An Empty File on the Lane Rope: When Data Refuses to Speak

On Tuesday morning I reopened the regional swim-meet tracking file I had spent three months building. The cursor clicked the first cell. Nothing. Event name: empty. Discipline: empty. Athlete list: empty. The time column: empty. The spreadsheet threw no error, showed no red exclamation mark. It simply stayed silent — the particular silence of someone asked exactly the question they do not want to answer.

In sixteen years on the job I have met dirty data, missing data, and data logged in the wrong time zone so that a final ended up filed on the previous day. This was different. A completely empty file, not one information point to hold on to. I sat looking at the screen and understood: what I was reading was a confession. How many adjectives a sport uses to describe itself, and how many data cells it keeps — those two numbers are usually very far apart.

I came to swimming before I came to spreadsheets. In 2026 I was the swimming reporter at Thanh Nien newspaper, equipped with a notebook, a pen and a phone used as a manual stopwatch. The results of a junior meet that day were published as a photograph of a scoreboard taped to a wall. I copied them out by hand. Three days later, to check a child's 100-metre split, I had to dig through my phone gallery. If the photo was deleted, the data was gone. None of that would matter if swimming were a sport of feeling. It is not.

Swimming holds the cleanest data of any sport I have covered. Football needs expected goals — a model built by people, with assumptions, with error, open to argument. Swimming needs one touchpad and one clock. The rival in the next lane cannot touch your time. No referee's whistle bends a stroke. When a swimmer completes the 200-metre individual medley in 2:09.84, that is an event that happened, not an opinion.

Which is why an empty file is harder to accept. If dirty data is a technical problem, empty data is a habit problem. We do not lack numbers. We lack people who keep them.

In 2026 I lost two million dong after following an older colleague's gut-feel tip. Annoyed, I sat down and built my own expected-goals tracker for ten rounds of a V-League club, found the team outperforming its expected goals by forty per cent, wrote a warning piece and was insulted to my face. By round sixteen they stopped scoring. Numbers do not lie, but they know how to hide something. That Saigon summer, I learned that data also needs watering: a sheet nobody tends every week dies.

Three years later, when world football froze, I spent eight months archiving 2,400 Serie A matches from 2026 to 2026 and regressing them against Asian handicap movement. Football stopped moving, but 2,400 matches kept whispering inside my spreadsheet. The lesson from those eight months was not the model. It was this: data only has value when it outlives the memory of the person who wrote about it.

That is why that Tuesday morning unsettled me.

When the spreadsheet returned zero, it forced me to recheck every step. Input source: none. Original content to extract: none. Entities identified: none. Time sensitivity: not assessed. Source quality: not classified. Six fields, six silences. And here is the interesting part: I could infer nothing from it. I could not say which athlete was improving. I could not say which nation dominated the lanes. I could not say a record was about to fall.

My trade carries a strong temptation: when there is no data, write in a confident voice. That temptation has ruined plenty of people. Invent a little here, extrapolate a little there, and the analysis still reads smoothly. But an analyst who lives on data and fills the gaps with guesswork is no different from a breaststroker taking two dolphin kicks in one arm cycle: everything looks plausible until the referee disqualifies you.

Swimming has its own rules, and those rules are strict for good reason. A lane is 2.5 metres wide, divided by lane ropes, and each swimmer must stay in their own. On the start and each turn, the head may stay underwater only for the first 15 metres. In breaststroke, only one kick is permitted per arm cycle. Those rules exist to protect the comparability of performances: if every swimmer were allowed to do something different, the number on the electronic board would mean nothing.

Yet when we write about swimming, we routinely skip the very thing the sport does best. We call Nguyen Thi Anh Vien a “pearl”, Nguyen Huy Hoang a “sea monster”, and children “prodigies”. Those words read beautifully. But when the silver medal in the men's 1,500-metre freestyle at the 2026 Asian Games reached Nguyen Huy Hoang, what forced analysts back to their desks was not the title. It was the split structure.

A 50-metre split is not a number. It is a confession. A 1,500-metre swimmer with even splits tells you about aerobic base and patience. One who lifts over the final 300 metres tells you about holding long-course rhythm. One who empties the tank in the first 200 and fades tells you about a race plan built on belief. Without splits you have only a final time — and a final time is evidence of a result, not evidence of a process. That is why an empty file is no small matter. It erases an entire layer of analysis.

There is another trap in swimming data that outsiders rarely notice: short course and long course. A 25-metre lane has twice as many turns as a 50-metre lane. Every turn is a wall push plus an underwater glide, and that is the most efficient stretch of the whole race. Short-course times are therefore usually faster, and converting between the two pools is always a limited calculation. Widely circulated conversion tables exist, but no table replaces an actual swim in a 50-metre pool.

The same applies to eras. Between 2026 and 2026, polyurethane suits appeared and skewed an entire layer of world records. In 2026 the world governing body banned them. Every cross-era comparison must therefore carry an adjustment factor. Anyone who reads a 2026 result, sets it beside a 2026 result and never mentions the suit era has not analysed anything — they are decorating.

Then there is age. Not every swimming event peaks at the same age. Sprint events, especially for men, tend to mature late. Distance events may mature earlier. For female swimmers, puberty is a biological variable that is very hard to predict, and some athletes break age-group records and never find themselves again. This is where data must stay humble. You can measure time; you cannot measure, in a spreadsheet, what is happening inside a fourteen-year-old girl's body.

Back to the empty file.

What made me write this piece was not the technical failure. It was my own reaction to it. For the first ten minutes I asked myself whether I could produce an analysis from what I remembered. I remembered Anh Vien dominating Southeast Asian Games lanes. I remembered Huy Hoang standing on an Asian Games podium. I remembered Tran Hung Nguyen, Pham Thi Hue, and a next generation training in national centres. I had enough material to build a smooth read.

That was exactly the trap.

A piece built on memory sounds persuasive, but it cannot answer the most important question: where the trend is going. Memory is a biased sample. It keeps the highlights and quietly deletes the heats lost at dawn. What I needed — splits, age curves, competition frequency, the rate of hitting Olympic qualifying standards — sits beyond memory's reach. When analysis dissolves into imagination, it stops being analysis.

Correlation is not causation. A fast pool does not create a fast swimmer. An overseas training camp does not automatically produce a national record. Age-group results do not predict senior results, because physiology, psychology and training environment all shift. This is why I object to the word “prodigy”: it attaches a conclusion to a process that still has a long way to run, based on a very small sample.

To be fair, data's humility is no excuse for not building data. On the contrary, precisely because swimming carries so many biological variables, it needs an archive long enough to reveal patterns. Without one, every debate about Vietnamese swimming will keep circling emotion: whether to believe in a name, rather than whether to believe in a trend.

An empty file told me one simple thing: it does not measure the sport. It measures the analysis machine behind it. An empty file is the signature of a question that was never asked properly. Swimming does not hide its data. The writer is the one who forgot to ask.

I closed the file and did what I should have done three months earlier: build from the bottom up. One column per touch. One column for splits. One column for pool type. One column for suit era. One column for age. And I left the conclusion section blank, because conclusions must come last.

What I want to watch over the next few seasons is not a medal. It is the data files that are still alive after the meet ends. If, after a Games, the results sheet is still there, normalised, tagged, then our swimming has advanced a stretch without anyone breaking a record. And if everything sinks back into social-media photos, then three years from now I will again sit in front of an empty spreadsheet, again wondering what I missed.

A spreadsheet cannot swim. It can only count. My job is to count correctly.