Man, I swear, half the projects I end up diving into start with me just getting really, really annoyed at something. This whole Granada CF positions thing? It’s all because of my brother-in-law, Gary. The guy is obsessed with La Liga, right? And he got into some ridiculously complicated betting pool where he has to predict the exact standings week by week.
Every single Saturday, he’d call me, screaming that the official app—you know, the big expensive one—was slow. Or it would change the layout of the table five times between Friday and Monday. He’d spend an hour just trying to find where Granada was sitting compared to Eldense, arguing over goal difference, and totally screwing up his prediction sheet.
The Moment I Said “Enough is Enough”
I finally hit my limit during Thanksgiving. We were supposed to be eating turkey, and Gary was slamming his phone on the table because the data wasn’t updating right. I told him, look, stop relying on some massive tech company’s messy app. If you need clean data, you gotta go and grab the clean data yourself. He challenged me right there. Said I couldn’t build something faster or more reliable than what the league pays millions for. That was the trigger.
I committed myself right then and there. I decided I wasn’t going to build an app—that’s too much hassle. I just needed the damn numbers, raw and fast. I needed to see where Granada CF stood when they played CD Eldense, but I needed a consistent positional history, not just the snapshot the app decided to show me.
The official websites were just too much fluff. Too many ads, too much slow-loading JavaScript, all designed to make money, not to deliver information efficiently. I realized the only way to get the pure, simple truth about those Posiciones was to bypass all the marketing junk.
Getting My Hands Dirty: Sniffing Out the Posiciones
The first thing I did was fire up my laptop, ignoring the mashed potatoes. I opened up the network tab in the browser while Gary navigated to the ‘official’ league site. That site? It was a disaster. It was loading a thousand things—ads, trackers, videos, and finally, tucked away in some complex data structure, was the actual standings table.
I started by isolating the data flow. I watched the traffic fly by. Most of it was absolute garbage that just slowed everything down. But there was one specific request that kept popping up, carrying just the standings list. It was a feed intended for their own internal display, not the public web page content, but it was sitting right there for the taking.
I zeroed in on that specific call. It wasn’t the easiest thing to read; the fields were named weirdly and buried deep in nested arrays. But once I found the repeating pattern—the identifier for the table position, the team name (GRC, ELD), and the total points (Ptos)—it was game over. That one specific data stream was the key to unlocking Granada CF’s real standing.
- I wrote a simple script, took me maybe an hour, just using a quick Python setup that knows how to grab web data.
- I told the script to ignore everything except that one single data stream that held the league table.
- I set up a schedule so that every time the league officially updated the standings (usually right after the final whistle), my script would fire, grab the fresh data, and save it.
This bypass system was critical. I skipped the entire messy website render and went straight for the source feed of the numbers. It didn’t care about my browser, my phone, or the latest app update; it just delivered the raw score.
Documenting the Data Structure and Granada CF’s Standing
When the script successfully ran the first time, I felt like a genius, even though it was the simplest thing in the world. What the official site showed as a complex, slow-loading graphic, I saw as this clear list of numbers.
I processed that raw feed and just extracted the few fields Gary actually cared about: Position, Played, Won, Drawn, Lost, and Points. Nothing else. No graphics, no fluff.
Here’s what my output showed for the relevant comparison following the recent matchday:
- Granada CF (GRC): Position 19, Points (Ptos) 18, Goal Difference -29.
- CD Eldense (ELD): Position 18, Points (Ptos) 22, Goal Difference -14.
The beauty of this approach is that it documented the position change immediately. For their recent match that focused heavily on climbing out of the drop zone, I could see exactly how the differential shifted. Before the match, Eldense was barely ahead; after the final whistle, the log file recorded the official, immediate gap. My script didn’t care about lag; it just read the output and logged the final Posiciones.
I built a small, ugly, text-based log file. Every week, it appended the new positional data. It’s totally bare-bones, just pure numbers. But that log file, man, it was faster and more reliable than the multi-million dollar app Gary was paying for. It’s simple text, which is the fastest thing in the world to load.
The Result: Pure Reliability
Gary tried to deny it at first. He kept checking his bloated app and then comparing it to my simple text file. Eventually, he couldn’t argue. My raw data was always there, structured, and didn’t require seven clicks to open. He finally stopped complaining about the data being inaccessible. He just checks the file I email him every Monday morning.
This whole practice just reinforced something I’ve learned over and over again: when commercial systems get too big, they get brittle and slow. They pile on features nobody needs until they break. If you need one specific thing done reliably, sometimes you just have to go behind the curtain and pull the lever yourself.
I started this because I couldn’t eat my turkey dinner in peace, but I ended up with a neat little tracker that handles his La Liga stress for him. Next time I need to track something obscure, be it a stock price or the standings of some obscure club in the Segunda División, I know the drill: go straight to the source, grab the raw list, and ignore the noise. It’s the only way to get true, fast Posiciones data without the headache.