Vietnamese Athletics and the Empty-Data Trap: When Analysts Must Learn to Say 'Insufficient Information'

I remember that morning the way I remember a starting line with no one behind it. On the screen was an athletics analysis table with every field laid out: performance, distance, opponents, season ranking, wind conditions, altitude, equipment status. But every field was empty. Not a single athlete's name. Not a single technical parameter. Only the same line repeating over and over: "insufficient information, cannot assess." Outsiders might see that as a failure of the analytical profession. But to me, someone who has spent nine years with numbers, it was the most honest moment this work can teach. And it opened a much larger question about Vietnamese athletics. Athletics is the foundational sport — where every record is measured in seconds and centimetres, where no side judge can hide an error. Precisely for that reason, it is the sport where data must sit at the centre. In Vietnam, we have athletes to be proud of: Nguyen Thi Oanh with her SEA Games gold medals in middle-distance events, Bui Thi Thu Thao with her long jump gold, and a rising generation in the sprint events. But if you ask me which curve their performances are following, at what age their career peak will fall, or where they stand against Olympic qualifying standards — I would have to answer with exactly the line I wrote on the screen that morning. Insufficient information. That is the core problem. It is not that we lack athletes. It is that we lack a data system thick enough to turn each individual into a verifiable story. And when data is thin, the biggest trap is not ignorance — it is a false confidence built on numbers that do not exist. I have been inside that trap before. I once tried to look up the data from a national athletics meet. What I found were scattered result tables, missing dates, missing competition conditions, and in many cases missing the athletes' full names. Compared with the database of the Japan Association of Athletics Federations, where every mark carries its date, venue, wind conditions and meet name, the gap lies not in talent — but in habit. The Japanese record first, analyse later. We tend to analyse first, and leave the recording for later. On the night of Russia 2026, I watched the data shatter before my eyes. I was seventeen then, taking meticulous notes on every Japan national team match at the World Cup. In the game against Belgium, Japan lost 2-3 in the round of sixteen. I recorded a number that still holds its value today: Japan touched the ball inside the opponent's penalty area only seven times, against Belgium's twenty-one, despite holding 55% of possession. I wrote an analysis, based entirely on statistics, arguing that Japan's decision to push their line high in the closing minutes was a tactical mistake. The piece drew fierce criticism from a group of fans. But I held my position, because data does not lie. What I learned was not how to defend myself against criticism, but how to begin every piece with the question "what do the numbers say" instead of emotion. From then on, I understood that an analysis is only trustworthy when every conclusion can be traced back to a specific data source. This brings me back to Vietnamese athletics. Picture an 800-metre runner. To assess him seriously, I need at least three layers of data. The first layer is performance: his personal best, his season's best, and his year-by-year progression. The second layer is conditions: wind, altitude, track surface, and whether the mark was set in a heat or a final. The third layer is opponents: where he stands against his contemporaries. Remove any one layer, and I cannot say whether he is improving or stalling. And if I speak anyway, I am selling the reader an empty belief. In esports, human reaction time is the limit of the data — and I find this principle holds for every measurable sport. The boundary of analysis lies not in how much data we have, but in whether we have enough to say something honest. When we do not, the correct answer is silence and an acknowledgement of the gap. That is not weakness. That is discipline. This discipline matters even more when I recall the empty-stadium season of 2026. When the pandemic forced the J-League to postpone for four months, I — a journalism student in Osaka — could not go to the stadium to watch Cerezo Osaka. I began building my own dataset from old match footage, logging 1,240 pressing situations from the club's 2026 season to analyse PPDA, the number of passes a team allows the opponent before pressing. When the league returned, I predicted Cerezo would slump because of the absence of home support. The result: they finished fourth, below my predicted second. I was wrong. And instead of blaming luck, I admitted the error and added a new variable to the model: crowd influence. An empty stadium, yet the numbers were still full of noise. That lesson shaped how I view Vietnamese athletics today. The problem is not that we lack perfect analytical models. The problem is that we lack the habit of recording data systematically from the very beginning. Every domestic meet, every fitness test, every time an athlete sets a personal best — all of these are data points. If they are not recorded, they disappear. And when they disappear, the future analyst will again sit before an empty screen. I collect mistakes, classify them, and then I know where a team is heading. In athletics, I do the same with every stride: every slow start, every loss of momentum on the bend, every fading final 200 metres. All of it is data. But only when it is recorded consistently does it become knowledge. In 2026, while analysing the Euros, I found that Denmark scored four of their six goals from pre-designed set pieces — far above the tournament average of 28%. I compared it with the 38 set-piece goals of RB Leipzig in the 2026-21 Bundesliga, where coach Julian Nagelsmann used running-position and ball-landing statistics to design training drills. I wrote a piece about Japan's lack of a comparable methodology. It was republished by a small football site and drew 15,000 reads. But my point here is not the piece's performance. It is the principle behind it: a system can only be improved once it is first measured. On the transfer front, I once analysed data on more than 200 players moving from the J-League to Europe and identified a correlation between kilometres run per match and success rate in the Bundesliga, with a correlation coefficient of 0.67. I contacted a scout at a German club and suggested midfielder Ao Tanaka — who covered 11.8 km per match, the highest in the J-League. He moved to Fortuna Düsseldorf on loan. My piece was cited across several foreign forums. But what I want to stress is this: if J-League data had not been recorded thoroughly, I could never have made that suggestion. What is more worrying is that when Vietnamese athletes compete abroad or move to international training centres, data about them often vanishes from the domestic system. An athlete may improve their personal best significantly overseas, but on returning home, no one has enough data to compare him with his own self of two years earlier. This is a silent form of information loss — not as loud as a defeat on the track, but no less dangerous, because it prevents us from seeing progress even while it is happening. In athletics, every hundredth of a second counts. A good data system does not make an athlete run faster. But it tells us who is running faster, and why. And this is where I must say something few want to hear. The biggest mistake in sports analysis is not a lack of data. It is pretending to have enough data when in reality you do not. An analysis table filled entirely with "insufficient information" is an honest table. But a table filled with estimated numbers, with judgements based on "expert feel", is a lie dressed up in terminology. In athletics — where everything is measured in fractions of a second — the difference between an honest analysis and a fabricated one is clearer than in any other sport. Because here, the data does not let us hide. Data does not create stories; it strips bare the stories of others. When a Vietnamese athlete posts a strong performance, the story is told immediately. But the real question is not "what was that performance", but "what does that performance mean in a broader context". And to answer that, we need a data system thick enough to compare, cross-check and verify. Without that system, every story is just a scattered fragment, beautiful but meaningless. I do not write these lines to criticise. I write them as a reminder to myself and to those in the trade. In Japan, where I live and work, the culture of recording sports data runs so deep that every club has its own analysis department. But I also know that applying those standards wholesale to Vietnam, without unpacking the cultural and practical differences, is a mistake. Vietnamese athletics does not lack talent. It lacks data infrastructure. And building that infrastructure is the work of many years, not of a single article. So where is the next signal? It is in the spreadsheet. Every probability hides a shock — and the analyst's job is to ensure that shock does not repeat. For Vietnamese athletics, the biggest shock will not come from a defeat on the track. It will come from us losing our own data — and then being unable to tell whether an athlete is improving or falling behind, until it is too late. The contract is only the ending; the beginning lies in the spreadsheet. And in athletics, that spreadsheet begins today — with the honest recording of every performance, every condition, every opponent. Not to produce attractive headlines. But to ensure that when the future analyst opens the data file, they will not see a blank space.