Alright folks, let me tell you about this thing I’ve been messing with: ben shelton predictions. Sounds fancy, right? Well, it started pretty simple.
First off, I got to thinking, “What if I could actually predict how Ben Shelton’s gonna play in his next match?” Not in a psychic way, of course. More like, “Can I use data and some basic calculations to get a decent guess?”
So, I dove in. The initial plan was to gather all sorts of stats. I mean everything:
- Serve percentage
- First serve points won
- Break point conversion rate
- Unforced errors
- Opponent’s ranking
You name it, I wanted it. I started scraping data from a few tennis websites. That was a bit of a pain, to be honest. Lots of messy HTML to clean up, but hey, gotta start somewhere.
After wrestling with the data for a bit, I threw it all into a spreadsheet. Excel, baby! Nothing too complicated at first. I just wanted to see if any patterns jumped out. Did a high first serve percentage usually lead to a win? Was there a sweet spot for unforced errors? (Spoiler alert: Yes, less is always better).
Then I got a bit fancier. Started playing around with weighted averages. Figured some stats are more important than others. Aced a bunch of times? Great, that probably counts for more than a few double faults, right?
I started tweaking these weights, trying to get a prediction that aligned with past matches. Lots of trial and error here. It was basically me guessing and checking, adjusting things until the numbers looked “right.” Pretty scientific, huh?
After a few weeks of tinkering, I had something that was… okay. It wasn’t perfect. It definitely wasn’t predicting every match correctly. But it was getting close enough to be interesting. Maybe 60-70% accuracy, which is better than just flipping a coin, right?
The next step? I wanna try and build a small Python script and automate the data gathering. Scraping is boring, and I’d like to throw this data into a simple model.
