N/A Is Not Failure: Data Voids and the Trap of the Sports Analyst

Saigon, July 2026. The city was under Directive 16 lockdown. I sat in front of a screen with an Excel file of 247 rows. Each row was a V.League injury between 2026 and 2026 that I had spent three months reviewing on tape, logging hip-joint muscle torque, consecutive minutes played, rest intervals between matches, and positional injury history. Column twelve read “recovery time.” Nineteen cells in that column were empty. Four years earlier, I would have filled them in. I would have used the average of the other 228 rows, or worse, my intuition about how long a winger usually sits out. That was how I used to work. That was also how I got Nguyen Van Quyet wrong. That night I stared at those nineteen empty cells for a long time. They were not a defect in the spreadsheet. They were a reminder that some questions cannot yet be answered by the data I hold, and that filling them would only make the file look fuller while the truth stayed the same size. The gaps in Vietnamese sports data do not make noise. They do not demand to be filled. Only the analyst demands that, because we are paid to speak, and silence is a hard product to sell. In March 2026 I was working as an analyst for a new sports channel in Saigon. The programme covered the V.League title race, and I was asked about striker Nguyen Van Quyet of Hanoi FC. I read the public medical report, saw the phrase “thigh injury,” saw an early line suggesting roughly two weeks, and said on air: two weeks, back within two matches. The reality was two months. The hamstring tendon tear was more severe than the public report described, and I had read the number without reading the mechanism. What I did not want to admit at the time was this: I had no data at all on Nguyen Van Quyet's hamstring. I had one line of news, one word — “thigh” — without an anatomical location, and a professional habit of filling gaps with experience. Experience is not data. It is only the data of the past retold through memory, and memory always favours what I want to believe. I once thought I was right. Nguyen Van Quyet taught me that a body does not need my agreement. After that incident I spent three months re-watching V.League injury footage from 2026 to 2026 and built a database of 247 cases with muscle-torque indices and match-history records. Not to predict faster. To predict less. The context of Vietnamese sports analysis today is a paradox. We have more televised matches than ever, more cameras, more discussion, more experts. But the volume of structured data grows far more slowly than the volume of opinion. A single V.League match can generate hundreds of commentaries and thousands of social posts, yet figures on workload, on accelerations above 5.5 metres per second, on sudden decelerations by a player returning from injury, are almost never published. I call this the “silent gap.” Fans cannot see it because there is nothing to see. Journalists do not write about it because it is not news. Experts do not mention it because mentioning it means admitting they do not know. And exactly at that intersection, the market creates a habit: filling the gap with belief. In sports medicine, that habit has a near-equivalent name — attribution bias. When we are forced to explain something without sufficient data, the brain grabs the nearest, most memorable sample and presents it as a conclusion. A player goes down in the seventieth minute without contact, and we say “dehydration.” A player returns after three weeks and goes down again, and we say “psychology.” Both statements may be true in some cases. Stated as rules, both are fabrications. What I learned from 247 V.League injuries was not the average recovery time. That is what people ask me most, and what I answer least. The average is distorted by those nineteen empty cells. What I learned is that the quality of an injury database depends on the quality of its blank rows — that is, on whether we record that we do not know. I divided the 247 cases into three groups. The first had full medical reports, recovery times and follow-up results — about 61 percent. The second had incomplete reports, listing only a general anatomical region such as “thigh,” “knee,” or “ankle” — about 33 percent. The third had no data at all, only a note that a player was “absent for unspecified reasons” — about 6 percent. The worrying group is the second. Not because it is large — it is only a third. But because it is exactly the group that media analysts are most likely to fill with invented numbers. One word, “thigh,” could mean a hamstring tendon tear, a strain, or a contusion. Three conditions, three clearly different recovery windows, one word in the report. Any commentator who reads “thigh” and answers “two weeks” has arbitrarily chosen one of three possibilities, usually the mildest, because it sells good news. There are injuries that are not in the tendon, but in how we look. Data is only dry bones; it needs context to become blood vessels. The 2026 World Cup in Russia was where I saw how large the data gap can be at national-team level. Thanks to my accumulated injury database, I was invited as a guest analyst by several domestic channels. I carried one question with me: is VAR changing injuries at major tournaments? The results from 48 group-stage matches made me stop. Non-contact injury rates rose roughly 34 percent compared with the 2026 World Cup, with 18 recorded muscle tears. These did not come from collisions. They came from movement — from sudden accelerations after standing still waiting for a decision, from defenders retreating earlier to avoid offside positions being reviewed, from play stopping and restarting repeatedly so that muscles had to fire before they had warmed again. I do not say VAR is the cause. I say the mechanism is a change in match rhythm, and VAR is one agent of that change. This is an important distinction that media often skips, because “VAR causes injuries” is a headline, while “changed match tempo raises muscle load” is not. Before blaming VAR, ask why we need it. My answer lies on the stands, not in the VAR room. We need VAR because we cannot tolerate refereeing mistakes, yet we easily forgive analysts' mistakes. That is an asymmetry. Referees are scrutinised frame by frame, while analysts are allowed to speak without proof. VAR did not kill football. It only exposed our fear of error. To this day I remember one small detail in Japan's round-of-16 match against Belgium. I predicted Japan could hold on through well-managed fitness without needing high-intensity pressing for the whole match. That prediction was right. But it was right for a reason I do not want to claim credit for: the data I used came from an incomplete database, and I was lucky to choose the right gap to doubt. The pandemic was when I decided to turn the gap into a research programme. In March 2026, world football froze. European leagues paused. In Vietnam, the V.League also stopped and then returned under unprecedented conditions. Rather than sit idle, I collected data from six European leagues after football resumed in June and found hamstring injuries up roughly 41 percent year on year against 2026. The number did not surprise me. What surprised me was that muscle injuries among players who had rested more than 45 days were about 2.3 times more likely than among those who rested fewer than 45 days. From that I built an index I call the Load Decay Index. The principle is simple: the human body does not forget load. When a player rests for a long time, the muscular system does not lose the capacity to bear load, but the threshold at which it can bear load falls. Returning to near-previous intensity, the body reacts as if attacked. The model correctly predicted 14 of 17 injuries when the Premier League restarted. It held again at Euro 2026. But what interested me more was that it failed on the other three, and those three taught me that an index is not truth — it is only a more disciplined way of asking a question. The pandemic taught me that data lies, but does not forget. In Vietnam, the V.League's post-lockdown return in 2026 showed a similar pattern, but the compressed schedule differed from Europe. Teams had to play in clusters, with fewer rest days and harder travel conditions. I did not have enough data to publish an independent conclusion for the V.League, and I did not publish one. That was one of the rare times I chose silence. A few colleagues complained that I “wrote too little,” but I think writing little and being right beats writing much and being empty. The 2026 World Cup in Qatar was my worst failure as an analyst. The tournament took place mid-European-season, and I was swept into a new meta: high-intensity pressing in the Ralf Rangnick school. I spent two weeks reviewing 364 injury situations in the tournament, trying to determine whether increased pressing raised injury risk. I wrote three pieces, and the three pieces reached three contradictory conclusions. My editor could barely publish them. In the end we ran all three as a series of notes under a single heading: “Insufficient data to conclude.” That was a classic execution failure of mine: too curious to stop digging, too analytical to close. Looking back, though, I think the lesson inside it matters more than any conclusion I could have drawn. When data is insufficient, forcing a conclusion does not make the data sufficient. It only makes me look as if I have an answer. Vietnamese sport is entering a phase where data gaps will be filled by artificial intelligence, by prediction models, by social media. I do not think that is bad news. But I think one principle is needed: a good model is not one that always produces a number. A good model is one that knows how to say “insufficient data.” Swimming in Vietnam is where I see this gap most clearly, because I came from there. An elite swimmer can generate hundreds of data points in a single race: reaction time off the blocks, time to 15 metres, number of underwater dolphin kicks, split times per length, stroke rate, distance per stroke, closing speed. When I was writing for a sports newspaper early in my career, we had almost none of those numbers. We had total time, and we had feeling. Feeling is not data. It is data read through emotion. And in swimming, emotion is governed by something very understandable: Vietnamese people want to see Vietnamese athletes win. When that desire speaks, the data gap fills itself with hope. With Nguyen Thi Anh Vien, we had one of the finest swimmers in Southeast Asia between 2026 and 2026. But what I found missing in the analysis of that era was not praise. It was comparative data on race structure between Anh Vien and her direct rivals in each event. I looked repeatedly for split data from Asian championships and could not find it. Not because nobody kept it. Because nobody published it. With Nguyen Huy Hoang, a swimmer with a long development arc in the 800 metres and 1,500 metres freestyle, the gap is even larger. Distance swimming is a sport where pacing strategy decides almost the entire result. But without time per 100 metres, we cannot judge whether a swimmer accelerated at the right moment or simply endured to the end. And when we cannot judge, we tend to describe with adjectives: “extraordinary effort,” “fighting spirit,” “transcending oneself.” Those phrases are not wrong. But they are not analysis. What I have drawn from years of writing about swimming and then moving into football is this: data gaps are not closed by writing better. They are closed only by logging more systematically, publishing to a higher standard, and above all by accepting that some answers we simply do not have. Of the nine standard dimensions of sports analysis — technical, performance, competition system, world hierarchy map, rules and anti-doping governance, athlete career, risk profile, public narrative, and industry ripple effects — there is one I always check first: which dimension is N/A. N/A stands for “not applicable” or “insufficient information.” In my work it tends to appear in exactly the cells I most want filled. When analysing a player returning from injury, for example, I need comparative data before and after the injury. If I have only the after, my analysis is not analysis — it is description. That is the first N/A dimension. When analysing a tournament, I need to know whether it is a friendly, a qualifier, or a peak event. Each requires a different reading of results. A fine performance in a friendly does not carry the same value as an identical performance at a championship. If the brief does not say, I mark that dimension N/A. That is the second. When analysing a tactic, I need to know whether the schedule is congested. The same pressing scheme produces a completely different outcome across three-day and seven-day turnarounds. No schedule, no conclusion. That is the third. When I count the N/A dimensions in a brief, I do not treat it as a weakness of the brief. I treat it as a risk map for the analysis I am about to write. If four of nine dimensions are N/A, I know I should only conclude weakly. If seven of nine are N/A, I know I should not write the piece. If nine of nine are N/A, I know I am standing in front of a void, not a subject. And this is what I want to say plainly: a void is not a failure. It is one of the most honest results an analyst can publish. The trap I want to warn about is not on the data side. It is on our side. In sport there is an almost physiological pressure to have an opinion, to make a remark, to file a piece, to offer a prediction. Nobody is paid to say “I do not know.” That pressure is especially strong in the transfer window — the period we are in now. In the transfer market, injury is the interruption everyone pretends not to hear. A player in negotiation with a history of hamstring injuries is more important information than any rumour about the fee. But fans usually care only about the fee, because the fee is a number, and numbers are easier to communicate than medical history. This is where the data gap turns from a technical problem into a financial one. Clubs hold internal medical data but do not publish it, because publishing would weaken their negotiating position. Agents hold data but have incentives to filter it. Journalists hold little data but need stories. Fans hold many opinions and very little foundation. In that chain, the gap is filled with belief, and belief is sold as an “exclusive.” What I have learned after many years is that when you do not have enough data, do not try to look as if you do. Publish the gap. There is a simple way to do this. Instead of writing “player X will miss two weeks,” write “the public report lists a thigh injury without severity, so recovery ranges from one to seven weeks depending on the specific damage.” The second version is less attractive than the first. But it is more honest, and it teaches readers that medical data has its own structure — it is not a number to be tossed out to please someone. This, I believe, is the difference between analysis and commentary. Commentary demands agreement. Analysis only demands verification. A good analyst is not the one who gets the most attention. It is the one after whom the listener knows exactly which parts to believe and which to doubt. Once a young editor asked me why I did not offer bolder predictions for matches. I answered with a question: “If you predict ten matches and get six right, are you better than someone who predicts two and gets both right?” She went quiet. I think she understood. Every injury is a story the body is trying to tell us. But we usually rewrite that story in our own voice, because the body's voice is hard to hear and unsuitable for television. If I could leave one thing to the next generation of sports analysts in Vietnam, it would not be a set of indices. It would be a discipline. That discipline lies in asking, before saying anything: which dimension of my analysis is N/A, and am I about to fill it with belief? The only conclusion I can state with the greatest certainty in this profession is this: the gap does not make noise. But it is there. And how we treat it determines whether we are analysts, or merely people who talk a little more than everyone else. That night in Saigon, I closed the Excel file with the nineteen empty cells still empty. I did not fill them. I saved it and added one line to the notes column: “19 cases with insufficient data. Need original medical records.” Three months later I obtained six. The remaining thirteen I have left blank to this day. Someone asked why I did not delete them for tidiness. I said they were the most honest part of the sheet. A sport can develop by adding data. But a mature sport is one that knows how to mark what it does not yet know, and keeps that mark until evidence arrives. That is what 247 V.League injuries, 48 group-stage matches at the 2026 World Cup, 364 injury situations in Qatar in 2026, and nineteen empty cells in an Excel file on a Saigon night have taught me. I no longer believe silence is a weakness. In a field where everyone must have a voice, the person who knows when to stay silent may be the only one actually telling the truth.