Alright, so yesterday I was messing around, trying to pull some data for a little personal project, and I ended up wrestling with getting the Miami Dolphins’ box score from a sports stats site. Figured I’d jot down what I did, ’cause you never know when this kinda thing comes in handy.
First off, scoping the battlefield: I needed the actual box score data, not just a summary. I started by hitting up a couple of the big sports sites, ESPN, CBS Sports, you know the usual suspects. I was looking for a clean, easily parsable layout. Some sites were a pain, heavy on the Javascript, others were a bit more straightforward HTML. Ultimately, landed on one that seemed…workable.
Digging into the HTML: I cracked open the developer tools in Chrome (right-click, “Inspect,” boom). Then I started poking around the HTML structure. The goal was to identify the specific tables or divs containing the stats I wanted. I was hunting for patterns, class names, anything that I could use to target the data with a script later on.
Initial Grab with Python: Python’s my go-to for this kinda thing. I used the `requests` library to grab the HTML content of the page.
import requests
url = "the_url_of_the_miami_dolphins_box_score_page" # I'm not posting the actual URL
response = *(url)
html_content = *
Pretty basic, right? But it gets the job done.
Soup’s On (BeautifulSoup, that is): Next up, parsing the HTML. I used BeautifulSoup. It’s like having a magic wand for navigating messy HTML.
from bs4 import BeautifulSoup
soup = BeautifulSoup(html_content, '*')
Now `soup` is a BeautifulSoup object, and I can start searching for stuff.
Targeting the Data: This is where it got tricky. I had to use the class names I found in the dev tools to pinpoint the right tables. I used `*_all()` to grab all the tables with a specific class.
tables = *_all('table', class_='the_specific_class_name') # Replace with the actual class name
This part was a lot of trial and error. Some tables looked promising but turned out to be something else entirely. It’s a lot of printing out table contents and seeing if it’s the right stuff.
Extracting the Good Stuff: Once I found the right table, I had to iterate through its rows and cells to extract the actual data. This involved more BeautifulSoup magic and some string manipulation.
for table in tables:
for row in *_all('tr'):
cells = *_all('td')
if cells: # Making sure the row isn't empty
data = [*() for cell in cells]
print(data) # Just printing for now, later I'd store it in a better format
This basically loops through each row, gets the data from each cell, strips any extra whitespace, and then prints it.
Cleaning Up the Mess: The raw data was a bit messy. There were extra spaces, weird characters, and stuff like that. I used string methods like `replace()` and regular expressions to clean it up.
Structuring the Data: Printing it out is cool, but I wanted something more usable. I decided to structure the data as a list of dictionaries, where each dictionary represented a player’s stats.
player_stats = []
#Inside the loop where I'm extracting data
player_data = {
'player_name': data[0],
'passing_yards': data[1], #Obviously depends on the table structure
'rushing_yards': data[2],
# ... more stats
player_*(player_data)
Final Step: Saving the Data: Finally, I saved the `player_stats` list to a CSV file using the `csv` library.
Lessons Learned:
HTML structures are a pain. Sites change their layouts all the time, so the script might break.
Error handling is crucial. Gotta handle cases where a table is missing or the data is in a different format.
BeautifulSoup is your friend. Seriously, learn it.
Next Steps: I’d probably wrap this in a function, add some error handling, and maybe schedule it to run automatically to keep the data up-to-date. It was a fun little project!
