Vietnamese athletics: when the data sheet comes back empty

One morning I opened the athletics analysis my colleague had sent over and found every cell blank. Nine sections — performance assessment, athlete condition, qualification mechanism, risk, communications — were all marked with the same line: insufficient information, cannot assess. No athlete name. No distance. No competition date. Not a single number to hold on to.

My colleague had done the one thing Vietnamese sports data rarely dares to do: say plainly that he did not know.

In more than a decade of reading numbers, I have learned that the hardest part of the job is not the calculation. The hardest part is holding your own hand back when the data is not there. A blank sheet is not quite a failure. It is a signal, and for Vietnamese athletics, that signal deserves to be read seriously.

I once worked as a data consultant for a football club in Hải Phòng before turning to athletics. The day football stopped, I began counting every stride again. No glamour, no grandstand, only a stopwatch and a question that repeated itself: is this number real?

A medal-rich athletics scene with thin data

Vietnamese athletics has reason to be proud. At the SEA Games 31 held in Hà Nội in May 2026, the Vietnamese delegation topped the medal table, and athletics was among the sports that contributed the most medals. A year later, at the SEA Games 32 in Phnom Penh in May 2026, Vietnamese athletes again asserted themselves across running, jumping and throwing events. Those medals are real, and they deserve recognition.

But results and data are two different layers.

At major international meets, every race is recorded as dozens of data points: reaction time at the start, split times for each hundred metres, wind speed, track temperature, a fatigue index over the closing stretch. A runner in the fifteen-hundred metres can be broken into four segments, each with its own pace, and it is the pace of the final segment that reveals how much was left at the bottom of the tank.

In Vietnam, most of those numbers are not kept. We have the final result — who finished first, and in what time — but we lack almost all of the process data. This is the biggest blind spot in domestic athletics: we measure the outcome, but not how the outcome was made.

Numbers are a mirror. Most of the market looks into it and sees only itself.

In football, where I began my career, data became part of the craft fairly early. V.League clubs started hiring analysts, installing tracking cameras and computing pressing indices for individual players. Athletics, despite a far longer history, trails in this respect. The mismatch does not come from a lack of passion. It comes from a lack of recording infrastructure.

Four layers of data and one gap

Picture a standard report on an athlete on the eve of a competition. It needs at least four layers of data.

The first layer is baseline performance: the personal best, the season's best, and the gap between the two. If the season's best is far below the personal best, that is a sign that form has not peaked. If the opposite is true, the athlete is in better shape than their own past baseline.

The second layer is competition conditions: wind, altitude, track surface. A mark set with a tailwind cannot be compared directly with a mark set in still air. Ignoring this layer creates a systematic error — one that is silent and repeats itself in every comparison.

The third layer is physical condition: injury history, training load, the strategy for peaking. An athlete in a base-building phase will produce very different results from one already in the key phase.

The fourth layer is competition structure: the qualification mechanism, entry standards, the schedule, the density of appearances.

These four layers stack on top of one another. Miss one, and the picture can still be drawn, but the lines will be blurry. Miss all four, and the picture does not exist — and the only honest thing to do is say that it does not exist.

That is exactly what the blank report did. It invented no athlete. It attached no figure to any name. It preserved the gap, and recorded that the gap was real.

One example shows why process data matters. At the SEA Games 32 in Phnom Penh, Nguyễn Thị Oanh competed in several events within a very short span. The final results were fully reported by the press. But to understand how she managed it, one needs the data between races: recovery time, heart rate, closing-segment pace in each event. That is the layer of data almost never recorded in Vietnam. And it is precisely the layer most worth learning from.

In the analytics trade, preserving a gap is a hard act. The pressure to "have something to say" is always present. Analysts are pushed by deadlines, by readers' curiosity, by professional ego itself. And the easiest way to satisfy that pressure is to fill the gap with a plausible-sounding guess.

I have seen such reports. An athlete never tested for endurance capacity, yet the report contains the line "good physical base." A competition that has not published its standards, yet the report still draws a path through qualifying. Those sentences are not wrong grammatically. They are wrong factually.

A season is a confession of tactics. And a data report is the same: it confesses the true extent of its author's understanding.

When correlation masquerades as causation

There is a subtler, more insidious temptation in the opposite direction. It arises when the data is real, but is read wrongly.

Suppose we have a table of athletes' marks across a season. The faster runners may overlap with training at a better-equipped centre. Looking at the table, one easily concludes: good conditions produce good marks. But correlation is not causation. It may be that the most talented athletes are the ones selected for that centre — meaning talent leads to both, rather than conditions producing the marks.

This mistake is dangerous because it looks so scientific. It has numbers, tables, trends. It lacks exactly one check: is the data sufficient to rule out other explanations?

For Vietnamese athletics, the answer is usually no. The sample is too small, the data too thin, and the variables too little controlled. A season with a few dozen athletes is not enough to derive causal rules about training. It is only enough to raise a hypothesis, and let the next season test it.

That is why I never close an analysis with an absolute claim. Every time I write, I leave a closing paragraph listing what the data still lacks, to state my own limits accurately instead of dressing them up. Readers deserve to know where I stand on the map of understanding.

People call me a data monk. A monk needs no cathedral — only the truth. And the first truth of an analyst is knowing what he does not know.

A signal for the next cycle

That blank report will not sit in a drawer forever. It is a marker. It shows that the data-collection process broke down somewhere — perhaps at the recording stage at the track, perhaps at the data-transfer stage, perhaps at the stage where the input questions are framed.

For Vietnamese athletics, the task is not to write many more reports. The task is to rebuild the data pipeline: record split times, record competition conditions, record athlete condition, and record the times when there is nothing to record. A good pipeline carries both clean numbers and silence, and turns that silence into information.

The ball rolls in one direction, but data can look in every direction. An athletics scene that wants to go far must learn to look also at the side it has not yet measured — where the next medals are waiting for someone patient enough to count.

And for the writer, the lesson stays the same: when the data is silent, the most honest thing is to let it be silent. And to record that it was silent.