An R package to quickly obtain clean and tidy men's basketball play by play data.
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Updated
May 7, 2024 - R
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.
An app to visually explore the density (and other related factors) of the schedule for NBA teams.
R package to interact with NBA api
Bayesian Regression Analyses from scratch - NBA data example
Build an R Package that can construct an NBA Shiny App
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.
How does playing in the Olympic Games impact a player's following NBA season?
Uses Random Forest to predict NBA players' positions based solely on their in-game statistics.
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.
NBA Player HUD RShiny Application
With the NBA bubble in Orlando, which teams are being spared the most/least from traveling to opponent arenas?
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.
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