The Empty Spreadsheet: A Lesson in Honesty for Football Analysis

Late at night in Incheon, the apartment was so quiet that I could hear the cooling fan of an old computer. I opened an analytical report sent by the system. The "information points" field was blank. Article title: none. Source: none. Entities mentioned: none. Nine analytical dimensions had all come back as "insufficient information to assess."

What made me pause was this: that empty report was more honest than many densely numbered reports I have read. I remember a scouting file sent to me in 2026, twelve pages long, with 42 metrics, and not one full name of a women's player. They wrote "number 7," "left-footed striker," "defensive midfielder." From the numbers, I see a person waiting to be named — and in that file, nobody was waiting to be named at all.

The context of a data pipeline

Modern football analysis runs on a data pipeline. A V.League 1 match is filmed, clipped, event-tagged, then pushed through models to produce xG, xA, and PPDA. Vietnamese women's football entered that machinery later, but not slower: after the 2026 Women's World Cup in Australia and New Zealand — Vietnam's first-ever appearance at the finals — the volume of public data on domestic women's players rose visibly.

Every pipeline has a breaking point. A blocked feed, a corrupted file, a failed parsing step, and the result is an empty dataset. The problem is not the emptiness. The problem is that people hate emptiness so much they will fill it with anything that sounds plausible.

Look at VAR to see the same mechanism. A decision reviewed for two minutes has already cooled the rhythm of the match. VAR itself is a pipeline: cameras, offside-line algorithms, referees, screens. When one link fails, you do not receive a "no data" notice — you receive a conclusion that looks very confident.

At club level, financial regimes such as UEFA's FFP or the Premier League's PSR are also data problems: revenue, wage bills, contract amortisation, all must reconcile. One wrong figure in a spreadsheet can turn into a points deduction. Verifiability there is not an academic matter.

Numbers and people

Three years ago I tracked the metrics of Incheon Hyundai Steel Red Angels for six months. A substitute striker named Choi Yu-ri had 214 minutes played but an xG of 3.2, the highest in the squad. That number does not say how good she is; it only asks one question: why did 214 minutes produce 3.2 expected goals? I went looking for the answer, and met a person. A month later she started the final match of the season and scored twice. What I took away is not that xG is always right. It is that a metric only has value when a specific person stands behind it.

The same holds for PPDA. Based on my experience following matches, I still remember Tokyo 2026: the Korean women's national team under Colin Bell took one point from three games, but their PPDA was 8.6, the most aggressive pressing figure in the tournament. Read the table and you see a failure. Read PPDA and you see a physically weaker side daring to press a stronger one. One dataset, two readings. Only one reading is honest, and it demands knowing how the number was produced, by whom, and under which definition.

For Vietnamese women's football, the gap is wider. Since Huynh Nhu became the first Vietnamese women's player to turn professional in Europe with Portugal's Lank Vilaverdense in 2026, attention on women's players has grown, yet most international platforms still record only goals, cards and minutes from a domestic women's match. Tackles, blocks, press escapes, distance covered — gone. When low-tier data does not exist, an analyst has two options: say "I don't know," or speculate and present the speculation as fact. The second option is always easier, and always more harmful.

I set a test for myself: ten matches is the minimum threshold for talking about form, three for talking about a trend, one is only enough to tell a story. Many reports I read breach that threshold without knowing it.

I once breached a different threshold. In 2026, at the U-20 Women's World Cup in France, I mispronounced the name of striker Ellie Brazil three times in the first half. That was not a pronunciation error; it was a data error, because I had not verified identity before going on air. Two weeks later I re-watched the tapes and learned the phonetics of all 352 players at the tournament. Misname once, remember for life; correct it, and the respect runs deeper.

A young colleague once asked why I always read players' names three times before going on air. I answered: because I have read them wrong before. In this trade, credibility is built by thousands of correct details and can be lost by one wrong one.

And some things sit in no model at all. At the 2026 Women's World Cup, goalkeeper Tran Thi Kim Thanh saved a penalty from the United States. In many datasets that moment is a single line: "save." For a generation of Vietnamese fans, it was the entire match. The data is not wrong. The data is simply not enough.

The paradox of the empty cell

Here is a paradox the analytical trade rarely admits. We fear empty data, while what actually harms is false data presented confidently.

A report that says "insufficient information to assess" across all nine dimensions is a useless report — but a harmless one. It forces the reader back to the source. A report that fills every cell with unverifiable numbers creates the feeling of having understood, and that feeling costs far more than an empty cell. In women's football, where data sources are thin and analytical budgets low, the temptation to fill cells is greater.

Look at esports and you see another kind of pipeline: digitalised training smoothing away individual play, turning players into products off an assembly line. When everything is standardised for measurability, whatever cannot be measured is treated as if it does not exist.

Nor do I want to invoke women's football to exempt it from criticism. A strict standard should hold for both the men's and the women's game. If a midfielder misplaces three of ten passes, say exactly that, and do not wrap it in euphemism. Real kindness lies in careful verification, not in painting things rosy.

What remains

At fifty, I understand that the pitch has no borders, but the heart has coordinates. Every time I sit before an empty dataset, I remind myself: do not write to fill. Let the empty cell stay where it is, then go and find the person waiting to be named. If Vietnamese football's data pipeline, from V.League to the women's competitions, wants to go further, the first task is not buying more metrics — it is agreeing that "I don't know" is also a valid answer.