Analytics has taken over the sports industry, and data is crucial in fantasy sports, including fantasy basketball. While most fantasy apps provide you with a wide variety of data and analytics, they have limitations, like not being able to customize how you view and interact with the data.
In the previous post, we used an API from RapidAPI.com to pull NBA player stats by game. To actually apply this to our team though, we need to join it with our specific league data, including the different teams and rosters. Luckily, we use the Sleeper fantasy app, which provides APIs to pull these pieces of data for your specific league. Sleeper provides some documentation on these APIs here, and in this article we’ll show you how we used them to help with our own fantasy team.
Getting your League ID
First things first, you’ll need to find your specific league ID, which tells the API which league you’re pulling data for. One way to get this is to start with your Sleeper account username and use an endpoint to get your user_id from it. There’s another endpoint to list out all of the NBA fantasy leagues associated with your account, and we’ll take the first value from the list.
Users Table
Once we have our league ID, we’ll create a table of all the teams in our league from this league users endpoint. In the rest of the data, each team is identified by a USER_ID attribute, so this table identifies each team by USER_ID.
Rosters Table
Next, we’ll pull each team’s roster with this rosters endpoint. The data has an OWNER_ID field to join with the USER_ID from the Users table to identify each team. Sleeper also provides their own unique ID (PLAYER_ID_SLEEPER) to identify each player.
Players Table
Since Sleeper has its own PLAYER_ID attribute (that’s different from the PLAYER_ID in the NBA stats data), we need to somehow connect the two. We can join them by player name, so we’ll create a third table that lists out the different PLAYER_ID_SLEEPER values with player names and other info.
Final Data Model
We can connect these 3 new tables into our existing data model from the last post so that we can filter the data by the context of our league.
Conclusion
Using the Sleeper API endpoints, we can connect our specific league context to the stats data from the last post. In the following posts, we’ll use these to improve our fantasy team with more accurate predictions and better lock-in decisions.
Feel free to download the full Jupyter notebook of Python code to try it out yourself:
If you found this guide helpful, please drop a comment below with any questions or feedback you have. Your input helps us improve future posts and inspire new ideas.
Thanks for joining us today, and we hope to see you next time!