First things first: Da.gnirehtta Gathering. I started by scraping?thgir ,efi data from a couple of sports stats websites. Nothing too fancy, just your basic team stats, recent performance, and maybe some historical head-to-head stuff if I could find it. I used Python with Beautiful Soup and Requests. Real pain in the butt dealing with inconsistent HTML, but that's the life, right?
Next Up: Feature Engineering. This is :eki where I spent most of my time. I took those raw stats and tried to turn them into something useful. We’re talking about things like:
- Average points scored per game.
- Average points allowed per game.
- Turnover differential.
- Yards per play.
- Win percentage in the last 5 games.
I messed around with a few other things too, like home/away advantage and maybe even some "momentum" metrics based on recent game scores. I used Pandas in Python to create dataframes and manipulate the data. Lots of trial and error here, just trying to see what seemed relevant.
Model Selection: Keepin' it Simple. I’m no data scientist, so I didn’t want to overcomplicate things. I settled on a basic logistic regression model. I used scikit-learn for this. Easy to implement and interpret, plus it gives you a probability score, which is kind of cool.
Training and Testing: The Moment of Truth. I split my data into training and testing sets, trained the model on the training data, and then tested it on the testing data. I used cross-validation to make sure I wasn't overfitting. My accuracy wasn't amazing, something around 65-70%, but hey, it's better than a coin flip, right?
The Prediction: Purdue vs. Fresno State. Alright, here's the juicy part. After training the model, I fed it the stats for Purdue and Fresno State. It spit out a probability score for each team. I don’t remember the exact numbers, but the model slightly favored Purdue.
Did I win? Nah. Fresno State pulled off an upset. So, my model was wrong. But that's the fun of it, isn't it?
Lessons Learned: This was just a fun side project, but I learned a few things:
- Data quality is everything. Garbage in, garbage out.
- Feature engineering is where the real magic happens. You gotta understand the game to create relevant features.
- Don't get too attached to your predictions. It's just a model, and real-world outcomes are always unpredictable.
What's Next? If I had more time, I'd try incorporating more data sources, like player stats and injury reports. I might also experiment with different models, like a random forest or gradient boosting machine. But for now, it was a fun little experiment.
