There are 4 repositories under nba-statistics topic.
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.
Feature requests for the MySportsFeeds Sports Data API.
Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)
Short, offhand analyses of the NBA
Stattleship API Ruby client
stats.nba.com library :basketball:
NBA game prediction model
Displaying team performance against player rotations during NBA games
NBA games' prediction
Statistical model on NBA basketball players' performance using multiple linear regression and stepwise search.
Find basketball players with similar shot charts
An easy-to-use Python utility to scrape basketball data off stats.nba.com.
R package to interact with NBA api
web scrapes performed for Kaggle datasets.
Using the nba_api to delve into interesting statistics!
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
Web app to track the NBA salary cap totals for every team.
Interactive Dashboard for NBA stat visualizations
NBA database in C#
Website to validate NBA game stats with HTML+CSS+Vanilla JS
NBA Dashboard using Dash and Plotly
NBA statistics website using the balldontlie API - made with Django,
NBA Discord Bot made with Python and Discord.py
:basketball: A helpful React site to search for NBA players by specific categories
I wanted to find out: Do NBA teams in the regular season only care about defense in the 4th quarter? -->Statistical analysis of 4th quarter in comparison to the first three quarters. The data are from the 2019/2020 NBA season