Okay, so I’ve been messing around with this NBA Elo ratings thing. It’s basically a way to rank teams, you know, see who’s really the best, not just based on wins and losses.
First, I had to grab the data. I found all these game results online, season by season. It was a bit of a pain to clean it up, making sure everything was in the right format. Dates, team names, scores – all that jazz. I mostly used some simple text editors and spreadsheets for this, nothing fancy.
Getting Started with the Calculations
Then came the actual Elo calculation. Now, I’m no math whiz, so I had to look up the formula. It’s not super complicated, but there are a few moving parts. You got your initial ratings, the expected outcome, the actual outcome, and this “K-factor” that determines how much the ratings change after each game.
- I started every team at 1500, which seems to be the standard.
- I picked a K-factor of 20. I saw some people using 32, others using 20, so I just went with the smaller number. Figured it would make the ratings a little less jumpy.
I decided I need a way to show this off. I went with a simple table, sorted by Elo rating. You can see the team name, their current rating, and that’s about it. I also added a little feature to track each team’s rating over time. It’s just a simple line chart, but it helps visualize how teams are trending.
The Actual Implementation
I wrote a script to do all this. I chose Python because it’s pretty easy to use, and there are tons of libraries out there for data stuff. I used pandas for handling the data, and I am just printing to the console, because I am not a frontend guy.
The script basically loops through each game. It calculates the expected outcome based on the current Elo ratings, compares it to the actual outcome, and then updates the ratings accordingly. It’s a pretty straightforward process, really.
Finally, I ran the script on all the data I had collected. It took a little while to crunch through all the games, but it eventually spit out the final ratings. And there you have it – my own little NBA Elo ranking system!
It’s not perfect, of course. There’s always room for improvement. I might play around with the K-factor, try different starting ratings, or even incorporate some other factors like home-court advantage. But for now, it’s a fun little project that gives me a different perspective on the NBA.
