The Discipline of N/A: Inside an Empty Football Scouting Report

2:47 a.m. in Shanghai. I open the scouting report for the weekend fixture and count seventeen blank fields. Not blank because I was lazy. Blank because the data feed arrived empty: no lineups, no metrics, no player names, not even a publication date.

I sat in front of the screen for twenty minutes, fingers resting on the keyboard, not typing. Fifteen years ago I would have written it anyway. I would have called three sources, woven a smooth enough story, and bolted on a few weighty terms to reassure the reader. Tonight I did not write. And I think that moment is the thing worth publishing.

An empty analysis is not the analyst's failure. It is the test.

Context

Modern football runs on content. Every matchday, every transfer rumour, every press conference generates a hunger for an answer. Readers in China and Vietnam consume football news at a speed nobody imagined a decade ago, and that speed turns silence into a defective product.

I work at the intersection of two markets. On one side are Chinese sports platforms, where every article is measured by its first 24 hours of traffic. On the other are Vietnamese readers, for whom faith in numbers runs higher than faith in the naked eye. Neither market rewards the phrase "we don't know."

The process I use has nine layers: tactics, club finance, results cycle, league context, rules and governance, dressing room, risk, media narrative, and industry transmission. When the input file is empty, all nine layers return the same word: N/A.

What caught my attention was not the N/A. It was my own reflex when I saw it.

Core analysis

Football analysis has an occupational disease: a fear of blank cells. A gap in a spreadsheet reads as incompetence. So people fill it. They fill it with rumours, with "sources close to the deal," with models borrowed from another league and given a fresh label.

But blank cells have a shape. When a club declines to publish injury data, that silence is not random. I have tracked hundreds of medical bulletins from Chinese clubs and found a pattern clear enough to call a rule: information is released when it serves the club's commercial value. A badly injured player whose contract is expiring stays quiet. A lightly injured player before a derby is announced loudly, to build drama. Medical secrecy is not a privacy policy; it is a pricing instrument.

The transfer market behaves the same way. A rumour with no origin is not a deal in progress. It is a mood. And a mood cannot be valued on Transfermarkt or audited in a financial report.

I learned this late. In 2026, aged 35, I published a preview before Shanghai SIPG faced Shandong Luneng on matchday 18 of the Chinese Super League. My model gave SIPG an xG of 2.8 against 0.4, and predicted a 3-1 win. Traditional pundits picked a draw. The final score was 3-1. The piece drew 50,000 views in 24 hours.

I retell that not to boast. I retell it because the following week I abandoned that series to chase a basketball betting model, and my editor spent fifteen minutes shouting at me on the phone. Early success taught me a bad habit: believing that a number which was right last time will be right next time.

The 2026 World Cup erased that habit. My model, built on PPDA and defensive height, correctly called South Korea beating Germany 2-0. I went online urging people to back it. Then in the round of 16, the model favoured Brazil over Belgium on defensive xG, and I said so live on air. Brazil lost 1-2. Clients lost money following me. I argued bitterly with a colleague on social media, then spent three weeks rewriting the code, adding tournament variables and a randomness term.

Every piece I have written since carries a warning line: the model is a probability, not a prophecy. It sounds humble. Only tonight, staring at seventeen blank fields, did I understand that the warning did not go far enough. Every model is wrong, but a few are wrong usefully. That sentence holds only when the writer accepts that sometimes there is no model left to be wrong.

Contrarian angle

There is another reading of emptiness, and I want to state it plainly because it argues against me.

My profession turns "insufficient data" into a shield far too easily. When a model fails, I blame noise. When data is missing, I call it discipline. Both sound noble. Both can be a way of dodging the work of analysis.

An empty dataset does not excuse me from reading the game. It only excuses me from inventing numbers. There is a wide gap between those two things, and most people in this trade stand on the wrong side of it.

When a club hides an injury, I can still watch how a full-back runs in the 70th minute. When transfer figures are unavailable, I can still count how often a young player has started in three months. Data going missing is not the loss of data — it is a category of data. Absence is evidence, provided I do not use it to fill a page.

Tonight my report goes in with seventeen N/A entries and a single paragraph stating that a complete input file is required before any judgement. My editor will not like it. Readers will not finish it. It is the only version I am willing to sign.

Takeaway

Next matchday I will return with a sharper question: if clubs treat injury data as an asset, who pays for the opacity — the player, the supporter, or the models of people like me?

Every spreadsheet is a meditation, except that when it ends you have lost money. Tonight the meditation ended and I lost nothing. That is the best result I can file.