The Empty Data Column in Vietnamese Athletics and the Discipline of the Chronicler

There was an evening when I sat in front of a screen with a spreadsheet already open, waiting for data to flow in after a race. The stopwatch stopped at the finish line, the referee recorded the result, the stands applauded. Yet my metrics column stayed blank. I checked the connection, reopened the source file, and realized something simple: at many domestic athletics meets, the thing I needed does not exist at all. No one records it. No one stores it. No one verifies it. The day football stopped, I began counting every stride again — but this time, there was not even a number to count the strides with.

I do not write that line for show. It is a job description. It is also a confession from a man who works with data: most of the time, I do not lack technique. I lack raw material.

An athletics scene that wins a lot and records little

SEA Games 31, held in Hanoi in 2026, was the edition where Vietnamese athletics topped the medal table for the sport. Nguyen Thi Oanh won multiple golds in the middle-distance events. Bui Thi Thu Thao left her mark in the long jump. Nguyen Thi Huyen asserted her standing on the hurdles track. Looking at the results board, anyone would see a strong athletics scene in Southeast Asia.

But a results board is only the outermost layer of a data mass that should be far thicker. Behind every medal are questions almost no one can answer with numbers: has that athlete gotten faster or slower over two years? What was her final 200-metre split? How many load-monitored training sessions in the peak month? What was her lactate threshold when she passed the mid-cycle test?

I once sat in a football team's meeting room, carrying the pressing metrics of a young midfielder named Vu Minh Hieu to ask for him a starting spot. Back then, the coaching staff's first question was not "how much does he run" but "where does this number come from". I could answer, because Vietnamese football has had a collection system for a long time. Athletics has not had an equivalent system.

That is the starting point of every analysis I do. It is not that I lack a model. It is that I lack input.

Where the gap actually sits

When people talk about athletics data, they usually think first of the final result: time, distance, placing. But the final result is only the first of four layers that a measurable sports system needs.

Layer one — competition results. Vietnam has this layer, but it is scattered. A race is recorded by a final figure, sometimes by a photo, rarely by split data.

Layer two — performance metrics. This is where data starts to have diagnostic value: speed per 100 metres, stride count, reaction time at the start, acceleration curve. For Vietnamese athletics, this layer is nearly empty. It does not mean athletes produce no data — they produce it every time they run. It means no one records it.

Layer three — training data. Load, session count, volume, intensity, recovery. Major centres around the world treat this as a strategic asset. Here, it usually sits in the coach's head, not in a file.

Layer four — biological and injury data. The physical development curve by age, injury history, load tolerance. This layer decides how far an athlete goes in a career. It is also the most neglected layer.

When three of four layers are empty, every conclusion about an athlete becomes a guess dressed up in terminology. And that is when I must say what this profession rarely wants to hear: a conclusion is not yet possible.

The transfer story and the missing anchor

During the transfer window, people talk a lot about money. In Vietnamese athletics, money is also a story, except it is rarely recorded as a number. Athletes move from one province to another, from one centre to another. Each time, people ask: why did she leave, why did he stay.

The most honest answer is: I do not know, because I have no contract data. No public remuneration figures, no published contract length, no buyout clause. All that remains is rumour, and rumour cannot be ranked by reliability when there is no anchor.

This is where data differs from emotion. A football transfer can be verified through fee, length, wage bill. A provincial move by an athletics athlete usually leaves only a single line of announcement. With no anchor, people easily assign motives to an athlete that the athlete has never stated.

A null result is still a result

In one analysis, I received an input file that was nearly blank. Every data field was left open. No athlete name, no discipline, no mark, no competition context. The only thing left was a single label: athletics.

The reflex of a green writer is to fill the gap. Pick a name, assign a number, tell a story that sounds plausible. The reflex of a disciplined writer is to stop and state clearly: there is not enough data to assess.

I chose the second path, even though it does not produce a catchy headline. Because I know where the trap lies. When people have no numbers, they reach for concepts that cannot be measured: class, fate, fighting fire, character. Those concepts are not wrong emotionally, but they cannot be verified, and once something cannot be verified, it cannot be corrected.

People call me a data monk. A monk does not need a cathedral — only the truth. But this time, the only thing I had in hand was a blank space. A null result is still a result. It shows where the system is broken; it does not license us to invent the missing part.

There is another temptation worth naming: mistaking correlation for causation. An athlete changes coach and then runs faster — we rush to conclude the new coach is better. But perhaps the athlete had just passed a growth phase, or just recovered from injury, or the track that day was wind-assisted. Without baseline data, we cannot separate the variables. Numbers are a mirror. Most of the market looks into it and sees only itself.

What to do next

Hai Phong taught me: a star is not on the shirt, it is in the metrics. Vietnamese athletics does have real stars, but it does not yet have a metrics shirt that lets them be seen correctly.

The work does not begin with a complex model. It begins with a simple habit: record. Record split speeds at every test session. Record load at every training session. Record the day of injury and the day of return. Three years of continuous recording will create what ten years of argument cannot: a baseline to compare against.

The ball only rolls in one direction, but data can look in every direction. What I hope for in the next round is not one more medal — but one more number column that is no longer blank.