Vietnamese Swimming: The Data Gap Nobody Wants to Measure

I received the file on an August afternoon in 2026. No athlete names. No lanes. No splits. No reaction times. Just rows of notes lined up like a graveyard of numbers: “insufficient information to assess.” The aggregation system returned an empty file for a meet I knew for certain had swimmers in the water. Six years working with swimming data, and I have grown used to watching numbers lie. I have seen an index say one thing while the scoreboard said another. This time was different. There was no number to be wrong. Only the total absence of data — something far more frightening than a failed prediction. In swimming, data is not a side dish. A 50-metre split tells the story of energy distribution. Reaction time on the blocks speaks about competitive psychology. Stroke rate and distance per cycle speak about technical efficiency. The number of underwater dolphin kicks after the dive speaks about tactics in a space the crowd cannot see. Without those indicators, all that remains is the final time. And the final time is the poorest indicator a swimming analyst can hold. It is like knowing a match ended 2-1 without knowing who scored, in which minute, or whether there was a penalty. The result is clear; the story is gone. What made me stop was not the empty file itself. It was the reaction of the people around me. Three colleagues, three ways of handling it. The first said to leave it and wait for the system to re-run. The second suggested writing from feeling, since readers do not need splits. The third quietly opened a notebook, wrote down the meet name, the date, and a short line: no one recorded it. The third one was right. But none of the three asked the better question: why is our swimming data so thin that an automated system can return zero? I began the trace with myself. Four years ago, I tried to build a performance prediction model for a national youth swimming meet. I collected final times for 280 swims. 50-metre splits existed for only 96. Reaction times: 41. Stroke rate and distance per cycle: almost none. Numbers do not lie, but people always find ways to lie about numbers. Here, no one needs to lie. They simply choose not to measure. I sorted Vietnam’s swimming data gaps into four layers, each with its own justification. Layer one, individual athlete data: not recorded because of missing equipment and missing operators — a technical reason, solvable with money and training. Layer two, domestic meet data: recorded but not centrally archived, with each organiser keeping a private hard drive that goes into a drawer after the meet — an organisational reason, solvable with regulation. Layer three, national team data: stored but not shared — an internal reason, the hardest to touch. Layer four, regional and continental comparison data: barely exists for Vietnamese swimming. We have had major swims from Nguyen Thi Anh Vien on the continental stage, yet the 100-metre splits for most of those performances were never fully recorded. A great achievement preserved with half its numbers. These four layers are not independent. The lower layers feed the upper ones. Without individual data, meet data stays empty. Without meet data, the national team has no baseline for comparison. With no baseline, every conclusion about an athlete becomes an eye test. I once treated models as scripture. Now a model is only a compass — but without it, we get lost. And in Vietnamese swimming, the compass spins with no needle. In 2026, when pools closed because of the pandemic, I thought it was dead time. I was wrong. It was the only stretch in which I could sit down and map what I actually owned. The result was not pleasant: after more than a decade of observation, I could name dozens of Vietnamese swimmers but describe the split structure of fewer than ten of them. The counter-intuitive point sits here: a system returning empty data is doing its job correctly. It is an honest system. If it had invented an average reaction time of 0.72 seconds, or filled in some average stroke rate, I could have written a piece that looked highly professional, packed with charts, and was entirely wrong. The empty file forced me to admit something the swimming world rarely says out loud: most Vietnamese swimming data never existed, rather than having been lost. People tend to underestimate emptiness. They say to re-run the system, to supplement later. But some gaps cannot be patched by re-running. They can only be patched by one decision: measure, or stop pretending you have measured. There is another trap. When data is thin, people tend to apply European or North American swimming models directly. I nearly fell into it: using split thresholds from large training centres to declare a domestic athlete’s pacing wrong. Wrong where? Wrong because I had no three domestic sources to cross-check. When the sample is a handful of swims, every conclusion must be labelled a hypothesis, not a law. I do not call the empty file a failure. I call it a map of the unread portion. It points precisely to where Vietnamese swimming has never placed a sensor: underwater, at every 50 metres, at the instant the foot leaves the block. Rather than shouting that the pool is empty, the work is to rebuild from the charred fragments of data, starting with small and concrete tasks. One fixed camera in every lane. One person pressing a stopwatch at every 50 metres. One hard drive submitted instead of kept private. If those three things happen within a single season, next year’s file will no longer be a graveyard of numbers. It will be a story worth telling. And the question I leave behind is not for the system but for people: next time, who will be the one pressing the stopwatch at every 50 metres, before we once again convince ourselves that we have measured?