Miami Marlins vs Yankees Match: Player Stats & Game Highlights

Okay, so yesterday I was messing around trying to pull player stats from a Miami Marlins vs. Yankees game. It sounds harder than it is, trust me. Let me walk you through it.

First thing’s first, I needed to find a decent data source. I started by just googling “miami marlins yankees player stats,” and a bunch of sports websites popped up – ESPN, MLB, you know the drill. I checked out a few, looking for a site that had the stats laid out in a way that seemed easy to scrape. I ended up settling on MLB’s official site because it looked relatively clean.

Next, I fired up my Python environment. I’m no pro, but I know enough to be dangerous, haha. I used the `requests` library to grab the HTML content of the webpage. So, basically, I did this:

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`import requests`
`url = “the actual url of the marlins vs yankees game stats page”`
`response = *(url)`
`html_content = *`

Pretty straightforward, right? Now I had this big string of HTML, which looked like a mess. That’s where `BeautifulSoup` came in. This library is like a magic tool for parsing HTML. So, I did:

`from bs4 import BeautifulSoup`
`soup = BeautifulSoup(html_content, ‘*’)`

Now, `soup` was a nicely structured object I could actually work with. The tricky part was figuring out how the player stats were organized in the HTML. I used my browser’s “Inspect Element” tool (right-click on the page and select “Inspect”) to poke around the HTML code and find the right tags and classes. It took some digging, but I eventually found the table containing the player stats. I noticed that the data was nicely structured in `

` tags, with `

` for rows and `

` for cells.

Then came the fun part – extracting the data. I used `*_all()` to find all the `

` tags. After inspecting them, I could identify the table containing player stats by its id or class attributes (using inspect element tool, as mentioned above!). Then, within that table, I iterated through each row (`

`) and each cell (`

`) to grab the stats. I extracted names, batting averages, RBIs, home runs – the whole shebang!

I stored the data in Python dictionaries, where each dictionary represented a player, and the keys were the stat names (e.g., “Name”, “AVG”, “RBI”). I ended up with a list of these dictionaries, one for each player in the game.

Finally, I printed out the results in a readable format. Just a simple loop to iterate through the list of player dictionaries and print out the stats for each player. Something like:


for player in player_data:
print(f"Name: {player['Name']}")
print(f"AVG: {player['AVG']}")
print(f"RBI: {player['RBI']}")
print("-"  20)

It wasn’t super elegant, but it worked! I got all the player stats I wanted from the Miami Marlins vs. Yankees game. It was a bit of a grind figuring out the HTML structure, but once I cracked that, it was smooth sailing. I guess this is the best way to spend my weekend, haha.