The Blank Cells in the Spreadsheet: Lessons from an Empty File and 23 Forgotten Players

At 2:17 a.m. on 14 March, in a rented flat in Chaoyang District, Beijing, I reopened a spreadsheet I had closed six months earlier. The file was named U20_Oberliga_2017_v7_final_real.xlsx. Inside were 23 rows, one per player, and 47 columns. I had spent nearly four months filling each cell. But when I dragged the cursor to the bottom of the sheet, I counted 41 blank cells. Not error cells, not hidden formatting. Just blank. And what kept me awake was not the number 41, but the fact that I had almost forgotten they existed.

Six months before that, I had filed my story with a full table. Nobody asked what those 41 blanks meant. Nobody asked why they were blank. I did not ask either. I let the process look complete, and in football a process that looks complete is always more dangerous than one that admits it is incomplete.

This piece is not about a match. It is about what happens long before a match, in a place no camera reaches — where data goes missing, and where people decide that silence is better than guessing.

Part 1 — The Oberliga map is still there; few have the patience to dig it up.

In August 2026, aged 35, I was the only reporter shadowing the Chinese U-20 selection squad through eight matches in the Oberliga — Germany's fourth tier, where games are played on artificial turf with rotting wooden stands, before roughly 300 spectators, half of them pensioners out for a walk. The coach was Sun Jihai. The team won only 2 of 8. In the press, that tour was called a failure.

I did not think so. But to say that, I needed evidence that does not live in the scoreline.

The Oberliga is a kind of forgotten sediment layer. No broadcast data package, no behind-goal cameras, no positional tracking. If you want to know where a 19-year-old midfielder receives the ball between the lines, you have to sit there yourself, stop a watch yourself, draw it yourself. Across those eight matches I built a system of 47 bespoke metrics for 23 players: 20-metre acceleration time, receptions between the lines, penetration pass rate into the final third, number of body rotations before receiving, average distance between consecutive touches.

47 metrics. 23 players. 8 matches. 41 blank cells.

That number 41 was no administrative slip. It was the result of a principle I set for myself in 2026, when I was still working at a local radio station: if I cannot measure it, I leave it blank. No inference. No estimation. No filling in by feel.

The biggest finding from that series concerned a midfielder named Yan Dinghao. After six weeks on tour, his ball-handling time fell by 0.4 seconds — from 1.9 seconds to 1.5 seconds in the same type of situation: receiving with his back to the opponent's goal, under pressure from behind. 0.4 seconds. At elite level, 0.4 seconds is the gap between a pass that gets intercepted and a pass that opens an entire defensive line.

What matters is that I did not discover those 0.4 seconds by rewatching video. I discovered them by hand-timing 14 occasions across 6 matches with a stopwatch and recording each one in pencil in a hardback notebook. After that series, some academies began cross-checking my numbers against their internal reports. They were surprised they matched. I was not surprised, because I knew what I had measured.

But they did not ask about the 41 blanks. And that is the problem.

Part 2 — Mbappé taught scouts that the weapon sits beneath the ankle, not in the scoreline.

In June 2026, aged 36, I took my personal measurement method to the World Cup in Russia. In the France–Argentina round-of-16 tie, I tracked 17 sprints by Kylian Mbappé and recorded something whose full meaning I only understood later: the gap between two consecutive sprints was always under 22 seconds.

Stop at that number, 22 seconds.

In movement physiology, repeated-sprint ability is a more important marker than peak speed. A player who can run 36 km/h in a single burst is a phenomenon. A player who can run 34 km/h seventeen times across 90 minutes, with under 22 seconds of recovery between efforts, is a system. The scoreline does not record this. The scoreline records two goals.

Three days after that match, Mbappé scored a brace against Argentina and my piece 'The Speed Structure of the New Football' was suddenly shared over 500 times. On day one it had 30 reads.

I do not tell this story to talk about late recognition. I tell it to point at a mechanism: the market reads football through results, while the nature of football operates through process. When process is placed correctly, results follow. When process is placed incorrectly, results arrive and then vanish.

And this connects directly to the 41 blanks. If I had drawn conclusions about Yan Dinghao from 47 metrics when only 6 cells were actually measured in a single match, I would have created an illusion of certainty. If I had filled those 41 blanks with guesswork, I would have created a file that looked beautiful but was hollow. And when that file is copied by an academy, then by a scout, then by a club, by the third layer nobody knows the 41 blanks ever existed.

Part 3 — The discipline of people who are not allowed to guess.

In the data industry there is a term rarely spoken outside meeting rooms: null handling. The principle is simple. When a field has no value, you declare it null. You do not substitute the mean. You do not substitute the nearest comparable case. You do not substitute intuition. Substituting a number for a null can make a chart look better, but it destroys the verifiability of the entire dataset.

In football, this principle barely exists.

Take transfers. A 20-year-old moves from the second tier to the top tier. The press reports: fee 3 million euros, no sell-on clause. In reality the contract has five clauses: four instalments, an appearance bonus, a national-team bonus, a sell-on percentage, and a penalty if the player is not registered in the second window. None of those clauses appears in the news. Yet dozens of analyses are written on the 3 million euro figure, judging whether the deal was expensive or cheap.

That is exactly 41 blanks filled with zero.

The core point sits here: the true value of a young player is not the number the market publishes, but the ratio between what is measured and what is ignored. When that ratio worsens, we do not lose data — we lose the ability to know we are losing data.

Throughout my career I have used 47 metrics. But I have never presented 47 metrics as though they carried equal weight. For each player I noted which metrics were measured directly, which were derived from two others, and which could not be measured at all. The fact that a metric cannot be measured is itself information.

For example: a midfielder's spatial reading could not be measured by any device in 2026 in the Oberliga. No eye-tracking, no positional data. I could count penetration passes. I could count how many times the player turned his head to scan before receiving — and I did count, sitting beside the touchline, recording each instance. But I could not infer what he saw from the fact that he turned his head. I left that cell blank and wrote in the notes: eye-tracking data required.

Years later, some European academies began using eye-tracking glasses in training. Reading their reports, I realised my 2026 blank had been filled — by a different tool, six years later. Had I guessed in 2026, I might have been right. But had I guessed, I would never have come back to check.

Part 4 — The trap of the empty file.

This is the part that made me write this piece.

In modern content-processing systems there is a class of failure more dangerous than any other: the failure that does not report as a failure. A pipeline runs, hits a problem at the input-retrieval stage, but instead of stopping, returns a structure that looks complete — correct format, correct headings, correct number of fields — with only the content empty. The recipient sees no error. The recipient sees a finished document.

In football this mechanism has another name: the fake scouting report.

A fake scouting report is not a wrong report. It is a report with a full Physical section, Technical section, Psychological section, Potential section, Risk section — and in each, the writer has entered a safe general sentence no one can attack. Good speed but needs to improve decision-making. Solid technical base with potential to develop. Needs more time to adapt to match intensity.

Those three sentences are not wrong. They are also not right in any verifiable sense. They are 41 blanks filled with language.

In 28 years observing the industry, I have never seen a young player lost because of a lack of data. I have seen many young players lost because the data on them looked far too complete.

When a report looks complete, nobody has an incentive to go back. When a report looks complete, a club makes a decision. When a report looks complete, the player is assigned a label — a label that follows him for three years, and across those three years he changes a great deal, but the label does not.

This is why, from 2026, I began attaching my own measurement sources to every piece. Not to show off method. But so the reader can see the blank cells. A table with honest blanks is more truthful than a table with none.

Part 5 — Six months frozen is not a gap; it is where value settles.

Back to the personal story. There was a period when I could not write for six months. Not through blockage. Because I was waiting.

During that time, a young player I had tracked since he was 19 tore a ligament and was out for nearly a year. No matches. No new data. Nothing to write. Every colleague had removed him from their watch lists. In their systems he moved from developing to inactive, then vanished from the database after two transfer windows with no news.

I kept him. But I did not write.

Those six frozen months turned out to be the most important phase of his development. During the layoff he changed how he ran — from forefoot to full-foot strike, reducing load on the knee. He trained reading the game through video, and notably began speaking about situations in the language of an organiser rather than an executor. On return his peak speed was 0.3 km/h lower than before the injury, but his receptions between the lines rose 34%, and his penetration passes per 90 minutes went from 4.1 to 6.8.

Judged on peak speed, he had got worse. Judged on impact on the match, he was markedly better.

This is why I say a blank in the data is sometimes not missing information, but information that has not yet settled. Like sediment. You cannot measure the thickness of a sediment layer the moment it is deposited. You have to wait for the water to drain.

But — and here I contradict myself — not every blank is sediment. Some blanks are simply nothing. Some players disappear because they are not good enough, not because the system was not patient. Anyone in this trade must tell the two kinds of blank apart. And the only way to tell them apart is to go back to the field, not sit in a room re-reading old data.

Part 6 — The weapon beneath the ankle, and what cameras do not record.

In my analyses I always try to avoid a common error: valuing players by the scoreline. But there is a subtler error, one I have committed: valuing players by official data.

Official data has one characteristic: it records only what happens while the ball is moving, and only what can be reduced to a number. Passes, tackles, distance covered, touches. That is the dust on the surface of the sediment layer.

The deep soil lies elsewhere.

I once spent three months rewatching a young full-back's actions without looking at the ball. I recorded: how many times he turned his head before the opponent passed, how he adjusted his body shape when the ball was on the far wing, where he stood when a teammate was preparing to pass to him. The official sheet says he had an 82% pass completion rate. That number says nothing about the body shape in which he received. And in modern football, receiving body shape matters more than pass completion.

A player who receives with his back to goal and passes backwards can post 95% accuracy. A player who receives half-turned, pushes the ball forward and plays into space might post only 70%. Read only the number, you pick the first. Watch the tape, you pick the second.

This is what I call the weapon beneath the ankle. It is not in the scoreline. Nor is it in the most advanced statistics table, because the most advanced statistics table still needs a human watching to label each action.

And in the Oberliga in 2026, nobody was labelling. There was only me, a notebook, and 41 blanks.

Part 7 — Market noise and the price of silence.

There is a force in professional football that always has an incentive to make data look more complete than it is: the intermediaries in transfer deals.

I am not speaking of a specific individual. I am speaking of a mechanism. When a young player is priced, the agent has an interest in pushing the number up, since their fee is a percentage. The most effective way to push the price up is not to lie, but to create an atmosphere of multiple interested parties. And the most effective way to create that atmosphere is to release so much information that nobody has time to verify it.

Silence has a price. A reporter like me, staying silent on an unverified deal, loses the speed advantage. But silence has a benefit: it keeps the database in my head clean.

I was once criticised by colleagues for not reporting a deal that three other outlets had covered. Two months later the deal collapsed for a reason none of those three outlets had ever mentioned. I was not praised for staying silent. Nobody praises the person who does not write. But had I written, I would have had to write three more pieces to correct it, and in those three corrections I would have had to use blanks filled with guesswork.

The value of anyone in this trade is not in the number of pieces written, but in the number of pieces that never need to be retracted.

This principle applies even to the most serious analyses. When an academy announces it has a 200-metric evaluation system per player, I always ask two questions: of those 200, how many are measured directly, and how many cells are empty. Nobody has answered straight. That itself is the answer.

Part 8 — Referees, technology, and the same mechanism.

In recent years I have spent a lot of time tracking referee decisions and video-assistance systems. There is a strange parallel between the refereeing problem and the scouting-data problem.

Both are moving from judgement to measurement. Offside lines are drawn to the millimetre. A toe past the line can cancel a goal. Technically, the decision is correct. In terms of the nature of the game, that decision is changing what we call football.

I do not oppose technology. I oppose using technology to create a sense of certainty in places where certainty is not the highest value. A striker must learn to slow by 0.05 seconds to avoid being offside. That is a new skill. It is also an erasure of instinct. And attacking instinct is something data cannot create, only record.

The mechanism here is identical to that of the empty file. Technology returns a result that looks very precise. But the question technology does not answer is: what assumption does that precision rest on, and is that assumption valid for what we are trying to measure?

When a line-drawing system uses 50 frames per second, it ignores the interval between frames. In that interval the ball and the players keep moving. The system picks the nearest frame and draws a line that looks decisive. But that decisive line is not physical truth. It is a blank filled with the nearest frame.

This does not mean we should abandon technology. It means we should be explicit about error margins. A declared error margin is a controlled error. A concealed error margin is an accumulating one.

Part 9 — What remains after the dust.

Back to the spreadsheet with 41 blanks.

After discovering them, I did something I should have done six months earlier: I listed every blank and sorted them into three groups. Group one: blank because I could not measure it under field conditions. Group two: blank because I did not think to measure it while I was there. Group three: blank because the metric cannot be measured by the method I have.

Group two is the most embarrassing, because it is not a technical limit but a limit of the practitioner. Group two had 19 cells. I could have measured them. I did not think in time.

Group three had 8 cells. Those eight I keep to this day. They remind me that there are things about a player I will never know, no matter how many matches I watch. And that does not make me less confident in my work. It makes me more confident, because I know exactly where my limits lie.

Of the 23 players in that U-20 selection squad, how many had international careers? The exact figure matters less than how we count. If we count by senior national-team caps, we conclude the tour failed. If we count by players with stable professional careers of 10 years or more, the picture is entirely different.

That U-20 side did not only produce players; it produced a way of thinking about football.

Part 10 — The reader of footprints on melted snow.

Modern football does not lack viewers; it lacks readers of footprints on melted snow.

What is a footprint on melted snow? A young player not called up to the first team for two seasons, but across those two seasons his reserve-team minutes fell 40%, and — notably — his key passes per 90 rose. Surface data says he is declining. The footprint says he is being used wrongly.

Reading that footprint requires three things the modern football industry supplies less and less of: time, silence, and the ability to accept that one is missing information.

Every year clubs spend hundreds of millions of euros on data, analytics tools and analytics staff. But the number of people who actually go back to the field to check how that data was generated is very small. Buying a measurement system is not the same as understanding a measurement system. And in many cases, buying the system actually reduces understanding, because it creates the feeling that the problem has been solved.

I think about those 41 blanks every time I read a scouting report with 200 metrics. I wonder how many empty cells are hidden behind round numbers. I wonder how many young players are being misjudged because a data file looks so complete that nobody bothers to check.

And I think of a question I never put to myself across 28 years in this trade: if I had to choose between a piece with five wrong conclusions and a piece with five unfilled blanks, which would I choose?

I choose the blanks. Because a blank can be filled tomorrow by a trip, a notebook, and an afternoon beside the touchline. A wrong conclusion takes years to retract, and sometimes is never retracted at all.

Closing — Not a summary, but a task.

The Oberliga map is still there; few have the patience to dig it up.

The value of a map lies in the lines left blank, not the lines drawn.

Every generation of good players begins as a generation of archaeologists who know how to wait.

I will end this piece with one concrete thing I will do next week, not a judgement. I will send the spreadsheet with its 41 blanks to three youth academies in Southeast Asia — places beginning to build their own data systems and standing at exactly the point where I stood in 2026. I will attach a single line of note: do not fill these blanks. Count them.

An academy that knows how many blanks it has is an academy that is growing up. An academy that believes it has no blanks is an academy steadily losing the ability to see players.

Six months frozen is not a gap; it is where value settles. But a blank in the data is different. It does not settle. It only waits for someone to return.

And in youth football, the one who returns is usually the one who arrives last — and stays longest.