The Empty Data File and the Discipline of the One Who Counts the Steps

That night I sat in front of a screen with a spreadsheet file already open. The file was named HP_2024_dienkinh_raw. Inside there was nothing. Not one timestamp, not one metric column, not one note. Only a bare header row – Split_100, Split_200, HR_max, Lactate – and beneath it, blank space stretching to the five thousandth row. I have received files like this many times. People send me a frame, a set of columns, and a belief that as long as there are enough cells, the data will simply flow in. Cells do not create truth. Only measurement creates truth. I picked up the phone and called the coach. "What do you want me to analyse from this file?" Three seconds of silence. "Just look at it and tell me whether this athlete is improving." That was the moment I understood I was facing a familiar choice: guess, or do not guess. Data analysts in Vietnamese athletics face that choice every day, and most of the time, the pressure leans toward the first option. Eighteen years ago, I left the football pitch to move into athletics. Not because I was tired of football. Because I realised something: in football, people had already started counting. In Vietnamese athletics, people were still measuring with their eyes. "The day football stopped, I began counting every stride again." I first wrote that line in 2026, when the pandemic shut down every competition and I sat re-examining the data from five V.League seasons and three major European leagues. Two thousand three hundred matches. But it holds true for athletics too, perhaps even more so. In athletics, everything can be measured. Reaction time after the starting gun, measured in thousandths of a second. Split times for every hundred metres, measured in hundredths of a second. Stride frequency, measured in steps per second. Blood lactate after the finish, measured in millimoles per litre. Maximum heart rate, measured in beats per minute. Athletics is the sport of the clearest numbers. No disputed goals. No offside. Only the track, the stopwatch, and a finish line that does not negotiate. Yet when I came home to work with training centres, I kept running into empty files like the one that night. Not because people were lazy. Because no one had taught them that data must be recorded at the moment it happens, not afterwards, from memory. In 2026, a training centre in the north invited me to assess a group of middle-distance runners preparing for a national meet. They handed me a file that looked complete. Each athlete had a results table, and each table had dozens of trial runs. I started checking. And I found something strange. One athlete, call him T., had an eight-hundred-metre time recorded as 2 minutes 04 seconds. But when I cross-checked against the four-hundred-metre splits in the same table, the two splits added up to 2 minutes 09 seconds. A five-second gap. Five seconds over eight hundred metres is an enormous distance. It is the boundary between a place in the final and a place at home. I checked three more times. The same result. Either the total time was wrong, or the split times were wrong. Both could not be right. I called the person in charge. He said: "It was probably hand-timed back then." That was an honest answer, and also a frightening one. Because a data table with mixed origins – half machine, half human – is a table you cannot fully trust. I did not write the figure 2 minutes 04 seconds into the report. I wrote: "Total time and split times do not match; re-measure before drawing any conclusion." Someone might ask: then what is the report for? What use is a report that only says the data is missing? It is useful in exactly one way. It prevents a wrong decision from being made on the basis of a wrong number. A competition place wrongly given to someone who does not deserve it, or worse, a place taken from someone who does, both begin with an unverified number. I always remind myself of the story from 2026. Before the World Cup, I published an article based on qualifying data: Germany had an average PPDA of 9.2 – far too high for the pressing standard of a champion – combined with a slow attacking tempo and a middling overall xG. I stated that Germany would be eliminated in the group stage. The online community mocked me, saying I only knew how to look at numbers. On the night of 27 June 2026, Germany lost 0-2 to South Korea despite taking twenty-six shots with an xG of 1.5. They were eliminated. My old articles were shared thousands of times. But what I learned that night was not "I was right." What I learned was this: a conclusion is only as strong as the data behind it. If I had guessed correctly without data, I would have learned nothing, and next time I would guess wrong. Winning by luck is a poor lesson. I did not see Germany lose because of a curse. I saw a chain of metrics that does not know how to lie. And precisely because of that, when I look at an empty athletics data file, I am not allowed to fill it in with intuition. Intuition is what I use to choose the question. It is not what I use to choose the answer. Back to the file HP_2024_dienkinh_raw. I had three options. First, I could write a report that sounded very convincing, based on reputation and general impressions. Second, I could refuse the job. Third, I could write a report stating clearly that the data was insufficient, together with a list of what needed to be measured so that next time a real analysis would be possible. I chose the third way. And I believe it is the only right way for someone who works with data. I drew up a list. It needed split times for every two hundred metres for each athlete across at least eight training sessions, not one. Reaction time at the start had to be measured with electronic equipment, not hand-timed. Weather conditions, temperature and humidity for each session had to be recorded, because a headwind and harsh sun can slow a time by several hundredths of a second. Maximum heart rate and lactate after each sprint had to be measured, to know each person's true fatigue threshold. That list ran to seventeen lines. I sent it to the coach with one line: "Give me these seventeen things, and I will give you back a true picture." Three months later, they sent it back. This time the file had real data. And the picture that emerged was completely different from what the coaching staff had believed before. The most highly rated athlete – the one with the best times in trial runs – turned out to be the one with the slowest starting reaction time and the fastest rate of decline in the final split. He won because his training rivals were weak, not because he was strong. Conversely, a young athlete who was rarely mentioned had the most stable final-split times across eight sessions, something no one noticed because he had never once finished first in a training session. This is the classic blind spot of Vietnamese sport: people measure by rank within a training session, not by the structure of performance. Someone who finishes first in a weak session is still the one who finishes first in the coach's eyes. But if you look at split times, you see a different story. Hai Phong taught me: the star is not on the shirt, it is in the metric. In 2026, at the age of thirty-one, I was a data consultant for a football club in Hai Phong. During an audit of the youth team's metrics, I found a young midfielder with an average PPDA of 6.8 – the highest in the academy – meaning he pressed extremely well but was overlooked because of a modest build. I brought the data sheet to the meeting room and asked for him to be given a chance. In the match against Hanoi FC on matchday 17, he won the ball fourteen times, provided one assist, and the team won 2-1. That lesson followed me into athletics. An athlete who does not stand out in training can be the one with the best underlying data. But to see that, you must have data. You cannot see the structure of performance with the naked eye. The naked eye only sees whoever finishes first. I want to pause here for a moment, because this is where I am often misunderstood. Many people think that someone who works with data like me is someone who believes numbers are always right. The opposite is true. A good data analyst is someone who knows which numbers cannot be trusted. A hand-timed split can carry an error of up to three-tenths of a second. A heart-rate sensor worn in the wrong position can produce heart-rate peaks that do not exist. A GPS device clipped to the back of a shirt measures a shorter distance than reality because of satellite error. "Data is a mirror. Most of the market looks into it and sees only itself." I use that line when talking about football transfers, but it holds for every measurement. A mirror does not create a face. It only reflects the face you bring to it. If you bring an empty file, the mirror will show you only your own emptiness. And here is the angle I consider the most counter-intuitive in this whole story: an empty data file is not a failure. It is a finding. We are often taught that the value of an analyst lies in the conclusions they produce. The more conclusions, the more certain, the better. But in real work, the greatest value of a data analyst sometimes lies in daring to say "I do not know." I do not know whether this athlete is improving. That is a correct answer, when the data file is empty. And it is more useful than a wrong answer dressed up in beautiful language. Imagine the opposite. I write a three-page report, using words like "potential," "good physical foundation," "high competitive spirit." The coaching staff read it, believe it, and award a competition place to someone based on those words. Three months later, that person fails, and no one understands why. The answer is very simple: because no one actually measured. People call me a data monk. A monk does not need a cathedral – only the truth. But a bad monk is one who locks himself in a room and declares that he knows everything. A good monk is one who opens the door and shows others where the light is still missing. I was once mocked for saying Germany would be eliminated. I was also once doubted when I said an unknown young midfielder deserved a chance. Both times, I did only one thing: read the data and present it. Nothing added, nothing removed. There is one thing I have learned after many years: the hardest part is not analysing data. The hardest part is staying honest when the data is insufficient, and still having the courage to say so in front of people who want a clear answer. Every coach wants to know whether their athlete is improving. That is a legitimate question. But a legitimate question does not automatically have a legitimate answer. If the data does not yet allow an answer, the right thing is to state clearly what is still needed, not to invent an answer for the sake of reassurance. I have come to realise that most of the problem in Vietnamese sport is not a shortage of talent. It is a shortage of recording systems. We have fast runners, but we do not have enough data to know which of them will be even faster in two years' time. We have good training sessions, but we do not keep enough detail to know which sessions actually produce progress. Athletics is the fairest sport by the rules. Whoever finishes first wins. But fairness on the track does not automatically create fairness in choosing who gets to line up. That choice depends on data, and if the data is incomplete, the choice will tilt toward the famous, the remembered, those who have won before. The quiet ones, the ones who are strong only in individual splits but have never finished first, will be left behind. That is why I do this work. Not to prove that I am good. But to give those who are underrated a chance to be measured properly. I return to that empty data file one more time, in a different way. Suppose I had to explain to a newcomer what to do when they receive a file with no data. I would tell them three things. First: do not guess. An invented number will outlive an overlooked truth, and it will do harm for longer. Second: state clearly what is missing. Do not just say "the data is insufficient." List exactly what needs to be measured, with which equipment, across how many sessions. A specific list turns a refusal into a plan. Third: record your own limits. In every report I write, I always set aside a final section to state what data is still missing and where my conclusion might be wrong. The reader has a right to know the degree of certainty of what they are reading. Those three things are not glamorous. No one shares them on social media. But they are the foundation of every trustworthy analysis. The ball rolls in only one direction, but data can see in every direction. On the track, the athlete runs only forward. But the analyst must look behind – injury history, years of training background – and to both sides – competition conditions, rivals, psychology. An empty data file shows you only one direction, and that direction is the direction of ignorance. I do not believe in prophecies. I believe in measurements repeated often enough to become truth. If an athlete runs faster across eight consecutive sessions, that is not luck. That is data. If an athlete runs fast once and cannot repeat it, that is not talent. That is noise. The difference between data and noise is repetition. And repetition needs time. That is why I always place a match, a session, a performance within a long-term context – five years, ten years. A season is a confession of tactics, and a training session is a confession of preparation. You can only read that confession if you have kept records long enough. The next morning, I sent the report to the coach. It was short. It contained no conclusion about whether the athlete was improving. It contained only a list of seventeen things to measure and a promise to analyse properly once there was enough data. He messaged back one line: "For the first time, someone told me they did not yet know." I kept that message. It reminds me that honesty, though unglamorous, is still recognised. And in a sport that is learning to count every stride again, honesty may be the most important measurement we are still missing. I will keep counting. Not because I believe every number is right. But because I believe that only by measuring will we know where we stand. An empty data file is not a full stop. It is an unanswered question, and that question is waiting for a device, a training session, and someone patient enough to record it.