams, read matches, apply models. But is he taught to recognise when he lacks sufficient evidence to conclude? In my experience, almost never. Our coach-education industry teaches confidence, not honesty. And confidence without evidence is one of the largest sources of error in football.

I will say this plainly where it is needed: I was once asked "what does a girl know about football" at sixteen. I do not want to hear it again. But years later, when I had data, diagrams, evidence, people began to doubt me in another direction: "Why do you say there isn't enough data? Can't you analyse it?" It is the same question in a different shape. And my answer remains: I show them a pressing trap that has been drawn and quantified, then I say, when this situation is not in the footage, I will not draw it.

They ask what a girl writes about football. I show them a pressing trap. And when there is no pressing trap to show, I give them four letters: N/A. Both actions serve the same purpose: keeping the work honest to what is real.

Takeaway: N/A is the future, not a retreat

The match does not end at minute 90; it ends when I find the pattern. But if I never find the pattern — if I search and still lack sufficient evidence to say something — then that match must end in an open state. Not in a forced conclusion.

I want Vietnamese football analysis to go a different way from the global content industry. I want us to reward data transparency, not only confidence. I want an analysis that can end with "we don't know yet" without being seen as a failure. I want a young coach to tell his students that knowing when you don't know is a top-tier skill, not a weakness.

In an age when AI can generate thousands of football analyses in an hour, what distinguishes a writer is no longer the number of conclusions they produce but the discipline that keeps those conclusions tied to evidence. AI is good at filling blanks. A person of integrity does not fill them. That is the last frontier of professional honesty.

I still draw by hand as in 2026. I still record sources and data dates at the end of every article. I still cross-check against at least two sources. And when data is empty, I still write N/A, accompanied by the full chain of reasoning explaining why I cannot go further.

Tactics are not magic. It is just that some people look a little longer. And sometimes, after looking a little longer, the most honest thing you can say is: I need one more source. One more match. One more camera angle. One more answer from the data, before I dare offer a conclusion.

If Vietnamese readers are ready to accept that — ready to read an analysis that does not end in a hard declaration — then our football-analysis culture has taken a step no metric can measure. That is not a retreat. That is growing up.