So, you want to know about La Coruña vs Levante standings? Man, that seems like a stupid, niche question, right? Who cares about some murky historical mid-table Spanish football data? Trust me, I didn’t care either, not until my life got completely turned upside down and I needed to scrape historical league data just to keep the lights on.
I was working a pretty sweet, though soul-crushingly boring, job doing enterprise software consulting. Then, about three years ago, the whole department got restructured. Translation: I got the boot. No severance, nothing. Just a cold email saying my services were no longer needed. They totally screwed me over, after ten years of busting my butt for them. I spent two months fuming, sending out polished resumes, waiting for calls that never came.
The Messy Start: Why I Needed Standings Data
I needed cash, fast. My old college roommate, Mike, he runs this small, shady data aggregation service for online bettors. He called me up one Tuesday, laughing, saying he had a job for me, but it was tedious as hell. He needed someone to verify the historical league performance data for two specific teams—Deportivo La Coruña and Levante UD—from the late 80s right up to the early 2000s. The problem wasn’t just tracking their position; the problem was tracking which division they were even in during those volatile years.
Mike’s existing source was cheap and dirty, full of errors from when La Coruña was yo-yoing, and Levante was stuck in the lower tiers, sometimes even the regional third division. He needed absolute, verified truth, because a big client was betting on some statistical anomaly related to promotion/relegation cycles.
I jumped on the opportunity because the alternative was eating ramen for a month. I told him I’d document everything, start to finish. I wasn’t just pulling stats; I was building a proof-of-concept for verifying messy, unstructured data.
The Digging Process: Battling Bad Data
My first move was the obvious one: I hit up the official LaLiga archive site. What a joke. For anything pre-2005, the data sets were incomplete, links were dead, and the formatting was inconsistent across seasons. It was useless for the granular, year-by-year placement I needed.
So, I pivoted hard. I knew I couldn’t rely on clean JSON files. I had to go archaic. I spent the next week just battling old, archived Spanish newspaper websites—El País, Marca archives—the ones preserved by the National Library. I fought tooth and nail against poorly rendered tables, PDFs that wouldn’t open properly, and scanned images of text I couldn’t even copy-paste. This wasn’t data science; this was digital archaeology.
Here’s the breakdown of the pain:
- I set up a quick-and-dirty Python script using BeautifulSoup, but it was less scraping and more targeted extraction, since the HTML structure changed literally every year on some of those fan forum archives.
- I dedicated hours to cross-referencing team names. La Coruña had three different common names used in the 90s archives. If I missed one, the data trail went cold.
- I built an enormous local spreadsheet where I manually logged the divisional tier (1, 2, 2B, etc.) before I even logged the specific final standing position (1st, 15th, etc.).
- The biggest realization dawned on me fast: La Coruña’s story was one of explosive, chaotic upward mobility followed by stability, while Levante’s was a slow, agonizing crawl out of obscurity. They barely overlapped in the same division during the period Mike cared about.
I specifically remember 1990/91. That year was the key. La Coruña was in the Segunda Division, fighting like mad for promotion. Levante was stuck down in Segunda División B. Trying to compare their “standings” during that time was statistically meaningless, yet Mike needed confirmation that their paths were distinct right up to the point La Coruña hit “Super Depor” status.
The Result and My Accidental New Career
After nearly two weeks of grinding, I delivered the goods. I didn’t just give Mike a spreadsheet; I gave him a fully documented record showing the precise divisional placement for both teams, year by year, highlighting where traditional data sets dropped the ball. I found five distinct, verifiable errors in his old source just by cross-referencing three different newspaper archives against two major fan sites. I confirmed that La Coruña was usually three tiers above Levante during the critical pre-2000 period, except for a few odd cup matchups.
Mike paid me well. But the real payoff? That awful, tedious project snapped me out of my consulting rut. I realized I wasn’t cut out for polished corporate pitches anymore. I was good at digging, verifying, and cleaning up the digital garbage nobody else wanted to touch.
I started marketing myself not as a software consultant, but as an unstructured data verifier for niche historical data—think compliance, legal research, and yes, weird sports betting syndicates. I built a reputation for handling the messy archives that required elbow grease, not just fancy APIs. I took that raw Python script I used for La Coruña and Levante and expanded it into a robust, personalized data retrieval framework.
That old corporate job? They called me a year later, asking if I was available for freelance work. I told them to pound sand. Now I charge five times what they paid me per hour, and I spend my days solving puzzles, all because I had to prove where two Spanish football teams were standing 30 years ago, just to make rent.