Alright, so today I’m gonna spill the beans on my little tennis project, the “maestrelli tennis” thing. Buckle up, it’s a bit of a ride.
It all started a few weeks back. I was itching to build something, anything really, and I’d been watching a ton of tennis. The idea kinda just smacked me in the face – why not try and simulate some tennis stuff? I’m no code wizard, mind you, but I figured I could hack something together.
First things first, I needed data. I spent a solid day just scraping results from various tennis websites. It was tedious as hell, but I managed to get a decent chunk of match data – scores, player names, dates, the whole shebang. I saved it all in a big, messy CSV file. Don’t judge, it worked!
Next up was cleaning that data. Oh man, what a nightmare. There were inconsistencies everywhere – different naming conventions, missing values, typos… you name it. I fired up Python and Pandas and just started wrestling with it. I swear, 80% of the project was just cleaning up that darn data. I learned way more than I ever wanted to know about regular expressions.
With the data somewhat tamed, I started thinking about what I actually wanted to do with it. I decided to focus on predicting match outcomes. Ambitious, I know, but hey, gotta aim high, right? I started messing around with some basic machine learning models. I tried a few different algorithms – Logistic Regression, Support Vector Machines, even a simple Neural Network. Honestly, most of them performed pretty badly.
I realized I was missing something crucial: features. Just feeding the raw match data into the models wasn’t cutting it. So, I started creating some derived features – win rates, head-to-head records, recent performance, that kind of stuff. It was a lot of trial and error, but slowly, things started to improve. I also started looking into Elo ratings and implemented a basic Elo rating system which seemed to help provide a bit more consistent base line for the models.
The best performing model ended up being a slightly tweaked Logistic Regression. It wasn’t perfect, by any means, but it was getting some predictions right, which was enough to keep me going. I even built a small web app using Flask to display the predictions. It’s ugly as sin, but it works!
Here’s what I actually did to get there:
- Scraped a bunch of tennis data from different websites.
- Cleaned that data using Python and Pandas (lots of cleaning!).
- Experimented with different machine learning models using scikit-learn.
- Engineered some features to improve model performance.
- Built a simple web app using Flask to display predictions.
Looking back, it was a fun little project. I learned a ton about data science, machine learning, and web development. And I even got to geek out about tennis a little bit. The predictions aren’t always accurate, but hey, nobody’s perfect. Maybe next time, I’ll try something even crazier!
What’s next? Well, I’m thinking about adding more data sources, like player stats and court surface information. That should hopefully improve the accuracy of the predictions. I also want to make the web app look a little less… terrible. But for now, I’m happy with what I’ve accomplished.
