Okay, so yesterday I was messing around, trying to see if I could predict the outcome of the Heather Watson vs. Karolina Pliskova tennis match. Thought it would be a fun little project to kill some time.
First thing I did was gather a bunch of data. I mean, tons of it. I scraped stats from a couple of different tennis websites – stuff like their recent win/loss records, head-to-head stats, how they perform on different court surfaces, you name it. Basically, anything I could get my hands on that might give me an edge.
Then came the fun part: cleaning up the data. Man, that was a pain. Different websites format things differently, so I had to wrangle it all into a consistent format. Used a bit of Python with Pandas – that library is a lifesaver, seriously. Spent a good hour just tidying everything up, removing duplicates, and handling missing values. You know, the usual data janitor stuff.
Next, I tried to figure out which stats actually mattered. I’m no tennis expert, so I had to rely on what seemed logical and do some experimenting. I looked at things like first serve percentage, break point conversion rate, and unforced errors. Played around with different combinations to see which ones seemed to correlate best with winning.
After that, I built a really basic model. I didn’t go crazy with anything fancy, just a simple logistic regression model using scikit-learn. I fed it the cleaned data and let it do its thing. Split the data into training and testing sets, of course, to see how well it generalized.
Then I ran the model on the Watson vs. Pliskova match. The model spat out a probability of who it thought would win. I gotta say, I was pretty nervous at this point. It’s one thing to play around with data, it’s another to actually make a prediction!
So, the big reveal? My model predicted Pliskova would win. And guess what? She actually did! I was pretty stoked, not gonna lie. Felt like I’d actually accomplished something, even though it was just a silly little experiment.
What did I learn? Well, data cleaning is a real drag, but it’s absolutely crucial. Also, even a simple model can sometimes give you surprisingly good results. And finally, predicting tennis matches is harder than it looks! Still, it was a fun way to spend an afternoon. Maybe I’ll try to refine the model and see if I can get even better predictions next time.
Would I do it again? Definitely! It was a cool learning experience, and who knows, maybe I can turn this into a serious side hustle, haha!
