Women's Football Data: The Gap Between the Spreadsheet and the Match

Minute 78, on a small stadium on the outskirts of Hamburg, seven degrees outside. A midfielder from the FC St. Pauli women's team receives the ball on the left flank, turns past a player in blue, and plays a line-breaking pass through the gap between the opposing centre-back and full-back. The ball reaches the striker facing goal. The shot drifts wide. No goal. But it was the best move of the match. I rewound that clip fourteen times. Then I opened the official league data sheet. The move did not exist. No "key pass" column, no "progressive pass" column, no "line-breaking pass" column. The sheet recorded forty-two successful passes for the home side, but none of them was the pass I had just watched. That was the first time I understood something that would follow me for nine years: in women's football, the absence of data is not a technical failure. It is a decision. And every such decision has someone who pays for it. I am twenty-five, I write biographies of female athletes, and I live in Hamburg. My job is to read matches through numbers. The more I do it, the more I see a paradox: women's football has more stories than anyone is willing to record, and less data than anyone is willing to acknowledge. On 30 March 2026, Camp Nou was packed with 91,553 spectators. Barcelona faced Real Madrid in the women's Champions League quarter-final. It was a world record for a women's football match at the time. Ten months later, again at Camp Nou, Barcelona hosted Wolfsburg in the semi-final and the figure rose to 91,648. International media called it the moment women's football entered the mainstream era. But when I opened the detailed data sheet for that Barcelona–Real Madrid match, I counted roughly half the columns of a men's match at the same level. No average position data per player. No heat maps per phase of play. No post-loss pressure metrics. There were spectators, there was a record, there were television cameras — but there was no data. The crowd came and went; the numbers stayed behind, and there were very few of them. This is the context I want to put on the table before the analysis. Women's football does not lack audiences. It lacks recording infrastructure. Those are two completely different stories, even though the media often merges them into one. I started following women's football in 2026, at sixteen, through a 0-5 defeat by the FC St. Pauli women's team. A 0-5 defeat is not about the loser; it is about the person who dares to stay and watch until the final whistle. I stayed. And I started taking notes. Back then I had no professional tools. I used a phone to record the screen, Excel, and an old laptop. I rewound fourteen tactical fouls by St. Pauli and found a pattern: every goal conceded came from the same gap between full-back and centre-back. It was a small discovery, but it taught me something big — if no one records it, that gap will never exist in any spreadsheet. Later I expanded to ten Frauen-Bundesliga matches. I built my own pressing tables, drew my own diagrams. People told me I did not understand women's football. I opened Excel, entered the data, and wrote it back. That was not an answer. It was a method. In 2026, when the men's World Cup in Russia took place, I tried applying the tactical principles I had learned to the German women's national team. I started the blog "Women's Data Lab" and wrote my first piece comparing German women's pressing with Spanish men's. A reader commented: "You have never played women's football, how dare you analyse it?" I did not get angry. I replied with a number: 78% of the German women's team's goals conceded in 2026 came from set pieces. I kept writing more than twenty pieces that year, each with clips and charts. The readership grew to about 1,200, mostly young coaches. In 2026, I began contributing to an independent outlet. That was the year I learned the discipline of writing from early-career observation — write only what you have verified, not what you think. In 2026, when leagues paused because of COVID-19, I was nineteen. The stadiums were empty. But I did not panic. I downloaded forty women's Champions League matches from 2026 to 2026, wrote my own Python scripts to analyse the average positions of central midfielders such as Amandine Henry and Dzsenifer Marozsán. I built an open dataset of 350 European women players and published it for free. An editor at the online magazine "Fussball im Norden" found it, got in touch, and invited me to collaborate. Empty time became an opportunity simply because I chose to type numbers instead of waiting. In 2026, I was assigned to cover the Swedish women's team at the Tokyo Olympics. In the semi-final against Australia, striker Kosovare Asllani suffered a thigh strain in minute 62. Colleagues rushed to the headline "Sweden lose their main striker". I calmly reviewed the footage and found that the coach already had a contingency: the team dropped deeper and relied on set pieces. I wrote a short analysis showing this. That night I received an email from one of the player's assistants, thanking me for not fabricating. Those experiences shaped how I work today. Data does not lie, but it does not feel pain either. I write to fill the gap between those two things. And in women's football, that gap is wider than anywhere else. Now I want to move to the core: what exactly is missing, how much, and why it matters more than the numbers that do exist. Let us start with pressure metrics. In men's football, PPDA — passes allowed per defensive action — has become the standard measure for evaluating a pressing system. A team with a PPDA below 8 is considered high-pressing. Above 12 is a deep block. The metric has appeared in every men's match report in Europe's top leagues since around 2026. In women's football, PPDA only began to be recorded widely from around 2026, and even then only in a handful of competitions: the women's Champions League, part of the Frauen-Bundesliga, the NWSL, and national-team matches at major tournaments. That means that during roughly the five most important years of tactical development in women's football — 2026 to 2026 — we have almost no pressure data. What does that mean? It means that when someone says "women's football is slower than men's", they have no evidence. And when someone says "women's football presses worse", they also have no evidence. Both sides are arguing from feeling, because the data to refute or confirm simply does not exist. I rebuilt part of that data myself. In 2026, I analysed forty women's Champions League matches and recorded the average positions of central midfielders. The result surprised me: Amandine Henry between 2026 and 2026 had an average position about seven metres deeper than a typical male central midfielder in the same role. Dzsenifer Marozsán was the opposite — she played higher, closer to the box, and was involved in more final-third combinations. Two players, the same league, the same club for years, yet two completely different spatial models. If you only look at goals and assists, you will never see it. And if no one measures average position, you will never know that Lyon was operating a far more complex midfield structure than the media described. This is the point I want to stress: women's football is not a smaller version. It is a world with its own rules. And those rules only become visible when someone bothers to measure. Let us move to another metric: xG, expected goals. In men's football, xG has been common language since around 2026. Male fans discuss xG the way they discuss the scoreline. In women's football, xG only became widespread after the 2026 Women's World Cup, and even today it is absent from many domestic leagues. I tried to calculate xG for some Frauen-Bundesliga matches manually — reviewing footage, marking shot locations, removing pressure, estimating probabilities. It takes hours per match. But the results showed something notable: many women's teams convert chances at a rate above their predicted xG, and this repeats across matches. With proper data, we could have detected a tactical trait of women's football years earlier. But there is no proper data. And so no one detected it. I want to tell a more specific story. In the 2026-2026 season, I followed a lower-tier German women's club closely. They lost seven of their first ten matches. On the table, they were a weak side. But when I recorded their counter-attacks myself, I counted an average of 4.2 clear chances per match — higher than the team sitting third in the league. Their problem was not attack. Their problem was conceding from set pieces, and no statistical column recorded that. Some goals conceded matter more than goals scored, if someone bothers to write them down. I wrote an analysis of that club. No one paid me. But their coach read it and sent a message of thanks. He said the coaching staff had never had anyone point out that pattern, because they had no tool to see it. That is the reality: many women's teams make decisions on feeling, not because they prefer it, but because data does not reach them. Now let us talk about money. This is the part I consider most important, and also the most misunderstood. People often argue that women's football has little money because it has few spectators. But audience data often shows the opposite. The record 91,553 at Camp Nou in 2026 is the clearest proof. Television viewership for the 2026 Women's World Cup final in France reached record levels in many countries. In 2026, the Women's World Cup final in Sydney drew 75,784 spectators in the stadium. So why does money not follow the audience? The answer lies in data infrastructure, not in public interest. Sponsors do not buy emotion. They buy measurability. A brand wants to know how many seconds its logo appeared on screen, how many viewers of which age, how much brand awareness rose. All of that requires detailed data — data that women's football often lacks. This is the crux: the data shortfall is not just a problem for journalists or coaches. It is a direct commercial barrier. A league cannot sell what it cannot measure. I have spoken with several media professionals in Germany. They say that when negotiating television rights for a women's competition, they often have to accept a price far below real value, simply because they lack the figures to prove that value. It is a vicious circle: less data leads to lower prices, lower prices lead to less investment, less investment leads to less data. I do not cheer from the stands. I type each number and rebuild the match. And precisely because of that, I see clearly that the fight of women's football is not a fight for spectators' affection. It is a fight for infrastructure. However, here I want to offer a counter-intuitive angle. There is a common assumption that if women's football had complete data like men's football, it would automatically be treated fairly. I do not entirely believe that. Because data is not neutral. Who decides what to measure, how to measure it, and whom to publish it for — those are political choices. For years, men's football metrics were designed for men's football. When we apply them to women's football, we risk measuring the wrong thing. For example: the "counter-attack speed" metric in men's football is usually calculated as the seconds from winning the ball to taking a shot. But if a women's team organises its counter-attacks differently — slower but more precise — the metric will undervalue them. We will conclude they counter poorly, when in fact they are playing a different kind of football. This is the biggest blind spot: applying the men's metric set to women's football without recalibration can do more harm than good. It creates an appearance of objectivity, but is in essence a form of imposition. A twenty-five-year-old with Python can read a match more clearly than an entire commentary box. But she also needs to know that the tool in her hands is not absolute truth. A tool is a tool. The user decides the meaning. So what is changing? I see three notable signals. First, major data providers are beginning to expand into women's football seriously. From around 2026 to now, more and more women's competitions are recorded with event-level detail, not just scores and cards. Second, the players themselves are starting to care about their own data. I know several women players in Germany who ask for their individual post-match reports. That is a cultural shift, not merely a technical one. Third, and most important, the independent analytics community is growing. People like me — belonging to no club, with no big budget, only a laptop and patience — are quietly filling the gaps the official system leaves behind. The 2026 World Cup gave me other people's football. 2026 taught me to find my own. I do not believe data will save women's football. Data saves no one. But data gives people a language to argue with, instead of only feelings to shout with. And in a field that for decades was judged by prejudice more than by evidence, having a shared language is the first step — not the last — towards making change irreversible. The gap between the spreadsheet and the match is still wide. But every time someone sits down, rewinds the footage, opens a spreadsheet and records what no one else records, that gap narrows a little. I do not know when it will close. I only know I will keep typing each number, until moves like that pass in minute 78 no longer disappear from history.