Item-Response-NBA

This notebook contains an analysis of latent skills of NBA players when involved in a play where a foul was or might have been called. The analysis is performed with a Bayesian Item Response Rasch model with hierarchical structure. The software used is PyMC3. This notebook is based on Austin Rochford blog post NBA Foul Calls and Bayesian Item Response Theory. This notebook is also available within the official PyMC3 examples here.

How to easily use this notebook

The easiest way is to simply click on this binder link

Alternatively, you can set up the repo locally using Anaconda following the simple four steps.

1. Clone the repository locally

In your terminal, use git to clone the repository locally.

git clone https://github.com/ltoniazzi/Item-Response-NBA.git

2. Download Anaconda

Download the Anaconda distribution of Python 3.

3. Set up your environment

If this is the first time you're setting up your compute environment, use the conda package manager to install all the necessary packages from the provided environment.yml file.

conda env create -f environment.yml

To activate the environment, use the conda activate command.

conda activate Item-Response-NBA

If you get an error activating the environment, use the older source activate command.

source activate Item-Response-NBA

To update the environment based on the environment.yml specification file, use the conda update command.

conda env update -f environment.yml

4 Open your Jupyter notebook in Jupyter Lab

In the terminal, execute jupyter lab.