Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm
-
Updated
Jul 25, 2023 - Jupyter Notebook
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.
Short, offhand analyses of the NBA
Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)
2017 Example NBA basketball website using nba_py for people to learn how to use NBA Stats Python API.
Stattleship API Ruby client
stats.nba.com library 🏀
Displaying team performance against player rotations during NBA games
Scraper for NBA data
NBA game prediction model
NBA games' prediction
Python API for stats.nba.com
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 application to see latest NBA news and stats
Generate NBA season simulation result
Add a description, image, and links to the nba-statistics topic page so that developers can more easily learn about it.
To associate your repository with the nba-statistics topic, visit your repo's landing page and select "manage topics."