Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm

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

Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)

Feature requests for the MySportsFeeds Sports Data API.

Short, offhand analyses of the NBA

2017 Example NBA basketball website using nba_py for people to learn how to use NBA Stats Python API.

Stattleship API Ruby client

NBA game prediction model

Displaying team performance against player rotations during NBA games

stats.nba.com library :basketball:

Scraper for NBA data

NBA games' prediction

Python API for stats.nba.com

Find basketball players with similar shot charts

Statistical model on NBA basketball players' performance using multiple linear regression and stepwise search.

web scrapes performed for Kaggle datasets.

Generate NBA season simulation result

An easy-to-use Python utility to scrape basketball data off stats.nba.com.

R package to interact with NBA api

No description provided.

Web application to see latest NBA news and stats

Using the nba_api to delve into interesting statistics!

No description provided.

Interactive Dashboard for NBA stat visualizations

Web app to track the NBA salary cap totals for every team.

Classification on the Kobe Bryant Shot Selection dataset (https://www.kaggle.com/c/kobe-bryant-shot-selection/data) using Decision Trees

Estimating player value based on Win Share and Salary data

Website to validate NBA game stats with HTML+CSS+Vanilla JS

No description provided.

NBA database in C#