Women's Football's N/A Gap: When Every Data Column Comes Back Empty

My spreadsheet has eleven columns. Seven of them came back as N/A.

It was a Frauen-Bundesliga matchday in Hamburg. I opened the data file after the final whistle, as I have done for seven years: expected goals, progressive passes, PPDA, ball recoveries within five seconds of losing possession, average distance between the two centre-backs, entries into the box from the left flank, duels won in the opponent's half. Four columns had numbers. Seven were empty. The same three characters sat in all seven cells: N/A.

Three days earlier I had opened a men's 2. Bundesliga match from the same matchday for comparison. Twenty-three columns. Not one empty cell.

I do not cheer from the stands. I type each number and rebuild the match. But there are nights when all I can rebuild is a map of empty cells.

What kept me at the desk was the contrast. Same day, same country, same recording system: a men's second-division match measured more fully than a women's top-division match.

On 20 August 2026, in Sydney, Spain beat England 1-0 in the Women's World Cup final. There were 75,784 people in the stadium. Across 64 matches, the tournament drew close to 1.98 million spectators. On 30 March 2026, at Camp Nou, 91,553 people watched Barcelona Femení play Real Madrid in a Women's Champions League quarter-final, the highest attendance ever recorded for a women's club match.

Full stands. Empty data columns. Both facts exist at the same time, and I have never read a single article that puts them in the same sentence. That is why this piece exists, and why I start from one specific match rather than a general appeal.

On 5 December 2026, the English Football Association banned women's teams from playing on grounds within its system. The ban lasted until 2026. Just before it, on 26 December 2026, the Dick, Kerr Ladies beat St Helens 4-0 in front of roughly 53,000 spectators at Goodison Park.

That number, 53,000, survives. Almost nothing else does: no detailed match report, no positional map, no fitness records, no ball-by-ball log. A match with a bigger crowd than most men's fixtures that year left almost no technical trace for anyone to read back.

Half a century erased from the pitch and, at the same time, erased from the archive. When the sports data industry formed in the 1990s and 2000s, it arrived first in men's football. Data follows broadcast contracts. With no broadcast contract, nobody pays someone to sit and log every pass.

The software is not at fault. That order was set long before the software existed, and it reproduces itself through every contract, every season, every financial report.

From the late 2010s, money began flowing into women's football at a scale never seen before. The world governing body published benchmarking reports on the development of women's football, devoting a section to data infrastructure. National leagues in England, Germany and Spain signed broadcast deals worth many times their previous value. Big clubs began hiring analysts for their women's teams.

But infrastructure does not grow at the speed of a contract. A broadcast deal can be signed in an afternoon. A full-pitch tracking camera system needs hardware, technicians and someone paying per match. And when a budget is split between a men's team and a women's team inside the same club, the last thing to be cut is always the thing that is hardest to see.

Modern football has two data layers, and women's football mostly lives in the lower one.

The first layer is event data: who touched the ball, where, in which minute, in which direction. Almost every professional women's league has this. It is enough to count goals, passes, tackles and possession share.

The second layer is tracking data: a camera system scanning the entire pitch, recording the position of twenty-two players and the ball at twenty-five frames per second. This layer exists in the Premier League, La Liga and the men's Bundesliga. In women's football, it is largely absent.

The gap between the two layers is not about goals. It is about everything that happens without the ball. Without tracking data, you cannot measure how far a player ran to open a gap, cannot measure who formed a pressing chain and for how long, cannot measure how compact a defensive block was second by second, cannot measure the true distance between full-back and centre-back at the moment possession is lost.

That last distance is what I first discovered at sixteen, watching FC St. Pauli's women lose 0-5 in Hamburg and rewinding the footage fourteen times. I had no tracking data. I had eyes and a sheet of squared paper. And I noticed that every goal conceded passed through the same gap.

Had I had the second data layer in 2026, I could have proved that gap with measurements rather than with a story. That is the entire difference between a careful viewer and a working analyst.

Based on my experience following matches across consecutive seasons, women's football suffers from a structural shortfall, and it is organised in four stacked layers.

The first layer is signal, described above.

The second layer is sample size, and it is harsher than most people assume. The Frauen-Bundesliga has twelve teams and twenty-two matchdays. The English women's league also has twelve teams and twenty-two matchdays. That produces roughly half the matches of a top men's league, which has twenty teams and thirty-eight rounds.

Tactical metrics need time to stabilise. A team's pressing figure over the first three matches mostly reflects which three opponents it faced, not the coach's philosophy. By the time a number is stable enough to read, the season is past halfway and the table has already fixed the narrative.

I ran stability checks on my accumulated data, splitting the season in half and comparing the two. The result made me stop and rewrite an entire conclusions section. But I will not publish that number here, because with a sample that small the number itself is a form of noise. That is the paradox of writing about women's football: precisely because data is scarce, I am not permitted to prove the scarcity with a sufficiently strong number.

The third layer is price. In September 2026, Keira Walsh moved from Manchester City to Barcelona for a fee reported by international media at around 400,000 pounds, then regarded as a world record in women's football. In 2026, Lena Oberdorf moved from Wolfsburg to Bayern Munich, ranked by German media among the most expensive transfers in the history of the domestic women's game.

Set that beside the following: a substitute in the fourth tier of English men's football can be valued at a comparable figure. On transfer valuation platforms, the most valuable women's players sit in the low hundreds of thousands of euros, while the most valuable men's players sit in the hundreds of millions.

That gap is a signal, and signals are what investment funds read before they read anything else. When the signal is distorted, capital flows are distorted with it. A women's striker who scores twenty goals in a season can still be valued below a men's defender who has never played in the top flight, and no spreadsheet corrects that error on its own.

The fourth layer is people. The number of full-time analysts working for professional women's teams remains in double digits in most countries. The number of female coaches holding the highest professional licence is growing far more slowly than broadcast revenue. A football economy with money but no one to read the data will let that money flow anywhere except into understanding.

These four layers form a closed loop. No data means no analysis. No analysis means no public debate dense enough to sustain attention. Without that debate, audiences grow more slowly than their potential. Slow growth means cheap broadcast rights. Cheap rights mean nobody pays the person logging the passes. And the loop restarts, once every season.

I have watched this loop long enough to know it does not break itself. It breaks only when someone agrees to work for free first and negotiates payment later. In 2026, when leagues paused during the pandemic, I downloaded forty Women's Champions League matches from 2026 to 2026 and wrote scripts to analyse the average positions of central midfielders including Amandine Henry and Dzsenifer Marozsán. I built an open dataset covering hundreds of European women's players and published it for free. Nobody paid me for that work. It is the reason I had work afterwards.

So far this reads like a complaint written in numbers. I want to turn and argue against myself.

The empty cell is not the biggest problem. The cell filled with a borrowed assumption is the problem.

Take one concrete example. The expected goals model, used to judge chance quality, is trained on hundreds of thousands of shots taken by male players. Shot distributions differ between men's and women's football: shooting positions, shot angles, shot power, goalkeeper reflex speed, even the density of bodies inside the box. Apply such a model to women's matches without recalibrating it and you get numbers that look scientific and are systematically wrong.

Take a subtler example. Every player valuation model rests on a career curve: value rises to a peak around twenty-five to twenty-seven and then declines. That curve is drawn from men's football, where a player is professionally trained from the age of twelve and accumulates physical development continuously through adolescence. Women's football has a different history: many players only began full-time training at twenty, and their physical peak arrives later. A model that does not know this will undervalue exactly the group of players currently performing at their best, and nobody will notice.

Take the most important example. Women's football today still has more space on the pitch, a slower tempo and greater structural variety. Some teams defend in a zonal block, some rely on individual technique, some live entirely on set pieces. If data-driven professionalisation pushes every team toward a single model, one imported wholesale from men's football, then within a decade we will have a league that is more uniform, more predictable and less interesting.

Women's football is not a miniature version. It is a world with its own rules. A model trained on data from another world, placed here, produces conclusions that sound highly professional and strip out the very thing that makes people want to watch.

The second risk of filling cells too quickly lies elsewhere: reporting pressure. Most professional women's teams still live inside the structure of a men's club, meaning their losses are covered by the men's team's money. As larger capital enters and clubs move closer to capital markets, the governing question shifts from whether this team is improving to how much it lost this quarter. The easiest metric to measure always beats the most important one, and women's football is where that pressure lands hardest, because the room to absorb losses here is thinnest.

There is another way to read this, and I think it is the truer one. The fact that women's football has not been measured to exhaustion is a gap, and every gap has two faces. The first is a disadvantage: it cannot convince investors, cannot protect players in negotiations, cannot show which part of a defeat was the real problem. The second is a temporary advantage: the models are not frozen yet, the teams are not forced into one mould yet, and the people working with data have a chance to redefine the measures before the measures define the game.

That is why I chose to write about one specific match in Hamburg rather than about a trend. A trend does not have a gap between full-back and centre-back. A match does.

On 20 August 2026, Aitana Bonmatí lifted the World Cup trophy. She won the women's Ballon d'Or that same year and again in 2026. Before her, Alexia Putellas won it twice in a row in 2026 and 2026, and Ada Hegerberg was the first recipient in 2026. Vivianne Miedema became the all-time leading scorer in the English women's league. These are real milestones with real dates and real numbers, and they are fully recorded.

But behind each milestone sit thousands of unrecorded passages of play. Ada Hegerberg scored in a match where nobody measured how many metres she ran three seconds earlier to break away from her marker. Alexia Putellas controlled the tempo with passes where event data captures only the destination, never the intent. Aitana Bonmatí received the ball in space that no model can explain, because no model recorded why that space opened.

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

A 0-5 defeat is not about the winner. It is about the person who dares to stay and watch until the final minute. Women's football has stayed a very long time. It stayed through half a century of prohibition, through seasons nobody broadcast, through training sessions on pitches with no stands. What remains, and what is hardest, is to sit down and write it all up.

Some conceded goals matter more than scored ones, if someone is willing to record them.

People told me I did not understand women's football. I opened Excel, entered the data and wrote it again. Years later I still do exactly that, except now I know better which cells should stay empty rather than be filled with a borrowed number.

What I want to see in the next decade is not empty cells filled faster, but models built from women's football's own foundation: expected goals trained on women's shooting data, career curves drawn from the real biographies of women players, pressing metrics that reflect the actual tempo of women's leagues. That takes far longer than copying an existing system. And it is the only route to a decade from now, when someone opens a spreadsheet after a Frauen-Bundesliga match and does not have to look at seven identical empty cells.