A Nine-Page Report With No Data: How Football Analysis Fooled Itself

Three in the morning in Manchester, rain against the window, and a nine-page tactical report lands in my inbox. Page one is a formation map. Page two is a metrics table. From page three to page nine, every single cell carries the same two characters: N/A. Forty-seven data cells, not one real number. Attached was one line: “Just write it first, I’ll drop the numbers in later.”

I sat still in the kitchen for a while. What chilled me was not the blank report. It was the fact that I have received exactly this kind of report at least twenty times in three years, and on some of those occasions I still went ahead and wrote.

My memory pulled me back to Doha, 21 November 2026, when England beat Iran 6-2 and a nineteen-year-old ran more than twelve kilometres on his World Cup debut. That night I wrote about Jude Bellingham from feeling — the sound of studs on grass, the way he sat between Iran’s lines and then suddenly burst into the box. Six months later he signed for Real Madrid and scored twenty-three goals in his first season. Plenty of people came back to apologise to me.

The lesson I kept was not that I was right. It was that I was right on instinct, and if I had been holding a blank spreadsheet that night, the same instinct could have produced a completely wrong piece that still looked thoroughly professional. That is the disease I want to talk about.

Modern football analysis runs on metrics. Every Premier League club has its own data department tracking xG, xGA, PPDA, progressive passes, packing, sprint distance, heat maps split by half. Broadcasters need numbers to fill airtime. News sites need numbers for headlines. Social platforms need numbers to start arguments.

So a production line formed: raw data poured into a template, the template spitting out an analysis complete with strengths, weaknesses, head-to-head comparisons and forecasts. That analysis gets published, shared, quoted. One small detail: most readers have no way of knowing whether the cells inside that template actually contained anything.

I used to be inside that production line. In 2026, nineteen years old and an intern at The Tactical Times, I was sent to Moscow. After England lost the semi-final 2-1 to Croatia, I wrote that Gareth Southgate was strangling England’s golden generation with caution. A former international replied with one line: “That kid has never played the game, what does she know about tactics?”

I rewatched the entire tape. I realised I had missed Croatia pressing high and breaking England’s midfield from the first half, not the second. My argument survived; my supporting evidence collapsed. Since that night I have held one rule: no line gets written before the tape has been watched. The kid they laughed at now teaches people how to watch football, and I learned it by being embarrassed.

But there is a bigger problem personal discipline cannot fix: an analysis can look formally perfect while being hollow inside. I call it empty analysis.

Empty analysis has a skeleton, section headers, structure. It lacks the only thing that gives it value: a finding nobody else has stated. The tell is simple — when you finish reading, you know nothing more specific about the match.

The clearest case is the heat map. For years I have received analyses opening with the claim that player X roams the entire left flank. The heat map is accurate. It only reports outcomes, never causes. If a wide midfielder hugs the touchline, it is often not because he likes hugging the touchline but because his team’s build-up forces him to stretch the pitch so the full-back can advance. The heat map cannot tell those two apart. It turns a tactical instruction into a personality trait, and the whole piece goes the wrong way from the second sentence.

I once spent nearly two weeks of a summer logging every touch of one midfielder in a national league, purely to test one thing: he appeared on the left flank three times as often as on the right, but that ratio changed the moment his club rotated its full-backs. In other words, his heat map was somebody else’s heat map. The heat map has become a new form of fortune telling, differing only in that it is drawn in software and printed in colour.

The second case is the back-three wave. Over the past two seasons, top European sides have switched one after another to a three-centre-back shape, and the analysis industry calls it progress. I do not buy it. Most of those switches are reputation-defence decisions, not tactical ones.

Look at the usual sequence: a team concedes three set-piece and crossing goals in four rounds, the media starts counting down to the manager’s sacking, and the following weekend the side lines up with three centre-backs. The new shape does not create a single better player. It merely makes each individual error look smaller, because somebody else is always nearby to clean up. That is a manager buying time, not a club improving.

Then there is the front I know even better: esports. There, a patch wields the authority of an invisible referee. One update shifts a champion’s scaling, and the world champions’ win rate falls from dominant to average within two weeks. Nobody blames the player. Yet when they win again the next season, the media praises their meta adaptability as if it were innate genius. It is the result of one analysis group reading the patch notes more carefully than their rivals.

What all three cases share is this: the hardest part of analysis is not collecting metrics, but verifying whether those metrics can genuinely carry your argument. Without that step, every analysis is just a table arranged neatly.

I have come close to failing this way myself. In March 2026, the Premier League stopped because of the pandemic, exactly when I was twenty-two and had just lost my part-time job at a sports café. The pandemic took my job, but I took back an entire community: I launched a podcast called Tactical Quarantine with an old friend. In episode three I said that Liverpool, twenty-five points clear, would still win the title but would not win it comfortably when football returned, because gegenpressing had drained them physically.

Thousands of comments called me a rebellious little girl. I held the line, but I also did something my nineteen-year-old self would not have done: I went back through three seasons of injury-cycle data instead of staring at the table. When football returned, Mohamed Salah’s Liverpool collected far fewer points than their own earlier pace and lost more matches than champions usually do. The podcast took off. What I kept was not the satisfaction of being right, but the awareness that I had nearly bet on a feeling rather than on a data run long enough to be tested.

From first-hand experience watching matches in the Premier League and at World Cups, I hold one principle: metrics do not lie, but the people using them do.

I can be wrong, and I want to be specific about where.

First, that N/A report may have been an act of honesty rather than laziness. Its author might have been telling me: the data is not in yet, do not write. If so, the empty template was a brake, and the person at fault is me, the one who still wanted to fill it with a piece.

Second, I spend a lot of ink attacking heat maps while relying on data for my own conclusions. The line between a metric used well and a metric abused is far thinner than it looks, and I have no formula for drawing it.

Third, my argument about back threes has a large blind spot: if a manager changes shape and his team genuinely defends better for ten straight games, I have to concede that the motive behind it matters less than the results on the pitch.

That night, after closing the nine-page report, I still wrote. But I wrote differently: I left blank the cells I had no data for, and I left those blanks in the middle of the piece instead of smoothing them over with a graceful sentence.

Tactics were never meant to be explained; they are meant to be felt with the heart. But feeling without verification becomes, three months later, a beautifully presented lie.

People call me hot, but what I burn is the truth they refuse to say.

My prediction, with explicit conditions: within eighteen months, at least one Premier League club will appoint a dedicated internal data-verification role — a person with the authority to block an analysis from the club’s internal board on the grounds that the metric lacks sufficient basis. If by the end of 2027 that has not happened at a single club, I will publicly state that I was wrong, in the same place I published this prediction.