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

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

Stattleship R Wrapper

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

R wrapper functions for the MySportsFeeds Sports Data API

Scraper for NBA data

A conceptual dashboard to visualize Expected Possession Value (EPV) in the NBA.

basketball reference datasets

An app to visually explore the density (and other related factors) of the schedule for NBA teams.

R package to interact with NBA api

Complete Project on Extraction, Management, Prediction, and Profitability Analysis of NBA Games. Fully based on R.

Bayesian Regression Analyses from scratch - NBA data example

A R Script to read Play by Play data and turn it into Offensive and Defensive Ratings

Build an R Package that can construct an NBA Shiny App

Project dealing with NBA salaries and contracts, researching the best way to allocate salary. See Final Paper and Presentation, R Shiny App and website below for results.

Using KMeans clustering and a decision tree classifer to select players who maximize a team's strengths in fantasy nba h2h category leagues.

Predicting NBA salaries using machine learning through R. Clustering players based on stats to determine player type in an increasingly position-less era of basketball.

Uses Random Forest to predict NBA players' positions based solely on their in-game statistics.

Clustering NBA players and teams through Model-Based methods

NBA Player HUD RShiny Application