The Data Monk and Four Blank Pages on the Track

In July, in Hai Phong, I sat in front of a spreadsheet of four blank pages. It was a monitoring file for a group of young track-and-field athletes, prepared ahead of a national meet. The competition name was blank. The names were blank. Track parameters, wind readings, altitude, shoe models — all blank. The person who forwarded the file said just one sentence: "Nothing could be collected." For most people in the trade, the story ends there. For me, it begins differently.

People call me the data monk. A monk needs no cathedral — only the truth. But faith in numbers carries its own trap: it is easy to believe that a lack of data means there is nothing to discuss. Four blank pages taught me the opposite. An empty result is not a zero result. It is a signal — and sometimes the most honest signal in an entire process.

This happened just as I was reviewing my own habits. After years of reading matches through metrics, I recognized a blind spot: when data is complete, I analyze quickly; when data is blank, I lose composure and tend to fill the gaps with guesswork. That is when the craft becomes dangerous. Guesswork dressed in the clothes of numbers is the hardest kind to detect, because it wears the appearance of evidence.

I remember the day football stopped. When the pitches closed, I moved to track and field with a simple belief: here, everything is measurable. The track does not lie. The stopwatch favors no one. But I soon learned that measurable does not mean correctly understood. A 100m run may be faster than that same athlete's personal best, yet if a tailwind above the permitted threshold is at his back, the number is no longer a result — it is just a number.

Track and field is the most data-harsh sport, and also the one most easily misread. Unlike football, where a goal can come from luck, the track returns a single, clean value. But that very cleanliness creates an illusion. Spectators see 10.85 seconds and think it is absolute truth. Practitioners must ask four more questions: how much wind? at what altitude? what surface? which shoe?

That is why, in my files, a performance line never stands alone. It always carries a block of context. For sprints and jumps, the permitted wind threshold for record recognition is +2.0 meters per second; beyond that, a mark still shows potential but does not enter the books. For endurance events, competing at altitude above roughly 1,000 meters becomes a reversed advantage — it assists sprinting but drains endurance. The same number, two entirely different readings. A performance only means something when placed beside the context that produced it.

I learned this early, and learned it through a small shock. In 2026, while working as a data consultant for a football club in Hai Phong, I found a young midfielder with an average PPDA of 6.8 — meaning his pressing ability was excellent, yet he drew no attention because of his modest physique. I brought the evidence to the meeting room and demanded he be given a chance. He entered the match and won the ball 14 times. The lesson was not in his number; the lesson was this: had I looked only at name and appearance, I would have missed a true data point. Since then I stopped writing by reputation and started writing by underlying metrics. Hai Phong taught me: the star is not on the shirt, it is in the metric.

Back to the four blank pages. In the past, I would have called it a failure of the data collector. Now I call it a result that must be recorded honestly. When an analytical process returns all empty cells — athlete name empty, distance empty, competition empty, dates empty — the careful reader sees two possibilities: either the source document was genuinely empty, or the data-extraction stage broke down. Both possibilities matter, and both demand a different action.

If the source was empty, that is a fact about source quality. If the extraction broke, that is a fact about the system. In either case, inventing a number to fill the gap betrays the very craft I chose. A good data person is not one who always has numbers to say, but one who knows clearly when they have nothing.

There is a principle I always remind myself of: absence of evidence is not evidence of absence. A missing signal does not mean the subject is clean, healthy, safe. In track and field, an athlete who does not appear in any testing list does not mean the list is empty — it may simply be that the list was never made. Inexperienced data readers confuse these two, and that confusion is more dangerous than a wrong number.

When the blank spreadsheet appeared, I forced myself to follow procedure: mark every cell as insufficient information, no inference, no filling of gaps with intuition. I call it the discipline of the blank. Not because I lack confidence, but because I have witnessed the price of beautiful conclusions built on sand.

Transfer season is when this lesson is most valuable. When rumors fly everywhere — an athlete about to change clubs, a scholarship slot, a ticket to an international meet — the pressure to write immediately, to conclude immediately, is enormous. But in track and field, the real signal usually lies in quiet places: an individual competition calendar, rest gaps between meets, publicly disclosed injury status, and the timeline of performance improvements. Those things do not shock, but they are trustworthy.

A season is a confession of tactics. And a data process is the same: in the end, it will confess whether it was truly run on discipline. Four blank pages told me nothing false about the athletes. It said only one thing about us — that in a world full of noise, the ability to stay silent when there is no data is a skill.

Of course, this is not a story with a tidy ending. The data I lack is genuinely lacking, and I will not pretend I have fixed it. What I have is only a framework solid enough to wait for the data to return, and honest enough not to deceive myself while waiting.

In the meantime, I think of the tracks back home. There are athletes whose results have never been properly recorded — not because they are slow, but because no one bothered to press the stopwatch. The fairness that data can bring still lies unfinished ahead. The task of a data person is not to invent enough to fill the blanks, but to keep the blank page standing as a reminder: work not yet finished should not be called done. Start again, from the first step, and this time count it right.