Alright, let me tell you about this little project I dove into recently: the Swedish Basketball League. I’ve been wanting to mess around with some sports data, and this seemed like a decent starting point.
First off, I started by scouring the web for data. I mean, seriously, hunting around. Ended up piecing together info from a couple of different sports stats sites. It wasn’t the cleanest data ever, but hey, gotta start somewhere, right?
Then came the data cleaning part. Ugh. This took way longer than I thought. Dates were all over the place, team names were inconsistent, you name it. I basically spent a whole evening in Excel, just fixing stuff up. I even wrote a few little Python scripts to help with some of the more repetitive tasks, like standardizing team abbreviations. Learned a few new string manipulation tricks in the process, which was kinda cool.
Next, I wanted to actually DO something with the data. I decided to focus on trying to predict game outcomes. Nothing too fancy, just wanted to see if I could get a basic model working. I used scikit-learn in Python, you know, the usual suspects. Started with a simple logistic regression model, just feeding it things like team rankings, average points scored, and win-loss records.
I split the data into training and testing sets, like you’re supposed to. Trained the model, and then…drumroll…it wasn’t that great! I mean, it was better than random guessing, but not by a whole lot. I think I was getting around 60% accuracy, something like that. Definitely room for improvement.
So, I started tweaking things. Tried adding more features, like home/away stats and recent performance. Also messed around with different models, like a support vector machine (SVM) and a random forest. The random forest seemed to do a bit better, but still, nothing amazing.
One thing I realized was that I needed more data. The Swedish Basketball League is cool and all, but it’s not exactly overflowing with historical game results. I started thinking about ways to maybe incorporate data from other leagues, or even just collect more stats on each team. That’s probably the next thing I’ll tackle.
Finally, I wanted to visualize some of the data. I used Matplotlib to create some simple charts, like histograms of points scored and scatter plots of team rankings vs. win percentage. It helped me get a better feel for the data and identify some potential patterns. I’m thinking about learning a bit of Seaborn to make the visualizations look a little nicer.
Overall, it was a fun little project. Didn’t exactly revolutionize the world of basketball prediction, but I learned a lot about data cleaning, machine learning, and sports stats. Plus, it gave me a good excuse to mess around with Python and Excel for a weekend. Now I’m itching to find another dataset to play with!
