Alright, let me tell you about messing around with that “ethan petry mlb draft” stuff. It was a bit of a rabbit hole, but kinda fun.
First off, I stumbled on this idea while just scrolling through baseball stuff, you know, the usual. Ethan Petry’s name popped up and I was like, “Huh, who’s this guy?” So, naturally, I Googled him. Saw he was a big-time college player, potential MLB draft pick. That’s when the tinkering started.
I started by digging into his stats. I mean, really digging. I wanted to see beyond the surface-level stuff. I scraped data from a couple of different baseball stats sites – batting average, home runs, RBIs, all that jazz. I used Python with Beautiful Soup to pull the info into a spreadsheet. It was messy at first, but I cleaned it up.
Next, I wanted to see how he compared to other draft prospects. So, I did the same thing for a bunch of other guys projected to go around the same spot in the draft. More web scraping, more data cleaning. My spreadsheet was starting to look like a beast.
Then came the fun part: trying to make sense of it all. I wasn’t trying to be some expert scout or anything, just wanted to see if I could spot any interesting trends. I messed around with some basic charting libraries in Python (like Matplotlib and Seaborn) to visualize the data. Scatter plots, bar charts, the whole shebang. Tried to see if Petry stood out in any particular area.
I even played around with some simple predictive models. Nothing fancy, just linear regression to see if I could “predict” his future performance based on his college stats. I know, it’s a long shot, but it was a good exercise. I used scikit-learn for this. Pretty straightforward stuff.
Of course, stats are only part of the story. I also watched some video of him playing. Tried to get a feel for his swing, his fielding, his overall game. There are tons of highlight reels on YouTube. I’m no scout, but I could at least see if he looked like a good player.
Now, I’m not going to pretend I discovered some hidden truth about Ethan Petry that all the MLB teams missed. But it was a fun project. I learned a lot about web scraping, data analysis, and even a little bit about baseball scouting. Plus, I now have a much better appreciation for the amount of work that goes into evaluating these players.
Key takeaways:
- Web scraping can be a pain, but it’s powerful.
- Data visualization makes everything easier to understand.
- Even simple models can be surprisingly insightful.
- Baseball is a lot more complicated than it looks.
All in all, a solid weekend project. Would I bet my life savings on my Ethan Petry draft analysis? Absolutely not. But did I have fun messing around with data and learning something new? You bet.
