Building a Data System for Vietnamese Athletics: The Track Never Lies

My Dinh, a late-May afternoon. The track still glistened after a sudden downpour, and in the stands thousands of people were waiting for the starting gun. I sat in the middle rows, an old yellowed notebook in hand, jotting down three numbers before the athletes took their blocks: an outside temperature of 31 degrees Celsius, humidity at 78 percent, and a light headwind blowing down the final straight. Nobody around me paid the slightest attention to those figures. They had come to see who crossed the line first, who raised their arms in celebration, who collapsed from exhaustion. I had come to hear something else — the voice of the track, a voice that only speaks when we bother to place it on the scale of data.

In twenty-three years of watching this sport, I have learned one simple thing: most arguments about Vietnamese athletics do not stem from a shortage of talent, but from a shortage of data with which to tell the story correctly. A SEA Games gold medal can be the crystallization of four years of hard training, or it can be the luck of a windless afternoon. On the results sheet, those two possibilities look identical. Only when we take the trouble to peel back each layer of data does the truth agree to show itself. When the numbers speak, I simply listen.

Context: A Sport Rich in Emotion, Poor in Data

Vietnamese athletics has carried a paradox for decades. We are one of the most serious investors in this foundational discipline in Southeast Asia, with a selection system running from provincial and municipal levels to national training centers, and hundreds of athletes training year-round across the country. Yet when an athlete steps onto the international stage, the publicly available information about them is often just a few dry numbers: personal best, year of birth, and the name of a coach. Fans do not know how many kilometers they run each week, where they accelerate within a race, how they recover from injury, or which phase of the four-year cycle their form is in.

This poverty of data is nobody's fault in particular. It is the product of a traditional approach: treating athletics as a sport of final numbers — time, distance, height — and ignoring all the intermediate numbers that produced those results. When I was still writing for international sports magazines early in my career, I realized something rarely said at home: the most fascinating part of athletics is not the finish line, but the space between the two lines. That space is where data lives.

In countries with developed athletics, every competition is recorded down to the hundredth of a second of each split, each stride, each breath. Training centers track training volume, intensity, sleep, resting heart rate, heart-rate variability, and even the psychological state of athletes through periodic questionnaires. Data does not only serve the media; it is a working tool. Coaches use data to adjust training plans, doctors use data to prevent injury, and athletes use data to understand themselves.

In Vietnam, we have taken our first steps. Some major centers have equipped GPS watches, heart-rate monitors, and training-management software. But the data remains scattered, unstandardized, and almost never shared beyond the training-room door. The result is that fans, journalists, and even administrators often make judgments based on intuition rather than evidence. And when judgments rest on intuition, we easily fall into two familiar traps: over-praising after a fine performance, and condemning too quickly after a defeat.

Reading a Performance Mark Correctly

Let us start with the most basic thing: what a performance mark in athletics actually is. Looking at a results board, we see a single number — say, 11.05 seconds in the men's 100 meters. But that number does not exist in a vacuum. It exists within a specific set of conditions, and if we cannot read that set of conditions, we are comparing things that cannot be compared.

A performance mark only means something when placed beside wind speed, altitude above sea level, track surface, and its position within the training cycle. International competition rules stipulate that a record in a sprint event is only recognized when the assisting wind speed does not exceed two meters per second. That means an athlete who runs 10.90 into a half-meter-per-second headwind has in fact performed better than one who runs 10.85 with a nearly two-meter-per-second tailwind. On paper, the second is faster. On the track, the first is stronger.

In Vietnam, I have witnessed heated social-media debates about who is the fastest runner in the country, based solely on results sheets with no mention of wind conditions or when in the year the mark was set. That is a debate without end, because both sides are right in their own way and both lack the data to rebut the other. My experience watching competitions shows that whenever an argument drags on without either side citing contextual data, the cause is almost always a data-collection problem, not a technical one.

Another overlooked aspect is the splits. A 400-meter race can be divided into four 100-meter segments, and the distribution of speed across them says a great deal about an athlete's tactics and conditioning. Someone who runs negative splits throughout is usually a good pace-keeper but lacks top-end speed; someone who starts too fast and fades in the third segment has usually not yet built the anaerobic base. Without split data, we see only a final time and have no idea how it was produced. With split data, we see a whole story of how the athlete allocated their body's resources.

I always remind my students of one line: do not read the result, read the process that produced the result. A medal is the endpoint of a curve; what matters is the shape of that curve.

Athlete Condition and the Age Curve

In athletics, age is not an absolute number but a curve. Each event peaks at a different age. Sprints usually peak early, between twenty-two and twenty-six. Middle- and long-distance events peak later, sometimes stretching into the early thirties. Jumps and throws require a blend of strength and technique, so their peaks can arrive late and last long. Understanding this curve helps us know where an athlete is on their journey, and what it is reasonable to expect of them.

When assessing condition, I always look at three independent indicators. The first is the trajectory of personal bests year by year — a steady rise, an abnormal leap, or a plateau? The second is current-season form relative to previous seasons, to know whether an athlete is advancing or receding. The third is injury history and signs of overload, because a base built on an unhealed injury is a base that will soon collapse.

Only when three independent indicators point the same way is there enough basis for a conclusion; a single indicator is merely a hypothesis. This is the principle I apply to both football and athletics, and it has saved me from many misjudgments.

There is a phenomenon I call the suspicious leap. When a young athlete suddenly improves their personal best sharply in one season, the question arises — and I apologize for having to raise it — of where that leap came from. There are three plausible sources: natural physiological maturation at a young age, a major change in training method, or a change in equipment such as carbon-plated shoes. Before praising or suspecting, an analyst needs to check all three. The checking is not to accuse anyone, but to understand the true nature of the performance. The distance never lies; it is only that we have not been patient enough to listen.

From this angle, I pay particular attention to an athlete's transition after a peak. Many fans see a declining season and immediately conclude that there has been a collapse. But if training data show that volume is being adjusted to rebuild the base, then what we are seeing is not collapse but restructuring. At the seventieth minute of a four-year cycle, the crowd sees a decline; the data reader sees a structure being rebuilt. The condition for reading it that way is patience and the presence of at least two independent indicators supporting that reading.

The Path to Standards and the Selection Mechanism

International athletics operates on a two-lane mechanism. The first lane is meeting the performance standard set by the world federation for each event, usually pitched high and reached only by top athletes. The second lane is accumulating world-ranking points through competitions over a set period. These two lanes do not replace each other but complement each other, and they create different strategies for each athlete and each national federation.

For a country like Vietnam, where the number of athletes meeting the direct standard remains modest, the ranking-points lane is often the more practical gateway. This means the competition calendar becomes a strategic variable. An athlete needs to enter enough meets, at the right level, at the right time, to both accumulate points and avoid wearing down before the major arenas. Too dense a schedule turns the advantage of point accumulation into a physical disadvantage; too sparse a schedule leaves an athlete short of points and out of a place.

This is an optimization problem, not an effort problem. Racing many meets does not equal progress; sometimes it only produces beautiful numbers without producing peak competitive ability. I once wrote about a young athlete who entered nearly twenty meets in a year, whose personal bests improved visibly, but who collapsed in the heats at the main arena through accumulated exhaustion. Looking at the year's results, everyone thought it was a step forward. Looking at the calendar and recovery data, I saw a base being wrung dry.

At the national level, the selection mechanism also needs to be read through data. Selection criteria should rest on performance at the right moment, under conditions close to those of the international arena, not merely on the best mark of the year. An athlete with a higher personal best but who routinely falters in big races may be a riskier choice than one with a slightly lower personal best who is consistent and durable. Data-driven selection does not remove the human element; it only gives human decisions a firmer foundation.

The Regional Power Map of Athletics

Southeast Asian athletics has long been a multi-layered map. At the top tier, a few events feature athletes of continental and world class, a prime example being the pole vault, where the region once produced vaulters clearing six meters — a mark that is rare worldwide. In the middle tier, middle-distance, long-distance, and throwing events are the playground of a group of countries with enduring training traditions. In the lower tier, most remaining events are an open contest where the gap between countries is a matter of hundredths of a second and can reverse after one good training season.

Where does Vietnam sit on this map? The answer depends on the event. In some middle- and long-distance events, Vietnam belongs to the medal-contention group and has repeatedly affirmed its standing at regional games. In sprint and jump events, we are usually in pursuit, where a medal requires a blend of talent, timing, and a little luck. In throws and combined events, the gap remains relatively large.

The power map is not a still photograph but a film. Countries rise and fall with investment cycles. A generation of talent comes and goes. What matters for an analyst is not to mistake a moment for a trend. A single medal does not make a mighty athletics nation, just as a barren season does not negate an entire system.

The most important data at this tier is squad depth. A country with only one leading athlete in an event is highly vulnerable to injury, form, and factors beyond the purely technical. A country with three or four athletes at a similar performance level has far greater resilience. That is why, when assessing a country's athletics strength, I always look at the number of athletes reaching performance thresholds, not only at the leader.

For Vietnam, an encouraging signal in recent years has been the emergence of many young faces in middle- and long-distance events, alongside athletes who have already made their names in the region. Athletes such as Nguyen Thi Oanh in middle-distance running, Nguyen Van Lai in long-distance running, Le Tu Chinh in sprint events, and Bui Thi Thu Ha in the marathon have formed a generation of relative depth. But that depth is only sustainable if the next cohort is nurtured with data and science, not with inspiration alone.

The Rules and the Anti-Doping Fight

Athletics is among the sports with the strictest rules and doping controls. Every international-level athlete must comply with the obligation to provide whereabouts information for out-of-competition testing, and every sample is stored for years so it can be re-analyzed with new technology. This is a data-based prevention system, and it reminds us that in modern sport, data is used not only to praise but also to protect integrity.

For an analyst, monitoring doping signals is not about suspecting every performance, but about understanding that every performance exists within a control context. A statistically abnormal performance mark can have many explanations, and the analyst's job is to present those explanations objectively, neither accusing nor blindly praising. Systematic doubt means keeping every possibility open until the data closes the door.

At the national level, compliance also includes technical matters such as venue conditions, compliant equipment, and entry procedures. A small procedural error can void a place, and this is a type of risk that is entirely preventable with a rigorous workflow. I have seen athletes lose opportunities for administrative reasons, and each time I think: if we managed data as well as we train, such losses would not happen.

The Training System and the National Team Machine

Behind every athlete is a system. That system is coaches, doctors, conditioning specialists, nutritionists, and those working on psychology. The quality of this system usually does not show up on results boards, but it determines the durability of results.

A good coach is not only one who issues training plans, but one who can read data and knows when to break the plan. Modern athletics demands that coaches be part physiologist, part data manager, and part spiritual companion. In Vietnam, we have dedicated and experienced coaches, but many of them still work mainly on intuition passed down from the previous generation. This is not wrong, but it has limits.

Those limits lie in the fact that personal intuition cannot encompass the volume of data an athlete's body produces each day. A coach can sense that a student is tired today, but cannot sense the trend of heart-rate variability over three weeks of training. A doctor can diagnose an injury that has already occurred, but cannot predict the injury risk accumulating. That is when data proves its value.

A modern training system also includes periodization. A four-year cycle is divided into phases with different conditioning goals, and every session has its place in the big picture. When we read an athlete's results without knowing which phase of the cycle they are in, we easily misjudge. A modest result in a base-building phase can be a good sign; a dazzling result in a rest phase can be a worrying one.

I always stress that a training system should be judged by its stability, not only by its peaks. A team that maintains stability over many years, continually producing qualifying athletes, is more trustworthy than one that occasionally produces a star and then vanishes. That stability, once again, can only be measured through long-term tracking data.

The Risk Map of a Season

In athletics, risk is not in one place but in many at once. I usually classify them into a few groups for easier tracking.

The first group is competitive risk. This comes from an opponent suddenly breaking through, a new generation of talent emerging, or a specialty event suddenly becoming more competitive. This type of risk can be tracked through opponents' results at previous meets.

The second group is injury risk. This is the costliest type, because an injury at the wrong moment can wipe out an entire four-year cycle. It can be mitigated by tracking training volume, sleep, and recovery indicators.

The third group is form risk. An athlete may peak too early or too late relative to the main arena. This is the hardest risk to control, because it depends on the interaction between physiology, psychology, and the competition calendar.

The fourth group is non-technical risk: procedures, administration, living conditions, adaptation to time zones and climate. These risks are often underestimated but have great destructive power, especially at away venues.

Risk is not something to fear, but something to name and track. Once we have listed the risks and assigned them a level and probability, we are halfway to controlling them.

Within this risk map, I always pay special attention to systemic risk. An athletics ecosystem that depends on a few outstanding individuals is a fragile one. If those individuals leave through age or personal reasons, the whole system can fall behind for years. That is why the most important indicator for assessing system health is not the medal count, but the number of young athletes approaching the international threshold.

The Media Story and the Expectation Gap

Sports media has a special power: it can turn an athlete into an icon in a single evening, and it can leave someone forgotten in a single season. With Vietnamese athletics, I notice a familiar pattern: expectations surge after each regional games, then quickly cool as the continental arena approaches.

The gap between expectation and reality is an important indicator. When expectations far exceed current ability, the pressure on the athlete grows, and excessive pressure often leads to wrong training decisions. When expectations fall below reality, opportunities are missed for lack of investment. The balance point lies in pricing ability correctly — and pricing ability correctly, once again, requires data.

The heat of the media usually runs ahead of the data foundation, and that gap is where misunderstandings breed. A young athlete hailed as a future star after one good performance can be placed in a position where they must constantly prove themselves, while their body still needs time to mature. A veteran with a modest season can be written off as finished, while the data show they are restructuring their base for the next cycle.

In my role, I choose to write based on data not to diminish emotion, but to give emotion an anchor. Fans have the right to be excited, to hope, to be disappointed. But I want those emotions tied to an honest picture, because emotion tied to truth lasts longer than emotion tied to illusion.

The Industry Flow and Long-Term Value

Athletics is not only sport; it is a value chain. A successful athlete can open commercial opportunities, attract investment in infrastructure, motivate the younger generation, and raise a country's standing on the international stage. But this value chain operates on a different logic from that of team sports.

In team sports, commercial value is tied to the league and club brands. In athletics, commercial value is tied closely to individual athletes and major events. This produces an important consequence: the sustainable development of athletics depends on building a generation of charismatic athletes, not just one or two isolated stars.

The long-term value of an athletics nation lies in a continuous flow of young talent, not in momentary peaks. A country may produce a champion, but without a next cohort, that champion becomes a lonely star in an empty sky.

From an industry angle, I see Vietnam's athletics potential in three directions. The first is developing high-quality domestic meets, creating regular competition for athletes. The second is investing in data and sports science, turning data into a competitive edge. The third is building athletes' stories professionally, so that each athlete becomes a personal brand with reach.

All three directions need data. Without data, we do not know which meet needs improving where, which investment pays off, and which story is worth telling. Data is the thread running through the mesh of the value chain.

A Counterintuitive Angle: The Discipline of Saying 'Not Enough Information'

Here I want to spend this section on something few analysts dare admit: sometimes the truest answer is that there is no answer yet.

I have lived through exactly such a situation in my own work. Once I received a request to analyze a young athlete's performance mark, and the input dataset was nearly empty: no information on competition conditions, no split data, no training-cycle context. By professional reflex, I could have written an analysis that sounded very reasonable, with judgments that sounded very certain. But doing so would have betrayed the very principle I pursue.

What I did instead was clearly set out what could not yet be concluded, and list what would be needed to conclude. I said that the analytical dimensions — event and performance, athlete condition, the qualification mechanism, the opponent map, the rules, the training system, risk, media, and the industry chain — were all waiting for data, and that rushing to fill the gaps with guesswork would produce something more dangerous than silence: misplaced confidence.

In a sport still young in data terms, the most important virtue of an analyst is to be able to say 'not enough information' without feeling ashamed. Because every time we fabricate a conclusion, we plant a seed of misunderstanding in the community, and those seeds sprout into wrong decisions at the management, coaching, and investment levels.

I realize that in Vietnam, our sports culture sometimes does not permit that humility. People want decisive answers. They want to know who will win, who will break a record, who will fail. And because of the pressure to answer, many of us choose to answer even without grounds. I have been in that position, and I understand the pressure. But I also understand that an analyst's credibility is not built on bold predictions, but on accuracy over time.

At the seventieth minute of a cycle, when the crowd has left because they think the race is decided, the data reader stays seated. Not because they are certain of the outcome, but because they know that what remains beyond the finish line is the most worth reading. An empty stadium does not make me lonely, because data is the echo of thousands of people.

What to Watch in the Next Round

So which signals deserve watching in the next round of Vietnamese athletics?

First is the signal of young-talent depth. The number of athletes under twenty reaching performance thresholds near the international level is the most important indicator for the coming five to ten years. If this number rises steadily year by year, the ecosystem is on the right path. If it stalls or falls, it is a sign to review the selection and nurturing system.

Second is the signal of data quality. When training centers begin publishing split data, recovery data, and periodization data, that will be a turning point. When data becomes transparent, the quality of public debate rises, and the quality of decisions rises with it.

Third is the signal of coaching-staff stability. A system that maintains stability in personnel and method over many years is a trustworthy one. Constant turnover of head coaches in national teams is a warning sign of systemic risk.

Fourth is the signal of the media story. If the public begins to care about intermediate metrics — splits, speed distribution, seasonal form — rather than only medals, then our sports culture is maturing.

I do not believe in luck; I believe in what has been repeated enough times. A sustainable athletics nation is not built from moments of brilliance, but from processes repeated every day: a session logged, an indicator tracked, a decision made on evidence. Those small things, accumulated over years, will build a structure that does not easily collapse.

For Vietnamese athletics, I believe talent has never been the problem. The problem is how to make that talent visible, measurable, nurtured, and honestly told. When we build that data system, every track will no longer be a solitary race between invisible names, but a story that an entire country can follow, understand, and believe in together.

The track has been speaking for years. Our job is to learn to listen, and to listen to the end.