Alright folks, let me walk you through how I dug into Steph Curry’s stats against the Rockets over the last 5 games. It was a bit of a journey, but hey, we got there in the end!
First things first, I started by hitting up the usual suspects: ESPN, *, you know, the big sports data sites. My goal was to quickly grab the raw game logs. I was hoping for an easy copy-paste situation. Spoiler alert: it wasn’t.
The initial problem I ran into was that the data was spread across multiple pages. Each game required its own page load, and I wasn’t about to manually copy-paste for each one. That’s just asking for errors and wasting time.
So, I decided to get a little more technical. I remembered seeing some tutorials on web scraping with Python. I’m no coding wizard, but I can usually hack something together. I fired up my Python environment and installed BeautifulSoup and Requests – those are the key libraries for this kind of thing. Think of BeautifulSoup as the tool that helps you navigate the messy HTML, and Requests as the thing that fetches the web pages for you.
Next, I spent a good hour or two inspecting the HTML source code of those game pages. This is where you try to figure out the patterns – what HTML tags are used to store the data you need? What are the CSS classes? It’s like being a detective trying to crack a case, only the case is “where is Steph Curry’s point total?”
After some digging, I figured out the right HTML elements to target. I wrote a small Python script to loop through the URLs of the last 5 games against the Rockets, fetch the HTML, and parse out the key stats: points, assists, rebounds, you know, the usual suspects. It was clunky at first, lots of trial and error, but eventually, I got it working.
Once I had the data in a Python list, I dumped it into a simple CSV file. CSV is just a plain text format that spreadsheets love. From there, I opened it up in Google Sheets. Much easier to work with there.
In Google Sheets, I cleaned up the data a bit, calculated the averages, and even threw together a quick chart. Nothing fancy, but it gave me a clear picture of Curry’s performance. The average points, assists, rebounds were clearly visible after calculation using the excel sheet.
Finally, I double-checked everything to make sure my scraping script wasn’t pulling any weird numbers. Always good to have a second look!
Lessons learned? Web scraping can be a lifesaver when you need to get data off a website that doesn’t have a handy API. Python and BeautifulSoup are your friends. And always, always double-check your data!
