To find the greatest-of-all-time using statistics. I started this project to explore different methods for estimating relative skill of NBA teams. My inspiration was FiveThirtyEight.com’s CARMElo system. The aim is to dive-deep into every method, derive the update equations and discuss the pros and cons. I intend to cover the following methods:

  1. Elo and common extensions.[DONE]
  2. Assumed Density Filtering.[IN PROGRESS]
  3. Expectation Propagation(given that this NBA data has already ocurred and can be used for batched inference.)
  4. Extensions to EP(score difference).

The complexity of methods increases according to the standard, universally accepted W3D difficulty rating system. You have been warned.

A note about the notebooks.

I wrote this tutorial before I was aware of hackmd.io. At the time, I would write massive Jupiter notebooks with all the math, code and figures. The nbviewer link will take you to the rendered notebook - this is the recommended way of viewing the project. The 2nd cell of the notebooks contains some javascript which hides all the input code cells for a pleasant reading experience. If you’re interested in the code, then please click on the here link in the 2nd cell.

Part 1. Elo:

https://nbviewer.jupyter.org/github/priyamtejaswin/nba-goat/blob/master/nb-elo_vanilla.ipynb

  • Tracking NBA franchises through changes in names and cities.
  • Explain Elo with its core assumptions and apply the vanilla Elo on nba data.
  • Extend the base model to account for score difference(mov-Elo).
  • Extend the base model to account for home-court advantage(hca-Elo).
  • Finish with interactive visualisation of Warriors and Bulls.
  • Discuss and segue to ADF and TrueSkill.

Scroll down to the last cell for an interactive visualisation for two of my favorite teams!