A Data Table Full of N/A Is Not a Clean Bill of Health

There was a document that sat on my desk in Hai Phong for three weeks. Nine pages, forty-two rows, and almost every cell marked with the same two characters: N/A. No athlete's name. No event. No mark. No date. No venue. The only thing that survived the entire extraction process was a single label: athletics.

The person who sent me that document attached one short request: analyse it. I read it from the first page to the last, read it again, then folded it up and replied that I could not analyse something that did not exist. Yet that very moment taught me more than any packed data sheet ever has. In the data-consulting trade, the most dangerous thing is not a wrong number. The most dangerous thing is a table that looks complete but holds nothing inside.

I have made my living reading matches through numbers for twenty-four years, starting in football before turning to athletics. At Hai Phong FC in 2026, I once brought a PPDA chart — the metric that measures pressing intensity — into a meeting room and persuaded the coaching staff to give a chance to a young midfielder with a modest frame but the highest pressing numbers in the academy. His name was Vu Minh Hieu; he won the ball fourteen times against Hanoi FC, and Hai Phong won 2-1. Hai Phong taught me: the star is not on the shirt, it is in the numbers.

A year later, I published a prediction that Germany would be eliminated in the group stage of the 2026 World Cup, based on an average PPDA of 9.2 — far too high for a champion's pressing standard — combined with slow attacking speed and a final xG stuck at an average level. Social media mocked me. On the night of 27 June 2026, Germany lost 0-2 to South Korea despite firing twenty-six shots with an xG of 1.5. I did not see Germany lose. I saw numbers that do not lie.

So when a nine-dimension athletics analysis sheet was placed in front of me — performance, athlete condition, qualification mechanism, national context, rules and anti-doping, training systems, risk, media narrative, industry transmission — I did not look at the number of rows. I looked at whether each row carried evidence. A nine-dimension frame sounds very complete, but complete in form has never meant complete in substance.

Start with the most important dimension: performance. An athletics mark only means something when it stands on a coordinate system. To know whether a 100m run of 9.85 seconds is excellent or ordinary, I must compare it with the world record, the national record, the qualifying standard, and the season's ranking. But before any of those comparisons, I must ask two questions: how much wind, and how high is the track? A tailwind above +2.0 m/s is the threshold at which World Athletics refuses to ratify a record. A track above 1,000 metres of altitude can hand an athlete hundredths of a second for free. And a shoe with a carbon plate and supercritical foam — what the trade calls super shoes — has shifted the baseline of track performances for nearly a decade.

In Hai Phong, I learned a similar principle in football: possession percentage is the most deceptive metric of all, because many teams grind out 60% with meaningless sideways passes. Athletics is the same. A performance number standing alone, without wind, without altitude, without shoe, is just a bare, unverified figure. I once saw a beautiful 200m result struck off the list because the judge measured a +2.3 m/s tailwind. The number still sits there, but it is no longer a record.

Then the second dimension: athlete condition. I never trust a single leap. If an athlete's historical yearly improvement at 100m is around 0.15 seconds, and this year it suddenly jumps 0.5 seconds, I immediately raise a question. A leap three times the historical average is a signal to verify, not a signal to celebrate. The personal-best curve must be drawn year by year, not collapsed into a single figure. At the same time, I must look at the injury map by event group: sprints and jumps tend to bring hamstring and Achilles problems, throws tend to damage shoulders and elbows, marathons tend to hit feet and spine. Without that map, any form forecast is guesswork.

The third dimension is the qualification mechanism. There are two routes into a major championship: hitting the qualifying standard directly, or accumulating points through the world ranking. Each route carries its own risk. A "one race decides everything" trial can unfairly cut an athlete in peak form; a three-per-country quota can keep a powerhouse's fourth-best at home even with a finals-worthy mark. These structures can only be read when I know exactly which meet, which date, and what the qualifying window is. Without those three facts, this dimension is entirely paralysed.

I routinely put PPDA and ball-recovery counts into every piece I write about young players, no longer writing on feeling or reputation. That principle carries straight over to athletics: I check the underlying numbers before trusting a name. An unknown athlete from a provincial town with a steady PB curve is more trustworthy than a media star with a single explosion. And during the four months when football shut down in the 2026 pandemic, I reviewed data from five V.League seasons and three major European leagues, gathering 2,300 matches to build a pressing model. That process itself taught me that long-term data is the only thing that can resist the glamour of a single moment.

The eighth and ninth dimensions obey the same rule. A media narrative — a record about to fall, a rising prodigy, a star returning — is only credible when its underlying layer holds up after wind, altitude and shoe have been subtracted. And the athletics industry's transmission chain, from youth development to equipment, from competition to commerce and broadcast rights, can only be traced when I know at least one real node. With no node at all, any industry analysis is mere boilerplate.

But here is where I must argue against myself, and where that nine-page document left its biggest lesson. A cell marked N/A does not mean "no risk". It means "no information". Those two things are worlds apart, and confusing them is the most expensive mistake in the analysis trade.

An empty anti-doping checklist is not a certificate that an athlete is clean. A column marked N/A under injuries is not confirmation that an athlete is healthy. A blank risk section is not a statement that everything is safe. When a report is presented with all nine dimensions, all the headings, all the tables, but every cell empty, it is no longer a report at all. It is a hollow frame waiting for someone to fill it with guesswork.

And precisely for that reason, I always separate three layers: what the source states explicitly, what is reasonable inference, and what is mere speculation. Numbers are a mirror. Most of the market looks into it and sees only itself. During the transfer window, when rumour noise drowns out signal, people easily turn a blank cell into a firm claim. Release-clause structure and the new wage bill are the real story, not the inflated figures on social feeds. Correlation is not causation. An athlete running faster after changing shoes does not prove the shoes did everything. A team winning more after raising PPDA does not prove pressing was the only cause. To make a claim, I need a sample big enough, long enough, and clean enough.

The day football stopped, I began counting every stride again. I learned that the true value of a data person lies not in filling every cell, but in daring to leave blank the ones without evidence. A season is a confession of tactics, but an empty analysis sheet is a confession of the writer. I did not see Germany lose. I saw numbers that do not lie. And I also saw that silence in the right place is itself a form of truth.