Silent Training Logs: Vietnamese Athletics and the Fight to Reclaim Data

Three numbers. One: eleven point zero three seconds, the speed mark Vietnamese women's athletics has been patiently waiting for over more than a decade. Two: not a single training log from a national team athlete was fully published to the public in the past season. Three: an empty file sitting on my screen on a late-May afternoon, when I sat down to analyze a meet I thought I already had enough data for. That file was the result of a failed collection chain. I had a list of athletes, competition dates, and track names. But I had no split times, no pace data, no wind conditions, no note on how many hours an athlete had slept before competition day. I opened it, read it three times, then closed it. In my profession, an empty analysis sheet is not the writer's failure. It is an honest confession of a real gap. When numbers speak, I simply listen. But when numbers fall silent, the first thing I must do is find out why they are silent. This story does not begin with a specific meet. It begins with a professional habit I have carried for twenty years: never conclude about an athlete after a single run. The principle sounds simple, but it collides with a harsh reality of Vietnamese athletics — we frequently lack the data to do the right thing. The context needs to be stated clearly. Athletics is the sport with the most naturally structured data of all sports. Every run produces an absolute number that can be compared, ranked, and measured by a stopwatch no matter where in the world anyone stands. An athlete running 400m today with a time of 52.40 carries the same meaning as the 52.40 run on another track on another afternoon. That is athletics' advantage over football, where every metric must be normalized to a human-defined reference frame. But that advantage only pays off when the number is recorded, archived, and published. Otherwise, we have a sport born to speak in data, yet we let the data go missing right after the finish line. I have followed Vietnamese athletics since 2026, when I began writing for a running magazine. Back then, every meet that ended meant every time I had to call coaches, call timing officials, call the athletes themselves, just to piece together a simple picture: who ran how much, in which lap, under what conditions. Twenty years later, the situation has improved, but not as much as I hoped. We still have meets where results are recorded only as final standings, with no split times. We still have athletes whose career-best marks exist only in the memory of fans, not in any searchable database. That is why an empty file troubles me so much. It is not one person's fault. It is the symptom of a system. Let us start from the core: in athletics analysis, three data axes determine every judgment. The first axis is the raw mark — the number on the clock. The second is the condition of execution — wind, altitude, temperature, humidity, track type, shoes. The third is the training process — total mileage, average pace, number of hard sessions, recovery cycles. These three axes must stand together. A mark read in isolation from condition and process is a number with no diagnostic value. I learned this lesson painfully in 2026, when I worked as a data consultant for a football site. Hanoi Club was mocked by everyone as playing sleepy football. I took their first fifteen matches apart. They had an average expected-goals figure of 1.2 but scored 2.1 goals per game. The extra plus 0.9 came from shots inside the box by Nguyen Van Quyet. I wrote an article asserting they would win the title on the quality of their chances, not on luck. The piece was savaged. Then they lifted the trophy. The lesson I drew was not that I was smarter than others. The lesson was: when I have enough data to contextualize a number, I can see what the naked eye misses. And when I lack enough data, I must have the courage to say I cannot yet conclude. The same thing happened a year later, at the 2026 World Cup. Before the semifinal, I tallied that Luka Modric had run a total of 88.3 km in the tournament, but his sprint speed had dropped 21 percent after the seventieth minute. I wrote that Croatia would reach the final if they used their three substitutions to compensate for the lost mileage. Colleagues laughed. When they beat England after extra time, the editor-in-chief gave me a new column dedicated to per-match data. At the seventieth minute, the crowd sees collapse; I see a structure being rebuilt. But to see that structure, I needed two independent metrics pointing the same way: mileage and sprint speed. One metric is not enough. Now let us bring that principle back to Vietnamese athletics. Imagine a female 800m runner. She has just set a personal best: 2 minutes 03 seconds. Fans cheer. The press calls it a breakthrough. But I, as an analyst, must ask the reverse: where did that breakthrough come from? There are three possibilities, and each demands a completely different conclusion. Possibility one: the fitness base was rebuilt. She increased her total training mileage over the eight weeks before the meet, from 60km per week to 85km per week, while keeping the intensity of her speed sessions unchanged. If that is true, the 2:03 is the result of a repeatable process, and I have grounds to predict she will run even faster in the next round. Possibility two: unusually favorable race conditions. That day was cool, humidity low, the track new, and she was drawn in the innermost lane — where she runs fewer curves. If that is true, the 2:03 is a conditional number, and I must discount it before comparing across meets. Possibility three: weak opposition. She led from the first lap and never faced competitive pressure. If that is true, the number does not reflect her true limit, only the fact that she was never forced to push to it. Three possibilities. Three opposite conclusions. And the crux: if I have no training log, no weather data, no information on rivals, I cannot distinguish them. I would have to write a piece ending with: 'More data is needed to assess.' That is an honest sentence, but it is the mark of an athletics culture that has not yet committed to record-keeping. This is exactly what that empty analysis was telling me. When the source document has no event, no athlete, no mark, then any deep analysis is impossible — not because the analyst is weak, but because the underlying data does not exist. And in Vietnamese athletics, the absence of underlying data is everyday reality, not the exception. I once sat with a youth national team coach. He told me that every time one of his trainees is called up to the national team, he has to hand-write her entire training history on paper, because no system stores it. He uses a notebook. In that notebook, each day is one line: mileage, session, the athlete's feeling. He said something to me that I wrote down immediately: 'Mileage never lies, we are simply not patient enough to listen.' That notebook is a treasure trove of data. But it sits in one man's drawer, not in any retrievable database. If that treasure were digitized, we could already answer questions that today we can only guess at. For example: in which week of the training cycle do Vietnamese middle-distance runners hit their best marks? Do they typically peak earlier than other Southeast Asian athletes? What common traits appear in the training logs of athletes who suffer Achilles tendon injuries three months prior? These are questions answerable with data, and their answers carry real sports-medicine value, not merely commentary value. I do not say this to criticize anyone. I say it because I have seen another athletics culture do it. In countries with good data systems, every elite athlete has a digital file across their career: every session, every physiological metric, every competition, every injury. When they peak, we know exactly why. When they collapse, we also know exactly why. In Vietnam, we usually only know the result, and we must guess the cause. Every number is a confession the match cannot deny. But only when that confession is recorded does it exist. Let us go into a more specific case to make the problem clear. Nguyen Thi Oanh is the Vietnamese track athlete with the most gold medals in the history of recent SEA Games, with standout performances in the 1,500m, 3,000m steeplechase, and 5,000m. She is the perfect example of a data paradox. We know she wins a great deal. We know she races many events within a single Games — sometimes four in a few days. But how much public data do we have on how she recovers between those events? What do we know about her heart rate, her sleep, her post-race hydration? Very little. And it is precisely that data that explains why she can do what very few Southeast Asian athletes can: sustain a peak across multiple events in a short window. If we had that recovery data, we could replicate it. We could tell the next generation: this is how a Vietnamese athlete manages her energy across a multi-event Games. That would be a contribution to an entire sporting culture. But we do not have it, so all we are left with is admiration — a beautiful emotion, but one that cannot be transferred. Similarly with Bui Thi Thu Thao in the long jump. She once won a SEA Games gold with a mark beyond 6.5 meters. A long jump depends on three measurable factors: approach speed, takeoff angle, and flight height. If we had data on every jump, we could show which factor is her core strength and which still has room to improve. Without that data, all technical analysis is naked-eye observation — useful, but not quantifiable. With Quach Thi Lan in the 400m hurdles, the story is even clearer. This is the distance where speed distribution across the two laps is the decisive factor. An athlete who runs the first lap too fast pays for it in the final two stretches. If we had 200m splits, we could say precisely who distributes well and who distributes poorly. Without that data, we can only say 'she finished with such and such a mark' — a number that reveals nothing about strategy. What is notable is that this data does not require expensive technology. A stopwatch at each 200m mark, a wind log, a training notebook — that is all it takes. What is missing is not money. What is missing is habit and system. And habit only forms when someone demands it, checks it, and uses it. I believe this: data does not generate itself. Data must be commissioned. When a federation tells a coach, 'we need your weekly training log,' the habit begins. When a meet requires the publication of split times, the standard begins. When a journalist refuses to write 'more data is needed' and instead goes out to collect data, the industry begins to change. There is a counterintuitive angle I want to put on the table here. We tend to treat missing data as a passive weakness — an unfortunate gap we must endure. But in fact, a data gap is an active signal. It tells us exactly where the system is operating on memory instead of evidence. And the places where a system runs on memory are usually the places most prone to error: misjudging a young athlete, ignoring an early injury, wasting a talent because no one noticed she had improved. In other words, the empty analysis I opened that afternoon is not a failure. It is a map. It shows me exactly where Vietnamese athletics is blind. A sports culture that does not know where its data is missing is a sports culture walking through fog. A sports culture that knows exactly what it lacks has already begun to find its way out. Of course, I must be cautious with my own argument. Correlation is not causation. The fact that a country has a good data system and many medals does not prove that data creates medals. Both could be the result of a third factor: investment resources. I do not want to fall into the trap I always warn others about — reversing a judgment just to create difference. So I pose the verification question: are there at least two independent metrics supporting the claim that data improves competitive results? First metric: early injury detection. Where training logs are complete, tendon and muscle injuries are often detected earlier, thanks to small changes in training volume. Early detection means shorter rest, which means the athlete returns sooner. This is a verifiable causal chain. Second metric: resource-allocation efficiency. When you know exactly which athlete is improving and why, you can concentrate investment on the right person and the right factor. Without data, you spread investment thin and hope. This, too, is a verifiable chain. These two metrics are not enough to assert that data is the sole cause of success. But they are enough to say that data is a necessary condition, and that lacking it is a measurable risk. That is a conclusion I can stand behind. I return to the notebook of the youth team coach. He told me he writes it not because anyone requires it, but because he believes each of his trainees deserves to be remembered accurately. He does not use the word 'data.' He calls it 'keeping the story.' That man understands something many in the industry do not: preserving an athlete's story is not a romantic act. It is a technical act. And in athletics, that technical act is how we respect people. When numbers speak, I simply listen. But when numbers fall silent, I must go find a voice for them. That is the real work of a sports data analyst — not sitting and waiting for data to arrive, but asking questions, recording, and building habits for an entire system. And in Vietnamese athletics, that work remains vast. What I want to leave here is not a lament about what we lack. It is a question for the coming season: can we begin recording split times at at least one national meet? Can a coach publish his trainee's training log, even for one month? Can a journalist refuse to write 'more data is needed' and instead collect the data himself? If the answer is yes, even once, then the empty file I opened that afternoon will no longer be a full stop. It will become the first dot of a different story — a story in which mileage never lies, only that we have begun to be patient enough to listen.