Okay, here’s my attempt at a blog post, following all your instructions. I’m going to talk about “Carlos Mannucci”, but since I was not provided with any context about it, I assume, it is a football club, and based on this, I’ll make up a story/experience about it:
So, the other day, I was messing around, trying to get some data on the Carlos Mannucci football club. I wanted to see if I could, I don’t know, maybe predict their next win or something, just for fun. I’m no expert, just a guy who likes to tinker with stuff.
First, I started by hitting up the usual spots. I mean, I just Googled them. Scrolled through a few pages, you know, the regular news sites, the official club page, some fan forums, that kind of thing. It gave me a general feel, but nothing really concrete to work with. It was all pretty surface level.
Then, I thought, “Okay, let’s get some actual numbers.” I tried to find some stats, like goals scored, goals against, possession percentages, the whole nine yards. This was tougher than I thought! I found some scattered data, but it was all over the place. Different formats, different websites, some of it was probably outdated, you know the drill.
I spent a good chunk of time just collecting this mess. I copied and pasted stuff into a spreadsheet. It looked awful, by the way. Just a giant, disorganized pile of numbers and dates. My eyes started to glaze over after a while.
Next, I figured I needed to clean this up. I went through the spreadsheet, line by line, making sure the dates were in the same format, the numbers made sense, and there weren’t any weird typos. This took forever. Seriously. It was like untangling a giant ball of yarn.
Cleaning Data, Ugh!
- Checked date formats.
- Fixed inconsistent team names (sometimes it was “Mannucci,” other times “Carlos A. Mannucci,” etc.).
- Deleted duplicate entries.
- Filled in some missing data where I could find it from other sources.
Once I had a (somewhat) clean dataset, I started playing around with it. I did some basic calculations, like average goals per game, win percentages, that kind of thing. Nothing fancy, just basic stuff to get a feel for the numbers.
Honestly, in the end, I didn’t really come up with any groundbreaking insights. The data I could find was just too limited. And, well, I’m not a data scientist, so there’s that. But it was a fun little project. It showed me how much work goes into even simple data analysis, and how important it is to have good, clean data to start with. I’ve got a newfound respect for those data nerds!
I will try other clubs next time, hopefully that data is less messy.
