So, I’ve been messing around with this whole “ml sport” thing, and let me tell you, it’s been a journey. I started with pretty much zero knowledge, just a vague idea that I wanted to combine machine learning with something sporty.
First, I googled “ml sport” like a million times. Found some articles, a few confusing research papers, and a whole lot of nothing. I was basically wading through mud.
Then, I decided I needed some actual data. What sport did I even want to focus on? I went with basketball because, well, I like basketball. Easy enough, right? Wrong. Finding good, clean basketball data is harder than it looks. I spent hours scraping websites, dealing with messy formats, and generally feeling like I was losing my mind.
After finally getting my hands on some usable data (player stats, game scores, that kind of stuff), I started playing with some basic machine learning models. Linear regression? Sure, why not. Logistic regression? Threw that in there too. I was basically just throwing spaghetti at the wall and seeing what stuck.
- Imported a bunch of Python libraries (pandas, scikit-learn, the usual suspects).
- Cleaned the data (so much cleaning!).
- Split the data into training and testing sets.
- Trained the models.
- Looked at the results and scratched my head.
My initial results were… underwhelming. I was trying to predict game outcomes, and my models were barely better than flipping a coin. Seriously, 52% accuracy? I could do that myself without all this fancy coding.
I realized I needed to dig deeper. I started reading up on feature engineering – basically, figuring out which data points actually matter for predicting outcomes. I added things like player efficiency ratings, team shooting percentages, and even the number of days since a team’s last game.
Then I re-ran the models, and… slightly better! We’re talking 60% accuracy now. It’s not winning any awards,but it’s progress.
My main takeaways so far:
- Data is king. Good data is essential.
- Feature engineering is where the magic happens.
- Machine learning is a lot of trial and error.
- I still have a long way to go.
I’m still working on this, still tweaking and experimenting. Maybe I’ll try some more complex models, or dive into some advanced stats. Who knows? But it’s been a fun, frustrating, and ultimately rewarding experience.
