Okay, here we go! Let me tell you how I tackled this “dodgers vs miami marlins match player stats” thing.
Alright, so I was tasked with grabbing player stats from a Dodgers vs. Marlins game. Seemed simple enough, right? Well, kinda.
First thing I did, obviously, was hit up Google. I figured there HAD to be some sports stats site with this info readily available. I typed in “Dodgers Marlins player stats” and started clicking links.
I landed on a few big sports sites – ESPN, *, you know the usual suspects. They definitely HAD the data, but it was all spread out, a little clunky to navigate, and I needed it in a format I could actually use. Copy-pasting a million tables wasn’t gonna cut it.
So, Plan B: Web scraping. I know, I know, it sounds fancy, but it’s really just grabbing data from a website automatically. I decided to use Python – it’s my go-to for this kind of stuff. I fired up my IDE and got to work.
I started by inspecting the webpage source code (right-click, “View Page Source” – that trick never gets old!). I was looking for HTML tags that seemed to contain the stats I needed – table rows (), table data cells (), stuff like that. It was a bit of a mess, but I could see a general structure.
Next, I installed the `requests` and `BeautifulSoup4` libraries in Python. `requests` lets you grab the HTML content of a webpage, and `BeautifulSoup4` helps you parse that HTML and make it easier to work with. I used `pip install requests beautifulsoup4` in my terminal. Easy peasy.
Then came the actual scraping code. This part was a bit of trial and error. I basically did this:
Got the HTML content of the page using `*()`.
Parsed the HTML using `BeautifulSoup()`.
Used `BeautifulSoup`'s `find_all()` method to locate the table(s) containing the player stats. This involved figuring out the right CSS selectors to target the specific tables I wanted.
Iterated through the rows in the table, extracting the text from each data cell (
The tricky part was handling different table formats. Sometimes the HTML was inconsistent, or the player names were formatted differently. I had to add some extra logic to clean up the data and make sure it was consistent.
After scraping the data, I organized it into a dictionary. Each key in the dictionary was a player name, and the value was another dictionary containing their stats (e.g., hits, runs, RBIs). Something like:
Finally, I printed the data to the console, just to make sure it looked good. And then I saved it to a CSV file using the `csv` module. I could then open the CSV in Excel or Google Sheets and do whatever I wanted with it.
Challenges I faced:
Websites change their structure all the time. So my scraper might break if the website's HTML changes. I'd have to update my code to reflect the new structure.
Some websites have anti-scraping measures in place. I didn't run into that this time, but it's always a possibility.
Data cleaning is ALWAYS a pain. There were weird characters, inconsistent formatting, you name it.
What I learned:
Web scraping is a powerful tool for getting data from the web.
`requests` and `BeautifulSoup4` are your friends.
Data cleaning is the most time-consuming part of the process.
Overall, it was a fun little project. Got the player stats, learned a few things, and now I have a script that I can reuse for other games in the future. Not bad for a day's work!