Alright folks, let me walk you through my recent basketball prediction adventure. It wasn’t exactly a slam dunk, but hey, that’s why we experiment, right?
It all started with me thinking, “I watch a ton of basketball, maybe I can actually predict some international friendlies.” I mean, the NBA is cool, but these international games have a different vibe, a different kind of excitement. So, I dove in headfirst.
First things first, I needed data. I started scraping game results and stats from a couple of sports websites. It was messy, took a while, but I got the basic stuff: team stats (points per game, rebounds, assists), recent game outcomes, and player info where available. Think of it like cleaning out your garage – a necessary evil.
Next, I figured, “Okay, gotta build some kind of model.” I’m no data scientist, but I know enough to be dangerous. I messed around with a simple regression model in Python, using scikit-learn. I wanted to see if past performance could predict future scores. Turns out, it’s not as easy as it looks on paper. I even tried throwing in some “expert” opinions I found online, like team rankings and injury reports.
Then came the fun part – testing my predictions. I picked a few upcoming international friendlies and ran my model. The results? Let’s just say they were…mixed. Some games I was surprisingly close, others I was way off. One game I predicted a blowout, and it ended up being a nail-biter that went into overtime. Humbling experience, for sure.
After a few rounds, I decided to add some new factors. I manually tracked things like “momentum” (were teams on a winning streak?) and “home court advantage” (does playing at home actually make a difference?). I even considered the referee assignments, trying to figure out if certain referees favored particular playing styles. This stuff was tedious, but I felt like it added a bit more nuance.
Did it improve my predictions significantly? Honestly, not really. But I learned a lot about the complexities of basketball and why it’s so hard to predict. So, I created a basic website to display all the matches I’d worked on.
My basketball prediction project wasn’t perfect, it was more like a chaotic mix of analysis, luck, and a whole lot of guesswork. But you know what? It was fun. I got to tinker with data, learn more about basketball, and challenge myself to build something from scratch. And that, my friends, is a win in my book.
