An Empty Data File and the Temptation to Name What Never Existed

Late at night in Shenzhen, I sat watching the screen, waiting for a match-analysis file to come back from the system. It arrived empty. No team name, no metric, no date, not a single line of description. I rested my hands on the keyboard and stopped, because I realised the most frightening thing in my line of work: I already knew what I would write next if I did not stop myself. Outside the window, a small football pitch stood without spectators. Only the sound of a ball hitting a brick wall — a student training late. In my head, a story had already taken shape: surely this was a match in which the home side pressed hard and then ran out of breath in the second half. I did not have a single number, yet I already had a conclusion, a cause, an antagonist. That moment shaped how I have worked ever since. Modern sports analysis runs like a two-stage pipeline. Stage one extracts raw facts: who played, when, where, how many shots, what share of possession. Stage two interprets: what those numbers say about the match. It sounds clean and scientific. But most of the risk in this trade lies not in interpreting badly, but in stepping into stage two when stage one never returned real data. When stage one returns an empty list, the natural human reflex is to fill the gap. The writer glances at the job title, catches the word "sport", and builds a plausible match in their head: a familiar line-up, a moderate scoreline, an argument that sounds insightful. The mistake is not that the numbers disagree. The mistake is that there are no numbers to disagree with. In the data-analysis trade, this is called silent subject substitution. You do not invent a grand lie. You quietly replace a missing figure with a guessed one, an unverified name with a plausible one, an unconfirmed date with a vague phrase. The report afterwards is still complete, still tabulated, still neatly titled. Only the truth quietly disappears. I have witnessed this at a much smaller scale. Once, my model showed a team's xG at just 0.35, while their opponent reached nearly 1.9. The weaker side won. Part of the readership reacted fiercely, arguing I had insulted their victory. I understood that reaction. But what I learned was not to drop the number to please the crowd. What I learned was to state plainly what that number measures and what it does not. xG does not lie; it simply never tells the whole truth. That is the fragile line. On one hand, I must be honest enough to state clearly: according to my model, confidence is around 80 percent, here is the data source, here is the date. On the other, I must refuse the gap by saying outright "there is not enough information to conclude", instead of filling it with a guess that reads smoothly. I do not build a spreadsheet for the match; I build a spreadsheet for doubt. The greatest worry for someone who contextualises data, as I do, is not a shortage of numbers. Sometimes the enemy is false completeness itself. A nine-dimension analysis, with every section, every heading, every polished format, can lead readers to believe a real analysis exists. But if every cell inside is "insufficient information", that finished exterior is merely draping emptiness. A skeleton by itself is not evidence. This is where I want to go against the crowd in my trade. Many believe that the more tightly structured, the more sections, the more detailed an analysis is, the more trustworthy it becomes. I think the opposite is sometimes true. A short piece that states decisively "this article lacks the data to be analysed" is more honest than a long report with every section filled, where every conclusion is speculation dressed up as data. In a world where publishing speed is ranked above verification, the person who dares to stop when the data has not arrived is the hardest one to find. The problem is more serious still because of the asymmetry of risk. The heaviest risks in football — an injured key player, unpaid wages, match fixing, governance failures in a league — are all silent risks. They only surface when someone actively goes looking. If the data list is empty, it means nobody has ever looked. The silence of a gap is not evidence of calm. It is only evidence that nobody has checked. I learned this during a strange period of football, when stadiums around the world stood empty of spectators because of the pandemic. Compiling data from hundreds of matches, I saw the home-win rate fall, and the PPDA index shift toward teams pressing harder yet scoring less efficiently. Had I looked only at the scoreboards and not at the empty stands, I would have missed half the story. Football is not in the cells of a spreadsheet; it is between the cells. Since then I hold one rule: a number may only appear when it comes with its context. A number with no crowd, no weather, no travel schedule, no tournament pressure behind it is a number that can be turned into a lie at any moment. Even the most neutral figure can become a weapon if people pull it out of the circumstances that produced it. Back to the empty data file that night. I wrote no analysis at all. I wrote a short note back to the system: input empty, insufficient data to conclude on any analytical dimension. Then I shut down the machine. No article was published that evening, and I count that as a small victory for the trade. Because in the end, I am not in this profession to invent stories. I am in it to check whether a story exists before telling it. A name not yet traced, a date not yet verified, a team not yet identified — all of them must be written down as they are: open gaps, rather than squeezed into some slot that sounds acceptable. Caution may make readers impatient. But a wrong conclusion, written in a confident voice, will make them believe wrongly for years. A gap is not something shameful. What is shameful is fearing the gap so much that we fill it with something that never existed. Whenever I stand on an empty pitch — whether a training ground in Shenzhen or a crowdless stand inside old data — I still hear a background signal. It reminds me that the true story always begins with admitting you know nothing, and that every number has value only when the writer is brave enough to say plainly what it cannot measure.