Fi I rst off, I grabbed some data. I'm talking about recent game stats, player stats, you name it. Found a decent dataset online – nothing too crazy, just the basics. Made sure it had enough info for both the Sixers and the Pistons.
Next, I cleaned that data. This is always the most boring part, right? Dealing with missing values, weird formats, all that jazz. Spent a good hour just making sure everything was consistent and ready to be used. Trust me, garbage in, garbage out.
Then, the fun part – I started playing around with some simple models. I'm no data scientist, so I kept it basic. Used a couple of regression models, nothing too intense. Just wanted to see if I could find any obvious correlations between the stats and the game outcomes.
I fed the data into the models and trained them. Split the data into training and testing sets, you know, the usual. Watched those numbers churn for a bit. Not gonna lie, it was kind of mesmerizing.
After the models were trained, I tested them on the unseen data. How'd they do? Well, let's just say they weren't perfect. But hey, it's basketball, right? Anything can happen.
The models mostly focused on things like points per game, rebounds, assists, and defensive stats. Tried to weigh them differently to see what would give me the best results. It was a lot of trial and error.
So, what did the models predict? One model leaned towards the Sixers winning by a small margin, while another one was a bit more confident. Ultimately, I just kind of averaged the predictions and made my own call.
Now, here's the thing – I'm not going to tell you who I actually picked. This wasn't about being right or wrong. It was about diving into the data, playing with models, and learning something along the way. And honestly, that's what makes it fun.
In the end, regardless of the results of the game, I learned something new and got to mess around with data. So I call that a success!
