The Empty Dataset and the Ethical Line of Sports Analytics

THE EMPTY DATASET AND THE ETHICAL LINE OF SPORTS ANALYTICS In November 2026, in a small office on Nguyen Thi Minh Khai Street in District 1, Ho Chi Minh City, I reopened a spreadsheet I had named three weeks earlier: Technical Dossier - International Qualifiers. It held fourteen worksheets, ninety-two column headers, and not a single row of data. I looked at it for four minutes, then typed three characters into cell A2: N/A. That was the entire work of that morning. No analysis. No forecast. Not one sentence of judgement about any swimmer. An outsider would think I had wasted three weeks of preparation on a dossier worth a few thousand dollars. In this trade, though, typing N/A into an empty cell is the hardest decision, and the correct one. Numbers do not lie, but they know how to hide something. When you hold no numbers at all, the only thing you can do is stay silent — or fabricate. Three weeks earlier, a sports media outfit had sent me a package: four articles, two news briefs, one interview excerpt, and a preliminary statistics sheet from a regional swimming meet. The brief was clear: rebuild technical profiles of the competing swimmers, benchmark them against international standards, and offer a judgement on their competitiveness. I accepted. Three weeks later, opening the package to begin, I found that nearly all the quantitative data had vanished during extraction. Not part of it. All of it. The preliminary stats returned a bare skeleton: correct headers, correct metric names, correct formatting — and not one value. The finish-time column was empty. The heat-result column was empty. The valid-turn-count column was empty. Data people call this a null-input case. Structurally valid, substantively zero. That is the moment that defines my profession. THE ORIGIN OF A PRINCIPLE I entered sports journalism in 2026, on the sports desk of a major daily, covering swimming. I was twenty-two, reporting on the SEA Games and domestic meets with a notebook and an old camera. Swimming is a strange sport: it has rules precise to the hundredth of a second, yet its public data infrastructure in Vietnam is startlingly thin. For years, all I had were printed result sheets, sometimes wrong in both names and times. That Saigon summer, I learned that data also needs watering. Without it, it dries out and dies, and you sit before a pile of dead branches believing you own a garden. In 2026 a harder lesson arrived. I lost two million dong on a football match because I followed a senior colleague's gut feeling. Angry, I built a manual expected-goals tracker across ten rounds of a V-League club. The result stunned me: the team scored 13 goals from just 9.2 units of expected goals. Forty percent over-performance. I wrote a warning that this was an unsustainable anomaly and was berated by readers. By round sixteen, the team went completely silent in front of goal. Since then I have held one rule: never write that a team is playing well without a specific number beside it. My first spreadsheet was called Chance Counting Data. At the 2026 World Cup, before South Korea met Germany in Group F, the world priced a heavy German win. I computed passes allowed per defensive action manually and got 11.2 — meaning Germany's midfield was allowing unusually intense pressing. PPDA is not a number; it is a confession. South Korea won 2-1. Total expected goals in the match was 1.4. In 2026 global football stopped. I was twenty-six, still a junior staffer despite three years of experience. Real-time data became worthless overnight. By ISTJ instinct I did not panic but planned: eight months archiving data from 2,400 Serie A matches between 2026 and 2026, then regressing it against Asian handicap movement. I found a classic away-team bias: bookmakers systematically underprice away sides by roughly five percent. Football stopped moving, but 2,400 matches kept whispering in my spreadsheet. When football returned in 2026, I was the only mid-level staffer in the company holding a structured forecasting system. But the biggest lesson was not the system. It was learning how to say no to data. NINE DIMENSIONS, AND THE PRICE OF FILLING A GAP When a dossier lands on my desk, I run nine analytical dimensions. In swimming, they look like this. One, technical analysis. Swimming is a sport where technique is legislated to a punishing degree. Start, underwater phase, turn, finish — four segments, each capable of deciding hundredths. The fifteen-metre rule caps underwater distance after the start and each turn in freestyle, butterfly and backstroke. Breaststroke permits only a single dolphin kick after the start and each turn. The backstroke start ledge was approved in 2026 and reshaped the timing structure of the entire backstroke programme. Without split data you cannot say anything about technique. You can say a swimmer looks smooth. Smooth is not a metric. Stroke rate and distance per stroke are what separate good swimmers from efficient ones. Katie Ledecky is famous for swimming at low stroke rate with a very long distance per stroke — which is why she holds speed at metre 700 of the 800m while rivals fade. If your file lacks those two columns, you are commentating, not analysing. Two, performance and data. A swim result means nothing outside a coordinate system: world record, all-time list, current-season ranking. Paul Biedermann's 400m freestyle world record of 3:40.07, set in Rome in 2026 in the polyurethane suit era, has stood for more than fifteen years and is among the most contested records in the sport's history. On the women's side, Ledecky holds the 400m free record at 3:56.46 from Rio 2026, the 800m at 8:04.79, and the 1,500m at 15:20.48. Times must also be separated by course. A 25m short-course pool allows more turns, so times are always faster than in a 50m long-course pool. Mixing the two in a single ranking is an elementary error, and I have seen it in at least four Vietnamese sports reports in the past year alone. Three, competition systems and entry mechanisms. Swimming is strictly tiered: Olympics, long-course world championships, short-course world championships, World Cup, continental meets, national meets. Each tier serves a different function. A World Cup in October is not a venue for judging Olympic form. The Paris 2026 qualifying standard in the men's 100m freestyle was 48.34 seconds for the A cut and 48.58 for the B cut. A B-cut swimmer only travels if their nation's quota is unfilled, which entirely changes how their heat result should be read. In Vietnam, the qualifying story is tied to one name: Nguyen Huy Hoang, born in 2026, silver medallist at the 2026 Asian Games in the 1,500m freestyle and a repeat Olympic qualifier across distance events. But reading Hoang's file by results alone reads only half of it. You need to know how many meets he swam that year, which were peak targets and which were accumulation. Four, the world map and event landscape. Global swimming runs three talent-supply models. The American depth model relies on the collegiate system, where thousands of athletes compete over four years and generate an extremely thick middle class. The Australian single-point-breakthrough model, where a few outstanding individuals carry the sport in specific events — Ariarne Titmus and Mollie O'Callaghan in women's freestyle, Kaylee McKeown in backstroke. And the Chinese event-cluster model, where resources are concentrated in a narrow band of events: women's butterfly, men's individual medley, and more recently men's freestyle through Pan Zhanle, who broke the 100m freestyle world record with 46.40 at Paris 2026. Read this map without nation names or event names and there is nothing to read. Five, rules and anti-doping governance. World Aquatics governs the sport globally, working with the World Anti-Doping Agency and national anti-doping organisations. This is a field where the smallest misstep can destroy a career, and also the field most prone to insinuation. The Sun Yang case is the great lesson: an initial eight-year ban, reduced to four years and two months, ending the peak career of one of the finest freestyle swimmers in history. The 2026 Gwangju incident, when Mack Horton refused to share the podium, showed that a doping case is never only about one individual. My position is simple: with no documentation, do not insinuate. Unfounded insinuation is defamation dressed as expertise. Six, athlete careers and team systems. Swimming's age curve differs sharply by event. The 50m and 100m peak between twenty and twenty-five. The 800m and 1,500m extend into the late twenties. Individual medley and butterfly sit in between. Some female swimmers peak at seventeen; others still race at elite level at thirty. Sport-specific injury risk matters too: swimmer's shoulder, breaststroker's knee. Without an injury history you cannot assess a swimmer's capacity to carry a dense competition calendar. Seven, risk profile. Competitive, career, anti-doping, rules, psychological and public-opinion, systemic. Six categories, each requiring its own input data. Without inputs there is no risk assessment — only guesswork dressed in jargon. Eight, public narrative and expectation. This is the most manipulable dimension of all. Vietnamese swimming passed through a phase of extreme expectation around Nguyen Thi Anh Vien, born in 2026 in Can Tho, who became the emblem of the sport with dozens of SEA Games golds. Expectations placed on her at continental and world level far exceeded the real gap in coaching infrastructure. The distance between public expectation and objective system capability is worth measuring, but only measurable when you have baseline data. Nine, industry ripple effects. Swimming moves the coaching market, the equipment industry, event business, the agency ecosystem, venue investment and derivative markets. An Olympic gold is never just a medal: it lifts coaching fees, floods a nation's learn-to-swim classes, and drives up broadcast rights for the national championship. At Paris 2026, World Aquatics announced roughly fifty thousand dollars per gold medal — a figure that turns a medal into a priceable asset. But to say any of that about a specific athlete, you need to know what they won, where, and when. WHERE I STOP Back to the empty spreadsheet. If I wanted to, I could write a two-thousand-word analysis from that package. I know the structure. I know the terminology. I know which names are famous enough to give the piece weight. I know how to weld a sentence about the fifteen-metre rule to a sentence about a world record and create the impression that I am saying something profound. But all I would produce is a building with no foundation — beautiful until the first reader asks where the numbers came from. There is a phenomenon I call the gap-filling trap. The human brain cannot tolerate blank space. Faced with an empty cell, the first reflex is to fill it. And with no real data, it fills with whatever is at hand: old experience, professional bias, or worse — feeling. I have watched analyses of Vietnamese swimming at the SEA Games written entirely from feeling, assigning swimmers technical traits no one had confirmed, not even their own coaches. And this trap does not live only in the analysis room. It lives in how we read sports news every morning. When football stopped during the pandemic, many people in this trade wrote about matches that never happened, about form with no basis, purely to keep publishing rhythm. I understand that pressure. I have been inside it. But I also know its price: trust. Emotion is the most expensive commodity on the transfer market. It is also the most expensive commodity in this craft. A COUNTER-INTUITIVE ANGLE The interesting thing is this: a null result is the most honest result of all. In statistics, finding no correlation is itself a finding. But sports media does not like that finding, because it does not sell. A headline saying we lack enough data to conclude gets no clicks. A headline calling someone the successor to a legend gets hundreds of thousands of reads. Over more than a decade watching swimming, the pattern I see most is this: a young swimmer posts a very fast time at a small meet, media instantly builds a succession narrative, and eighteen months later that swimmer has vanished from the international map. Not because of a lack of talent, but because the sample was too small to say anything, and pressure built on a small sample has nowhere to land. Correlation is not causation. A swimmer changes coaches, swims faster, and wins a medal. Those four variables occur together in one timeframe. Nothing yet proves the first caused the fourth. With Vietnamese swimming, I believe a reverse bias exists. Most people believe we lack talent. Looking at the SEA Games medal table, that seems right. But look at the infrastructure — the number of competition-standard 50m pools, the number of internationally certified coaches, the number of twelve-to-fifteen-year-olds receiving structured training — and the problem is not talent. It is the filtering mechanism. A country with a hundred thousand children who can swim but only five elite training pipelines will always lose to a country with thirty thousand children who can swim and fifty elite pipelines. The first number looks more impressive. The second is the number that decides. That is the kind of thing surface data hides. And to see it, you must accept that there are questions you do not yet have the data to answer. SIGNALS FOR THE NEXT ROUND My spreadsheet still has no data. Three weeks on, the media outfit has not resent the originals. Perhaps they forgot. Perhaps the fault was in the transmission line. Perhaps the source article I was assigned to analyse was simply never ingested into the system. I do not know. And my not knowing is itself information. What I do know is this: in transfer season and in competitive season, noise will always outrun signal. Someone will always tell you a swimmer is about to break a record, that a national team has found a formula, that an Olympic quota has been decided before qualification even begins. Most of those claims will have no data behind them. The reader's job is not to believe or disbelieve. The reader's job is to ask: where is the number. Every goal is a data point, but not every data point is a goal. So it is with every empty cell in a spreadsheet — it is not ignorance. It is honesty, recorded in a format. My profession is not about knowing a lot. It is about distinguishing what I know from what I do not, and writing precisely to that line. In three more weeks, the file may be filled. Then I will write. For now, cell A2 still holds three characters: N/A. And that is the most important row of data in the entire file.