Swimming and the Blank-Data Problem: Why Analysis Needs an Input Gate

A Blank Spreadsheet and a Lesson from Kazan

On a Tuesday morning in Brisbane, I opened the spreadsheet for my swimming analysis session and found the central data column completely empty. No finish times, no 50-metre splits, no athlete names, not even a meet name. The only surviving element was a domain label: swimming. Thirty years of watching this industry have taught me that a blank dataset does not prove that nothing happened; it is a statement that the process dropped something along the way. In a sport measured in hundredths of a second, what gets dropped is usually the decisive moment.

I have seen the same thing at a larger scale. In 2026, in Kazan, I watched Germany hold 74% of possession and still lose 0-2 to South Korea. I wrote then that the team produced only 11 passes into the box and an xG of 0.7, lower than their opponent's. A week later, FIFA confirmed every number. Kazan was the day I learned that a 99% probability can still die on the betting table. But Kazan also taught me the opposite: a number only has value when it exists, when it is recorded, checked and cross-referenced. When the number disappears, we have nothing left to argue about, only belief.

And belief, in sports analysis, is the most expensive and most fragile thing there is. Numbers have no gender, but the people who read them do. I say that not to soften a blank spreadsheet, but to remind that behind every data cell there is a person waiting for a correct conclusion.

Swimming: A Sport That Does Not Allow Fabrication

Swimming is ruthless in its own way. There is no xG, no decisive pass, no contested ball. There is only time, splits, stroke rate, distance per stroke, the number of underwater kicks, and the distance covered before surfacing. An athlete can win or lose by 0.01 of a second. At Paris 2026, the men's and women's 100-metre freestyle finals were settled by a margin as thin as a breath. That is why data here is not decoration; it is the race itself. When a split is missing, we lose the ability to distinguish between an athlete surging in the final 25 metres and an athlete coasting to the wall.

That is why I always open any swimming analysis with a question about data completeness, not with a conclusion about a person. I once took part in the valuation race over Daniel Arzani, where I concluded the deal would fail based only on an average run of 8.2 kilometres per match, a dribble frequency of 2.1 per game, and a history of two torn ligaments. The sporting director objected, saying I looked at people like machines. Two seasons later, the player had logged a mere 20 minutes. Valuing a player is not a calculation; it is a battle between belief and the spreadsheet. But that battle is only fair when both sides hold the same dataset. When one side holds a blank sheet, the battle becomes a guessing game.

Technique: Where One Empty Cell Erases a Whole Story

In swimming analysis, the technical section is usually the most revealing and the easiest place to lose data. The four pillars of a swim are the start and underwater phase, turn and finish technique, stroke efficiency, and adaptability to the venue.

The underwater phase is the clearest example. In butterfly, backstroke and freestyle, athletes may stay submerged for up to 15 metres after the start and after each turn. That distance, along with the number of dolphin kicks, often decides the winner. I have reviewed hundreds of swims and found that missing data on the surfacing point alone can lead you to misread the entire tactic: an athlete who surfaces at the 12-metre mark at high speed looks like someone surging, when in truth they are simply riding the glide.

Turn technique is the same. A deep somersault, a touch that misses the wall by half a beat, all of it only becomes visible when you have 50-metre splits. Without splits, you are left with a single aggregate number, and an aggregate number conceals every technical error inside it.

In breaststroke, the rules are even stricter: both hands must touch the wall simultaneously, and each cycle allows only one kick. A single extra kick can invalidate an entire swim. In breaststroke, data is not just for analysis; it is for confirming validity. When that data is blank, you do not know whether you are discussing a record or a disqualification.

The venue is the final decisive variable. The same athlete over the same distance produces two different worlds in a 50-metre pool and a 25-metre pool. A short-course pool has more turns, and every turn is an opportunity to accelerate. Treating the two as equivalent is the most basic error a newcomer can make, and also the easiest to make when venue data is missing.

Performance and Data: The Coordinates of Truth

When data is complete, positioning a performance becomes a coordinate problem. You place the time on three axes: the world record, the all-time list, and the current-season ranking. Each axis yields a different gap, and that gap is the story.

Ariarne Titmus once pushed the women's 400-metre freestyle below the 3:56 mark at the 2026 World Championships in Fukuoka. Placed against the all-time list, that number does not stand alone; it sits within a continuous line of improvement by that same athlete over several years. That is the signature of a durable physical foundation, not a lucky one-off explosion.

Katie Ledecky, with her records in the 800-metre and 1500-metre freestyle, is an example of sample stability. When an athlete holds the peak across multiple Olympic cycles, her data sample is thick enough for us to trust the trend. Conversely, a fast time that appears once, at a small meet, with no comparable rivals, is a thin sample, and a thin sample cannot support a conclusion.

This is where the era factor and the suit factor enter. World records do not exist in a vacuum. The high-tech suit era of the early 2000s left behind a layer of records that is fundamentally different in physical nature. A responsible analyst must filter by era before comparing. Skip that step, and you will place two different things side by side and call it fair.

And behind all of it remains the question of source. Numbers have no gender, but numbers also do not generate themselves. They come from Opta, from Stats Perform, from official federation documents. A blank spreadsheet has no source, and a spreadsheet without a source cannot be cited. And what cannot be cited cannot be defended before a panel, let alone before an angry crowd.

Competition System and Qualification Mechanism

A decent swimming analysis does not stop at the time. It asks: what kind of meet is this, and what does this result mean within the Olympic cycle?

There is a vast difference between a performance at an early-season invitational and a performance at an Olympic trial. In the United States, the selection system is famous for its top-two model: only the first and second finishers in an event earn a place, regardless of reputation. In many other countries, selection relies on a comprehensive evaluation, where results across several meets are accumulated. These two systems produce two different mentalities: one is the pressure to peak on the right day, the other is the pressure to be consistent all year.

Competition density also shapes results. An athlete swimming five events across four days will enter the last final with shoulders already fatigued. If we do not know the schedule, we will call that decline a loss of form, when in reality it is a physical arithmetic problem. The same number, two explanations, and the correct explanation only appears when we have schedule data.

This is also where officiating risk surfaces. An early start, an extra breaststroke kick, a touch that is not simultaneous, any of these can turn a medal into a footnote. Without detailed data, we cannot separate a technical failure from a rules failure.

The World Map of Swimming

Swimming is a sport where the map of power changes slowly but surely. The United States and Australia remain the two largest poles, with impressive depth across most events. China has risen strongly in butterfly, breaststroke and women's freestyle. European nations such as Italy, France, Great Britain, Hungary and Sweden contribute isolated but sharp names.

When I draw this map, I do not draw it by medals but by the stability of the throne. In the women's 400-metre freestyle, the throne belongs to a small group of athletes capable of swimming under four minutes. In the women's 100-metre backstroke, Kaylee McKeown has almost defined an entire standard. In the men's 50-metre freestyle, Cameron McEvoy is an example of an athlete who found his own formula for a high-speed event.

What is notable is not who holds the throne, but the talent supply chain behind it. The United States relies on the NCAA university system, where thousands of young athletes compete every year. Australia relies on a system of sports institutes and clubs. Other nations rely on centralised national systems. Each model has a different production rate, and that rate determines who survives after every four-year cycle.

I once said at a seminar that scouting networks in developing countries find prodigies and also create lottery tickets and broken families. That was not a throwaway line. It is the consequence of a talent supply chain that measures only medals and never measures the price paid.

Rules and Anti-Doping Governance

Swimming is one of the most heavily drug-tested sports. A positive result can erase years of a career, and an unverified allegation can destroy a person before a sports tribunal ever speaks.

Here, the principle of separating fact from opinion becomes more important than ever. A case can be a confirmed violation, a contamination dispute, a procedural issue, or simply an allegation. These four categories carry four different levels of risk, and lumping them together is an ethical error, not merely a technical one.

Other governance issues are also running hot: sporting nationality switches, neutral status, and regulations concerning eligibility. Each issue has a data layer and an emotional layer, and the emotional layer is always louder.

I learned one thing from my own career: an empty anti-doping section does not mean there is no doping risk. It only means there is no content to evaluate. The silence of data is not a declaration of innocence.

Athlete Careers and Team Systems

Career analysis is the most human part and the easiest to mechanise. An athlete is not a simple curve; she is a body in change, a mind learning to endure, and a person living inside a system.

For young female athletes, the puberty barrier is the biggest and least-discussed risk. A changing body shifts the strength-to-weight ratio, and times that once came easily suddenly drift out of reach. An analyst who only looks at the spreadsheet sees a decline; an analyst who looks at the person sees a biological process unfolding and asks whether the training system adapted in time.

Injury is also part of the data. Swimmer's shoulder, breaststroker's knee, these are occupational risks documented in sports medicine. A young athlete swimming butterfly and freestyle at high volume each week is accumulating a debt that the body will call in at an unwanted moment.

And behind every athlete is a team: coach, strength specialist, doctor, recovery specialist. A coach's success rate when moving to lead a new squad is not a figure everyone publishes. But it exists, and it explains why the same talent, in two different environments, produces two different outcomes.

Risk Profile

Every decent piece of sports analysis must end with a risk profile, and that profile must be honest to the point of discomfort.

Competitive risk comes from rivals. Career risk comes from injury and age. Rules risk comes from thin boundaries. Psychological risk comes from the pressure of a single swim before thousands of spectators.

But the biggest risk I have encountered in this profession is not in the pool. It is in the process. A complete but distorted dataset can do more harm than a blank one. A blank spreadsheet that passes through a system without being stopped will become a fabricated conclusion, and a fabricated conclusion, once published on a reputable outlet, becomes truth. The highest risk is not a wrong prediction; the highest risk is a prediction with no basis that is presented as if it has one.

That is why I propose an input gate: no data points, no analysis. No source, no citation. No citation, no conclusion. These three sentences sound rigid, but they cost far less than the price of a single loss of trust.

Public Narrative and Expectations

Swimming generates grand narratives: the prodigy, the record night, the king's return, the transition, and the controversies. Each narrative has a heat cycle: budding, accelerating, climaxing, then receding.

What I always check is the gap between market expectation and objective reality. When the public expects a world record every time someone enters the pool, and when the data shows such records arrive only a few times a decade, that gap is where disappointment is born. That disappointment is not the athlete's fault; it is the fault of a narrative built on expectation rather than on fundamentals.

I have lived through this. After Kazan, I was attacked online by one group of fans and hunted by another. But a week later, when FIFA published the official numbers, every attack became meaningless in the face of an incontestable dataset. I do not trust emotion. I trust a data chain longer than your emotion. But I also know that emotion is what makes viewers turn on the television, and an analyst who denies emotion is an analyst cutting away half of the data.

Industry Ripple Effects

Above the pool sits an entire ecosystem. Upstream is youth development, the coaching market, and the talent supply. Midstream are the athletes and the meets. Downstream are broadcasting, sponsorship, equipment, and derivative markets.

A swimming star does not stop at winning a race. She drives sales for a swimwear brand, boosts enrolment in learn-to-swim programmes back home, and opens a new stream of media content. But that effect is only measurable when there is data on sales, on enrolment, on viewership. Without data, the ripple effect is merely a good story.

And this is where I am most careful, because ripple analysis is the most inference-heavy part. It is the easiest place to fabricate, and also the place where fabrication looks most plausible. When the input is blank, the correct behaviour is not to fill it with guesswork, but to say plainly: there is not enough data to conclude.

The Counter-Intuitive Angle

The counter-intuitive point here is this: a complete dataset can do more harm than a blank one.

That sounds absurd, but think it through with me. A blank sheet is obviously blank. It forces us to stop, recheck the process, and go find the source. A complete but distorted sheet, however, looks perfect. It fills every cell, it has units, it has clean formatting. It makes us believe we are analysing, when in fact we are decorating an error.

This is the greatest lesson of Kazan. Before Kazan, I believed that correct data was enough. After Kazan, I understood that correct data is only a necessary condition. The sufficient condition is correct data in the correct context, read by someone who knows that behind every calculation is a person with a gender, with emotions, and who can die even when the probability is 99%.

Correlation is not causation. An athlete who swims faster after changing coaches does not prove the new coach is better. Perhaps she has just passed through puberty. Perhaps the pool is faster. Perhaps she simply slept properly for the first time in months. Data gives us correlation; assigning causation is a human act, and that is where humans err most.

The blind spot of the swimming analysis industry is not a shortage of data. It is excessive confidence in the data it has, to the point of forgetting to ask whether that data is actually saying what we think it is.

What Remains

At the end of every analysis, I always set aside a section to draw a map of limits: the zone where data can assert, the zone where data is ambiguous, and the zone that must rely on intuition, where five years underwater, growing up in Vietnam and working in Australia give me an advantage to speak without numbers.

A blank spreadsheet, on a Tuesday morning in Brisbane, has never been a disaster. It is a reminder. It reminds us that before asking who swims fastest, we must ask who is holding the clock, and whether that clock is running true.

The next round of this story is not in the pool. It is in the process. And the signal I will be watching is not a new record, but the question of whether we have the courage to say there is not enough data before we say I know for certain.