Node application using D3 to Analyze Data from the NBA
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Updated
Jun 7, 2017 - HTML
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Node application using D3 to Analyze Data from the NBA
Python API for retrieving sports data from various known sources. Currently focused on MLB advanced metric data.
Mobile application for the Trashtalk Fantasy League Game.
A project that examines advanced data collected from basketball reference
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.
NBA 2k and Stats Exploratory Data Analysis and Modeling. View project here: https://htmlpreview.github.io/?https://github.com/willyiamyu/nba2k_analysis/blob/master/nba.html
Decoration LED Scoreboard to display your favorite games from NHL, NBA, MLB, and La Liga.
An airflow pipeline for building and scoring NBA daily fantasy models
Python Script containing trained ML models that can be used to predict NBA All-Stars based on stats in the last 5 years. Stats obtained from Basketball Reference: https://www.basketball-reference.com/
Breaking down NBA play-by-play data to show how teams perform when each player plays or not.
This program uses ESPN Fantasy Basketball API to get the stats of the top 50 players with the Best Last 7 Day Average stats
Milwaukee Bucks Game Outcome Prediction using Tensorflow
NBA Player HUD RShiny Application
Basketball Simulation and Management Game
Machine Learning project to predict NBA Salaries of a player based on factors such as PPG, Age, Win Shares, etc. using a multiple linear regression.
Adds live NBA scores for today's games in VS Code's status bar.
Add a description, image, and links to the nba topic page so that developers can more easily learn about it.
To associate your repository with the nba topic, visit your repo's landing page and select "manage topics."