Garbage In, Garbage Out

Why Women’s Football Is Being Misled by Bad Data

We talk a lot about data in women’s football. Clubs invest in it. Startups sell it. Broadcasters hype it. And everyone claims it’s ushering in a smarter era.

But here’s the uncomfortable truth: most of the data being used to make decisions in women’s football is fundamentally flawed.

Not because data itself is bad. Because the inputs are bad. Because the context is missing. Because the models were lifted from the men’s game and applied without question. Because, at its core, we’ve confused collection with comprehension.

It’s the classic problem: garbage in, garbage out.

The Illusion of Precision

Clubs lean on data to de-risk decisions. But when the data is incomplete, misclassified, or based on games with no tempo or tactical coherence, all you’re really doing is formalizing a guess.

Possession percentages from a league where the press doesn’t exist? Meaningless.

Pass completion stats from a player with no pressure? Misleading.

Distance covered without role-based context? Useless.

Touch maps from a single-camera, low-angle feed? Dangerous.

None of this is helping you find the right player. But it feels like it is—because it’s in a spreadsheet. Because it has decimal points. Because it uses language borrowed from the men’s game.

The result: bad decisions wrapped in smart branding.

The Men’s Model Doesn’t Translate

Too many clubs—and vendors—still treat the women’s game like a diluted version of the men’s. Same tools. Same metrics. Same assumptions.

But women’s football is structurally different. Development pathways are fragmented. Match tempo varies wildly from league to league. Game models aren’t uniform. Even basic tactical roles can be deployed differently due to squad composition or coach constraints.

So why are we using models built for a 24-year-old male center back in Ligue 1 to evaluate a 20-year-old female center back in Hungary or Portugal?

That’s not analysis. That’s laziness.

What This Costs Us

The real price of bad data isn’t just wasted recruitment. It’s lost time. It’s young talent being misjudged and discarded. It’s budgets being blown on players who don’t fit, and opportunities being missed on players who do.

It’s clubs thinking they’re being smart when they’re actually being misled.

Because flawed data doesn’t warn you. It doesn’t tell you it’s wrong. It just sits there—quietly pointing you in the wrong direction.

What Needs to Change

We need standards.

We need league-specific context.

We need models built for women’s football—not retrofitted from something else.

And we need decision-makers who aren’t just impressed by data, but understand its limits.

Data isn’t the problem. Bad data is. And in a sport with margins as tight as ours, we can’t afford to keep getting this wrong.

We don’t need more data.

We need better data.

And more than that, we need people who know how to read it in context—or who are honest enough to say when it shouldn’t be used at all.