When the Swimming Spreadsheet Returns an Empty Cell

Three in the morning in Saigon, August. I open my swimming tracking spreadsheet — an Excel file I built in 2026, each sheet a meet, each row a swim. That night I needed to cross-check the numbers from a final. I click into the sheet, and every cell is white. No finish time, no 50m split, no reaction time, no athlete name. Only the column headers and a stretch of emptiness as long as a pool no one has swum across.

Sixteen years in this job, and for the first time I saw an analysis with not a single data point inside. Not missing numbers. Not wrong numbers. Completely blank. The strange part is that analysis still had all the headings, all the tables, all the "conclusions". Only the core was empty.

For someone who analyses swimming like me, that is the most frightening kind of data. Outsiders see a tidy document. I see a trap.

Swimming is a sport where numbers are not there just to count — they are the entire story. No numbers, no athlete. No splits, no tactics.

I learned this early, at Thanh Nien newspaper, when I was a reporter covering the blue lanes. Back then I wrote about swimming by feel: this athlete "surging with astonishing speed", that one "finishing with real courage". An older editor called me over, handed me a 50m split chart, and asked: "You say he surged. So how many seconds faster was his second half than his first?" I had no answer. Since that day, I never write the word "surge" unless two numbers stand beside it.

Swimming differs from football here. Football can hide inside xG, inside PPDA, inside a whole web of metrics wrapped around what the eye cannot see. Swimming is naked. Flat water, straight lanes, time is time. A swimmer in the 100m freestyle can be dissected into dozens of data pieces: reaction time off the blocks, the underwater phase, the moment of surfacing, average speed per lap, stroke rate, distance per stroke, turn time at the wall, and the touch. Every piece is measurable.

That is why an empty swimming dataset is a paradox. It is like a pool with every lane rope, every whistle, every official in place, but nobody jumping in.

In 2026, when almost every sport stopped because of the pandemic, I returned to swimming — my original sport. I understood that live data had suddenly become useless trash, and by the instinct of an ISTJ, I did not sit and wait. I archived. I gathered every result from the swimming meets I had followed, big and small, standardised them into one format, so that when the lanes reopened I would have a lasting foundation rather than a few scraps of paper.

That is why I am sensitive to gaps in data. An empty cell is not a small matter. It is a sign that a process broke somewhere — at collection, at entry, or at cross-checking. And in an environment where decisions rest on numbers, a broken process is more dangerous than a wrong number.

I once saw something close to that in the analysis world. A results table compiled beautifully, full of metrics. But when I checked its origins, not one result could be traced. Everything was "aggregated" figures. Nobody knew which meet, which round, which date it came from. Formally, the table was complete. In substance, it was no different from a blank sheet with gridlines.

In swimming, a number without a source is itself a warning. If you do not know which meet, which date, and whether a 50m split was recorded in a short course or long course pool, that number cannot be used for comparison. A 25m pool and a 50m pool produce two entirely different stories, even for the same athlete and the same distance.

This is the point many readers of results tables overlook. A 100m freestyle time swum in a short course pool always has a built-in advantage because there are more turns — each turn is a push-off, an almost free acceleration. Compared directly against a long course result, the number becomes meaningless.

A simple example to picture it. When I follow an athlete in the 200m individual medley, I do not record only the finish time. I split it into four legs by stroke, then compare each leg with that same athlete's previous swims. If their breaststroke leg is 0.8 seconds slower than at the last meet, that may signal a change in kick technique. But if I have no pool type and no meet date, that 0.8 seconds says nothing.

There is a technical detail viewers usually miss: after the start and after each turn, a swimmer may travel underwater for up to 15 metres. That underwater phase, among the top butterfly and backstroke swimmers, can be faster than the swimming on the surface. Their speed is created in a part of the pool the crowd barely sees.

Another variable newcomers often forget: the equipment era. In the late 2000s, high-tech swimsuits completely reshaped the world rankings. When the rules changed and those suits were banned, people had to divide the record books into two periods: before and after. A record set in the old era cannot be compared directly with today's times. That is why, before saying anything, I always check the year, the equipment and the pool type.

Since then, in all my tracking sheets, every number must carry four mandatory details: the meet, the date, the pool type, and the athlete. Miss one of the four, and the figure goes into a "pending verification" column and is barred from appearing in any judgement. That discipline has saved me more than once from standing behind a wrong conclusion.

Now back to the blank table from the start. When I look at it a second time, I no longer feel disappointed. I see a signal. An empty analysis does not say swimming lacks data — swimming is one of the most data-dense sports there is. It says that somewhere, the collection stage failed. And more importantly: if I try to interpret it, I will invent numbers.

That is the line a sports-data worker must never cross. I can analyse a bad number. I can warn about a controversial number. But I cannot fill in an empty space and call it analysis.

The line between analysis and fiction is thin. It is only as thick as the one time you allow yourself to "estimate" instead of "verify".

In the swimming betting world — where I worked for years — getting this wrong is suicide. Swimming markets settle on margins measured in hundredths of a second. A reaction time off by 0.05 seconds can flip an entire assessment of an athlete. People ask me why swimming data is harder to analyse than football. The answer lies in its very precision. Football can paper over error with probability. Swimming cannot. Swimming strips that camouflage away and leaves a bare number that cannot be argued with.

That is why, when you see an empty swimming dataset, read it differently. Do not rush to fill it. Ask: what happened before this? Who collected it? Where is the source? Because in this sport, a blank space is also data — it is just data about people, not about lanes.

That Saigon summer, I learned that data also needs watering. A number without a source is like a tree without roots: it may still look green, but the day it falls is only a matter of time.

An empty dataset is a confession. It confesses that at some stage, people abandoned the process.

Here I want to say something that runs against the instinct of the crowd. When someone meets a blank dataset, the first reaction is usually to find another source to fill it in, so that "the article can still run". I think that is precisely the mistake. In swimming, and perhaps across sports analysis in general, the greatest value of a good process lies in its willingness to stop when the data is not enough. A system that refuses to analyse a blank space is more trustworthy than one always ready to deliver a conclusion.

A good analyst is not someone who can answer every question. It is someone who knows they are not permitted to answer before the evidence exists.

I keep that blank table on my machine, undeleted. Every time I open it, it reminds me of one thing: 2,400 matches or 2,400 swims, all of them only hold value when each number still remembers where it came from. Swimming stopped moving, but the lanes still whisper in my spreadsheet — and I must listen to them the way they deserve to be heard.