When the Data File Returns Zero

Saturday night, I sat in front of three spreadsheets open side by side, the clock reading 11:47 PM. The data file I had just pulled contained only an empty column: no team names, no metrics, not a single note. I clicked twice, then three times, as if repeating the action could turn blank space into a number. It did not. In that moment, I faced the hardest question of the trade: what do you write when the data says nothing?

Ten years of covering sports taught me that an empty file is not meaningless. It is a signal. Blank space in data is always an event; people simply ignore it to publish on time. When a system returns zero, there are only two possibilities: the match had nothing to measure, or the collection process broke somewhere. Both are news.

This is not the story of one evening. The sports analytics industry, from football to esports, lives in an age where data has become currency. Every major tournament, every transfer window is wrapped in hundreds of metrics: expected goals, passes allowed per defensive action, age-based player valuation. Data journalists like me live by reading them, and so we are also the first to be accountable when they are wrong or simply do not exist.

In esports, this is even clearer. Win rates by patch, champion ban-pick rates, kill-death statistics can all be extracted in seconds. But that very ease creates a trap: a number that is technically correct can still lead to a conclusion that is wrong in meaning.

In the summer of 2026, when I was a first-year student in Shenzhen, I calculated expected goals by hand from shot data. In the France-Belgium semi-final, I found France reached only about 1.6, while Belgium had 0.8, yet France won 1-0 through Umtiti's header from a set piece. I sat with it for a month, re-watching the footage, and understood: my model was not wrong, it was only incomplete. xG does not lie; it just never tells the whole truth. Since then, every time I present a number, I attach its error margin and source.

Years later, when the pandemic emptied the stadiums, I collected data from 240 matches and found the home-win rate fell from 47% to 39% without crowds. I stood in the empty stadium and heard football's ambient sound. That report taught me the remaining lesson: never separate a number from its context.

Back to that empty file. If I lived by speed, I would fill the blank with guesswork: Team A is sharper than Team B, Player X is declining, Tactic Y is outdated. It all sounds reasonable. And it is all baseless. That is the line between a data journalist and someone who lives on clickbait headlines.

My first principle is simple: no source, no number; no number, no conclusion. It sounds obvious, but publishing pressure makes many writers skip it. They build an entire story from one early statistic, forgetting the contextualization step — the step that turns a dry number into a verifiable truth.

I have had to defend that principle publicly. At a major tournament, an underdog unexpectedly beat a favorite with a very low expected-goals figure. I published the real number. Some readers felt I was insulting the underdog's win. I did not take the piece down; I wrote a follow-up using movement and positioning data to explain why the favorite controlled possession but defended loosely in the two decisive phases. Defending an argument with data is always stronger than defending it with emotion.

Since then, each of my pieces has three parts: the raw figures, the contextual analysis, and an anticipation of rebuttals. I raise my own weaknesses before readers can question them.

But I also admit a paradox. Data models are penetrating deeper into the dressing room. Conclusions from a spreadsheet are sometimes presented as truth, while the real rhythm of a match — a player's breathing, the crowd's roar, the hesitation in a pass — sits in no cell at all.

Here I want to argue against myself. People say data does not lie. That is true, but it easily leads to a dangerous belief: that data tells the whole truth. That empty file was the most vivid proof. If I trusted the system absolutely, I would conclude the match had nothing worth saying. If I stay clear-headed, I must ask: why did the system go silent?

Correlation is not causation, and the absence of data is not the absence of truth. A number stripped of context easily becomes a deliberate lie. A rushed conclusion from an early statistic can turn a writer into a headline seller. I have seen flawless reports on paper overturned by the reality of the pitch within a week.

So whenever I close a piece, I ask myself: what data cannot measure this moment? If I cannot answer, I cut that number out. I add a section describing outside factors — crowds, weather, travel schedules — because I believe football is not in the spreadsheet cell; it is between the cells.

That empty file ultimately did not become a piece about a match. It became a piece about my own trade. Whether the stadium has a crowd or not, the match still needs someone to tell it. If the system returns zero again tomorrow, I will not panic, and I will not invent. I will call sources, reopen the footage, and ask what is happening behind the blank space. In sports, the right question is sometimes worth more than a fast answer.