The Data Void in Women's Football: What Happens When the System Refuses to Keep Records

Winter 2026, on the side stand of FC St. Pauli's ground in Hamburg, the match ended 0-5. The main stand lights had gone out long before, but behind the goal about twenty people stayed seated. I was one of them, notebook in hand, logging every movement of the St. Pauli women's back line.

At home I rewound the video I had shot on my phone, cut it into fourteen situations and numbered them one to fourteen. Fourteen times the opposition broke through the defensive line. Eleven of those came through the same zone: the gap between the left full-back and the left centre-back. What kept me at the screen longest was that no report, no statistics page, recorded any of it. A 0-5 defeat is not about the loser, but about the person who dares to stay until the final whistle — and about what that person sees that nobody bothers to write down.

Frauen-Bundesliga was founded in 2026, seven years before I was born. Going into 2026-18, detailed data for each match in the top tier of German women's football still consisted of a few fixed fields: the score, the goalscorers, the cards. No player coordinates. No passing maps. No pressing metrics.

The absence of data was not a technical accident. It was the outcome of a chain of resource-allocation decisions, repeated steadily year after year. Cameras were not rigged from enough angles to extract coordinates. No data company sent anyone to the ground. No contract obliged anyone to do it. And without data, every decision on tactics, recruitment and player valuation rested on memory — something that cannot be verified and cannot be argued with.

That wall began to crack, but very slowly. In 2026 the Ballon d'Or Féminin was awarded for the first time, to Ada Hegerberg. In 2026-22, the UEFA Women's Champions League introduced a sixteen-team group stage for the first time, replacing two-legged knockout ties. In 2026, Spain's Liga F was recognised as a professional league. The 2026 Women's World Cup in Australia and New Zealand drew close to two million spectators across sixty-four matches, according to figures published by FIFA.

The money arrived first. The crowds arrived first. The data infrastructure is still a long way behind.

In 2026, when competitions stopped because of the pandemic, I was nineteen and had nothing but time. Empty stadiums. I downloaded forty Women's Champions League matches from 2026 to 2026 and wrote Python scripts to trace the average positions of central midfielders, including Amandine Henry and Dzsenifer Marozsán. The early output was an open dataset on roughly three hundred and fifty European women players, published free on my own blog.

The data void does not merely impoverish statistics; it shapes how women's teams play and how people judge them.

Reconstructing those forty matches, the first thing I noticed was not the goal count. It was the shape. Mid-table women's teams often press as a block, but that block operates differently from what I had watched in men's football. A four-player midfield holds its horizontal spacing very tightly, almost a straight line, and when the ball switches flanks the whole line shifts like a single plank. That organisation holds up against short-passing sides, but opens two vertical channels whenever the ball gets past the first line.

The average positions of central midfielders showed this more clearly. In many matches, the defensive midfielder of the underdog side had an average position level with or below her centre-backs, while the opposing central midfielder stood up to fifteen metres beyond the opposition midfield line. That gap creates a buffer zone the underdog cannot seal if the midfield line stays flat.

Back to the St. Pauli match. Eleven of the fourteen goals conceded came through the channel between full-back and centre-back, and it recurred not because any individual was weak. It recurred because in that shape the full-back is the only player responsible for the entire flank, and also the player who must advance when the team has the ball. When possession is lost in midfield, she has the longest recovery run in the entire back line. If the midfield does not drop in time to close the inside lane, the centre-back must choose between tracking a runner and holding position. Both choices concede.

People told me I did not understand women's football. I opened Excel, entered the data, and rewrote it.

What I rewrote was not the story of a weak team. It was the story of a structural fault repeated in silence over many matchdays, because nobody had enough data to see it sooner. A coach watching the tape back sees a goal conceded. Someone with a dataset sees a pattern. The difference between those two people is not the eye; it is whether anyone sat down and counted.

In 2026, at the Tokyo Olympics, I was assigned to cover the Sweden women's national team. In the semi-final against Australia, forward Kosovare Asllani pulled a thigh muscle in the 62nd minute. Colleagues immediately piled onto the line that Sweden had lost their main striker. I rewound the footage and saw something else: Sweden dropped deeper afterwards, organised set pieces more carefully, and their long-pass share rose noticeably. That night I received an email from a member of the player's staff thanking me for not inventing anything.

Data does not lie, but it does not feel pain either. I write to fill the gap between those two things.

In Vietnam the void is wider still. The Vietnam women's national team reached the World Cup finals for the first time in 2026 in Australia and New Zealand. Huỳnh Như moved to Lank FC in Portugal in 2026, becoming the first Vietnamese woman to play professionally abroad. Those milestones were covered widely for a few days. Detailed data on Vietnam's national women's league — actual minutes played per player, running volume and positional zones per line — barely exists in public form.

A Vietnamese player who wants to be properly assessed must rely on one televised match, a few short clips, and the memory of viewers. That is the scouting method of the previous decade, not this one. When a player moves abroad, her new club has no dataset to read about her running distances, her preferred receiving positions, or her tendency when pressed. They have footage, if that.

The counter-intuitive point sits here. When money enters women's football, what arrives with it is not better data but a metric set imported wholesale from men's football. Data providers charge by matches tracked, and the cheapest way to track is to piggyback on the broadcast signal. The camera covers one area of the pitch. Automated data therefore only measures what the camera sees: the player on the ball, the pass, the shot.

What decides women's matches often sits outside the frame: the off-ball run that drags a centre-back out of position, the winger's retreat that seals a channel, the distance a full-back must cover after losing possession.

That imported metric set has two consequences. First, it rewards players who play in easily logged ways and overlooks those doing structural work. Second, it pushes clubs to optimise for what is reportable rather than what wins matches. When a women's club has to convince a sponsor, it needs good numbers to put in the file. Those numbers do not necessarily reflect playing quality.

I have watched Bundesliga women's matches long enough to see how this operates. A team shifts from high pressing to a mid-block; goals conceded fall, opposition shots fall, but goals scored fall too. On a media metrics board they look worse. On the league table they look better. Those two boards do not measure the same thing.

Financial pressure accelerates the gap. When a club looks to list or raise capital, the dossier it needs is one with flattering numbers that compare easily with men's football. The more a metric set resembles men's football, the easier it sells. The result is that sporting decisions bend towards the criteria of the finance department, and the specific values of women's football get pushed to the margins because there is no way to measure them.

This does not make data useless. It means the first dataset women's football received was not designed by women's football. A league measured with a ruler built for another sport will always look like it is missing something.

Women's football is not a miniature version of men's football. It is a world with its own rules, and those rules demand their own set of measures.

Building that set does not require a corporation. It requires someone willing to sit down, rewind the tape, count each phase, and write it into a spreadsheet. Fourteen situations in a 0-5 defeat in Hamburg in 2026 taught me that. A twenty-five-year-old with Python can read a match more clearly than an entire commentary box, as long as she opens the file and starts counting.

The question is no longer whether women's football deserves to be recorded. It has deserved that for a long time. The question is who in Vietnam will be the first to open the spreadsheet and do the work.