The Blank Report and the Discipline of Counting Athletics Data

That night I opened a report with nine sections. All nine carried the same line: insufficient information, cannot assess.

No title. No source. No date. No mark, no athlete name, no competition name. Only the scaffold, and inside the scaffold, blank cells marked with improbable care.

A young colleague called at eleven at night: just write something, we have to publish in the morning. I said no. He thought I was being difficult. I printed the blank report on A4, pinned it to the wall, right next to the 2026 PPDA chart.

The day football stopped, I started counting strides again. In those months without football, I sat with athletics data. It is a sport measured to the hundredth of a second, and also the sport where people lie most easily, because a bad run has nobody to check it for you.

That blank report turned out to be the most useful document of the month.

Context: a two-stage pipeline and its weakest joint

At forty, I work as a data consultant for a football club in Hai Phong and I write about athletics. Two jobs, one principle: before concluding, know where you are standing.

The pipeline my engineering team and I built has two stages. Stage one reads the source article and extracts information points: title, source, article type, marks, names, competitions, timestamps. Stage two takes those points through nine professional dimensions — performance positioning, athlete condition, qualification mechanism, event landscape, rules and anti-doping, training system, risk matrix, public narrative, industry transmission.

That night, stage one returned nothing. There was nothing to extract. Stage two still had to run.

The easiest path is to let it improvise. Machines are very good at making things up under pressure. Give one a prompt like analyse this and it will produce an athlete who does not exist, an event that never happened, a medal never awarded, all of it fluent. That is what frightens me most in this trade.

Stage two did not improvise. It returned nine sections, each stating plainly: insufficient information, cannot assess. Then it added a warning at the bottom: the upstream pipeline has failed; re-run stage one before using this output.

I read that warning three times. It sounded like a line I had written on my personal blog in 2026, when I built a pressure index model from 2,300 matches: if you have no data for a cell, leave the cell empty. An empty cell is data. A fake empty cell is a crime.

I started writing in 2026, at a running magazine. Twenty-four years later I keep one habit: every piece must contain at least one place where I admit I do not know.

The core: nine dimensions, and what is genuinely missing

When an athletics analysis comes back blank, what is missing is not prose. What is missing is coordinates.

Performance positioning. An athlete runs 100 metres in 9.95 seconds. It sounds like big news. But if that run had a tailwind above 2 metres per second, it cannot be ratified as a record. If it happened above 2,000 metres of altitude, thin air helps enormously over middle and long distances. If it happened on a new-generation synthetic track, or in shoes with a carbon plate close to 40 millimetres thick, then the mark is the sum of three variables: the human, the surface, the equipment.

World Athletics shoe rules, in force since 2026, capped road-racing sole thickness at 40 millimetres, limited the sole to one rigid plate, and tightened track spikes as well. Once there is a cap, every cross-era comparison must subtract technology's share. People forget that when they place this year's record beside a record from 2026.

A mark without coordinates is not yet a mark. It is a point outside every axis.

With a blank report, there is no mark to place on an axis. No event, no wind reading, no altitude, no track type. There is no way to say whether the athlete has met a standard, where they rank in the season, or how far they sit from rivals of the same tier. Any answer would be invention.

Athlete condition. An athlete is not a point but a line. That line is drawn by personal bests year after year. Sprint events usually peak between 24 and 28. The marathon peaks later, roughly 28 to 33. If the curve suddenly spikes at 31 after three flat years, that is a signal for cross-checking, and cross-checking here means biological data, not applause.

Alongside the curve sits injury history. Hamstring, Achilles, foot — the three familiar trouble spots of a runner. Then peaking strategy: is the athlete concentrating on one race a year, or racing densely to collect ranking points?

A personal-best curve is the one testimony an athlete cannot edit.

The blank report has no name. No curve. No way to place the athlete in any phase of a career: rising, peaking, declining.

Qualification mechanism. This is the part readers see least and coaching staffs care about most. There are three roads into a major championship: hit the entry standard, accumulate world ranking points, or win a national selection race. The three carry completely different physical costs.

The men's marathon standard for the Paris 2026 Olympics was 2:08:10, the women's 2:26:50. To hit it, you must choose the right race, the right weather, the right rhythm. The points route works the other way: the more you race, the more points you collect, but you only have two legs.

Chasing ranking points is a reverse gamble: you spend fitness today to buy a ticket for tomorrow, and nobody refunds you if your legs fail first.

No competition is named in the blank report, so there is no way to say what tier it is, when the qualifying window closes, or where the athlete stands in the race for a place.

Event landscape. Every athletics event has its own power map, and that map changes very slowly. Men's distance running has been an East African story for decades. Short sprints are shared between the Caribbean, the United States and a handful of West African nations. Throws usually belong to Europe and the United States. Pole vault and high jump have countries that hold traditions for decades.

Dominance patterns have types too: a single absolute ruler, two rivals sharing, a broad contending group, or a generational handover. Those four types produce four different readings of the same result.

No event is named in the blank report. There is no map. The right move is to write cannot assess rather than draw a fake one.

Rules and anti-doping. This is the part a decent sports writer has to touch, uncomfortable as it is. The Athlete Biological Passport tracks blood and hormone markers over time to detect anomalies. Whereabouts rules oblige athletes in the testing pool to file their location, and three missed tests in twelve months constitute a violation.

Then come eligibility rules, the most famous legal dispute of which revolves around testosterone levels and athletes who must choose between suppressing testosterone or leaving their event. Add the waiting period for changing sporting nationality, and the shoe rules — all of it belongs to the same bundle.

A medal can be re-awarded ten years later, once a higher finisher is disqualified. The results sheet waits for no one.

The blank report raises no rules scenario. No anomaly signal, no precedent, no sanction scenario. This cell must stay empty.

Team and training system. Elite track and field athletes typically sit in one of three models. The state model, where a national training centre covers everything from housing to doctors. The professional model, where a personal coach and an agent share responsibility. And the overseas camp model, where the athlete flies to a high-altitude base for a few months each year.

Each has different strengths. The state model is strong on recovery and medical support, weaker on individualising the plan. The professional model is strong on specialisation, weaker on financial safety after injury. The overseas camp is strong on environment, weaker on cost and continuity.

Periodisation is also readable from data: is the athlete loading for one race a year, or spreading across four? No coach, no centre, no country is named in the blank report. This cell stays empty too.

Risk matrix. This is where I want to linger, because it is where data people fool themselves most.

A decent risk matrix needs six groups: competitive risk, doping risk, financial and career risk, rules and eligibility risk, media and brand risk, systemic risk. Each needs a level, a probability, an impact, and a mitigation.

With no data, the reflex of the majority is to write low in the empty box. It feels safe. It is a polite lie. There is no basis for calling the injury risk of an unnamed athlete low. There is no basis for calling the doping risk of an unidentified competition low.

Cannot assess is a conclusion. Rating something low out of habit is just a habit.

Public narrative. Every athlete entering a major championship carries a label the public has stuck on them: record hunter, emerging prodigy, king returning, legend saying goodbye, or a doping story waiting to break. Every label has a life cycle, and that cycle is usually shorter than people think.

The job is to measure the distance between outside expectation and internal capability. Market expectation, data assessment and actual result are three different lines. The gap between them is where media shocks are born.

At regional level the pressure is even clearer. A SEA Games hosted at home loads national teams with far more expectation than their underlying strength justifies. I once sat through a regional games in Hanoi and logged the splits of every running event. What stood out was the heats, where average speed was markedly lower than in the finals — evidence that most athletes knew how to save energy.

The blank report has no label to test. No expectation, no result, so no gap to measure.

Industry transmission. An athletics performance spreads across three layers. Upstream is youth development and equipment research. Midstream is athletes and competitions. Downstream is broadcasting, commerce and derivative markets.

The carbon shoe race is the clearest upstream example. When one brand releases a shoe whose carbon plate saves a few percent of energy, other brands must follow and federations must legislate. The 40-millimetre cap was born from a commercial race, not from a pure debate about fairness.

Downstream is more familiar to Vietnamese fans: a regional medal brings sponsorship contracts, scholarships, and money flowing back into youth training centres.

No athlete, no competition, no brand appears in the blank report. The transmission chain breaks at the first link.

The contrarian angle: my job is not to produce numbers

There is a common misunderstanding in newsrooms. People think a data specialist is paid to turn a match into as many metrics as possible.

My actual job is deciding which metrics do not yet exist.

The irony is that the market pays for speed while quality comes from slowness. A blank report has nothing to sell. A report stuffed with numbers sells immediately, even when half of it is inference. Everyone knows this, and everyone looks away, because deadlines do not care about truth.

I have made that mistake myself. For a period I allowed myself to estimate metrics for matches without full tracking data. First one match, then a whole season. When I checked back, I found that every estimate leaned toward the hypothesis I wanted to prove. People call it confirmation bias. I call it systematic self-deception.

Since then I separate two kinds of cell in every dataset. Cells with evidence, and cells without. The second kind is not allowed to contain numbers.

The natural counterargument is this: if everyone writes insufficient information, where does the content come from? The answer lies elsewhere. Readers do not need me to invent. They need a reliable enough filter to know which stories to read and which to skip.

In 2026 I once carried a metrics sheet into a club meeting in Hai Phong. Reviewing academy data, I found a midfielder named Vu Minh Hieu with an average PPDA of 6.8 — the best in the development system — meaning he pressed extremely well but had been overlooked because of a modest physique. I asked the coaching staff to give him a chance. Against Hanoi on matchday 17 he won the ball 14 times, provided one assist, and the team won 2-1.

Based on my experience watching matches, that was one of the rare occasions a metrics sheet changed a coaching decision. Hai Phong taught me: the star is not on the shirt, it is in the metric. But that same match taught me the opposite lesson — had one cell in my PPDA sheet been wrong, I would have taken an opportunity from one young player and given it to another on the basis of junk data.

There is one more point the blank report reminded me of. Correlation is not causation. My model showing that teams with a pressure index below 8.5 average 1.8 points per match does not prove that good pressing produces points. Those pressing teams may simply be the ones with better budgets, and budget may be the real variable. A model cannot explain itself. The writer has to annotate that part for the reader.

Data is a mirror. Most of the market looks into it and sees only itself.

What to carry into the next cycle

That blank report gave me something no model could: a to-do list. Re-run the extraction stage on the original article. Check whether the fault sits in the reading step or the scaffolding step. Tag this record as an extraction failure and remove it from every aggregate, so it does not blur the picture for the whole batch.

The transfer window is at its peak. Hundreds of contract lines appear daily, and most of it is noise. Amid that noise, a decent sports writer holds an advantage few notice: the right to stay silent until there is data.

I still keep that A4 sheet on the wall. It reminds me that whenever my hand wants to fill an empty cell with a plausible sentence, that is the moment my trade is being tested. And next time you read an athletics analysis packed with numbers, try to find whether any cell in it was honestly left empty.