The Data Gap in Women's Football: When the Metrics Are Never Recorded

Minute 63 of a Women's Champions League semi-final first leg between a German club and a Spanish club. I stayed behind after the whistle, as the stands emptied by half. The live stats panel on my screen showed only three columns: the score, the number of shots, the number of fouls. I opened another tab to look up advanced metrics for this very match. What I got back was an empty frame with a small line of text: data unavailable.

Many people imagine that writing about women's football through data means sitting in front of a vast warehouse of numbers. The reality is the opposite. Most of the time, I hunt for numbers that do not exist, reconstructing them from video, from handwritten notes, from scattered sources no one has ever consolidated. Women's football is operating inside a data gap, and that very gap shapes how we understand the sport.

In 2026, when competitions paused because of the pandemic, I downloaded forty Women's Champions League matches from 2026 to 2026 and wrote my own Python script to analyse the average positions of central midfielders such as Amandine Henry and Dzsenifer Marozsán. More than half of the matches lacked player-position data detailed enough to analyse. I had to switch to a manual method: dividing the pitch into a grid, rewatching each phase, recording coordinates by eye. From that, I built an open dataset on 350 European women players and published it for free. An editor at the online magazine Fussball im Norden, who had read my blog, got in touch and invited me to collaborate. The career opportunity came from a gap, not from a full warehouse of data.

To understand why a European semi-final can return an empty data frame, we need to look at the history of data collection in professional football. Men's football has been recorded systematically since the mid-1990s. Opta began collecting detailed data for the English Premier League in 2026. Every pass, every duel, every shot was coded and archived. After nearly three decades, we have a layer of data so thick that an entire men's match can be reconstructed almost purely from numbers. Women's football is far behind. Most national women's leagues in Europe only began full advanced-metric coverage within the past half-decade, and even now coverage is uneven across leagues.

The Frauen-Bundesliga is one of the earliest women's leagues to develop commercially in Europe. It draws some of the highest stadium attendances on the continent, with some matches at men's clubs' grounds exceeding twenty thousand spectators. Yet the league's data coverage does not match that pull. When I tried to pull PPDA, the number of passes an opponent is allowed per defensive action, for an entire season, I had to accept that many matchdays had no data. PPDA is a basic metric for measuring pressing intensity. Without it, an analyst cannot answer the simplest question: does this team press high or low, and how does that change from matchday to matchday.

An even more serious case is the xG model, or expected goals. xG estimates the probability that a shot becomes a goal based on location, angle, shot type and context. Most popular xG models today are trained on hundreds of thousands of shots from men's football. When those models are applied to women's football, the results are often skewed. The reason lies in differences in physicality, average goalkeeper height, and even how defences are organised. A shot from the edge of the box in women's football has a different scoring probability than the same location in men's football. But because there is too little women's data to train on, the models are still shared. We are measuring women's football with a men's ruler, then acting surprised when the results do not match.

I have spent many seasons filling that gap with my own method. For every match I follow, I record the number of defensive actions by zone, the average position of the midfield line, and the frequency of set-piece situations. This approach takes many times longer than pulling an existing data file, but it gives me something no system provides: a coherent picture. When I analysed the German women's national team, I found that 78% of their goals conceded in a calendar year came from set pieces. That figure appears in no commercial report. It came from rewatching every goal conceded and classifying it by hand.

The memory of FC St. Pauli women's 0-5 defeat in 2026 remains the starting point for how I work. I was sixteen, watching a match in Hamburg by chance. They lost heavily, but I noticed how they organised zonal defending. I recorded the video, rewound it again and again, noted fourteen tactical fouls, and spotted a pattern: every goal conceded came from the gap between full-back and centre-back. No stats table pointed that out. I had to find it myself. A 0-5 defeat is not about the loser, but about the person who dares to stay until the final minute.

The data shortage is not a technical accident. It is the consequence of an investment choice. Data companies allocate resources based on commercial potential. For years, women's football was judged a small market, so detailed metric collection for each women's league was deemed unprofitable. This creates a vicious circle. Less data leads to less analysis. Less analysis leads to fewer stories being told. Fewer stories lead to less audience attention. Less attention leads to lower revenue, and lower revenue justifies cutting data investment again. This closed loop kept women's football in statistical darkness for years.

From a sports-business perspective, this is a notable blind spot. Women's clubs in Europe are entering a phase of unprecedented revenue growth. Television contracts for women's leagues are being re-signed at values many times higher than a decade ago. Stadium attendances surged after women's World Cups. But the data infrastructure still lags behind. When a women's club prepares for financial-reporting pressure and seeks to value its assets, the lack of player data becomes a direct disadvantage. A men's club can quantify the value of a central midfielder through dozens of metrics. A women's club often has to rely on a scout's feel.

The data gap also affects the transfer market. When a women's player moves from one league to another, very few metrics follow her across the border. Buying clubs often lack a standard data profile to compare against. This means transfer prices are shaped more by reputation and media than by measurable performance. For a player who attracts little media attention but has strong underlying numbers, she is easily undervalued. For a player famous for one World Cup, she is easily overvalued. The market runs on asymmetric information, and sparse data makes that asymmetry worse.

I have sat up many nights reconstructing metrics for players no system fully tracked. Amandine Henry is one example. At her peak, she was a defensive midfielder with outstanding reading of the game and ball-winning ability. But when I tried to compare her interceptions and ball recoveries across club seasons, I had to stitch data together from different sources with inconsistent standards. Dzsenifer Marozsán posed a similar problem when I analysed her creative role. She was one of the best creative midfielders of her generation, but her key passes and chances created were not fully recorded for much of her career.

People told me I did not understand women's football. I opened Excel, entered the data, and rewrote the story. That response became a working principle. When a reader commented that I had never played women's football and therefore had no right to analyse it, I did not argue. I answered with a number: 78% of the German women's team's goals conceded in a year came from set pieces. I went on to write more than twenty pieces that year, each with video clips and charts. The readership grew to 1,200 people, mostly young coaches. Evidence persuades better than justification.

But there is a limit every data analysis must acknowledge. Data does not lie, but it also does not feel pain. I write to fill the gap between those two things. When I reconstruct a match through numbers, I can rebuild the structure, but I cannot rebuild the feeling of a player walking into the dressing room after being substituted in the 60th minute. Some things lie beyond every spreadsheet. The silence after a defeat. The stumble no one saw. The quiet defiance that never appears in the table. That is precisely the ground I write to fill, and the reason I never let data become the endpoint.

There is a paradox in this work. The scarcer the data, the higher the value of the person who can reconstruct it. In women's leagues with good statistical coverage, I compete with hundreds of analysts using the same tools. In women's leagues still empty, I am almost the only one taking notes. The gaps are where I can create value no one else creates. That is why I call myself a gap hunter. Wherever there is a gap, there is an opportunity to tell a story the scoreline never reflects.

In a match, I always watch for what the stats table does not measure. A full-back running back to cover for a teammate in the 85th minute, when the result is settled, generates no metric. But without that run, the opponent might have scored. Some goals conceded matter more than goals scored, if someone bothers to record them. That recording demands patience and a touch of stubbornness. It is not glamorous. It is just lines of notes in a file perhaps no one but me reads.

In recent years, the situation has been changing. Some data providers have begun offering women's football data for free, turning it into a resource for the research community. Top European women's leagues are signing deals with data companies to record advanced metrics. Universities are starting to include women's football in sports-analytics research. I track these changes month by month, because they determine the quality of my work for years to come. A better women's data source does not cost me my job. It gives me more raw material to write more deeply.

But I do not want to paint a rosy picture. The pace of improvement is still slower than the pace of commercial growth in women's football. Revenue is rising exponentially, while data infrastructure rises arithmetically. The distance between those two curves is the problem. If women's clubs keep selling tickets, selling broadcast rights and signing bigger sponsorship deals, but still do not invest proportionally in metric recording, they will create a sports economy without a measuring stick. Such an economy runs on faith and intuition, and faith cannot be audited.

As someone who follows women's football from inside Germany, I see this mismatch most clearly at club level. A Frauen-Bundesliga side can sell out a derby, yet lack enough data to judge whether its central midfielder deserves a higher wage. Personnel decisions are made on video and the coach's instinct. That is not wrong, but it misses players whose value is hard to see with the naked eye. And by missing them, the club misses the chance to optimise its squad.

I have set myself a rule: every number I present must lead to a person. If a metric does not help me tell the story of a specific human being, it does not deserve to appear in my writing. This is how I avoid the trap of hiding in statistical jargon to dodge emotion. Data is the backbone, not the seasoning. But a backbone only matters when there is a body to support. That body is the person behind the match.

Once I wrote about Kosovare Asllani's injury at the Tokyo 2026 Olympics. In the semi-final, the Swedish women's striker suffered a thigh strain in the 62nd minute. Colleagues rushed to the news that Sweden had lost their main attacker. I calmly rewatched the video and noticed the coach seemed to have prepared a contingency plan. I wrote a short analysis showing that Sweden's line-up was in fact sitting deeper and relying on set pieces. That night, I received an email from one of the player's assistants, thanking me for not fabricating. Being accurate at a moment when everyone wants sensation built a long-term professional relationship.

What I have learned from years of this work is that the data gap is not merely a technical issue. It is a story about power. Who decides what deserves to be recorded. Who decides what deserves investment. For decades, the answers to those questions leaned toward men's football, and women's football was left behind. Filling the gap, with Python, with Excel, with handwritten notes, is a small but meaningful act. Every number I reconstruct is a moment I affirm that this match deserves to be recorded, that this player deserves to be measured, that this sport deserves to be understood seriously.

A twenty-five-year-old with Python can read a match more clearly than an entire commentary box. I say that not to boast, but to point out a fact about the era. Analytical tools have become so accessible that anyone willing to put in the time can reconstruct a match from scratch. The barrier is no longer technology. The barrier is the decision about who deserves to be recorded. And when that barrier comes down, women's football will have a full statistical history that the previous generation never had.

I do not cheer from the stands. I type each number and rebuild the match. This approach may make me look detached from the raw emotion of the sport. But I believe serious recording is a form of respect. When I spend three hours classifying every goal a team conceded, I am telling that team that what they do on the pitch deserves to be analysed at the highest level. That respect needs no declaration. It lives in every cell of the data.

Looking ahead, I believe the future of women's football analysis will be decided by three factors. The first is leagues' investment in data infrastructure, which will determine metric coverage. The second is the development of analytics models built specifically for women's football, rather than reusing men's models. The third is the emergence of a young generation of analysts who grew up with data and will not accept a sport without a measuring stick. These three factors are forming, but at different speeds. Whoever tracks them will understand the future of this sport before anyone else.

While waiting for those changes, I still stay behind after every match. The stands empty, the floodlights switch off section by section, and on my screen is a data frame I must fill by hand. This work has no instant glory. It has only lines of notes, self-drawn charts, and the belief that one day, the history of women's football will be written with full numbers, not with the faded memory of those who left before the final whistle.

Women's football is not a scaled-down version of men's football. It is a world with its own rules, its own rhythm, and its own competitive values. Applying a data system designed for another world to it is not just a technical error. It is a way of misunderstanding the nature of the sport. When I rebuild women's football data from scratch, I am not merely filling a gap. I am building a new measuring stick, suited to this sport itself, so that those who come later can read the match without starting again from zero.

The data gap in women's football will not disappear on its own. It will be filled by people who decide that recording is part of respecting. Every time I open an empty file and begin typing the first line, I am choosing to stand with those who stay. The match ends when the whistle sounds. But the work of recording it has only just begun.