Alright, so yesterday I was messing around with some baseball data, specifically trying to pull player stats from the Dodgers vs. Oakland Athletics game. Thought I’d share how it went down.
First things first, I had to figure out where to even get the data. I started by hitting up the usual suspects – ESPN, *, stuff like that. A lot of sites have these stats, but grabbing them programmatically? That’s the tricky part.
I ended up settling on using a Python library called Beautiful Soup. I’ve used it before for web scraping, and it’s pretty decent for parsing HTML. I also used requests to, well, request the webpage data. Nothing too fancy.
The basic flow was like this:
Use requests to grab the HTML content of the webpage with the Dodgers-Athletics stats.
Feed that HTML content into Beautiful Soup to create a “soup” object – basically a navigable tree of the HTML.
Inspect the webpage source (right-click -> “View Page Source” in your browser) to find the specific HTML tags that contain the player stats I wanted. This is the most tedious part, honestly. You gotta hunt around for the right tables, divs, spans, whatever.
Use Beautiful Soup‘s methods (find(), find_all(), etc.) to extract those specific elements from the “soup.”
Clean up the extracted data. Web scraping is rarely perfect, so you usually end up with extra whitespace, weird characters, or just the wrong data entirely. I used some basic string manipulation to tidy things up.
Okay, so getting into the specifics… I remember the page I was scraping had the player stats in a table. So I did something like this (simplified version, of course):
import requests
from bs4 import BeautifulSoup
url = "some_url_to_dodgers_athletics_stats" # Not a real url
response = *(url)
soup = BeautifulSoup(*, '*')
table = *('table', {'class': 'the-stats-table'}) # Made up class name
Once I had the table, I needed to iterate through its rows and extract the data from each cell. Each row represented a player, and each cell in the row represented a stat (at-bats, runs, hits, etc.). It’s really about digging through the html.
There were a few snags. For example, some players might not have played in the game, so their row would be empty or missing certain cells. I had to add some checks to handle those cases gracefully. And sometimes, the website would use slightly different HTML structures for different games, which meant I had to tweak my scraping code to adapt.
After pulling the raw data, I needed to get it into a usable format. I decided to store the stats in a Python dictionary, where the keys were player names and the values were dictionaries of their stats.
Finally, I printed out the results to the console to make sure everything looked good. It wasn’t perfect – there were still a few inconsistencies and errors – but it was good enough for a quick analysis.
In the end, I got a decent dump of player stats from the Dodgers vs. Athletics game. It wasn’t super polished, but it served its purpose. I learned a few things about web scraping, and I got a glimpse into how baseball stats are structured online. Plus, it was a fun way to spend an afternoon.
