Nine Sections of Analysis, Not One Line of Data

The file arrived at 6:12 a.m. Liverpool time. Eight pages. Nine sections. It had tables, a six-row risk matrix, a three-tier transmission diagram with arrows, an appendix titled “Glossary of Professional Terms,” and a disclaimer at the bottom. I read it through once. Then I read it again, slower, certain I had missed something.

I had missed nothing. Every cell in every table contained the same entry: N/A. Not one number. Not one date. Not one player, club or competition name. No passing accuracy, no minutes played, no transfer fee, no league position. Every conclusion carried a stamp reading “Confidence: Low.” Thirty-two times. I counted with a pencil, out of old habit.

And at the top of the file, in the covering note, a single sentence: “See if you can run this.”

I came into this trade in 2026, starting at a local radio station, where I learned the only lesson worth learning: if you have no numbers, keep your mouth shut. Back then we had no passing maps, no expected goals models, no optical tracking cameras. We had a notebook, a pencil, and a responsibility for every sentence we spoke. Forty-seven years later, the notebook has become a data cloud, and the responsibility appears to have been dropped somewhere along the way.

The 2026 World Cup closed on 19 July, after 104 matches involving 48 national teams across three countries. In the four weeks after the final whistle, I received more “deep professional analysis reports” than in the entire previous year combined. That is the nature of this industry: every major tournament brings a flood of content, and people swim through it on whatever floats. But that eight-page file was different. It floated with nothing inside it. It was an empty buoy, inflated by form alone.

Football analysis has four tiers. The first sells raw data to competitions. The second sells models to bookmakers and clubs. The third sells scouting reports to sporting directors. The fourth — the largest, the loudest, the sloppiest — sells reports to readers. In Vietnam, the fourth tier has swollen so fast that within a few seasons the number of people calling themselves “analysts” has multiplied, while the number of people who actually read a data table to the end has barely moved.

That is the context. And that eight-page file is the perfect product of that context.

The frightening part is not that the report was empty. The frightening part is that the machine producing it knew it was empty, labelled it as empty, and shipped it anyway.

Let us walk through each section, slowly, the way I walked through it.

Section one: Tactical and technical. The table had four rows. System sophistication. Execution. Personnel fit. Key data. All four read N/A. What does a real tactical section need in order to exist? It needs possession sequences, pressing triggers, build-up shape, passes allowed per defensive action, field tilt, shot quality. Without any of that, the table is a picture frame on a wall. A reader skimming it would assume the team was an unknown quantity. The harsher truth is that the team was never examined at all.

Section two: Club finance and the transfer market. Broadcasting revenue. Commercial revenue. Wage expenditure. Net debt. Four cells, four N/A entries. Attached was a deal assessment with three lines: total deal price N/A, fair valuation N/A, premium rate N/A. Here I have to be blunt: a financial report without numbers is not a financial report, it is a job application. Anyone who has sat in a club boardroom knows the only figure that wakes a president at midnight is the wage-to-revenue ratio. Without it, every remark about “sustainability” is poetry.

Section three: Results and the opinion cycle. This section stated plainly: sample size zero matches. Pressure on the manager: N/A. Pressure on key players: N/A. Pressure on the board: N/A. I read “sample: 0 matches” and laughed out loud. A person working honestly would stop there. No matches means no form; no form means no opinion cycle; no opinion cycle means no article. But the machine does not stop. It simply relabels N/A as “cannot assess” and moves on.

Section four: League landscape and team positioning. A four-tier diagram was drawn beautifully: title contenders, European places, mid-table, relegation zone. Four arrows pointing at four N/A entries. The resource comparison table — squad value, financial power, academy output — was equally blank. This is the section I regret most, because it is the easiest. Squad value is a public number. Academy graduates are countable. Someone simply had to open a website. Nobody did.

Section five: Rules and governance. Financial fair play: N/A. Transfer registration rules: N/A. Disciplinary sanctions: N/A. Competition eligibility: N/A. A sanction scenario model offered three branches — worst case, central case, optimistic case — and all three read N/A. A compliance table with no status checks nothing. It is a medical form reading “blood pressure: undetermined” on all four lines, signed and stamped.

Section six: Management and the dressing room. Owner investment, recruitment decision quality, structural stability — three rows, three N/A entries. Dressing-room health was “cannot assess.” The key-personnel table had columns for age curve, contract status, injury risk and media pressure. The single data row read N/A in all four columns. I have written about football for close to half a century, and I can state this: the dressing room is where every beautiful model dies. Ignore it and your analysis is mathematics on paper.

Section seven: Risk profile. Six risk categories: sporting, financial, personnel, rules, public opinion, systemic. Each with level, likelihood, impact and mitigation. All six rows read N/A. Overall risk rating: N/A. And directly beneath it sat a sentence I wanted to frame: “No basis for rating.” The machine had spoken its own truth. It knew it had no basis. It still rated, by not rating.

Section eight: Media narrative and expectations. Current narrative: N/A. Heat cycle phase: N/A. The expectation-gap table had three rows — team results, player performance, transfer operations — blank in both columns. Transfer rumour credibility: N/A. This is the section I care about most, as someone who lives in Liverpool and reports on football for the English market. Transfer rumour is the easiest thing in this industry to verify. A source has a tier. An agent has a motive. A club has a spending history. Put those three together and you have an assessment in ten minutes. Here, those ten minutes were never spent.

Section nine: Industry transmission. A three-tier diagram: upstream academies and talent pipelines, midstream clubs and competitions, downstream broadcasting and derivative markets. All three tiers read N/A. The impact table split into six segments — academy chain, agent ecosystem, broadcasting and commercial, capital networks, derivative markets, national-team ecosystem. Each with four columns. Six times four equals twenty-four cells, twenty-four N/A entries.

Then came the summary. And this is where I take my hat off.

The summary read: “The stage-one deconstruction result contains no usable information. No analysis can be performed. The input provided is entirely blank.” The information value table awarded one star out of five across all four dimensions: sporting value, industry value, timeliness value, reference value. The key risk warning, rated high: “Critical input failure.” And the recommendation: “Regenerate the stage-one deconstruction with a valid article source.”

By that point I understood everything. This machine is not stupid. It is terrifyingly intelligent. It diagnosed its own illness precisely, named the medicine, specified the dosage — and still handed the client an empty box with a full label.

I was once wrong about the 2026 World Cup. And that is the most expensive lesson I own. During the Croatia–Denmark round-of-16 tie, I mispronounced the name of Ivan Rakitić three times in a row on live air. For the following month I sat in a dark room, rewinding tape, counting every pass from Croatia's midfield. Luka Modrić, Ivan Rakitić and Marcelo Brozović held an 89 percent passing accuracy through the knockout rounds. I wrote an article rebutting myself to explain how Croatia reached the final. That piece did not apologise. It corrected.

The difference between me in 2026 and the machine in that eight-page file comes down to one point: I had bad data to correct, and it had no data at all. I was wrong at the level of speech. It was empty at the root. One can be fixed. The other can only be filled.

And remember 2026, when at 56 I published a prediction on my personal blog that the whole trade laughed at: Mohamed Salah, Roberto Firmino and Sadio Mané would score at least 84 goals in all competitions for Liverpool in 2026-18. By the end of the season the three of them had scored 91. Salah 44, Firmino 27, Mané 20. A new sports platform republished the piece, and it drew 120,000 views in its first week.

The number 91 is not a lucky number; it is the destination of a plan. I did not pull it from the sky. I went back and watched Liverpool's previous season match by match, counted each man's shots from each position, measured how often they touched the ball inside the box, and worked out how much the new system under Jürgen Klopp would inflate chance volume. The figure 84 was the output of a chain of calculations I could show step by step. If I could not show the steps, I would never have published.

People call me reckless, but numbers have never lied. Only the people who use them lie. And the most common lie of this era is not invented data — it is a very long page with no data on it at all.

Look at the structure of that file once more. It has everything a professional report needs on the surface. Hierarchy. Tables. Diagrams. Confidence labels. Risk warnings. Recommendations. A hurried reader sees seriousness. An automated scoring system gives it high marks. A search algorithm ranks it above, because it is long, structured and full of technical keywords. And so the empty is pushed ahead of the full.

This is the price of an analysis industry that has morphed into a content industry. People measure by pages, not by data points. By length, not by information density. A 900-word piece with three verifiable numbers loses to a 3,000-word report with zero.

And I have a more specific worry, tied to the market I live in. In England, football commentary has passed its golden age of writers. Today, a large share of Vietnamese-language content about English football is produced not by people sitting in England, but by machines sitting somewhere else, reading a summary and writing a longer one. I have nothing against tools. I object to using tools to cover a void. What needs covering is not the gap in the article — it is the gap in understanding.

I once spent a year writing a history book, on Catholicism, war and the making of a political figure. That book taught me something that applies to football exactly: to say one sentence about the past, you must read three sources. With one source, your sentence is an opinion, not a fact. And opinions must be labelled as opinions.

The eight-page file was labelled. It labelled itself with a honesty bordering on cruelty. That is the point I have to grant it.

And here is where I might be wrong.

There is another reading, and I want to put it forward seriously. Perhaps that machine is the most honest actor in the room. It was asked to analyse when there was nothing to analyse, and it refused to invent. It filled every cell with N/A instead of stuffing in a flowery sentence. It wrote “cannot assess” instead of “this team has great fighting spirit.” Set it beside a pundit willing to talk for twenty minutes about a team he has never watched, and the machine is the more ethical party.

I do not deny that. I only say that honesty at the diagnostic layer does not rescue meaninglessness at the product layer. A doctor who correctly records “I have no test results” and then prescribes medicine anyway — is that honesty or negligence? The line sits here: an honest person stops. A machine is not programmed to stop.

Here is the second point on which I might be wrong. Perhaps I am too harsh about form. For forty-seven years I have demanded numbers. But some things in football are not measurable. The atmosphere at Anfield on a European night. The pressure on a twenty-year-old stepping up to a penalty at minute 88. The fear of a manager who knows the next match is his last. Perhaps part of this trade must be written by feel, not by table.

I agree. But data does not kill emotion. It gives emotion a frame. Emotion without a frame is just noise, and noise is something anyone can shout.

As for home advantage — a subject I have chased for years: the home ground is no longer a fortress. The pandemic proved it. When matches were played in empty stadiums during lockdown, home win rates across many top leagues fell close to neutral, and the home-away gap narrowed to a point that could be ignored. When crowds returned, that gap did not fully recover. That is a finding with data, with method, with verifiability — exactly the kind of thing the eight-page file lacked entirely. To say “home advantage is dead,” I need thousands of matches in hand. To say something empty, I need one page.

So what produced that eight-page file? Not one person's laziness. A system of incentives. When article count is the target, when speed is the target, when formal completeness counts as quality, the step of “check whether I actually have data” is the first to be cut. And it is cut harmlessly. Nobody is punished for publishing a piece with no numbers. Somebody is punished for publishing too few pieces.

That is why I set myself a rule after the 2026-18 season: no data, no writing. The rule has cost me opportunities. There are weeks I publish nothing, because I cannot find a single detail solid enough to serve as a fulcrum. My readers in England and Vietnam are used to the fact that within the first two sentences of anything I write, they will meet a number. That is not a trick. That is an entry check.

And football waits for no one. It waits only for those willing to ask questions.

I did not send that eight-page file back to its sender. I did not reply with a curt refusal either. I took it out, read it a third time, and kept it in a drawer. It is a valuable document, in the way a documentary about an empty room is valuable: it shows what happens when an industry learns how to speak and forgets how to know.

What I want to put on the table for the coming season, now that the 2026 World Cup has closed and domestic leagues are preparing to start, is a different measure. Not pages. Not words. Not sections. Verifiable data points per thousand words. If a two-thousand-word piece contains three properly sourced numbers, it beats an eight-page report containing none. And if anyone sends me another file with nine sections and thirty-two “low confidence” labels, I will ask exactly one question: your first data cell — what number?

The answer to that question is everything that separates an analyst from a busy man.