Okay, here’s my attempt at writing a blog post about my “pirates vs royals prediction” project, keeping the tone and style in mind.
Pirates vs Royals Prediction: My Messy Attempt
Alright, so I had this crazy idea, right? To predict who would win between the Pirates and the Royals. I know, I know, sounds kinda dumb, but hey, gotta try, right?
First off, I grabbed a bunch of data. Like, a ton of it. Game stats, player stats, weather conditions (because why not?), the whole shebang. I scraped it all from some sports websites – a real pain in the butt, let me tell you.
Then came the “fun” part: cleaning the data. Oh man, what a disaster. Missing values everywhere, weird formats, you name it. I spent, like, a whole evening just trying to make sense of it all. Python and Pandas became my best friends (and worst enemies) during this process.
Next up, I tried a couple of different models. I started with a simple logistic regression, figured that’d be a good baseline. Threw the data in, tweaked some parameters, and… well, the results were… underwhelming. Like, barely better than flipping a coin.
So, I thought, “Okay, time to get fancy!” Tried a random forest. More parameters to mess with, which meant more chances to screw things up. Did a bit of cross-validation, fiddled with the number of trees, and got… slightly better results. Still not great, though.
- Data Collection: Web scraping, lots of it.
- Data Cleaning: A nightmare. Seriously, a nightmare.
- Model 1: Logistic Regression – Flop.
- Model 2: Random Forest – Meh.
I even tried adding some more features. Like, how well each team does against left-handed pitchers, or how many home runs they hit on Tuesdays. Complete shot in the dark, really. Didn’t seem to make a huge difference.
The Big Reveal? My predictions were… not accurate. Let’s just say I wouldn’t bet my life savings on them. Both models kinda sucked, to be honest. I think the Random Forest did a little better, but not by much.
So, what did I learn? Well, predicting baseball games is hard! I also learned a lot about data cleaning, which is way more important (and tedious) than I thought. And I learned that sometimes, even if you put in the effort, you still end up with a pile of garbage. But hey, that’s part of the process, right?
Would I do it again? Maybe. But next time, I’d probably start with a simpler dataset and a less ambitious goal. Like, maybe just predict the number of hot dogs sold at the stadium. That seems more achievable.
