The Empty Spreadsheet: When a Sports Data Journalist Learns to Wait

On Saturday night, my screen had three files open. The first was a serve statistics table from a quarterfinal. The second was a break-point distribution chart. The third was a match log, where I record every rally minute by minute. All three were blank. Not because I had forgotten to download them, but because the data source had sent nothing back. In the data journalism trade, that silence has a name: the silent feed. I sat still for about ten minutes. Not to wait for the files, but to ask myself: if the files never arrive, what will I write? The honest answer is: nothing. But this profession does not reward silence. The newsroom needs copy. Readers need content. And when there are no numbers, the greatest temptation is to use words in place of figures. Data is never in a hurry. It is the hurried who are wrong. I learned this on a night in 2026 at Lach Tray Stadium. It was Hai Phong FC against SLNA, and I spent three days reconstructing the match through xG. The home side generated 1.92 xG; the opposing goalkeeper saved 11 shots, 3.8 times his personal average. The result: 0-1. The media called it a decline. I called it random injustice. The article was mocked for two weeks. Then the head coach of Hai Phong FC cited my numbers in a press conference. Not to praise them, but to explain why his team had not played as badly as the scoreline suggested. From that night on, I set an unbreakable rule: no conclusion without verifiable data. But that rule is easy to apply only when data is present. The harder question is: when data is absent, what should a writer do? This is a problem bigger than one match. In modern football, we are used to xG, PPDA, progressive passes, expected threat. But those metrics exist only at a certain tier of competition. Below the First Division, the Second Division, or the qualifying rounds of a national cup, the spreadsheet empties out. In some youth tournaments, match minutes are not even recorded. In tennis, Challenger events carry only basic serve statistics, with no break-point data by game. In athletics, beyond the finishing time, there is almost nothing. So what does a data journalist do when the data layer thins? There are three choices. The first choice: lower the standard. Use crude metrics like possession share, shot count, corner count — things everyone has. This is the safe path, but it turns analysis into a scoreboard bulletin. The second choice: replace it with observation. Sit down with the footage, take handwritten notes, reconstruct the match with your eyes. This is the most time-consuming path, but sometimes the only one. The third choice: write without a foundation. This is the path I refuse. Every shot is a hypothesis. xG is how we test it. But when there is no xG, the hypothesis still exists — we simply lack the tool to test it. And a hypothesis that cannot be tested should not be presented as fact. I remember an evening in Hai Phong, watching a youth match. No statistics table. No analysis camera. Just me, a notebook, and eleven players per side. I recorded every pass, minute by minute. Three hours later, I had a crude but honest dataset: 247 passes, 38 turnovers, 12 counterattacks. The numbers were not pretty. But they were true. That is the lesson of data's line of humility. When the spreadsheet is empty, the writer has two roads: invent the numbers, or admit that there is not enough data to judge. The second road is harder, because it demands the courage to say I do not know. In a sports journalism culture where everyone wants an opinion, I do not know is the hardest sentence to say. But it is also the most honest. There is a paradox I have observed over many years: the less data there is, the more people write. At major tournaments, where every metric is available, analysis tends to be short and cautious. At small tournaments, where no one can verify anything, people write long, write with certainty, write as if they can see what others cannot. That is the paradox of scarcity. Data gaps are not filled with caution, but with confidence. Because when there is no number to contradict you, every judgment looks correct. I once watched a colleague write about a match he had not seen. He relied on reports, on other people's memories, on similar matches. The article flowed. Readers believed it. But three weeks later, when the footage was released, everything he had written was wrong. He had not lied. He had simply filled the gap with speculation. And speculation, in this trade, is a polite form of lying. At major tournaments, data arrives fast and thick. But there is another paradox: the more data there is, the easier it is to get lost. A single match can carry hundreds of metrics, and writers tend to pick the ones that support their argument. That is the moment data becomes a tool of bias, not of truth. I once re-examined one of my own analyses three months later. I realized I had chosen three metrics out of twenty available, and all three supported the conclusion I wanted. I had not invented numbers. I had only selected. And selection, in many cases, is more dangerous than fabrication, because it wears the mask of objectivity. So what is the solution? I think the answer lies in distinguishing two kinds of gaps. The first is a fillable gap — when data exists but has not been collected, the writer can collect it. The second is an unfillable gap — when data does not exist, and never will. With the second kind, the only way is to acknowledge it. Audiences can leave the stadium, but physical data never rests. This is true in the sense that data always exists somewhere. But we cannot always reach it. And when we cannot reach it, admitting our limits is a professional act, not a failure. In the final analysis of every season, I always keep a separate section. I call it Things I Do Not Know. It is a list of questions that data cannot answer. It does not make the article weaker. It makes it more honest. Back to Saturday night with three empty files. I did not write that article. I called the data source and asked why the feed had gone silent. It turned out the server had failed. Three days later, the data arrived. I reconstructed the match from scratch, and discovered something I would have missed had I written that very night. The losing side had won 61 percent of rallies lasting five touches or more. They lost only in short rallies. That is a story about tactics, not about spirit. And it only emerged once the data was complete. Had I written that night, I would have written a different story — a story about decline, about spirit, about things that cannot be measured. A story that sounded plausible. But wrong. People remember results. I remember the conditions that formed the results. In this major tournament season, when every eye turns to national teams, the pressure to write fast is even greater. But I believe the value of a data writer is not in speed. It is in knowing when to wait. An empty spreadsheet is not the enemy. It is a reminder that every conclusion needs a foundation. And when the foundation is not yet there, the good writer is the one who knows to put the pen down. Data is never in a hurry. It is the hurried who are wrong. Next time you read a sports analysis and everything seems clear, ask yourself: is the writer seeing data, or filling the gap with confidence? The answer to that question decides whether you are reading journalism, or a story being retold. And in this season, when every match comes with hundreds of metrics, the clear-eyed reader is the one who knows that the most beautiful number is sometimes the absent one.