Alright, so yesterday I was messing around trying to pull player stats from a Mets vs Padres game. It sounded simple enough, right? Famous last words, I swear.
First off, I started by thinking I could just scrape some data off ESPN or something. I mean, they have all the stats laid out nice and neat. So, I dove headfirst into Beautiful Soup and Requests in Python. I spent like an hour trying to figure out the right CSS selectors to grab the tables I needed. It was a total mess. The HTML was a rat’s nest, changing all over the place depending on the page. I got some stuff, but it was super inconsistent and unreliable. Scraping felt like whack-a-mole.
Then, I thought maybe there’s an API I could hit. I googled around and stumbled upon a couple of sports data APIs. Some were free, some were paid. I checked out a free one first – can’t remember the name now, but it was giving me the basic game info but didn’t have the detailed player stats I was after. Total bummer.
I figured, okay, if I want decent data, I gotta pony up some cash. I looked at a few of the paid options. I ended up going with one that seemed pretty reasonable. After signing up, getting an API key, and wading through their docs, I finally started making some progress.
The API spat out a huge JSON blob. It took me a little while to get my head around the structure, but eventually, I managed to extract the stats for each player from both teams. I used Python and the json library to parse everything.
Next step was getting the data into something useful. I decided to throw it into a Pandas DataFrame. I mean, who doesn’t love a good DataFrame? I massaged the data, cleaned it up a bit, and renamed the columns to be more descriptive. I ended up with separate DataFrames for the Mets and Padres.
Here’s a quick example of what the data looked like:
- Player Name
- At Bats
- Runs
- Hits
- RBI
- Etc.
After that, I started doing some basic analysis. What was the highest batting average? Who had the most RBIs? Stuff like that. I plotted a couple of histograms and scatter plots just to visualize the data. Nothing fancy, just exploring.
Lessons learned:
- Web scraping can be a huge time sink and often unreliable. APIs are the way to go if you can find one.
- Paid APIs are usually worth the cost if you’re serious about getting clean, consistent data.
- Pandas is your best friend for data manipulation and analysis.
In the end, I managed to get the player stats I wanted. It took way longer than I expected, but hey, that’s coding, right?
