When the Data Spreadsheet Is Empty: Lessons for Vietnamese Sports Media

On the night before the final, I opened the spreadsheet and cell A1 was empty. Not a formatting issue, not a corrupted file — simply nothing had been recorded. On the news websites, the golden headlines kept flashing the usual buzzwords: “miracle”, “rise above yourself”, “reaching continental heights”. I sat in front of an empty cell and asked myself: when a sports media ecosystem is used to singing victory songs on emotion alone, does anyone notice that what is disappearing is not the medal, but the truth? My name is Đặng Quân, 28 years old, a sports betting analyst in Hải Phòng. Before this job, I was a swimmer. Eight years in the pool taught me to count every stroke, every breath, every touch of the wall — and taught me that water never lies. Sports media, however, can. When I say this, colleagues think I am too cynical. They do not understand that a person who counted strokes for eight years sees a fate in every number. The numbers speak, but no one ever asks how many times they have cried. People often ask where I started. I tell them: from an Excel spreadsheet and a match that no one in Vietnam wanted to revisit in detail. In 2026, I was a second-year student of Sports Science at Bac Ninh University of Physical Education and Sports. The SEA Games 29 was held in Kuala Lumpur, and a lecturer asked me to track the match between Vietnam U23 and Thailand U23. No specialized software, no automatic data feed — I built a spreadsheet and counted every pass by hand. The result: 37 passes in the final third and an expected-goals figure of 0.68 for Vietnam. We lost 0-3. The press talked about the score. I looked at the table and saw a bigger problem: our midfield had been strangled in the middle of the pitch, not because of a lack of technique but because of a lack of ideas for escaping pressure. SEA Games 2026 taught me that even poor data can open up an entire universe. But at that time I was not brave enough to say this out loud. I only quietly wrote long analytical pieces on a personal blog — not about goals, but about pass counts, spaces exploited, and receiving positions. Few people read them, but each time I wrote, I understood the match a little better. Since then I have kept one habit: never write an analysis without data. In 2026, the World Cup was held in Russia. I spent the whole summer analyzing all 64 matches. In the group stage, Germany lost to South Korea. That night I stayed up until almost dawn, rewatching every phase. Die Mannschaft produced only 0.9 xG in that match, while their qualifying average was 1.8. The defensive line pushed high but the press fell apart; their PPDA was 12.4, while South Korea’s was 8.9. They did not lose because of bad luck. They lost because the system had stopped working. On the day Germany collapsed, I understood that probability never walks alongside faith. I wrote a 4,000-word analysis and posted it on my blog. Nobody read it. Everyone only wanted to discuss why head coach Joachim Löw had not brought Leroy Sané. That was the moment I learned the first lesson of this profession: the crowd does not need the truth, the crowd needs a story. A story needs a culprit, a tragedy, a hope. Data does not provide a culprit, so data is left at the roadside. A sports media without data will invent a story. And an invented story is often easier to read than the truth recorded with patience. From then on, I changed my approach. I open a piece with a concrete moment, a person, a scene on the pitch; only then do I bring in the numbers as evidence. Data is the backbone, and the story is the lamp that shines on it. Readers do not need to memorize the definition of xG — they only need to understand that their team produced 0.68 expected goals and scored zero, which means the problem is not finishing but the inability to create real chances. Just like in the pool, I often tell young swimmers: being 0.3 seconds faster is just one missed breath. Every complex number can be translated into a familiar image. In 2026, the pandemic stopped every competition. I was writing my master’s thesis and suddenly had no fresh data to analyze. When the Bundesliga returned after five weeks, I took on a strange task: rewatching all 98 matches of the 2026-20 season and charting the space between lines in an empty stadium. The result startled me. Home teams won only 23% of matches, down from 45% before the pandemic. Football without spectators was no longer football. I wrote a 30-page report and sent it to a German analyst. He shared it on Twitter; within two days, the post had more than 2,000 retweets. An empty stadium is a strange marriage between data and loneliness. The lesson I kept from 2026: data does not exist in a vacuum. The same statistic, placed in a stadium with or without a crowd, tells two different stories. An analyst must always ask how external conditions shaped the result. A team resting three days versus an opponent resting seven are not equal teams on a spreadsheet. A swimmer in a 50-metre pool and a swimmer in a 25-metre pool should not be compared with the same ruler, because the number of turns and wall touches completely changes the structure of the race. In 2026, the Euros took place while the pandemic was still complicated. I was an assistant analyst at a sports betting company with headquarters in Hanoi, tasked with predicting results for VIP clients. I kept an eye on Italy from the group stage — not because they had superstars, but because they pressed with a PPDA of 8.5, the best in the tournament, while the other big teams were all above 11. I presented my thesis to my boss: the team with the best pressing midfield in the tournament would win. He frowned, saying Italy had no personality, no stars, no reason for faith. I said: data does not need personality. We backed Italy to win at 11/1, and they won. The company earned a record profit. That success led to me being asked to build a predictive model of our own. But it also taught me something more humble: I learned how to place the right bet, but I still had not learned how to refuse an empty report. At the company, every week I received requests for analysis without source data. Clients sent a name, a match, a hunch, and asked me to “write something”. Some colleagues chose to invent numbers that fit the story. They reasoned that readers could not verify, that probability was only an estimate, that inventing a plausible number was better than leaving a blank. I never did that. I sent back an email with the subject “Early warning” and a short content: “Not enough data to price. Do not bet.” VIP clients were annoyed, my boss was annoyed, but I slept better. Behind every number is a life-or-death decision of the bettor, and I do not want to be the judge of a case without a file. If I write a number with no basis, I am no better than a journalist writing about a match he never watched. I realized that