Vietnamese Swimming: The Discipline of an Empty Data Table

On the final night of a national swimming meet, I stayed behind in the technical area after the stands had emptied. On my screen was the data file I had just downloaded: more than three hundred swims by the athletes, but the split-time column was blank, the turn-time column was left open, and the list of athlete names showed only half its entries. A young colleague stopped by, looked at the screen and asked: "You'll write it now, right? It has to go up tomorrow." I shook my head. There is one thing worse than a slow analysis, and that is a wrong one. In swimming, where every conclusion rests on numbers measured to the hundredth of a second, the error does not lie in missing data — it lies in filling the gaps with guesswork. Swimming analysis differs from football analysis in one fundamental way: almost the entire story is told through time. A 200m freestyle swim is divided into four 50m segments, each with its own time, stroke count, stroke rate, and underwater time after the start and after every turn. At expert level, analysts also measure the breakout time after the first dive and the number of metres of glide before surfacing. Lose one of those indicators, and the picture of an athlete can be turned completely upside down. In Vietnam, the data infrastructure for swimming is still young. Many national meets publish only an overall results table, with no split times. Dedicated analysis cameras are not always deployed on every lane. Electronic timing systems can suffer synchronisation faults, and when that happens, the analyst must decide: publish a conclusion built on incomplete data, or stay silent until there are enough sources. This is the point I believe Vietnamese swimming needs to face directly. The first principle I set for myself years ago is never to publish a number from memory. In 2026, writing about a major match, I stated that a team had pressed successfully 21 times, when the real figure was 14. A reader caught it the same night. I had to correct it, and from then on every piece I write has to pass a two-source check before it goes out. In swimming, that discipline is even stricter, because the margin of error here is measured in hundredths of a second — something the naked eye cannot detect. When I opened that empty data table that night, I did not rush. I checked three possibilities. The first was an export fault: the timing system had recorded the data, but the output file had an encoding error that left the number columns blank. The second was a lane-synchronisation fault: a few lane timers failed to transmit their signal to the server, so every swim in those lanes was flagged as missing. The most worrying possibility was that the data had never existed at all — the organisers recorded only the total time, not the splits. I contacted two independent sources: a timing technician and a coach who was present. The result showed the second possibility was correct — two lanes had suffered a sensor fault at the turn, so the entire third- and fourth-segment times of the athletes in those lanes were lost. That meant any conclusion about their pacing strategy was unreliable. This is where the principle of "never fill a gap with guesswork" earns its keep. I chose to publish an analysis table that stated plainly: which indicators were reliable, which were missing, and why. For the two lanes with the sensor fault, I wrote "insufficient information to assess" instead of speculating. It may look like an unfinished analysis, but in fact it is an honest one — and in swimming, honesty with data matters more than a conclusion that sounds complete. Only once the data is sufficient do I begin to read the bigger picture. A swimmer in the 200m freestyle whose first segment is clearly faster than the second is usually pacing on an early-attack strategy — a tactic that works only if the fitness base allows them to hold speed at the end. Conversely, a swimmer who negative-splits, meaning the second half is faster than the first, usually has a strong endurance base and the ability to surge late. But to tell the two apart, I need the time of each segment, not just the total. That is why a data table missing split times cannot be used to talk about tactics. Numbers only retell the race; tactics begin with a mistake. From a systems perspective, the problem for Vietnamese swimming is not a shortage of analysts, but the collection infrastructure. A football league can rely on tracking data to draw movement maps, but a national swimming meet that wants equivalent data needs underwater cameras, touch-pad sensors accurate to the hundredth of a second, and synchronisation software strong enough not to drop data. When those things are not yet aligned, the analyst is forced to work with incomplete data — and how they work with it is what shapes the quality of the output. Stepping into the world of Vietnamese swimming data, I learned to stay silent before the numbers. Take a familiar example: Nguyễn Thị Ánh Viên once dominated Vietnam's women's lanes across many SEA Games. If you look only at her best times, you see a star. But if you look at data across many seasons, you see something else: consistency between swims is what made that dominance. A swimmer with an impressive personal best but wide fluctuations often has a competition-psychology or endurance problem. Conversely, one with a more modest best but greater consistency is the more reliable performer on the big stage. But to measure consistency, I need data from many meets, not one. This is why I always say that data accumulated over many seasons is where an athlete's skeleton is revealed. The awkward truth is that in sports analysis, people are often persuaded by pieces with decisive conclusions. A piece saying "this swimmer won through a negative-split tactic" sounds far more compelling than one saying "I don't have enough data to conclude." But that very decisiveness is the trap. When you force an insufficient set of numbers into a pre-set story, you are not analysing — you are storytelling, and the story may be wrong from the foundation up. I believe the value of an analyst lies not in how many conclusions they produce, but in knowing when to stop. An analysis table that plainly marks "insufficient information" where it is needed is more honest than a smooth piece full of holes. For Vietnamese swimming, where the data infrastructure is still being built, admitting the limits of the data is the first step to improving it. You cannot fix a system if you keep pretending it is already perfect. So the next time you read a swimming analysis, pay attention to what the author does not write. A piece brave enough to say "I have no data here" is more trustworthy than one confident on every line. For Vietnamese swimming, the road ahead is not about having many more conclusions, but about having much more reliable data — and enough courage to stay silent when the data has not arrived. Because data is a mirror, not a lamp: it reflects what is there, it does not illuminate what we wish to see.