Okay, so yesterday I was messing around, trying to see if I could predict the Bulls vs. Nets game. I’m no expert, just thought it would be a fun little project.
First thing I did was grab some data. I scraped stats from a couple of sports websites – points per game, win/loss records, recent performance, that sort of thing. I’m talking about both the Bulls and the Nets, obviously. Used Python with BeautifulSoup for the scraping, pretty straightforward stuff.
Then, I started messing with the data. Cleaned it up, got rid of any missing values, and made sure it was all in the right format. This part always takes longer than you think. After the cleaning, I did some basic exploratory data analysis (EDA). Just looked at averages, distributions, that sort of thing, to get a feel for the data.
Next up was feature engineering. I created a few new stats, like the difference in points per game between the two teams, and a “momentum” score based on their last five games. Figured those might be useful for the model.
For the model itself, I decided to keep it simple and used a basic linear regression. I know, I know, it’s not fancy, but I wanted to see what I could do with something easy. I split the data into training and testing sets, trained the model, and then ran it on the test data to see how it performed.
The results weren’t amazing, to be honest. My model was okay at predicting the point difference, but it wasn’t super accurate. I think I need to add more features, maybe try a different kind of model. A random forest or something might do better.
But hey, it was a fun experiment! I learned a lot about data scraping and model building, even if my prediction wasn’t spot on. I’m thinking about trying this again with more data and a more complex model. Maybe next time I’ll actually get it right!
Anyway, that’s what I did. Just a quick little project to kill some time and learn something new. Let me know if you have any tips or suggestions!
