An API Client package to access the APIs for NBA.com

Visualization and analysis of NBA player tracking data

Using data analytics and machine learning to create a comprehensive and profitable system for predicting the outcomes of NBA games.

Repository which contains various scripts and work with various basketball statistics

Predicts Daily NBA Games Using a Logistic Regression Model

An R package to quickly obtain clean and tidy men's basketball play by play data.

Python wrapper for the MySportsFeeds Sports Data API

sportsdataverse python package

NodeJS wrapper for the MySportsFeeds Sports Data API

NBA play-by-play data from stats.nba.com, data.nba.com, pbpstats.com, and also shots information with season 1996/97

Stattleship R Wrapper

Feature requests for the MySportsFeeds Sports Data API.

Short, offhand analyses of the NBA

Create NBA shot charts using data scrapped from stats.nba.com and R package ggplot2.

🏀 JavaScript Client for stats from NBA.com

Hover on an NBA player's name on any web page to quickly view their career stats. Thousands of active users across Chrome, Firefox, Opera, and Edge.

R wrapper functions for the MySportsFeeds Sports Data API

2017 Example NBA basketball website using nba_py for people to learn how to use NBA Stats Python API.

Python package for filling in information about players on court in NBA play-by-play data.

In this series, we're going to learn the fundamentals of the popular Python data science tool called Pandas.