Alright folks, gather ’round! Let me tell you about this little project I tackled recently: tracking triple-doubles in a basketball season. Sounds simple, right? Well, it got pretty interesting.
It all started with me just being curious. You know, watching games and wondering who’s racking up those triple-dubs. So, I thought, “Hey, why not build something to keep track of it myself?”
First thing I did was figure out where to get the data. After a bit of digging, I found a couple of sports stats APIs. Some were free, some weren’t. I ended up using one that had a free tier, good enough for what I needed. Had to sign up for an API key and all that jazz, but it wasn’t too bad.
Next up, coding! I decided to go with Python ’cause it’s my go-to for quick projects. I started by writing a script to hit the API and pull down the game data. This was a bit of a pain, had to mess around with JSON parsing and figure out the API’s weird data structure. Lots of trial and error, let me tell you.
Once I had the game data, I needed to figure out how to identify triple-doubles. Basically, I looped through each player in each game and checked if they had 10 or more points, rebounds, and assists. Simple enough, but gotta make sure you’re handling the data correctly!
Then came the fun part: storing the results. I didn’t want to just print them out to the console. I wanted to see the data over time. I opted for a simple CSV file. Easy to read, easy to update. I wrote some code to append each triple-double to the file, along with the player’s name, date of the game, and the stats line.
After that, I wanted to visualize the data. So, I used Matplotlib (also in Python). Created a simple bar chart showing the number of triple-doubles per player. It wasn’t fancy, but it got the job done. I could easily see who was leading the pack.
Challenges I Faced:
- API Rate Limits: The free API tier had rate limits. I had to be careful not to make too many requests too quickly, or I’d get blocked. I added some delays to my script to avoid this.
- Data Inconsistencies: The API data wasn’t always perfect. Sometimes player names were misspelled, or stats were missing. I had to add some error handling to deal with these issues.
- Time Zones: Dealing with dates and times across different time zones was a headache. I had to convert everything to a consistent time zone to make sure I was comparing apples to apples.
In the end, it was a fun little project. I learned a lot about working with APIs, data processing, and visualization. And now I have a cool tool to track triple-doubles throughout the season. Maybe next year, I’ll add some more features, like tracking double-doubles or even quadruple-doubles!
So yeah, that’s the story. Nothing earth-shattering, but a good way to spend a weekend and learn something new. Give it a try yourself, you might be surprised what you can build!
