When a Swimming Analysis Is Left Blank: Why Data Journalists Must Say “Not Enough Information”

At 4:47 a.m. in Miami, I received an email from my editor. Attached was a PDF labeled “Technical Analysis – Stage 2.” I opened it, expecting heat maps, split-time tables, stroke-rate charts, or a list of athletes ranked by risk. Instead of numbers, I found a long chain of “N/A” running from “Technical Analysis” to “System Risk.” No swimmer, no event, no result, not even a competition date. A long analysis document that contained no content that could be analyzed.

The editor asked one short question: “Where is the article?” I answered: “The data is insufficient. If you want an analysis, we must go back to the extraction stage.” When the editor says no, I learn to listen to the data.

Context: Every analysis begins with extracting information

In my working method, before writing any technical judgment, the system must pass through a step called “Stage 1.” This is when the original article is broken into small pieces of information: title, source, date, people, events, numbers, the author’s viewpoint, and its level of reliability. Stage 1 is like locating a swimmer before pressing the timer. If you do not know which lane, which meet, or whether the pool is 25 meters or 50 meters, every subsequent number is meaningless.

The document I received was the result of Stage 2, but the input data for Stage 1 was empty. The whole analytical framework returned messages such as “insufficient information,” “cannot assess,” and “no comparison possible.” On the surface, this looks like a defective product. Look closer, and it is actually correct behavior from a system designed not to invent stories.

In swimming, if I have no data for the first 15 meters, I cannot say whether an athlete’s underwater technique is improving or declining. If I have no stroke-rate data in the middle of the race, I cannot conclude that a gold-medal candidate will “explode” in the final 50 meters. In football, without tracking data, my expected-goals model cannot produce a prediction. A data reporter should not jump from emotion to conclusion. Emotion can be the starting point of a hypothesis, but it should never be the ending point of an article.

This “blank” analysis is still valuable because it reveals what we do not know. To a regular reader, a list of N/A items seems boring. To a professional, it is a map of data gaps. To praise a swimmer’s finishing speed, you need split times. To suspect an athlete of doping, you need test results. To report a transfer rumor, you need contract clauses, fee structure, and the agent’s actions. When all of these are missing, the honest choice is to stay silent.

Core data analysis: When the system says “no” to an unsafe guess

I have spent years learning that a good prediction model is not one that always gives an answer. A good model must also know when to refuse to answer because the information does not meet a confidence threshold. The analysis I read had nine major blocks, from “Technical Analysis,” “Performance Data,” and “Competition System” to “Compliance Risk” and “Industry Impact.” In every block, the system ended with the same phrase: “cannot assess.” This behavior reminds me of a car that automatically shuts off its engine when oil pressure is too low. The shutdown may annoy the driver, but it protects the engine from long-term damage.

Based on my experience following swimming trials for many years, I have learned a rule: the results of young swimmers are often inflated by observations that lack clean data. A boy who swims fast in an early-morning practice may be compared to a national record if the observer uses only the naked eye. But when the data is placed on a workbench, without height, arm span, pool depth, water temperature, or timing method, every compliment cannot become a reliable judgment. I do not argue emotions; I present the data chain. The data chain in this article is an empty one.

In the “Hidden Information” section, the system wrote clearly that nothing could be inferred when no original article existed. I find that a strong point. Many analysts have a habit of looking for “hidden intentions” of swimmers, coaches, or federations without evidence. They see an ambiguous sentence on social media and immediately conclude that the national team is about to change its coach. They see a closed practice session and conclude that an athlete is hiding an injury. In data journalism, that kind of inference is dangerous. If the input is empty, the “hidden information” section must also be empty. No exceptions.

The system also refused to simulate disciplinary scenarios when no incident existed. This is especially important in the anti-doping field. When there is no positive test, no disciplinary process, and no official sanction, speculating that an athlete “might be doping” is just a way to release toxic rumors online. A sports journalist can write about risk, but must state clearly that it is a hypothetical risk, not a conclusion. The system in this document did the right thing by refusing to simulate any disciplinary scenario. I consider that a standard.

Contrarian angle: Writing less but staying honest

Many sports newsrooms consider an analysis without a strong conclusion to be a failure. Pressure for page views, pressure from deadlines, and pressure from news competition push editors to want aggressive headlines: “He will win gold,” “That team is finished.” But being right too early is also a form of rejection. If a journalist reaches a conclusion before data is ready, the article has two possible destinations: history refutes it, or readers detect the logical holes inside it.

I once had a football analysis rejected because my editor thought an expected-goals chart was too difficult. That article sat in a drawer for two weeks; after that, I published it on my personal blog. It did not create a wave, but it was shared by an analyst in Europe. More importantly, if I had written a strong conclusion without supporting data, I would never have learned to accept uncertainty. The blank analysis today teaches me a different version of that lesson.

There is a thin line between “writing an article without a conclusion” and “having nothing to say.” If a swimmer has no results, no technical data, and no competitive context, we should not try hard to speak. We should return to the extraction stage and find the right question. When a data chain is too short, every answer is only noise. In the middle of a noisy stadium, I choose to sit with the numbers. Today my numbers table has no digits, but that does not make me feel empty.

Takeaway: Return the data to the first stage

The biggest mistake a data reporter can make is using imagination to fill a data gap. If I do not know who is swimming, I am not allowed to write about their technique. If I do not know whether a transfer deal includes a release clause, I am not allowed to announce “the player has agreed.” In the current transfer window, noise from social media accounts often drowns out real signals. Fans want to believe in a blockbuster transfer so badly that they share a rumor with no source. In times like that, saying “we have insufficient data” is the only way to protect the credibility of the profession.

The logical next step is not to delete this document. It should be sent back to the Stage-1 extraction unit and run again. If, after extraction, there is still no athlete, no event, and no data, I will propose publishing one short line: “We do not have enough data to provide a responsible analysis.” Readers may find this answer bland. But it is more accurate than a long analysis filled with fabricated numbers.

The match is over, but the data is still playing added time. This time, the extra time is not in the pool but in the information-extraction room. When nothing is ready, printing the words “not enough data” may be the biggest finding we have that day.