Vietnamese Athletics Enters the Age of Verification: Every Stride Needs a Number Behind It

VIETNAMESE ATHLETICS ENTERS THE AGE OF VERIFICATION: EVERY STRIDE NEEDS A NUMBER BEHIND IT A number appears on the electronic board, the stands rise to their feet, and the roar erupts. But the first question I ask is not "how fast," but "how strong was the wind, was the track sloped, and did the shoes contain a carbon plate." The fan sees a moment. I see a chain of data that must be verified before it is allowed to become truth. In athletics, a number never stands alone. It rests on a frame of reference: wind direction, altitude above sea level, the slope of the track, the material of the racing shoe, and even the eligibility rules governing the athlete. Ignore that frame, and we are no longer analysing sport — we are merely retelling a feeling. I write from Hai Phong, the city that taught me that a star is not worn on the shirt but lives inside the metric. The day football stopped, I began counting every stride again. When the pitch was no longer my daily workplace, I moved to athletics — a sport where everything can be measured, and therefore everything can be misread if we are too lazy to verify. For more than twenty years I have travelled from the meeting room of a football club to the data desk of athletics. The biggest lesson did not come from a victory; it came from a moment when I nearly stated something I could not prove. That is why this article does not open with a conclusion. It opens with a question: how are we reading athletics, and are we reading it correctly? CONTEXT: WHEN ATHLETICS BECOMES A DATA SPORT For most of the twentieth century, athletics was remembered through names and moments. People told each other about an athlete who ran like the wind, a jump that exceeded imagination, a night when the whole stadium held its breath. Sporting memory is woven from emotion, and there is nothing wrong with that. But over the past two decades, a layer of data has settled on top of that layer of emotion. World Athletics runs a system of results, athlete profiles, and technical standards detailed almost beyond belief. Every track is measured with specialised equipment. Every throwing and jumping event has equipment rules. Every wind reading is recorded and archived. Athletics, one could say, is the sport where truth is protected by paperwork. In Vietnam, this process has been slower but is accelerating clearly. National meets are increasingly staged with electronic timing, athlete performance records are being digitised, and the professional community is beginning to talk more about metrics than about medals alone. Figures such as Nguyen Thi Oanh and Nguyen Thi Huyen have helped the public grow familiar with the idea that an athlete can be assessed across several events, through personal bests and season's bests, rather than through a single medal. But precisely because athletics measures so much, it is also the sport most easily misunderstood. A fan sees the figure of 10 seconds in the 100 metres and immediately thinks of class. An analyst sees the same figure and immediately asks: what was the wind, what was the altitude of the venue, was this a final or a heat, and how many times has this athlete run this week. The same fact, two readings, two conclusions that may be entirely opposite. This is the starting point of every serious athletics analysis: accepting that raw data is not yet truth. It is only material. And material must be refined before use. I once witnessed a heated debate among professionals about a long-jump mark. Some insisted it was a breakthrough; others argued that the conditions had "gifted" the athlete part of the result. After the wind reading was checked, both sides had to adjust their positions. That is the nature of this work: it does not create belief, it removes beliefs that cannot stand. CORE: THE FOUR LAYERS OF VERIFICATION FOR AN ATHLETICS MARK To understand why an athletics mark needs verification, one must pass through four layers: environmental conditions, equipment, competitive structure, and the athlete's biological profile. Each layer can change how we read the number on the board. The first layer is wind. In sprinting and jumping, wind is the decisive variable for the validity of a record. World Athletics rules that a mark is recognised as a record only if the tailwind does not exceed 2.0 metres per second. Behind that figure lies a simple physical fact: a tailwind pushes the athlete forward, and over 100 metres a strong gust can save several hundredths of a second — enough to turn a good mark into a historic one, or the reverse. So when I read a sprint result, I look for the wind reading before anything else. If it exceeds the threshold, the performance is still impressive, but it cannot serve as a yardstick against records. The second layer is altitude. At venues above 1,000 metres, the air is thinner, drag is lower, and sprint and jump events enjoy a clear advantage. This is why certain stadiums are famous as "fast tracks," where fine marks appear more often than normal. A responsible analyst must ask: did this mark come from the athlete's ability, or from the altitude of the venue? Both may be true, but only one of them can be reproduced elsewhere. The third layer is equipment, and this is the most contested layer of the past decade. The arrival of shoes with carbon plates and supercritical foam has changed the game, especially in distance events. Technically, this is a form of "equipment dividend": the same athlete, with the same physical foundation, can run significantly faster in the new footwear. World Athletics was forced to introduce rules on sole thickness and to require that a product be available on the mass market before it can be used in competition. This means that when comparing marks across eras, one must subtract the dividend that technology provides. Otherwise, one is comparing an athlete running in the shoes of 2026 with an athlete running in the shoes of 2026, and declaring the latter inferior. That is a methodologically unfair conclusion. The fourth layer is the athlete's biological profile. This is where athletics touches anti-doping, and also where data analysis becomes most subtle. The Athlete Biological Passport is a long-term monitoring tool for blood and hormone markers, allowing abnormal changes to be detected over time even without a direct positive sample. To me, this is the clearest proof that data has power when it is collected continuously, not in isolated instances. A single test sample may say nothing. But a series of data points over many years can tell a story. These four layers form a frame of reference. When I read an athletics mark, I work from the outside in: first the measurement conditions, then the equipment, then the competitive structure, and only at the end the athlete. This order matters, because it forces us to eliminate the simplest explanations before offering a complex judgement. And this is where competitive technique enters the picture. In middle- and long-distance events, the most important data is not the final time but the distribution of speed throughout the race — the splits. An athlete who finishes with a good time while running a negative split (the later segment faster than the earlier) usually demonstrates a more solid physical and tactical foundation than someone finishing in the same time with a positive split (going out fast and fading). To me, reading splits is like reading the heartbeat of a race. It shows how the athlete calculated, controlled emotion, and prepared physically. The best figure on the board can conceal a badly paced race. Conversely, a mark that is not yet a best, but with sensible splits, signals a genuine step forward. At the same time, one cannot ignore the structure of the competition. A mark set in a heat, when an athlete only needs to advance while conserving energy, cannot be compared with a mark in a final, when everything is pushed to the maximum. A mark set at a national meet cannot be placed beside a mark at an international meet with denser competition. In athletics, competitive context is part of the number, not decoration around it. From a data perspective, an athletics season is like a confession of tactics. It reveals what an athlete wants to hide, and also what their coach has not yet noticed. The problem is that most spectators read only the final page of that confession — the result — and skip the rest. At the preparation level, the concept of peaking is a clear example. An athlete cannot be at their best all year. They must plan cycles: a base-building phase, a special-preparation phase, a taper, with the peak falling on the target competition. When an athlete competes densely yet maintains form, that is usually a sign of an exceptional physical foundation. But when they skip certain meets, that may not be a sign of decline; it may be a sign of a long-term plan. Fans often read absence as bad news. The data analyst reads it as a signal. Finally, at the system level, Vietnamese athletics faces an opportunity and a challenge that are one and the same: building a database long enough and clean enough. A young athlete cannot be judged on a single competition. They must be tracked across seasons, across events, across different competitive conditions. Only with sufficient data can we distinguish a genuine leap from a lucky day, a durable talent from a passing phenomenon. This is quiet work, without applause, but it is the foundation of all lasting success. CONTRARIAN VIEW: CORRELATION IS NOT CAUSATION There is a trap into which even veteran data people easily fall: mistaking correlation for causation. We see an athlete with a strong mark, we see them training under a new programme, and we conclude that the new programme is the cause. But two events happening together do not automatically have a causal line between them. This is what I must remind myself of constantly, especially when facing an attractive story. In athletics, this trap appears in many forms. An athlete moves to train abroad and their marks improve. People immediately assume the foreign training environment is the decisive factor. But perhaps the athlete is simply entering their prime years, or competing at meets with more favourable conditions, or recovering more fully after a run of injuries. If we cannot control for those variables, we are building a conclusion on sand. This is why I always require one section in every analysis: a listing of what data is still missing. Without data on sleep, training load, injury history, and the wind reading of each competition, every conclusion must be framed by a degree of uncertainty. An honest analysis is not one that delivers a certain answer. It is one that states clearly what it knows and what it does not. I once witnessed a case in which the online community argued about whether an athlete was at peak form, based on a single mark. No one asked about the conditions, the opponents, or which round it was. An entire debate was built on one data point, and of course it could lead nowhere. A single mark is never enough to describe form. It is only a slice. I call this the problem of the "empty input." When you begin an analysis without sufficient input data, you have two honest options: pause and go collect more, or state clearly that your conclusion is only hypothetical. The third option — filling the gaps with plausible-sounding speculation — is the worst, because it creates the illusion of knowledge. A report with a full title, full sections, and full tables, yet with every cell reading "insufficient information," looks complete, but in fact says nothing about the world. And worse, it can be misread as a confirmation. Data is a mirror. Most of the market looks into it and sees only itself. We tend to find in data what we already believe. If we believe an athlete is rising, we find the numbers that support that belief and ignore the numbers that do not. If we believe a team is falling, we do the same. Athletics, with its vast data store, is fertile ground for this kind of thinking. So the data professional has a duty to actively seek counter-evidence. If none is found, the conclusion has value. If some is found, we must change our view. I call this self-correcting counter-argument. An analyst is not someone who is never wrong. They are someone willing to publicly correct themselves when the data forces them to. Over my career I have had to retract my judgements many times. Each time was uncomfortable, but it is the price of honesty. Another aspect of the contrarian view is the flexibility of standards. We often want to apply a single yardstick to everything, because that makes comparison easy. But athletics does not permit that simplicity. A mark in the 100 metres cannot be compared directly with a mark in the marathon. A national record in a shallow event cannot be placed beside a national record in a deep event. Before comparing, we must check whether the two things are genuinely of the same kind. If not, every comparison is a fallacy. This is what I always tell younger colleagues: do not rush to standardise. Standardisation has value only when it reflects the true nature of the data. Applying a template to an entity it does not fit is not science; it is a form of imposition. A LESSON FROM A NEAR-MISS There is a memory I still recount when asked about professional ethics. Years ago, I faced pressure to deliver a judgement on a young athlete immediately after a major meet. I had a few figures in hand, enough to sketch an attractive story. But when I checked again, I realised those figures came from too small a sample, and the conditions of that meet differed from those the athlete would face in future. I decided not to deliver a conclusion. I presented only the data and stated clearly what was missing. Some time later, that athlete made a leap forward, and I realised that had I rushed a conclusion that day, I might have been wrong in a way that is hard to fix. Not because I guessed the outcome wrong, but because I had tried to turn a slice into a panorama. Since then I have built a rule for myself: before concluding, ask whether you have enough data to conclude. If the answer is no, the right thing is to admit it. In a sporting culture that always wants fast answers, saying "I don't know yet" is an act of courage. It runs against the instinct of media, which rewards decisiveness. But precisely for that reason, it is necessary. A mature sport is one that can tolerate uncertainty. I have seen fierce debates about an athlete simply because each side held half the data. One side looked only at the mark, the other only at the conditions. No one sat down to join the two halves together. The result was a war of beliefs, not a discussion about truth. And the one who suffers in the end is always the athlete, judged on half a story. I think Vietnamese athletics is at exactly the point where it needs patient data people more than fast commentators. We have enough passion. We have enough talent. What we still lack is a habit of verification, a culture that accepts that a number must be read within its context. I call myself a data monk, and I always tell younger colleagues: a monk does not need a cathedral — only the truth. Truth in athletics is not found in sensational headlines. It is found in quiet lines of data, patiently collected over years, and read with full humility. When a sport learns to read its own data, it will no longer depend on prophecies. It will be able to see the road ahead for itself. PROGRESSIVE VIEW: A SIGNAL FOR THE NEXT CYCLE If I had to pick one signal to track in the coming cycle, it would be the input-data quality of domestic athletics meets. Not the medal count, not the record count. But whether we can collect wind readings, track measurements, splits, and competition histories in a complete and consistent way. Because every conclusion about the future depends on the quality of today's material. A sport with clean data can correct itself. A sport without clean data can only argue. And in a world where information travels faster than any athlete can run, the ability to self-correct is the greatest competitive advantage. The question I leave to those working in Vietnamese sport is not "how much talent do we have," but "do we have the courage to measure them honestly." Because sometimes the most honest act is not to deliver a conclusion, but to admit that we do not yet have enough data to conclude. A season is a confession of tactics. And an honest database is the only way that confession gets recorded correctly.