The Empty Cell: A Confession of the Number-Caller

Three in the morning. A small apartment on Van Cao Street, Hai Phong. Sea wind slipped through the gap under the window, carrying the smell of salt. On the screen, a spreadsheet opened with seven columns and not a single row of data.

I sat like that for nearly forty minutes. The coffee beside me had gone cold long ago, a thin film forming on its surface. My fingers rested on the keyboard, ready to type. But all I had was a blank page — and a deadline in the morning.

That night I learned something no curriculum teaches: the craft of sports analysis is not hard because of finding the number. It is hard because of knowing when to stop, when to open your mouth and say you do not know.

Seven columns. Not one row. And a very large temptation: to invent a story to fill the empty space.

I did not invent one. But I had done so before. And those times taught me more than any regression model I have ever built.

The empty cells nobody wants to read

In 2026, I was nineteen, a second-year student in Movement Science at the Bac Ninh University of Physical Education and Sports. A lecturer asked me to compile statistics for the U23 Vietnam match against U23 Thailand at the 29th SEA Games in Kuala Lumpur. I built an Excel sheet tracking 37 passing sequences in the attacking third. The result: 0.68 xG for Vietnam, despite a 0-3 defeat.

That number did not save the match. But it said something the scoreline did not: our midfield was squeezed out in the central zone, the ball could not move forward, and all three goals came from turnovers in our own half rather than sustained pressure.

The media the next day spoke only of the score. Nobody asked about the central zone. Nobody asked what those 37 passes meant.

That was the first time I realized: data does not speak for itself. Someone has to translate it. And the translator must be honest to the point of cruelty.

I started writing long pieces on a personal blog. Not about goals. About pass counts, about the spaces being exploited, about where each line received the ball. A friend messaged back one short line: "You write too dry." He was right. But I did not yet know how to fix it.

The summer of 2026 and the four thousand words nobody read

In 2026, I spent the entire summer analyzing all 64 World Cup matches in Russia. I was twenty, unemployed, living on a student stipend and cheap canteen meals.

When Germany were eliminated by South Korea in the group stage, I spent nearly three weeks gathering data. Die Mannschaft generated only 0.9 xG in that decisive match, well below their 1.8 xG qualifying average. The defensive line pushed high but the pressing was disjointed. Germany's PPDA reached 12.4, while South Korea's was 8.9. In other words: the team considered stronger allowed its opponent far more comfortable passing.

I wrote a 4,000-word analysis. Nobody read it. The entire forum only debated whether coach Löw should have brought Leroy Sané.

That was the first slap. Raw data is not enough. A correct report can still be ignored if it does not take the reader by the hand and lead them through the door.

From then on I changed how I wrote. I started with a scene, a person, a shocking moment. Only then did I thread the numbers through to prove it. I added a section called "decoding advanced metrics" — where I translated PPDA into "the number of passes an opponent is allowed before being closed down", and xG into "the goals that should have been scored if every shot were average".

The pieces got longer. But also easier to read. And I began to understand that telling stories with numbers is not a betrayal of numbers. It is a form of respect for the reader.

2026: when football stopped breathing

In 2026, at twenty-two, I was writing my master's thesis when the pandemic hit. Every league was suspended. There was no new data to analyze. My spreadsheet sat empty for weeks.

I decided to rewatch all 98 Bundesliga matches of the 2026-20 season from recordings. I meticulously logged the gaps between lines when stadiums had no spectators. Ten hours a day in front of a screen, pausing on every phase, measuring every distance.

When football returned after five weeks, I found something: home teams won only 23 percent of matches, compared with 45 percent before the pandemic. Not because they had become weaker. Because what is called "home advantage" largely came from the roar of the stands, from the psychological pressure a crowd applies to referees and to opponents — not from the pitch or the dressing room.

I wrote a 30-page report and sent it to a German analyst. He shared it on Twitter. Within two days it had more than 2,000 retweets.

Once again, I learned something important: football data must be placed in context. The same number, in a different context, means something entirely different. Since then, every report of mine has a dedicated section on home venue, weather, and rest days between matches.

Euro 2026 and the first time a number paid me

In 2026, I graduated with my master's and became an assistant analyst at a sports betting company in Hanoi. I was tasked with predicting outcomes for VIP clients throughout the Euros.

I fixed my attention on Italy when I noticed they had a PPDA of 8.5 — the best in the tournament. Every other major team was above 11. I persuaded my boss to back Italy to win at 11/1 odds. They won. The company booked its highest profit in its operating history.

My boss asked me to build a proprietary prediction model for the company.

That was the turning point in how I wrote. I moved from pure analysis to structured prediction reports: hypothesis, supporting data, probability, risk. The prose became more concise and more logical, less emotional — because I knew every sentence could affect the decisions of hundreds of people.

But it was also from that point that the memory of the three-in-the-morning spreadsheet returned. Because this profession has a very particular temptation: when there is no data, people still want a conclusion.

The hardest discipline for an analyst is not finding the truth, but accepting that some gaps will never be filled.

When the spreadsheet is empty, that is not a glitch — that is data

In my workflow there is a step called input validation. Before analyzing anything, I must confirm what I actually hold: match name, date, lineups, raw metrics, sources.

Sometimes this step returns empty. The information column sits blank. There is no core viewpoint. Entities are unidentified. Time sensitivity has not been assessed. Source quality has not been graded.

The reflex of a newcomer is to fill it in. The reflex of someone who has done the job long enough is to stop.

I once watched an older colleague handle this differently. He had a match with nearly all data missing. He still produced seven hundred words of tactical analysis, in very certain language, with very decisive judgments. It read smoothly. But it was not based on anything.

That smoothness is the most dangerous thing in this profession.

An empty cell, in the end, is a signal. It says the collection process failed, that the source was not loaded properly, that someone is asking you to conclude about something never recorded. And that signal matters more than any judgment you could invent to fill the space.

In swimming, this is clearer than in any other sport.

Water hides nothing, but Vietnamese swimming data does

I spent eight years swimming. Long enough to understand that a pool is the most transparent environment in sport. Water lets no one take a shortcut. Every stroke, every breath, every kick is counted in seconds and hundredths of a second.

But swimming data in Vietnam is one of the poorest information zones I have ever worked in. There is no standardized stroke-rate system. There are no regularly published 25m and 50m split sets. There are no energy-efficiency metrics. At the 2026 SEA Games, I had to time each swim myself because no official source existed.

Nguyen Thi Anh Vien was once the most notable case. She was the first Vietnamese swimmer to make a mark on the continental stage, but when I looked for split data for her best swims, I found only a handful of scattered figures from on-site scoreboards. No continuous time series. No stroke-rate analysis by competition.

A 200m medley swimmer may spend an entire career optimizing each fifty-metre segment. But if nobody records those segments, every analysis is guesswork.

And this is where the temptation appears. When there are no split times, people start talking about "spirit", about "character", about "class". Those words sound beautiful. But they measure nothing.

Nguyen Huy Hoang's 1,500m freestyle swims are an example I return to often. He had final laps slightly slower than his average in the heats, and faster in the finals. Looking only at total time, that means nothing. With per-hundred-metre data, it says he paced his effort well and did not fade in the decisive segment. But to say that, I must have split data. I must have it for real, not invented.

An analysis without source data is a poem — perhaps a good one, but it is not analysis.

The counterintuitive angle: silence is worth as much as a chart

When a sports reporter says "there is no data", audiences usually assume he is lazy. Within the industry, people avoid that sentence for fear of being judged unprofessional.

I want to argue the opposite.

In a market flooded with judgments issued from thin air, refusing to conclude is the most honest act and also the most professionally rigorous one.

Look at how headlines are made. A player scores in three consecutive matches. The headline says "blazing form". But how many minutes were those three matches? Who were the opponents? What was the expected goals, that is, should those goals have gone in? If a player scores three from three long-range shots, that is luck. If he scores three from three close-range finishes, that is a system.

Two entirely different stories. The same headline.

I once wrote a transfer-market report hypothesizing that free-agent signing fees are more harmful to financial oversight than transfer fees. The reason is simple: transfer fees are booked, while signing bonuses and agent fees are not. A fifty-million-euro deal for a free agent can be distorted into three different line items in the financial statements.

That was a data-driven hypothesis. I did not write it as fact. I attached the phrase "with current confidence" and left open the possibility that new data would change the conclusion. Six months later an independent audit appeared and I had to revise two of my three original conclusions.

I wrote a correction. I did not see it as losing face. I saw it as process. Whenever the data changes, the writer must change with it. Otherwise he is no longer an analyst; he is a propagandist.

Correlation is not causation, and that is where many people fall

There is another kind of error, subtler than fabricating data: misreading real data.

At the 2026 World Cup, many post-mortems of Germany's elimination reached the same conclusion: the team had lost its hunger. But the data said otherwise. They still ran a lot. They still held the ball a lot. What was missing was the quality of the pressing — the distance between lines when out of possession. German players ran the right number of metres but in the wrong places.

That is the difference between running and running effectively. A distance figure cannot distinguish the two.

In the spectator-free 2026-20 Bundesliga, some concluded that home advantage no longer mattered. Wrong. Home advantage still mattered; only the crowd's contribution to that advantage had been stripped away. What was lost was not home advantage. What was lost was the roar.

Those two examples taught me a rule I keep to this day: when a variable changes, do not ask how much it changed. Ask what it changed.

And this is where I must speak about Vietnamese swimming once more.

The medal obsession, and the price paid

Vietnamese sport has a structural problem I have observed for twelve years: we measure by medals, not by process.

A swimmer who finishes fifth and breaks a personal record is treated as a failure. Another who finishes third with a time worse than the national record is celebrated. This inversion of values repeats at every SEA Games.

In my eight years of swimming, I learned that progress does not come from winning. It comes from swimming faster than yourself. But to know how much faster, you must have your own data, recorded continuously, over many years.

That is what most Vietnamese athletes do not have. They have coaches, training plans, six-hour sessions. But they do not have a decent logbook of numbers.

The consequence: when an athlete slows down, nobody knows why. People blame age, injury, motivation. Rarely do they blame data — because the data does not exist.

Every number ignored is a life left unrecorded. A sports writer has an obligation to count the numbers that weep in silence.

The ethics of the number-caller

There is a sentence I wrote in an internal report and was told by a superior was too heavy: the numbers speak, but nobody asks how many times they have wept.

I kept that sentence.

A number-caller holds great power and a corresponding responsibility. When I write that a player has a low conversion rate, I am writing about a twenty-five-year-old man who may read that piece, whose family may read it, whose agent may read it. My number can affect his contract value.

So my rules are strict: never publish a number without context. Never call a player inefficient without noting where he played, in what role, alongside whom.

And there is a stricter rule still: never invent a number to make a piece better.

I once came across an article citing a metric I knew for certain did not exist. I emailed the author. He replied that the number came from "an aggregated source". That source did not exist.

That was the moment I understood that the problem with Vietnamese sports media is not a lack of data. It is that the lack of data gets converted into the appearance of data through unverifiable figures.

What an empty spreadsheet taught me

Back to that three-in-the-morning night.

I did not invent anything. I sent an email to the person requesting the report, saying the input data was insufficient for analysis, and proposed collecting it from scratch. I listed exactly what was needed: match name and source, at least one concrete information point, the list of athletes involved, a time marker, and an assessment of source quality.

They received that email at seven in the morning. They agreed.

It cost two more days. But the final report was real. And to this day it remains the report I am proudest of — not because it was good, but because it was true.

In this profession people talk about the big moments: a model that predicted correctly, a tournament called right, a transfer window read like a book. But for me, the most important moment was an email refusing to write.

An empty stadium is not a sporting event. It is a strange marriage between data and loneliness. And my job, in the end, is to sit inside that emptiness and record everything that can be recorded — then be honest about what cannot.

Signals for the next cycle

In the current regular season, there are three signals I am tracking, and all three stem from the same principle: data left blank is still data.

First, rest days between matches. In the second half of the season, as the calendar thickens, teams' pressing efficiency usually declines before their results do. A rising PPDA is a more reliable signal than points.

Second, the quality of my own data sources. I am building a source-grading column for every metric I use, in three tiers: verified, present but unverified, and unclear origin. Any metric in the third tier will not appear in my writing.

Third, repeating gaps. When the same category of data is missing across many matches, it is no longer a collection error. It is a structural feature of the sport, and it deserves its own piece.

No model of mine predicts the 90th minute plus three. No spreadsheet of mine can say what a twenty-year-old athlete is thinking as she steps onto the starting block. But I know one thing for certain: whenever I feel the urge to fill an empty cell with a guess, that is the moment I must put the pen down.

The only thing I can promise readers is the only thing I can control: if I have no numbers, I will say I have no numbers. If I have numbers but am not certain, I will say I am not certain. And if I am wrong, I will write it again.

Has the loneliness of any Vietnamese athlete ever been recorded by a number? I still cannot answer that. But I know for certain that if I invent a number to answer it, I will never be able to answer it again.

My spreadsheet still has many empty cells. I am leaving them that way.