Okay, so yesterday I was messing around, trying to get a feel for some player stats and match predictions. I figured, why not dive into a head-to-head: Rublev vs. Broady.
First thing I did was hit up some sports data sites. You know, the usual suspects. I wanted to grab their recent performance, any history between ’em, and, crucially, their stats on different court surfaces. I scraped together all this raw data into a big messy spreadsheet.
Next up, data cleaning! Oh man, what a pain. The data was all over the place – different formats, missing values, you name it. I spent a good hour just normalizing the numbers and filling in gaps with some averages from their previous matches. Basically, I made it so the computer could actually understand what was going on.
After that, I started fiddling with some simple models. I’m no expert, so I just threw a bunch of stuff at the wall to see what sticks. I started with a basic weighted average – giving more importance to recent matches and head-to-head results. Then, I tried a slightly more complex regression model, throwing in variables like serve percentage and unforced errors. I used Python with scikit-learn, because it’s what I’m most familiar with.
Ran the models, tweaked the parameters, ran them again. The whole process was pretty clunky, lots of trial and error. The initial results were all over the map. One model favored Rublev heavily, the other said it was a coin flip.
Then I thought, “Hey, let’s visualize this!” I plotted their win percentages, serving stats, and head-to-head records using matplotlib. It made it easier to spot trends and see where the models were getting stuck. Turns out, Broady’s performance on grass was throwing off some of the averages.
So, I adjusted the models to give more weight to the surface they were playing on. After a few more tweaks, the models started to agree a bit more. Still not perfect, but at least in the same ballpark.
Finally, I ended up with a prediction that gave Rublev a 65% chance of winning. But honestly, I wouldn’t bet the farm on it. These models are just a starting point. There are so many factors they don’t account for – player form on the day, weather conditions, even just plain luck.
All in all, it was a fun afternoon project. I learned a bunch about data wrangling and got a bit more comfortable with predictive modeling. Plus, I have a semi-educated guess for who’s going to win the match. Now, let’s see if I’m right!
