Previsione dei punteggi ottenuti nell'intero campionato NBA 2015/2016, utilizzando la regressione di Poisson che tiene conto della forza offensiva e difensiva delle squadre coinvolte in ogni partita

A master repository for basketball data and projects

Unsupervised approach to discover patterns between NBA compensation and skillsets

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.

Breaking down NBA play-by-play data to show how teams perform when each player plays or not.

NBA Player HUD RShiny Application

predicting 2020's Most Improved Player, as well as candidates for next year

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.

Predicting outcomes for the suspended 2019-20 NBA season

predicting contracts for 2020 nba free agents

Individual Project investigating fan engagement with the NBA.

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.

A machine learning approach to categorize NBA players by role using shot characteristic data, highlighting the game's evolution over three decades.

Cluster-specific logistic regression models for whether an NBA team will make the playoffs given the current statistics of that team. Specifically uses population averaged models (PA) based on generalized estimating equations (GEE); Also, uses cluster-specific (each team) random effects models

Stephen Curry Under Armour Analysis

http://sumitrodatta.shinyapps.io/nba-free-agency-dashboard/