The Empty Cell Is More Dangerous Than the Wrong Number: Swimming Analysis and the Null-Data Trap

The Empty Cell Is More Dangerous Than the Wrong Number: Swimming Analysis and the Null-Data Trap

Rome, summer of 2026. The Foro Italico pool. Forty-three world records erased in eight days of competition, a summer that forced the whole swimming world to rewrite its textbooks. Sixteen years later, what keeps me awake is not that figure of 43. It is the silence sitting between two columns of my own spreadsheet.

That night I sat in front of the screen, gathering data for a qualifying-round analysis. I pulled time, reaction time, 50m split, stroke rate, distance per stroke. The spreadsheet opened. Some rows were full. Some were gaping empty. Reaction times for several swimmers were never recorded. The second 100m split was missing in three lanes. Stroke rate appeared only for the group that finished first. My job changed from that night on. I no longer only read numbers. I read the gaps.

An empty cell is not a zero. It is testimony never taken, waiting for someone to fill it in with a guess.

Swimming is cruel to data in its own way. Football gives you ninety minutes to be wrong and then correct yourself. Swimming gives you one hundredth of a second, and no replay. Everything happens under the surface, where cameras cannot reach, where the eye only catches a wake. So swimming data must be built from fragments: starting-block sensors, underwater camera systems, electronic boards, and the referee's hand. Four sources, four speeds, four margins of error.

When one of those four sources goes quiet, a gap appears. And a gap in swimming never stays still.

Based on my experience tracking thousands of swims across many seasons, I have drawn one conclusion: modern swimming data is becoming fuller, not emptier. Starting-block sensors measure reaction time to the thousandth of a second. Underwater systems count stroke cycles per length. Electronic boards sync with officials. The gaps mostly sit in small meets, in heats, where infrastructure has not caught up. At a major meet, with an elite swimmer, the data is dense enough to suffocate.

So the problem is not full versus empty. It is that we read the two kinds of data with two different attitudes.

Start with the young athlete files, the area I have tracked longest. A fifteen-year-old swims the 200m individual medley with a fine time. The time column is full. The height-growth column is empty. The physical-development-cycle column is empty. The shoulder-injury-history column is empty. The spreadsheet looks neat, because everything potentially uncomfortable has been left blank.

The risk in a young talent is not the performance figure. It sits in the columns nobody bothers to fill.

This is why I call puberty the silent wall of swimming. An athlete can break an age-group record at fourteen and vanish at seventeen, not because they trained badly, but because the body changes proportions, the arm span lengthens, the centre of gravity shifts, and the whole technique built on the old body collapses. The data does not record that moment. It only records the falling times. A lazy reader concludes: no potential left. That conclusion is as cheap as a lottery ticket, and exactly as dangerous.

Now the other side, numbers that are too complete. Rome 2026 is the classic case. Forty-three world records in a single meet, a record-breaking rate swimming history had never seen and will not see again. When FINA banned high-tech suits from 1 January 2026, people finally realised that the data of 2026-2026 did not measure people. It measured fabric.

This is the lesson I give new staff whenever they get excited about a beautiful results table. Before comparing two eras, ask what that era swam in. Textile or polyurethane. A 50m or a 25m pool. Water temperature, depth, bottom current. A lovely rising performance curve can simply be the curve of textile technology.

A full dataset can lie in a way an empty one cannot: it lies with confidence.

And here I must address the biggest prejudice in my profession, the belief that a number means a truth. I once believed it. In 2026 I lost money following a claim with no data behind it. So I built an Excel file, named it Chance-Counting Data, and promised myself I would never again say a team is playing well without a figure attached. But that same file taught me the opposite: once you have numbers, you easily forget which numbers you are missing.

Cross-checking is the answer. With swimming, I never judge an athlete on time alone. I need at least two other data groups. First, split structure. Does the swimmer go out fast or come home fast? A 100m freestyle swimmer who opens in 23 seconds and closes in 26 is a different creature from one who goes 24.5 and 24.5, even at the same total. Second, technical indices, stroke rate and distance per stroke. Winning by a high stroke rate and winning by a long stroke are two different paths, and they age differently.

When I have time, splits, stroke rate and distance per stroke, I begin to see the swimmer's true shape. When one of those two groups is missing, I am left with a bare number. And a bare number in swimming is usually someone else's number, from another condition, another body, another era.

Once, during a transfer window, a large sports company sent me an athlete file to review. Fine performances. A rising curve. But the shoulder-injury-history column was empty, and the weekly training-load column was empty. I wrote a single line in the report: these two empty columns cost more than the rest of the file combined. They did not sign the deal. Six months later, the athlete retired with a shoulder injury.

I tell this story not to brag. I tell it to point to a rule: in swimming, data gaps cluster exactly where the risk lies. Nobody forgets to record times. People forget to record injuries, forget to record training load, forget to record sessions skipped through pain. Those columns are blank not by chance. They are blank because they are not attractive.

Empty cells are not distributed randomly. They distribute along the gravity of a truth somebody does not want written down.

Emotion is the most expensive commodity on the transfer market, and an empty cell is where emotion lives.

There is another data type I value nearly as much as times, yet few bother to analyse: reaction time. In swimming, the start reaction lasts a few tenths of a second, but in races decided by hundredths, it is the entire race. A swimmer averaging 0.68 seconds and one at 0.74 can be half a body apart the instant they hit the water. Yet reaction time remains the column most often left blank in heat sheets. It only gets recorded in finals, when it is far too late to adjust.

And I must mention the pool itself, the most underrated variable. The same swimmer, the same body, but racing in a 25m pool and a 50m pool are two different problems. A short course has more turns and more starts per metre, so the advantage belongs to the good turner. A long course destroys that advantage. A short-course time dropped into a long-course comparison without conversion is a number placed in the wrong slot. It is not wrong in value. It is wrong in context.

In 2026, when football and nearly all sport stopped, I learned that real-time data is the most perishable thing there is. I spent eight months archiving old data, then regressed it to find market bias. That lesson transfers intact to swimming: a properly arranged historical archive answers questions a live scoreboard never can.

Here I must be careful, because I stand before the trap I keep warning about. Once you are too used to reading gaps, you start seeing gaps everywhere. You look at a complete file and suspect it. You look at a perfect spreadsheet and ask what it hides. That is the state of a person who has lost faith in data, and it is also a prejudice, only in the other direction.

Most spreadsheet readers make the same mistake. With full data, we doubt. With empty data, we fill the hole with feeling. We see a blank injury column and assume nothing happened. We see a blank development-cycle column and assume it is normal. Yet if that column held a number, we would spend hours dissecting it. That asymmetry is the largest source of error in swimming analysis.

The paradox sits here: a data gap can be a signal, but it can also just be a record-keeper's laziness. There is no way to tell the two apart without tracing the source. Who recorded this data. With what equipment. At what moment. Was it cross-checked against a second source. Correlation is not causation, and an empty cell is no confession until you know who left it empty.

In my trade, there is a line I use like a prayer before every report: numbers do not lie, but they know how to hide something. Empty cells are the same. They do not lie. They only hide, in a more dangerous way, because they leave no trace for you to doubt.

Back to Rome 2026 one last time. If someone that summer had split the swimmers into textile suits and high-tech suits, they would have found something strange: in the high-tech group, the gaps between swimmers were compressed abnormally. Many records, small margins. That is the signature of an outside variable squeezing the whole sheet together. Nobody needed underwater slow-motion to guess it. You only had to read the compression of the margins. That data did not lie. It only hid the fact that the swimmer no longer decided the result.

For the season ahead, as the Olympic cycle accelerates, I will watch three things. One, the share of results tables carrying all four data groups: time, splits, technical indices, and training-and-injury history. Two, young athlete files with an empty physical-development column, which I treat as a priority to check, not a priority to skip. Three, any meet with an abnormal rate of record-breaking, where I will ask about equipment and pool conditions before asking about the people.

Swimming keeps teaching me a lesson sixteen years in the trade have not finished teaching: the value of data is not in how many cells it fills, but in showing which cells remain empty and why. A good analyst is not the one with the fullest spreadsheet. It is the one who bends down to look at the empty cell, and does not rush to fill it with a story that sounds reasonable.

One question I cannot yet answer, and may keep for myself: when a gap in the data has existed so long that everyone treats it as normal, is that the record-keeper's fault, or the decision of someone who does not want it written down?

My spreadsheet is still open. The cell is still empty. And I have not filled it.