The Silence of Football Data

On Tuesday night, in the press room at Mestalla, a young colleague pushed a twelve-page document toward me. The first page carried a bold line: "In-depth tactical analysis." I turned to page two. The section "Club financial structure" was blank. Page three, "Squad risk," held a single short line: insufficient information. He gave an embarrassed smile and explained that the data feed had crashed the night before, but the software had still produced a full twelve-page report, with every heading, every section, every professional flourish of a genuine document.

I sat in silence for a long time.

What chilled me was not the empty data. It was that an empty report could look exactly like a real one.

One and a half metres from the pitch, yet close enough to feel the breath of the match — that is the distance I have kept for years standing at the ground. And from that distance I realised that in football today, the most dangerous thing is not a lack of data. The most dangerous thing is a machine that looks as though it has data.

Over the past decade and more, Spanish football has turned itself into a digital industry. Metrics like xG, xA and PPDA, once confined to the analysis rooms of a handful of pioneering clubs, now spill into the eleven o'clock television bulletin. La Liga clubs invest in data departments, hire specialists, buy software licences. The media has not stood aside either. Every match now arrives with hundreds of data points, dozens of charts, thousands of lines of positional data.

I have stood on the Mestalla terraces through several generations of players. From an era when reporters like me had only a notebook and our legs, to an era when every touch of the ball is recorded to the hundredth of a second. That change has brought good things: we understand the game more deeply, and we are fairer to players the naked eye once overlooked. But it has also carried a new trap, and that trap took the shape of the young colleague I met on Tuesday night.

The trap is called "silent failure". A feed drops, a file fails to upload, an algorithm runs on an empty set — and the system does not raise an alarm, does not stop, does not shout. It quietly fills the gaps with "insufficient information," and the report still rolls out, still complete in its headings, still heavy enough that whoever reads it believes they have just learned something.

In my trade this is nothing new. Years ago, unable to verify a transfer rumour in time, a careless writer would publish anyway, because the article had a headline, a club name, a handsome number. Only the body was hollow. Today that hollow body wears a suit of technological armour: charts, tables, arrows, colour. The shinier the armour, the less willing people are to turn it over and ask one simple question: is this data real, and is it alive?

I write one heartbeat slower, so as not to miss the moment a boot touches grass. That slower moment is usually when I notice something odd. A player running far less than usual, yet a handsome number still showing on the sheet. A side pressing forward, yet a strangely low possession figure. Those small contradictions are not football's fault. They are the fault of data read without being checked.

As a reporter who follows the team day by day, I get to see what a spreadsheet can never measure. I once spent a whole afternoon outside the training-ground corridor, watching a young midfielder stay behind until dark simply to drill a single touch with his weaker foot. The data sheet will record that he completed eighty-two percent of his passes. No line records that he stayed when everyone else had gone home.

That is also why I do not place full trust in transfer valuation models. From what I have observed, those models tend to inflate the potential of young players — because youth is a variable easy to quantify, easy to discount into future value — while underrating something far harder to measure: dressing-room chemistry. A nineteen-year-old with a handsome metric can be priced at a small club's entire season budget. But whether he can drag a whole squad through a bleak January, no algorithm can answer.

In 2026 I was sitting in Doha during the Portugal versus Ghana match when I took a call from the agent of a Valencia player. He told me the player wanted to leave, and asked me to keep it quiet. It took me three days to write nothing. In those three days I went to ask twelve Valencia supporters in the cafés around the media zone, recording their mood before a rumour had even taken shape. When the article appeared, what I carried was not the transfer fee figure, but the faces of people who had heard the news before it was real.

That caution traces back to another lesson. In 2026, at the World Cup in Russia, I sat in a bar in Moscow with some fifty Spanish supporters after a one-nil win over Iran. The whole bar did not talk about the victory. They talked only about the coach being sacked on the eve of the tournament. Some backed the decision, others called it a disgrace. I sat in the middle, noting every curse, every drop of beer falling on the table. That night I understood one thing: a good writer is not the one who picks a side, but the one who can hold both voices on the same page.

If empty data can still pass itself off as an analysis, then the same happens with a story. An article with a full headline, full of names, full of quoted figures can be entirely empty of meaning. It says a great deal while telling very little. And readers, like the supporters in that Moscow bar, only notice the hollowness when someone dares to say: wait — where does this data come from?

The counter-intuitive point I want to make is this: the problem with modern football is not too little data. The problem is so much data that people stop checking where it comes from. The more metrics, the more tables, the greater the pressure to appear "knowledgeable". And once that pressure wins, the writer starts filling the gaps with guesswork dressed as analysis.

I take no side; I only record how the beer falls and how a generation swears. But I have to be honest: the football writers of today face a far greater temptation than my generation did. When a single click produces twelve pages of professionally formatted report, the line between analysis and decoration grows thin. Readers have no time to check every source. They trust appearance.

One line in the young colleague's email stayed with me. He wrote: "I thought as long as the headings were there, the piece was fine." That line is not only true of a piece of faulty software. It is true of a great many football pages I read every morning.

There are evenings I choose to stay at the ground instead of going home, and in return I get a story no one has told. Those evenings taught me that what is worth writing most often lives in the gaps — the silence before a team walks out, the frown of a coach whose GPS numbers do not match his own instinct. A machine can automatically write a report. But in the silence, only a human being can hear someone hiding a truth.

The lesson from that night at Mestalla is not to abandon data. I still use figures every day; they help me see what the eye misses. The lesson is this: when an analysis has every heading but not a single real data point, the right thing is to stamp it "cannot yet conclude" — rather than quietly let it slide into the evening bulletin.

In the coming week, as the team enters the run-in of the season, watch how many reports look real while actually running on an empty dataset. Because if we do not ask that question ourselves, then we — not any machine — are the ones filling the gaps with belief that no one ever verified.

And I will still write one heartbeat slower, slow enough to hear it when football data falls silent.