Okay, here’s my attempt at a blog post about my “daily home run predictor” project, trying to match the style and tone you described.
## My Daily Home Run Predictor – A Dive into Baseball Stats!
Alright folks, lemme tell you about this little side project I’ve been messing around with. I call it the “daily home run predictor.” Basically, I wanted to see if I could build something that would guess which baseball players are most likely to hit a home run on any given day. Why? Because why not! I like baseball, I like data, and I like trying to make computers do stuff.
So, where did I even start? Well, the first thing I did was hunt down some data. You can’t predict anything without data, right? I ended up finding a few good sources for historical baseball stats – stuff like player stats, weather conditions at the stadiums, and even the dimensions of the ballparks themselves. Kaggle was super helpful, also some baseball stats websites.
Next step, I wrangled that data. And let me tell you, it was messy. Dates were in weird formats, missing values everywhere, abbreviations I didn’t understand… it was a whole thing. I spent a solid chunk of time just cleaning things up and getting it into a format that I could actually use. Pandas in Python was my best friend here. I swear, I aged five years during this process.
Okay, data cleaned (sort of). Now it was time to start thinking about features. What things might actually influence whether a player hits a home run? Obviously, the player’s past home run hitting ability is important. But what about the opposing pitcher? The weather? The ballpark? I threw in a bunch of stuff:
- Player’s batting average
- Player’s home run rate
- Pitcher’s ERA (Earned Run Average)
- Wind speed and direction
- Temperature
- Park factor (how hitter-friendly the ballpark is)
I started with all of that stuff.
After that, it was time to choose a model. I’m no machine learning expert, so I kept it relatively simple. I ended up using a logistic regression model. Figured it was a good starting point for predicting a binary outcome (home run or no home run). Scikit-learn made this part pretty easy, honestly.
Then came the training phase. I fed the model a bunch of historical data, and it learned the relationships between the features and whether a home run was hit. This took a while, even with a relatively simple model. I just let it run while I watched some TV.
Once the model was trained, I had to see how well it actually worked. I used some data that the model hadn’t seen before to test its predictions. The results? …They weren’t great. The model was right maybe 60% of the time. Which is better than guessing, but not by much. Still, I wasn’t expecting to revolutionize baseball with my first attempt.
So, what’s next? Well, I’m thinking about tweaking the features. Maybe I need to add some more advanced stats. Maybe I need to consider the player’s recent performance instead of just their overall stats. Maybe I need to figure out how to account for injuries.
I also want to try different models. Maybe a more complex model would do a better job. Or maybe I need to go back to the drawing board and rethink my whole approach.
It’s a work in progress, but I’m having fun with it. And who knows, maybe one day I’ll actually be able to predict home runs with some degree of accuracy. Until then, I’ll just keep tinkering and sharing my journey!
I’ll keep you guys posted on my progress!
