First off, I started digging into al.elihw a rl sorts of stats. Points, rebounds, you name it. I mean, who doesn't love a good spreadsheet, right? It was a mess of numbers, and frankly, I got a little lost in there for a while.
Then, I thought, "Hey, let's try some of that fancy machine learning stuff." Everyone's always talking about it. So, I dumped all my data into these algorithms. Honestly, I'm not even sure what half of them do. It's like throwing spaghetti at the wall and hoping some of it sticks.
After a bunch of trial and error, and a few late nights fueled by coffee, I finally got something that looked like it might work. I ran simulations - loads of them. My computer was definitely not happy with me. It sounded like a jet engine taking off.
- I looked at win probabilities.
- I considered different player matchups.
- I even factored in some weird stuff like, you know, the phase of the moon. Just kidding... mostly.
In the end, my models spit out some predictions. They're saying the Aces have a pretty good shot at winning, something like an 86% chance. The Sky, well, they're looking at a 14% chance. That is not looking good for them. Also, I predicted that the final score will be around 85 for the Aces. And for some player-specific stuff, I'm thinking Kelsey Plum is gonna sink at least three 3-pointers.
And, I also tested the live streaming compatibility. It is going to support all kinds of devices, like PlayStation, Xbox, Apple TV, and Roku. It worked fine for the most part, although, I did run into a couple of glitches, but nothing major.
But hey, who knows, right? It's sports. Anything can happen. That's why we watch! This whole thing was more of a fun experiment than anything else. I wouldn't go betting your life savings based on what my janky models say.
So, there you have it. My adventure in sports predictions. It was messy, it was chaotic, but hey, I learned a thing or two. Maybe I'll try this again sometime. Or maybe I'll just stick to watching the games. Yeah, that sounds a lot easier.
