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

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

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

R wrapper functions for the MySportsFeeds Sports Data API

Scraper for NBA data

R package to interact with NBA api

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

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.

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

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

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

basketball reference datasets

NBA Player HUD RShiny Application

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

Build an R Package that can construct an NBA Shiny App

How does playing in the Olympic Games impact a player's following NBA season?