Okay, so check it out, I was messing around with some college basketball data, trying to see if I could predict Villanova’s performance. Figured it’d be a fun little project.
First thing I did was grab a bunch of data. I’m talking historical game stats, player stats, all that jazz. Found a few decent datasets online, nothing too fancy, just raw numbers. Spent a good chunk of time cleaning it up, you know, getting rid of the garbage, making sure the formats were consistent. Data cleaning is always a pain, but it’s gotta be done.
Then came the fun part, figuring out which stats actually mattered. I started by looking at things like points per game, field goal percentage, three-point percentage, rebounds, assists, turnovers – the usual suspects. I wanted to see which ones had the strongest correlation with winning. I used some basic statistical analysis, nothing crazy, just simple correlations in Python using Pandas and Numpy.
Next, I decided to build a model. I started with a simple linear regression model, just to get a baseline. Threw all the data in there and saw what happened. The results were… meh. Not terrible, but definitely not good enough to brag about. It was overfitting like crazy. I needed to reel it in.
So, I started messing with the features. I added some interaction terms, like multiplying points by field goal percentage, trying to capture the synergy between those stats. I also experimented with polynomial features to see if non-linear relationships existed. Still, the model wasn’t performing as well as I’d hoped. I think the features were too noisy.
Alright, time to try something different. I switched to a Random Forest model. Figured that with all the features I had, a decision tree-based model might be able to pick up on some patterns that the linear regression missed. And it actually did! The Random Forest performed significantly better than the linear regression.
But, here’s where it got tricky. I was getting good accuracy on my training data, but the model was still struggling to generalize to new, unseen data. Overfitting was still rearing its ugly head. So, I started tuning the hyperparameters of the Random Forest. I messed around with the number of trees, the maximum depth of the trees, the minimum samples required to split a node – basically, all the knobs and dials you can tweak.
I used cross-validation to evaluate the performance of the model with different hyperparameter settings. That helped me avoid overfitting to my specific training set. After a lot of trial and error, I found a set of hyperparameters that gave me a decent balance between accuracy and generalization ability.
Finally, I tested the model on some recent Villanova games that I hadn’t used in the training data. The results were… not perfect, but surprisingly decent! The model correctly predicted the outcome of a good chunk of the games. I even managed to predict a couple of upsets, which was pretty cool.
Now, I’m not saying I’ve cracked the code to predicting college basketball games. There’s a lot of randomness involved, and my model is definitely not foolproof. But it was a fun exercise in data analysis and machine learning. And who knows, maybe with a little more tweaking, I can get it to be even more accurate. Maybe I will add some more advanced features like player injuries or opponent quality. But for now, I’m happy with what I’ve got.
Key takeaways:
- Data cleaning is crucial.
- Feature engineering can make a big difference.
- Hyperparameter tuning is essential for avoiding overfitting.
That’s my Villanova prediction journey! Maybe I’ll try predicting another team next time. We’ll see.
