Nine Dimensions of Athletics Analysis and the Lesson of an Empty Data Sheet

That April morning, I opened the data sheet for a domestic athletics meet and found exactly one thing: blank space. The wind-speed column was empty. The 200m split markers were empty. The reaction-time column was empty. Even the name of the meet was written in a single faint line, with no date and no venue. Someone had sent me a report complete in form — a title, a table of contents, nine clearly numbered sections — but inside there was not a single verifiable fact to hold on to.

I read it three times. The first time, I looked for my own mistake. The second time, I looked for the sender's. By the third time, I realised there was no mistake here at all. There was only an uncomfortable truth the trade rarely dares to say out loud — that sometimes the data simply does not exist, and the analyst's job is to say exactly that.

It was the first time in years on the job that I had to sit back and ask a question that had nothing to do with the track: when the data is empty, what is an analyst supposed to do?

I have one rule, and it is not one I invented. It is the rule of the craft: every conclusion must be anchored to a verifiable point of information. When there is no anchor, the most honest conclusion is "not enough data." It sounds simple. But in a field where everyone wants an answer before the race starts, saying "not enough" is the hardest professional act I have ever performed.

The craft of reading athletics through data

I came to athletics from football, and I brought an old habit with me.

In 2026, while working as a data consultant for a club in Hai Phong, I built the PPDA metric — passes allowed per defensive action — for the youth team. During a review, I found a forgotten midfielder whose average PPDA was 6.8, the highest in the entire academy. He pressed superbly but was overlooked by the coach because of his modest frame. I brought the numbers into the meeting room and demanded he be given a chance. In a match against a strong opponent on matchday 17, he won the ball 14 times, provided one assist, and the team won 2-1.

From that day, I understood something I have carried through my whole career: metrics do not replace people. Metrics give back to people what the naked eye misses.

Athletics is the sport of the purest measurement. There are no team-mates to shield you. There is no complex tactic to blame. There is only the track, the stopwatch, and the wind. But precisely because of that purity, athletics is also where data is most easily misread — because people assume a time figure is a complete truth.

It is not complete. A 10.20 run in the 100m can be the leap of a young talent, or it can be the result of a tailwind of 2.0 m/s sitting exactly on the legal threshold. Under World Athletics rules, sprint marks are only ratified as records when the tailwind does not exceed 2.0 m/s. Above that threshold, the figure is still a figure, but it is no longer a record. Two entirely different stories, the same number on the board. Without the wind data, I have no right to choose either story.

The men's 100m world record is 9.58 seconds, set by Usain Bolt in Berlin on 16 August 2026, with a tailwind of 0.9 m/s — within the legal threshold. Had the wind been stronger that night, the 9.58 figure would not exist in the record books, even though the legs ran exactly as they did. That is why I always begin any analysis with a question about the data, not about the athlete: where is the source, when was it measured, under what conditions, and what is still missing.

Athletics also taught me something football never could: patience with incomplete data. In football, a match lasts 90 minutes, and you can draw a conclusion after 90 minutes. In athletics, an Olympic cycle lasts four years, and a performance curve may need a whole decade before its true shape emerges.

In 2026, when the pandemic halted every competition, I lost my live data feed. Instead of waiting, I spent four months re-examining data from five domestic seasons and three major European leagues. I collected 2,300 matches and computed a new pressure metric combining PPDA, defensive distance, and pressing speed. The result: teams with an average PPDA below 8.5 earned 1.8 points per match, clearly above the rest. I published that model on my personal blog, and it drew the attention of several domestic analysts.

The lesson from that project was not the 1.8 figure. It was that I had to accept that 2,300 matches is still a small sample, that European football and Vietnamese football do not share the same yardstick, and that every model has limits. Flexible standardisation — before comparing two things, check whether they are measured in the same unit.

Nine dimensions, and the cost of an empty cell

When I received that empty report, I did not throw it away. I read its structure the way one reads a blueprint. It had nine dimensions. And each dimension, when left blank, taught me something about how data works.

Dimension one — event and performance. A performance only means something when placed on a coordinate system: world record, Olympic record, qualifying standard, season ranking. Remove the coordinates, and a time figure becomes a meaningless string of characters. I have seen more than a few news boards publish training marks as if they were competition marks. That is one of the most dangerous traps in sports media: the aura of a figure that was never ratified. An athlete who runs 9.8 in training is not the second-fastest man in the world. He is simply a man running fast in a session with no officials, no crowd, and no certified electronic timing.

Dimension two — athlete condition. The personal-best curve by age is almost impossible to fake. A 22-year-old breaking his personal best three times in a season is a very different signal from a 31-year-old doing the same. The young one is rising; the older one is resisting the natural curve. But if I do not know the age, the personal best, or the current season, I cannot place anyone in their peak window. The empty cell here is not the writer's shortcoming. It is a warning sent to the reader.

Dimension three — competition structure and qualification mechanism. Modern athletics runs on two parallel paths: hitting the qualifying standard, or accumulating world-ranking points. These two paths carry different physical costs. An athlete chasing points all season to secure a slot may pay for it with their very peak at the biggest meet. Without data on the competition calendar and the points sequence, I cannot say who is trading away what, or whether the trade is worth it.

Dimension four — event landscape and national strength. Athletics is a sport of dominant regions. Some events have been ruled by a single nation for decades. Others are going through a generational handover, where the old guard has not let go and the new guard is not yet ripe. Reading this power map is reading half the result. But the map can only be drawn when there are names of people, nations, and competitions. Without them, I have only a blank sheet and a pen.

Dimension five — competition rules and anti-doping. This is the dimension where I never allow myself to guess. An anomaly in the biological passport, a whereabouts failure, a sample re-analysed years later — each is a separate story, with its own procedure, and with the presumption of innocence until a formal conclusion. Talking about doping without facts is not analysis. It is defamation, and it destroys the very thing this trade needs most: trust in the data.

Dimension six — team and training system. No elite athlete walks alone. Behind every stride is a coach, a training group, a recovery system, and a certain level of technology adoption. The training cycle — when to load, when to deload, when to peak — is what determines the final result, not the athlete alone. But the training system is closed data. Outsiders see only the result on the board, not the thousands of hours in the gym. This is the dimension the media skips most often, because there is nothing in it to sensationalise.

Dimension seven — risk landscape. I sort risk into six groups: competition risk, doping risk, financial and career risk, rules and eligibility risk, public-opinion and brand risk, and systemic risk. An honest analysis must list all six, including the ones nobody wants to hear. But when there is no subject to assess, the very inability to rank risk is itself a risk — a data-pipeline risk, not a track risk. This is the point I want to stress: a fault at the data-collection stage can poison the entire analytical chain downstream, and no one sees the poison until the conclusion has already been published.

Dimension eight — public narrative and expectation. This is the dimension I care about most when writing. Every athlete arriving at a major meet carries a story: a record chase, an emerging prodigy, national pride, a comeback from injury, or a farewell. The story can fuel performance, or it can strangle it. The gap between public expectation and objective assessment is measurable — if the data exists. When expectation rises faster than actual strength, that is when risk appears. Without data, that gap becomes mere noise, and noise is always louder than the truth.

Dimension nine — industry transmission. From youth recruitment, to athletes and competitions, to broadcasting and commerce. A national record can make domestic running-shoe sales surge. A medal can open up a whole new generation of talent. Or both can fade after a single season. This transmission path needs market data, commercial data, and youth-development data. Without them, I cannot draw even one arrow, let alone a trend.

What an empty sheet really teaches

I sat with those nine empty cells for a long time, longer than the job required. And I realised this: an empty sheet is not a failure. It is a mirror.

Data is a mirror. Most of the market looks into it and sees only itself. When the data is complete, people see exactly what they want to see — the star they love, the team they hate, the conclusion they had already written in their heads. When the data is empty, they are forced to face the opposite: that they do not know.

My job, in the end, is not the job of giving answers. My job is to ask the right questions, and to have the courage to leave blank the cells that have no data. A sheet with three honest empty cells is worth more than a sheet with three cells filled by guesswork. People call me a data monk. A monk needs no cathedral — only the truth.

I think of the times I was wrong. In 2026, I published an analysis saying a major national team would be eliminated in the World Cup group stage, based on a low pressing metric and slow attacking speed. The online crowd mocked me, saying all I knew was looking at numbers. Then that team lost, and my old articles were shared thousands of times. But what I remember is not that victory. What I remember is that I was lucky — I had enough data to speak, and the data stood on my side. Had the data been empty that day, I would have had to stay silent. And silence would have been the right answer.

The contrarian angle: correlation is not causation

There is a temptation every analyst has felt: seeing two series move together, and instantly telling a causal story.

Teams with a low pressing metric lose a lot. True. But that does not mean low pressing causes defeat. The team may have lost because its defence was weak, and because it lost it had to defend, and because it defended its pressing metric was low. The arrow of causation runs entirely opposite to the attractive story.

Athletics is the same. An athlete who trains more hours and runs faster does not prove that training more creates speed. Fast runners tend to have better recovery, better nutrition, better coaches, and a better genetic base — and those are the real variables. Training hours are merely an accompanying variable, and they are very easily mistaken for a cause.

This is why I never conclude from a single variable. I need cross-checks, comparison groups, and long-run context spanning 5 to 10 years. A season is a confession of tactics. But a single match is only an unfinished sentence.

And this is the hardest part of the trade: I have to accept that there are questions I cannot answer. Not because I am weak, but because the data is not enough. Saying "I don't know" in a meeting where everyone is waiting for my numbers is a braver act than any beautiful chart. I learned that not from books, but from the times I nearly spoke beyond the limits of the data.

What to watch in the next round

If you read athletics the way I read it, start with the empty cells.

When a news board publishes a performance, ask: how much wind, what altitude above sea level, is the equipment compliant, is this a final or a heat, and what was the athlete's previous personal best. Every missing answer is a reason to delay belief. Not to doubt the athlete, but to respect the truth.

The day football stopped, I began counting every stride again. And I learned that the first stride is not the fastest one. It is the most honest one.

The empty data sheet from that April is still on my machine. I have not deleted it. Sometimes an empty cell teaches more than a full one.