Man, tracking the Liga Iberica, Spain’s top women’s football division, is not for the faint of heart. Everyone thinks ranking teams is just looking at the official league table and shouting out the points. Nah. That table is often rubbish. It doesn’t tell you who is actually playing the best right now or who is about to collapse.
I decided a while back I needed a proper side project, something totally technical but also totally useless in the grand scheme of things, just to keep the gears turning. I picked this league precisely because the official data streams are a total mess. If you want real, granular performance metrics, you have to go out and fight for them.
Wrestling with the Data Monster
My whole process started with a single, aggressive verb: I scraped. I wasn’t relying on some clean API. I spent the first three weeks just battling the official federation websites and a couple of those hyper-specific Spanish sports news sites. They load slower than molasses in winter, and every single time they change the layout, my scripts break. It’s like trying to catch smoke.
First thing I had to establish was a clean, consistent data pipeline. I needed raw match results, but more importantly, I needed the details: who scored when, yellow/red cards, and crucially, expected goals (xG) data, which is barely tracked officially in a standardized way. This meant I often had to translate detailed match reports from Spanish publications and manually input key metrics that my basic scraper missed.
The core struggle was merging everything. I had results from Site A, which was fast but missed cards. I had detailed lineups from Site B, which loaded like a 56K modem was running it. And Site C had the best goal-time data, but sometimes mistranslated team names. So, I built a gigantic SQL table designed purely to handle discrepancies. If Team A was “FC Barcelona Femení” on one site and “Barça Women” on another, my script had to know they were the same entity. That process took forever—just normalization, hours and hours of it.
Once I had the raw data cleaned up—results, recent form, location, and disciplinary records—I moved into the ranking algorithm phase. I threw the standard league points table out the window. My ranking focuses heavily on three factors:
- Recent Form Weighting: Wins in the last five games are worth 50% more than wins from ten games ago. I wanted a snapshot of right now.
- Goal Difference vs. Expected Goal Difference (xGD): How lucky or unlucky is the team? If they are scoring way more than their chances suggest, that luck is going to run out. I punished teams that over-performed their xG dramatically.
- Opponent Strength Multiplier: Beating the bottom three teams doesn’t boost your score much. Beating the top four teams? Huge boost.
Why Bother with Obscure Data?
Now, you might be sitting there thinking, why the hell would a grown person spend their evenings wrestling with inconsistent Spanish football data? This project started during one of the most stressful periods of my life, actually. It’s how I know these systems inside and out.
See, I was working as a contractor on a massive internal data migration project—total hell. Long hours, no breaks, pure stress. But then, the absolute kicker came: my main contract was abruptly terminated because the client decided they were “re-evaluating IT spend.” Poof. Two years of planning, gone.
I had some savings, sure, but I was suddenly sitting around, high-strung, needing something repetitive and structured to keep my brain from exploding while I looked for the next gig. I needed a project that had clear rules, clear inputs, and a definitive, measurable output. The real world was chaos; my data project had to be control.
I started with the simple idea of tracking my favorite team, but it quickly escalated. The data was so scattered and unreliable that turning it into a structured database became a challenge—a mental distraction—far greater than just watching the games. It was pure, repetitive, analytical work, perfectly divorcing me from the nightmare of job searching and dealing with recruiters who ghosted me mid-interview.
I spent those stressful months building my ranking model from the ground up. Every time a recruiter wasted my time, I refined the opponent strength multiplier. Every time an automated job application went into the void, I perfected the xG comparison script. This ranking isn’t just numbers; it’s a monument to forced patience and finding order in a messy situation.
The Current Top Performers (My System)
Based on all that messy work—the scraping, the normalization, the ridiculous weighting system—here’s what my data is screaming right now. This is who is genuinely dominating, not just who happens to be on top due to lucky bounces.
- Barcelona: Obviously, they are always on top, but my system shows their xG is terrifyingly low for how many goals they actually score. They are efficient monsters, but the margin between expected and reality is thin. They are performing well, but maybe not as dominant as the table suggests.
- Levante UD: These guys are punching way above their weight class according to standard metrics, but my current form weighting has them skyrocketing. Their last five performances show incredible defensive tightness and consistency. They are the most in-form team outside of the clear leaders.
- Real Madrid: They are the definition of stable. They hit their xG targets consistently. They don’t have massive peaks, but they don’t have sudden dips either. They are mathematically sound, a reliable engine.
If you want to know who is actually playing the best football, not just who got lucky on penalties, you have to dig into the details. And trust me, I dug. I know this because when the real world shut down on me, I needed the most complex, frustrating, and structured side project I could find to keep my mind sharp. And wrestling with Spanish women’s football stats was exactly that.