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

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

With the NBA bubble in Orlando, which teams are being spared the most/least from traveling to opponent arenas?

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

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

Bayesian Regression Analyses from scratch - NBA data example

R package to interact with NBA api

basketball reference datasets

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

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

Scraper for NBA data

R wrapper functions for the MySportsFeeds Sports Data API

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

Stattleship R Wrapper