The Wrong Numbers:

Why Women’s Football Keeps Getting Itself Wrong

Let’s start here: the data in women’s football is wrong.

Not because it’s incomplete. Not because it’s biased. But because it was built for someone else’s body, someone else’s environment, someone else’s game. And because it’s wrong, the recruitment systems built on top of it are wrong, too.

This is the fundamental flaw no one wants to say out loud.

The data architecture in women’s football is copied from the men’s game—statistical models, performance benchmarks, physical thresholds, tactical tracking systems. It was not designed for women. It was inherited. And because of that, it distorts more than it reveals.

We’re measuring the wrong things, in the wrong ways, with the wrong baselines. Then we’re using those outputs to judge, rank, recruit, and discard players—many of whom are being evaluated in part-time environments, outside of cycle alignment, while managing dual careers and social expectations the men’s data never accounts for.

So we end up with elegant dashboards and broken conclusions.

xG models that don’t reflect the biomechanical differences in power generation or decision speed. Heat maps that misinterpret shape due to different pitch compression. Sprint stats logged against a physiological profile that isn’t cycle-adjusted. Duels won without considering body contact norms or refereeing tolerance in the women’s game. And psychological profiling that’s either nonexistent or imported from a completely different competitive ecosystem.

Then we wonder why talent falls through the cracks.

What’s worse—people assume more data is the solution. It’s not. If the underlying model is flawed, more data just means more false confidence. We aren’t short on numbers. We’re short on relevance.

This is why QQSI doesn’t rely on automated scouting models. This is why we don’t “data scrape” our shortlists. Because in women’s football, data without context is just noise. We work backward from the player, not forward from the metrics. Our potential model integrates performance and developmental multipliers. Our assessments are adjusted for cycle variation, club environment, cultural transition, tactical usage, injury recovery, and financial stability. Things that aren’t visible in Wyscout. Things that change a career.

Because a woman’s potential isn’t linear. It’s conditional.

And that’s what so few in this sport understand. They talk about equality. We talk about accuracy. Equality says, “Let’s give women access to the same tools as men.” Accuracy says, “Let’s build tools that actually work for women.”

This isn’t ideology. It’s correction.

Until the scouting frameworks, development models, and recruitment strategies in women’s football are rebuilt for women—not recycled from the men’s side—most data-driven systems will keep producing elegant (and costly) mistakes.

The game deserves better. The players already are.