Okay, so let me tell you about this little side project I tackled recently. It’s all about Ted Higashioka – you know, the Yankees’ catcher? I’m a big baseball fan, always tinkering with stuff, and I thought, “Hey, why not build something around his stats?”
First, I started with the data. I mean, you can’t do anything without the numbers, right? I scraped data from some baseball stats websites – FanGraphs, Baseball-Reference, the usual suspects. I’m talking about batting averages, RBIs, home runs, all that jazz. Got it all into a nice, tidy CSV file.
Next up, I decided to use Python. It’s my go-to language for data stuff. I fired up Jupyter Notebook, imported Pandas (because who wants to wrestle with CSVs manually?), and started cleaning the data. There were some missing values, weird formatting issues, the kind of garbage you always find when you scrape data from the web. Nothing too crazy, just the usual wrangling.
Then came the fun part: analysis. I wanted to see if I could find any interesting trends in Higashioka’s performance. I looked at his year-over-year improvements, his splits against different pitchers (lefty vs. righty), and even his performance in different ballparks. Nothing earth-shattering, but some cool insights emerged. For example, he seems to hit better at Yankee Stadium (shocker, I know).
After the analysis, I thought it would be cool to visualize the data. I used Matplotlib and Seaborn to create some charts and graphs. A simple line chart showing his batting average over time, a scatter plot of his home runs vs. RBIs, that kind of thing. Made everything a bit easier to digest.
Now, here’s where it gets a little more interesting. I wanted to make this data accessible to others. So, I decided to build a simple web app using Flask. I know, I know, there are fancier frameworks out there, but Flask is quick and easy for small projects like this.
I created a basic HTML template with the charts and graphs embedded. Used Flask to serve the data and the visualizations. It was pretty barebones, but it worked! You could go to the website, see Higashioka’s stats, and explore the visualizations.
Finally, I deployed the app to Heroku. It’s free (for basic stuff), and it’s super easy to set up. A few command-line commands, and boom, the app was live. I even added a custom domain name I had lying around.
The result? A simple web app dedicated to Ted Higashioka’s stats. It’s nothing groundbreaking, but it was a fun project. I got to practice my data scraping, data analysis, and web development skills. And hey, maybe Ted himself will stumble upon it one day!
Things I learned:
- Data cleaning is always the most time-consuming part.
- Flask is awesome for quick and dirty web apps.
- Heroku makes deployment ridiculously easy.
All in all, a solid little side project. Maybe I’ll add more features later – who knows? For now, I’m happy with it. Time to move on to the next baseball-related endeavor!
