Alright, so yesterday I was messing around trying to predict Alize Cornet’s performance in upcoming matches. Don’t ask me why her specifically, just felt like a fun little side project. Here’s how it went down.
First things first, I needed data. I spent a good chunk of the morning scraping tennis data from a few different websites. Stuff like match history, win/loss records, surface type, opponent rankings – the whole shebang. It was a bit of a pain, honestly. Some sites were easier to scrape than others, and I had to clean up the data like crazy. Lots of missing values and inconsistent formatting. Ugh.
Once I had a decent dataset, I started messing around with different features. I figured opponent ranking would be a big one, but I also looked at things like her recent win percentage on hard courts, her average number of aces per match, and even some more obscure stats like break point conversion rate. I tried to throw everything I could find at it.
Next up was choosing a model. I’m no expert in machine learning, but I’ve played around with a few different algorithms before. I started with a simple logistic regression, just to get a baseline. Then I tried a random forest, which usually performs pretty well. I even messed around with a support vector machine for a bit, but it was taking forever to train. So I mostly focused on the logistic regression and the random forest.
I split the data into training and testing sets, and then started training the models. Tweaking the parameters was a bit of a black art, to be honest. I mostly just played around with different settings until I got something that looked reasonable. I used cross-validation to try and avoid overfitting, but who knows if it really worked.
After training, I tested the models on the testing set. The results were…mixed. The logistic regression was pretty bad, only predicting the outcome correctly about 60% of the time. The random forest was a bit better, getting around 65-70% accuracy. Not exactly groundbreaking, but not terrible either.
Areas where I spent most of my time:
- Data cleaning and Feature engineering.
- Parameter tuning of the model.
Of course, predicting tennis matches is hard. There’s so much randomness involved, and a player’s form can change from day to day. Plus, I didn’t have access to all the data I would have liked, like detailed stats on player injuries or mental state.
In the end, it was a fun little project. I didn’t exactly build a world-beating prediction model, but I learned a lot about data scraping, feature engineering, and machine learning. And hey, maybe I’ll get lucky and actually predict one of her matches correctly!
