Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm
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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.
2017 Example NBA basketball website using nba_py for people to learn how to use NBA Stats Python API.
NBA game prediction model
Data Extraction (from https://stats.nba.com) and Processing Scripts to Produce the NBA Database on Kaggle (https://kaggle.com/wyattowalsh/basketball)
Scraper for NBA data
Stattleship API Ruby client
Statistical model on NBA basketball players' performance using multiple linear regression and stepwise search.
Short, offhand analyses of the NBA
R package to interact with NBA api
Web application to see latest NBA news and stats
Python API for stats.nba.com
Displaying team performance against player rotations during NBA games
Feature requests for the MySportsFeeds Sports Data API.
NBA games' prediction
NBA Player of the Week Visualizations using ggplot
NBA Player of the Week & Salary Prediction using Scikit-learn
Estimating player value based on Win Share and Salary data
A wrapper API for data.nba.net written in Elixir
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