The Empty Spreadsheet: When Silence Is the Only Credible Answer

I have just opened a document labelled “Stage-Two Deep Analysis”. Fifteen classification items, from tactics, form and institutions to media risk, were filled with the same repeated phrase: insufficient information. No tournament name, no player name, no number. To an ordinary reader, it is a blank page. To me, it is a warning letter: stop before pressing the publish button.

In sports newsrooms, an empty analysis is rarely treated sincerely. Summer is the transfer-window season; sources race to release information faster than the ability to verify it. Editors need articles, readers need answers, and when faced with blank cells, people often grab any number to fill them and call it “analysis”. I have done this job since the 2026 World Cup, and I have enough experience to repeat an old principle: every number has a genealogy; I need to know its ancestors. If a data table cannot declare its source, it is not qualified to enter a newsroom.

Quality analysis does not begin with an answer

Quality analysis does not begin with an answer; it begins with a question. Before using a metric to judge a player or comment on a match, I must answer three foundational questions: where does this data come from, how was it processed, and what is it hiding? Good analysis is about asking the right question, not having a beautiful answer.

I left the meeting with that empty document and remembered the first shock of my career: the 2026 World Cup in Russia. That year I was 16, writing a sports blog in Hanoi. The most discussed match was not the final, but Germany’s 0-2 loss to South Korea in the group stage. I had written an article based on Germany’s 87% possession and concluded that possession means victory. The result on the pitch rejected my entire article. Germany were eliminated, my blog received more than 200 mocking comments, and I spent three weeks reviewing ten Germany matches.

The Russia World Cup shock taught me: false data is more dangerous than instinct. What I needed was not possession rate, but what modern football calls PPDA – the number of passes allowed to the opponent before regaining the ball. In that match, South Korea’s PPDA was only 6.8. That number showed that Shin Tae-yong’s team defended actively and fiercely, rather than retreating under German pressure. Possession is only surface statistics; it does not reflect whether the ball was actually moved into dangerous areas. From then on, I abandoned the habit of using general indicators, looking for clearly sourced data and fixed situations instead of percentages repeated as truth.

Small samples, beautiful models and the paper-season trap

In 2026, when football was suspended because of COVID-19, I built my own Bayesian model to predict the Bundesliga after the league returned. The model was based on ten seasons of data and gave RB Leipzig a 54% title probability. The actual result: Bayern Munich won eight consecutive matches, while Leipzig collected only four points in their final five matches. My mistake was not in the algorithm; it was in the model’s failure to include a variable that has no column: empty stadiums. When I reviewed forty matches to verify, I realised that Leipzig’s young squad lost 27% of their home pressure when fans were absent. That is a variable outside my ten-season data table.

The paper season only looks good while the model has not met reality. I publicly wrote a correction article, explained where the logic failed, and added an assumptions section to every later analysis. The phrase “under normal conditions” is not a safe phrase; it is an admission: the model is not reality, it is only one way of reading reality. When an analysis document returns to me full of “insufficient information” cells, I understand that the variables without columns are reminding me to be humble.

Vietnamese badminton also has data gaps

I have moved from football to following Vietnamese badminton for years, and I see the same disease: a lack of structured data. At major European tournaments, tracking systems measure every step, shuttle speed and contact position. At many Asian badminton tournaments, the match report only records score, number of serves and time; information about defensive quality, ability to turn the match around, or scoring efficiency from the rear court is rarely published consistently.

That does not mean writers are allowed to invent numbers. On the contrary, it means we must ask the right questions: did the latest victory come from tactics or from an exhausted opponent? Is that player really improving defensively, or was the opponent simply suited to their style? Old head-to-head records only matter if we know under what conditions the match took place, on what surface, and at what stage of the season. A number separated from context is no different from a puzzle piece without a picture.

Transfer windows and the temptation to fill blank cells

The transfer window is always the season of greatest temptation. Transfer fees, release clauses and wage budgets are highly mentioned numbers but rarely verified. Fans want to know who their club will buy, at what price, and whether the deal makes sense. That is why a rumour attached to a specific number is often shared faster than an analysis showing the data is insufficient for a conclusion.

I am not against transfer reporting. I am against publishing numbers whose origin cannot be traced. Every rumour needs to be classified by level of evidence: club source, agent source, local media source, or mere social-media gossip. I often apply a simple rule: if a piece of information cannot answer the question “who confirmed this”, it should not appear as an assertion. An article can raise a question about a potential transfer, but it should not turn an ancestorless number into a fact.

During the pandemic, I saw many articles use the exact phrase “according to statistics” to create an objective impression. But statistics are not objective by themselves. If the writer does not explain the method, does not disclose the source and does not state the margin of error, such statistics are only a shiny coat of paint on a house without foundations. I trust data, but I trust process more. The process of checking three sources, cross-checking figures before publication, and publicly correcting errors when a predictive model misses – these are what build the credibility of a sports article.

Empty data is a signal, not an excuse

The counter-intuitive point I want to make is this: an empty analysis table can be valuable. It tells us that the topic does not yet have enough conditions for deep analysis. It stops us from making hasty judgments. It reminds us that not every situation needs a long article; some situations need silence while waiting for more data.

Silence is not failure in modern journalism. Silence is a way of saying we value truth more than speed. I understand the pressure to publish constantly, especially during the transfer window when every passing hour brings a new rumour. But if a rumour only existed for five minutes before being denied, publishing it adds no informational value. It only creates noise.

When I encounter a blank analysis, I do not try to use imagination to fill the cells. I return to the raw numbers, find independent data sources, check them crosswise, and consider whether there is enough evidence to make a defensible claim. If there is not enough evidence, I will say so directly. Writing the sentence “there is not enough data to conclude” is more reliable than writing a thousand words to hide one’s insufficiency.

Lessons for Vietnamese sports journalism

Vietnamese sports journalism has a great advantage as readers become more interested in professional metrics. But that advantage only works if we know how to verify data. I have spent years watching matches, building models and comparing results with reality; that experience gives me one lesson: on a sports journalist’s desk, an unverified number is as dangerous as an unsubstantiated statement. Whether we write about football, badminton or transfers, we all need to ask the same question: where does this number come from? If there is no answer, let the blank space show itself.

The Russia World Cup was not an anomaly; it was a reminder about small sample sizes. One match cannot answer a question about a team across an entire tournament; one transfer window cannot answer a question about a player across an entire career. When the sample is too small, a good writer does not exaggerate the conclusion. A good writer expands the field of observation, collects more data and is prepared to wait.

I will not publish an analysis built on an empty data table. I will not advise any colleague to do so either. Instead, I will set the document aside, note the questions that need answering, and look for data from reliable sources. If, after all that, there is still not enough information, I will write a short note about what is not yet known. That is how I protect readers from numbers that know how to lie.

xG does not sign contracts, but it helps me know where I am putting my pen. Every time I pick up a pen, I ask myself: if readers traced the genealogy of every number in this article, would I confidently raise my hand to confirm it? If the answer is no, I will put the pen down and wait. Timely silence does not reduce a journalist’s value; it shows that the journalist understands that truth is worth more than a timely article.