NBA Data Analysis

Check out the deployed web app.

Summary

passing: creating a graph from player passing data, random walks to simulate possessions done on the frontend

positions: classifying NBA players into positions using a KNearestNeighbors classifier on season statistics

styles: clustering NBA teams and players based on their play styles determined by the frequencies of play types

tiers: clustering NBA players into tiers using the k-means clustering algorithm on advanced statistics

Data

All the data was scraped from the NBA's publicly available stats

Getting Started

All of the needed libraries can be installed using pip install -r requirements.txt in the repository directory.

Install the package using python setup.py install.

Unless you have the required database URI, change the package config.py file to have data_source = 'local'.

Prior to running the app, you will likely want to scrape the data, to do this run python nba_analysis/scraping/*.py. Do this after changing the package config so that the data is downloaded locally. All the data should be downloaded into the directory nba_analysis/data/.

To run the web app, run python runserver.py and point your browser to http://localhost:5000/.

Alternatively, to run individual analyses, navigate to the nba_analysis/analysis directory (must enter subdirectory due to hard-coded data paths) and run python <analysis_script> <params>.

Disclaimer

All of these experiments/mini-projects are more for proof-of-concept and practice than true analysis. The analysis is rudimentary and the scikit-learn algorithms used could be tuned much further by manipulating parameters.