Pressing Is Geometry: Lessons on Data Verification in Modern Football

In July 2026, I sat in the stands of the Shanghai stadium and recorded every movement of Shanghai SIPG in the derby against Shanghai Shenhua. The match ended 1-3, and in my analysis I pointed out that SIPG won thanks to 54 pressing actions in the final third. A former male star on national television mocked me publicly: "What does a woman know about football?" My article was flooded with negative comments for a week. I stayed silent. When Opta published tracking data confirming the number 54, several colleagues apologised to me privately.

That moment shaped the way I work to this day. An unverified number is more dangerous than a wrong judgment. A wrong judgment can be challenged with argument; a wrong number spreads like truth and poisons the entire debate behind it.

Modern football runs on a data foundation that most spectators never see. Every match in the top leagues generates thousands of data points: passes, duels, distance covered, and more complex metrics such as xG (expected goals) or PPDA (passes allowed per defensive action). These metrics shape how coaches prepare for matches, how clubs value players in the transfer market, and how the media tell the story of each round.

The evolution of football data has passed through several stages. From hand-made tables that counted only goals and passes, the industry has moved to tracking systems that record the position of every player and the ball many times per second. Data has become ever more detailed, but human capacity for interpretation has not automatically grown with it. Machines can measure to the centimetre, yet deciding which numbers matter remains human work.

There is a paradox rarely discussed: the more data there is, the greater the chance of misreading it. An xG figure is calculated by a probability model based on position, shot angle, defender pressure and the body part used. Two different data providers can give two noticeably different xG values for the same shot. The first question I always ask is not what the number says, but who measured it, how they measured it, and what interest that person is protecting.

Take pressing as an example. For years, the media described pressing as a physical race: the team that runs more presses better. That reading is wrong in essence. Croatia 2026 taught me: pressing is geometry, not a sprint. Before the Croatia-England semi-final, I wrote that Croatia would win because they controlled the central corridor through the rotating triangles of Modrić, Rakitić and Perišić. I used the statistic of Modrić touching the ball 128 times in the quarter-final against Russia to prove that the tempo of the match belonged to Croatia. The article was doubted because the media leaned heavily towards England. When Croatia won 2-1, several major newspapers cited my name.

From then on, I began drawing diagrams of triangles and spatial corridors instead of simply naming players. Pressing geometry is not on the screen; it is between the runs. An effective pressing block is built from three elements: the cutting angle, meaning who blocks which passing lane; the closing line, meaning who covers behind a teammate; and positional coordination, meaning the whole team moves as one unified block. Measuring it by distance covered is a common mistake, because the team that runs the most is often the team that is behind and chasing.

Another concrete example comes from the PPDA metric itself. It measures how many passes a team allows its opponent before making a defensive action. The lower the PPDA, the more aggressive the pressing. But a low PPDA does not automatically mean effective pressing. A team can press aggressively yet be stretched out of shape and expose space behind. Another team can deliberately press low, let the opponent hold the ball in harmless areas, then counter-attack. Reading PPDA without reading the positional map is reading half the story.

In build-up analysis, I always separate two concepts that are often merged: controlling the ball and controlling space. A team can hold 65% possession yet still trail, because most of its passes happen in non-threatening areas. Conversely, a team with only 35% possession can constantly create chances from fast transitions. A possession figure, standing alone, is an almost meaningless metric. It only has value when placed beside a positional heat map, the number of chances created, and the quality of those chances.

Similarly, the pass-completion metric does not distinguish between a five-metre sideways pass and a thirty-metre through-ball that opens up a chance. Both count the same, yet their value differs enormously. This is why advanced metrics such as progressive passes were created, to measure the real value of each action rather than merely counting volume.

In 2026, another lesson came to me. When the Bundesliga returned after lockdown, I analysed Borussia Dortmund's match at an empty Signal Iduna Park. The data showed the home side won only 58% of duels, a sharp drop from 76% when there were spectators the previous season. I wrote the piece "A Silent City: Is Atmosphere a Player?" arguing that crowd pressure had masked part of Dortmund's pressing weakness. The empty stadium of 2026 showed me the limits of tactics. Football does not operate in a vacuum; it operates in an atmosphere that has weight. 2026 made me realise: football is emotion before it is data.

Set-piece analysis is another textbook example of data complexity. A team can score from 15% of its corners, but that rate says nothing about the quality of future corners if the sample contains only a few dozen situations. Small sample size is the biggest trap in football analysis. A player who scores three goals in two matches can be praised as a new discovery, when in reality it is just a run of luck in a sample too small to conclude from.

Verification also plays a pivotal role in the transfer market. Every summer, hundreds of transfer rumours appear each day, and most come from sources that cannot be verified. A published transfer fee can include many add-ons: performance bonuses, a sell-on percentage for the former club, and instalment payment terms. When a newspaper reports a fee of 80 million euros, the actual figure the club pays can be significantly lower or higher depending on the method of calculation.

Data does not lie, but the people who collect it do. An agent wanting to inflate his client's price will emphasise favourable metrics. A club wanting to sell a player will cherry-pick the finest matches for the dossier. A data platform wanting to attract paying users will present metrics in the most striking way. No one lies outright, but everyone selects.

This is why I built a strict rule for myself: never use any number that has not passed three rounds of independent verification. I do not predict with data alone; I predict with data that has passed three rounds of verification. The first round is source checking: who provided it, is that organisation reputable, what interest do they have in presenting the number a certain way. The second round is method checking: was the metric measured by the human eye or by a tracking system, how large is the sample, is it affected by the opponent and the match context. The third round is cross-checking: comparing with at least one other independent source, and seeing whether the conclusion holds when a few outlier data points are removed.

But there is a blind spot that even deep data analysts often overlook. When every club has access to the same data pool, the competitive advantage from data disappears. What remains is the capacity to interpret and decide. A coach can read every metric correctly yet still fail because he cannot communicate his ideas to the players, or because the dressing room no longer believes in him.

This is also where I revisit a popular belief in analysis circles: that gegenpressing is the pinnacle of modern football. Reality is more complex. Gegenpressing has been decoded at the tactical level. Top teams know how to escape pressing with long balls over the top or with a striker dropping deep as an anchor. As gegenpressing became the standard, mid-table teams began using it as a purely physical tool, turning football into a disguised athletics event. They run more, but run without structure. The result is tense but idea-poor matches, where physicality replaces tactical intelligence.

Another blind spot lies in how the media build their stories. After each round, a player who scores twice becomes a hero, regardless of his overall performance. A coach who loses three matches in a row faces questions about his future, regardless of whether his team-building process is sound. This results-driven storytelling erases the quiet work of the collective: the off-ball runs, the spaces created and then erased, and the silent order that governs the chaos on the pitch.

I see a similar problem in youth development. Many former stars open academies bearing their name, but most are commercial gimmicks rather than genuine investment. Systematically training grassroots coaches is the sustainable foundation, and that investment is severely lacking. A glamorous academy can produce a few standout players, but a solid grassroots coaching system produces an entire generation.

On the business side, I track the effect of shirt sponsorship on the bond between club and local community. When global sponsor logos replace brands tied to the city, that bond gradually loosens. Sponsors care only about brand exposure and return on investment, not about whether the club remains the pride of a neighbourhood. This is a trend I have followed for years, and it is more troubling than the financial balance sheet shows.

As a new season approaches, I remind myself that the analyst's job is not to give the fastest answer, but to ask the right question. In a team's last three matches, the PPDA may dip slightly, but is that a sign of a new tactic or merely a consequence of opponents deliberately conceding the ball? A striker may score in four consecutive matches, but is he improving or merely benefiting from an easy fixture list? A team may win four of five matches, but will that form hold when stronger opponents arrive?

The Shanghai derby forged in me a healthy instinct to doubt data. I do not doubt to deny, but to understand more deeply. Every number has a story behind it, and that story only becomes trustworthy when we take the trouble to trace its origin. The season ahead will bring countless new numbers, countless new headlines, and countless hasty conclusions. Readers deserve more than that. What I hope for most is that each of us, whether journalist, coach or spectator, learns to slow down one beat before believing a number.