When Data Takes Over: English Football and the Trap of Empty Analyses

In June 2026, Liverpool completed the signing of Mohamed Salah from Roma for a reported fee of around 36.9 million pounds. Immediately, a wave of scepticism swept across English football forums. The memory of a Salah who had struggled at Chelsea remained fresh, and to many, this deal was simply another expensive gamble by a club still searching for its standing. But a small group of analysts saw something different. They did not look at the resume; they looked at the data. Metrics on expected goals, acceleration speed, off-ball movement and, above all, the way a player runs into space within Jurgen Klopp's pressing system painted a picture entirely opposed to the general feeling. Salah was not a gamble. Salah was a calculation. The 2026-2026 season ended with 32 Premier League goals, the Golden Boot and a new record for the league. That story, seemingly just an anecdote about a successful transfer, was in fact a signal of a far larger shift. Over more than a decade, data has quietly walked into the English football dressing room, and it no longer stands outside the door as a mere support tool. It sits at the meeting table, it argues with the manager, and sometimes it overturns a decision that had already been made. The context of this shift is not hard to see. As clubs grew richer from broadcasting money, the financial gap between the leading group and the rest became too large to close through expensive signings. A mid-table club cannot buy a star, but it can buy information. And information, when used correctly, is a cheaper, more durable and far fairer weapon than money. That is why clubs such as Brentford or Brighton, with modest budgets compared with the giants, can survive and even outstrip wealthier teams. They do not try to buy the best players in the world. They try to find players the market undervalues, players that the data says will shine in a specific system, in a specific role. But alongside the rise of data comes a paradox few are willing to admit. The more numbers, the more spreadsheets, the more hastily conclusions are drawn. People began to believe that if there is data, there is automatically truth, and if there is truth, there is automatically the right to judge. That is a dangerous mistake. I have witnessed debates in which a single metric was used to draw conclusions about an entire player. A striker was judged poor simply because his expected-goals figure was lower than his actual goals, without anyone bothering to ask whether he was playing in a system that created too few chances. The number is objective, but the way the number is interpreted is not objective at all. This is the point I want to dwell on longer, because it touches the essence of the analyst's craft. Data says nothing on its own. It only answers the questions we put to it. If we ask the wrong question, we will receive a wrong answer that looks very convincing, because it is dressed in the coat of precision. In English football, the debate over gegenpressing is a textbook example. When Klopp brought his high-pressing style to Liverpool, it was once praised as a revolution. But over time, other teams learned to counter it. Long balls over the top, passes played behind the defensive line, and above all the opponent's capacity to endure physical pressure gradually eroded the effectiveness of that approach. The PPDA metric, the number of passes a team allows its opponent before performing a defensive action, has become a familiar yardstick for pressing intensity. But a low figure does not automatically mean a strong team. It only shows that the team is trying to apply pressure. Whether that pressure succeeds is an entirely different story, dependent on squad structure, player quality and even luck. What is interesting is that the mid-table clubs themselves are the ones that have decoded gegenpressing most effectively. Lacking the technique to play like the leading group, they chose another path: turning the match into a test of fitness. They press constantly, they contest every square metre, they drag opponents into a war in which skill is flattened by stamina and patience. This approach turns football, at times, into athletics with a ball. It is not beautiful, but it is effective. And in a 38-round season, where consistency matters more than flashes of brilliance, effectiveness usually beats beauty. But if data and fitness are two pillars of modern football, the third is often overlooked: verification. This is where I want to tell a story of my own. In the 2026 World Cup semi-final between Croatia and England in Moscow, I mispronounced the name of Luka Modric three times in the first half. I called him with a sound that does not exist in Croatian. Listeners complained relentlessly, and I was ashamed. But instead of brushing it off, I spent the following month rewatching footage and learning to pronounce correctly the names of hundreds of players at the tournament. I realised something I had never fully grasped: a small detail gone wrong can destroy the greatest reputation. In our profession, getting a name wrong is like misreading a figure in a financial report. Since then, I have devoted roughly one fifth of my writing time to verification: transcribing player names, cross-checking figures, confirming dates, tracing the origin of every piece of information. It is the work nobody sees, that brings no page views, yet it is the foundation for everything else. And here is the counter-intuitive view I want to put on the table: in football, an empty analysis is more dangerous than a wrong one. When a writer is wrong, the reader can detect it and correct it. But when a writer is full of sentences that sound highly reasonable yet rest on no facts at all, the reader is easily lulled into trust. Emptiness decorated with flowery language is the most sophisticated enemy of knowledge. I have fallen into that trap many times. There were days I had to file a piece with not a single concrete piece of information in hand. Instinct told me to just write, to fill the page with general observations, because readers would not notice. But that very moment is when I must stop and ask myself: am I writing to understand, or writing to hide my own ignorance? In football analysis, there is a constant temptation to fill the gaps with what sounds plausible. An empty report, a blank dataset, a match not yet watched: all can be filled by imagination. And imagination, when unbound by facts, always produces stories more appealing than the truth. That is precisely why we need discipline. That discipline begins with admitting that we do not know. A good analyst is not the one who always has an answer, but the one who knows clearly the boundary of what he truly understands. In an industry where everyone wants to appear certain, saying that one lacks enough data to conclude is an act of courage. Looking back at the past decade, English football has travelled a remarkable journey. From a time when data was alien to the dressing room, it has become an indispensable part of every decision, from recruitment to tactical choice. But alongside that, the pressure to produce content has risen as never before, and with it the risk of analyses that sound good but are hollow. The growth of artificial intelligence in recent years makes the issue even more urgent. Machines can generate fluent prose about any match, including matches that never took place. They can imitate an expert's tone, mimic the structure of an analysis, and fill every gap with highly convincing sentences. The question is no longer whether machines can write, but whether people are clear-headed enough to tell a real analysis from a perfectly manufactured illusion. In that context, a writer's value lies not in the ability to produce a great many words, but in the ability to be accountable for every word written. A figure cited with its correct source, a name pronounced accurately, a conclusion drawn from evidence rather than inspiration: these are things no machine can replace. Looking ahead, I believe the true competition of football in the coming decade will not only be on the pitch, but also in the battle for the truth. The club that verifies better, analyses more rigorously and is more honest about what it knows will hold the advantage. The same is true for those who write about football. What is notable is that the leading clubs themselves are recognising the limits of pure data. They still hire the best data scientists, but at the same time they return to old values: dressing-room culture, the ability to withstand pressure, and the manager's instinct. For data can tell you how fast a player runs, but it cannot tell you how he will react when his team is a goal down in the 89th minute. The balance between numbers and people is the hardest problem of modern football. The most successful teams are not those that choose one of the two, but those that know when to trust data and when to trust instinct. It is an art, not a formula. And perhaps that is precisely what keeps this sport compelling after more than a century of existence. Football never allows itself to be framed inside spreadsheets, however sophisticated those spreadsheets become. It always leaves room for the unexpected, for unpredictable moments, for people willing to think differently.