When the Analysis Comes Back Empty: Witnesses, Memory, and the Limits of Football Data

This morning I received an in-depth football analysis. I opened it expecting to read something intelligent. The first page stopped me. Title: none. Source: none. Information points: empty. Every field across its nine sections was stamped with the same phrase: "insufficient information." Not a single judgment on tactics, finance or personnel was issued. The author chose not to invent.

In an age when anyone can generate a plausible-sounding analysis out of thin air, that disciplined silence made me sit down. It touched the very question my profession must always answer, though we seldom say it aloud: when there is nothing to say, do we dare stay silent?

Thirty years ago, at thirty-nine, I wrote a piece about a match I could not attend. I reconstructed it from short wire items, a colleague's memory, and one supporter's account over the phone. It read smoothly. And it was wrong in three details. From that day I set myself a rule: better a short piece that is right than a long piece that is smooth. This morning's empty analysis, oddly enough, obeys exactly the rule I learned through one mistake.

CONTEXT: FROM A SCORESHEET TO THE DATA ROOM

Football took a long journey to learn to trust data. In 2026, when I began working with local radio stations, "data" was a scoresheet and a few scrawled lines about the scorer. To know how far a player ran, you asked him. Today, optical cameras in the stadium record every stride; algorithms assign each shot a probability of becoming a goal; software paints a heat map for each player like a personal signature. Broadcasters run statistics beneath the goal frame like the pulse of the match. Fans open their phones and see everything quantified: who ran most, who passed most accurately, who is worth how much on the transfer market.

The promise of the data age was beautiful: data will tell us the truth. But this morning, holding an empty analysis, I understood that the promise is only half true. Data can tell the truth, but it does not generate the truth by itself. An empty analysis does not lie; it merely confesses it has nothing to say. In my profession, that is the rarest kind of honesty.

At 69, I sit between two football cultures, Vietnam and China, two different ways of loving the game that share one foundation: the memory of the stands. Perhaps that is why I always distrust reports that are too smooth. An empty report leaves you knowing what to do with it. A report stuffed full but with no source, no date, no concrete names — that is the dangerous thing, because it invites you to believe, to write, and to invent.

THE CORE: AN ANATOMY OF A REPORT

To understand why an empty analysis taught me so much, look at the structure of a good report. A serious data report must answer three questions: whom it concerns, when, and on what source. Without the first, we do not know the subject. Without the second, we do not know the time context. Without the third, we do not know whether to trust it. This morning's analysis lacks all three. And the third absence is the deepest wound.

Let me tell an old story to make this clear. Memory is a kind of witness, and a witness must be written down while it is still warm. On the night of June 30, 2026, in Kazan, I sat in the press room as France met Argentina in the World Cup quarter-final. Kylian Mbappé was 19. He dribbled past pressing wave after pressing wave — I counted eleven over the match — and scored twice in France's 4-3 win. The post-match data gave me everything: Mbappé's distance covered, his top speed on the second break, the number of times he received the ball in the space between Argentina's full-back and centre-back. But no field in the table told me how the stands felt that night. No algorithm measured the moment tens of thousands held their breath and then burst. I had to write that part myself, from my own memory.

The heat map has become a new kind of astrology. It wears the coat of science, printing bright red patches where a player moved most, and people read it like a horoscope. But that red patch does not tell us what the player was doing inside the team's tactical system. A central midfielder may cover the whole middle third in red because he is the only one asked to cover, while a teammate who runs less is holding the rhythm of the whole machine. The heat map tells us the story of movement and hides the story of role. That is why I always place the heat map beside my notebook, never in place of it.

There is one indicator I still use to test whether a player truly matters, one the heat map never shows me: how often he touches the ball in space before receiving it. A good striker is not only the man who receives and scores, but the man who moves to open space for himself before the ball arrives. That act is usually invisible on a heat map, because it happens in a split second, in a corner of the pitch where movement density is low. To see it, you must rewatch the footage, slow it down, put your eye in the right place. Data invites us to look at the big picture; a journalist's job is to look at the split second.

This is equally true of women's football, where public data is far thinner. I once followed a women's national team match and realised that most published statistical tables lack depth: few heat maps, few pressing metrics, little individual analysis. That shortfall is also a kind of empty analysis, except it does not confess. It still appears complete. And that appearance of completeness makes us forget half a match left untold.

Back to the structure of a report. I learned a rule in editing: one item should carry one topic. If a source covers several topics, split it. The reason is practical: when you blend topics into one item, you unwittingly hide the weakest topic behind the strongest. A transfer story mixed with a tactical story makes readers think both have been verified equally. Split them, and each must stand on its own feet. If it cannot, we know at once.

Another rule: never use relative time. "Yesterday", "this week", "recently" are words that kill verifiability. A piece that uses them decays over time, until no one knows which day it described. A piece that states absolute dates will outlive its own author. I write slowly, partly because I spend time checking the date of every event. I write slowly, because football is in no hurry - it only waits for whoever is patient enough to understand.

Then there is naming. People abbreviate, use pronouns, write "he", "that club", "this player" to tighten a sentence. But each time we do, we drop a thread connecting us to reality. A player's full name, a club's full name, is how we tell the reader: I am speaking of a real person, not a shadow. This is what I remind the young people in the newsroom almost every week.

The true value of a report lies not in its length, but in its traceability. The reader has the right to ask: where did you get this number? And the writer must have an answer. In my profession, a claim that cannot be traced is not data; it is a belief dressed up with a percentage sign. That is the line between a journalist and a wandering storyteller. Both tell good stories, but only one is accountable for what he tells.

There is a metric I like very much and fear very much: PPDA, the passes an opponent is allowed before each defensive action. The lower it is, the more aggressively a team presses. It sounds tidy. But a team can press ferociously in the first half and collapse in the second from exhaustion, and the match-average figure will hide that collapse entirely. To see it, you must split the metric into fifteen-minute blocks. Aggregate data is a blanket laid over the truth; it keeps you warm but hides the shape beneath.

xG, the chance-quality metric, is another example. A team that wins three games in a row through lucky long-range shots will have a lower xG than its actual goals. A fluent reader of data will say: this winning run is hard to sustain. And usually they are right. But some teams play exactly that way and keep winning, because football is not a pure game of probability; it has belief, momentum, nights when a whole team plays on an energy no statistic can capture. Data predicts what usually happens; it does not narrate what did.

Then the transfer market. A player is priced by models of age, form, minutes played and resale potential. Those figures are useful to a club, but they never tell the story of an afternoon when the player took the pitch while his small child had a fever. Market value is the language of the boardroom. It is not the language of the stands.

There is a cycle I have seen dozens of times in my career: a young player rises after a few good games, the press lionises him, the stands chant his name, and a few months later the same papers analyse why he disappointed. That cycle runs faster than any human being's rate of growth. Data helps speed it up, because each time a pretty metric appears, we gain another excuse to declare. What data cannot do is remind us that behind every metric is a person trying to learn how to live with attention.

I follow movements on the prediction markets as I would follow an index of crowd psychology, never as advice. When the odds shift before kick-off, what matters is not that they predict who wins, but that they show what the crowd fears. But I will never write a piece advising anyone to bet. My job is to help people understand the match, not to help them wager on it.

This morning's analysis was divided into nine sections: tactics, club finance, results and public opinion, league landscape, rules, management and dressing room, risk, media, and industry transmission. It sounds magnificent. But each section needs one minimum thing to exist: a name. Without a player's name, a club's name, a competition's name, all nine are just nine empty frames. And what is admirable is that the author did not stuff them with fake names.

There are two kinds of data, and I learned to tell them apart. The first is data for the coach: it serves a specific decision, a half-time adjustment, a set-piece plan. The second is data for the reader: it serves understanding. Confusing the two is the common disease of modern sports journalism. The writer hands readers figures that only mean something to a coach, then is surprised that readers find them alien.

Back to Mbappé and the night in Kazan. The data told me he ran fastest. What it did not say was this: that night, a 19-year-old boy taught an entire football nation that had fallen asleep that youth needs no permission to change a community's heartbeat. Youth is not only speed, but the way a person chooses to charge forward. Four years later, in December 2026, in Qatar, I sat before a screen again as Lionel Messi, 35, touched the ball for the last time in Argentina's colours. The final ended 3-3 after extra time, and Argentina beat France on penalties. Data measured the passes, the shots, even the kilometres Messi covered. It did not measure the weight of a farewell. And I, 65 then, suddenly realised I was ageing alongside the supporters I had recorded in the Ban Cong Xanh project. At 69, I learned that the world still runs faster than I do, but longing always stands still.

I learned this most painfully in the pandemic season of 2026. The stadiums of Chengdu fell silent. Sichuan Jiuniu, once drawing 43,000 fans a match, had only cicadas on empty terraces. The data department still sent reports, full of metrics as usual. But sitting before the screen, reading statistics about a match with no crowd, I found them pale as chalk on a blackboard. The empty seats still ring with song, because longing is also a kind of supporter. It was in that silence that I launched the Ban Cong Xanh project: video calls to fifty veteran supporters, three hundred minutes of recording about the afternoons they stood waiting for the ball to roll before old loudspeakers. No metric in any data report captured what I gathered from those calls.

If I were asked this morning to rewrite that empty analysis into a full report, where would I begin? I would not begin with a cell of figures. I would begin by establishing the subject: which match, which two teams, which players. Then I would fix the absolute time: day, month, year. Then I would state the source: where this data came from, who published it, when. Those three layers — subject, time, source — are the spine of a report that can stand. Without them, every analysis behind them is a castle of sand.

And there is a fourth layer I learned only after many years: the added value of information. A piece deserves to exist only if it tells the reader something they never knew. If everything I write can be found in ten other places, I am not practising journalism; I am practising copying. Readers today are not short of information. They are short of information that is selected, verified, and set inside a story that helps them understand themselves a little better. That is the part no algorithm can do for us.

THE COUNTER-INTUITIVE ANGLE

The irony is that we usually think data is objective and memory subjective. I hold that the division is wrong at both ends. A heat map is programmed by people, its scale chosen by people, its sampling window decided by people — it carries the judgment of its maker. It has an author. Meanwhile, the memory of the stands, however emotional, has a quality data rarely achieves: it is faithful to what happened as we experienced it.

The biggest blind spot of collective memory is that it remembers the scorer and forgets the man who cleared the path. But data's blind spot is worse: it remembers every stride and forgets that some strides exist only to let others shine. If I must choose between two blind spots, I choose to keep the first, because at least it still has room for human beings.

There is something both overlook, and it is the last thing I want to raise: absence. This morning's analysis was empty for lack of data. But there are other gaps that are not about lack, but about our choosing not to look. A player absent through injury appears in no statistical table, yet his absence is present everywhere on the pitch, in the spaces his teammates cannot fill. Supporters do not die; they merely move to the empty seat to watch the pitch a little longer. And data, with its habit of counting only what is present, has missed the entire world of those who have gone.

AN OPEN ENDING

The file is still blank this morning. I close my notebook, switch off the press-room light, and walk home through Chengdu's nipping cold. Tomorrow, perhaps, the data will be full again, and I will read the blazing red heat maps once more. But I will read them with the eyes of a man who has once seen an empty analysis. The pitch never betrays anyone; people simply forget that it also knows how to embrace. And the next generation of writers — you, who will take up this pen — will you choose to trust the cell of data, or the breath of the stands behind it?