Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)
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Updated
Feb 12, 2017 - Jupyter Notebook
Machine Learning Model for Sport Predictions (Football, Basketball, Baseball, Hockey, Soccer & Tennis)
Kaggle Competition for Predicting NCAA Basketball Tourney Games
Repository which contains various scripts and work with various basketball statistics
SkillCorner Open Data with 9 matches of broadcast tracking data.
An Implementation of the ANT+ Network on top of ant-arduino
Python wrapper for the Sportradar APIs ⚽️🏈
A Tennis dataset and models for event detection & commentary generation
Sport stats UI components
Stattleship R Wrapper
Based on NFL game data, we want to predict the success of a play. This can be used to insert different strategies before the play is called to determine the success probability.
R wrapper functions for the MySportsFeeds Sports Data API
Sports Analytics on Kaggle's IPL Dataset
An R package to quickly obtain clean and tidy college football play by play data
A scraping and aggregating package using the CollegeFootballData API
Neural network that predicts the number of wins for a baseball team based on the importance of different statistical categories and their influence on a team's success.
Stattleship API Ruby client
An Ionic + Firebase based PWA for tracking foosball matches
Machine Learning Predictor for NBA games that takes into account player stats, team stats.
Analysis and visualization of various aspects and facets of the game of cricket, with respect to the Indian Premier League(IPL).
Feature requests for the MySportsFeeds Sports Data API.
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