Using Machine Learning to predict the winner of NCAA Men's March Madness given statistics from previous games and seasons. Compares hand built Linear Perceptron and kNN algorithms to pre-made sklearn perceptron and kNN packages.

Using Kaggle data to create predictions for 2018 NCAA Men's Basketball Tournament

Logistic Regression model to assign probabilities of outcomes for any possible matchup in the 2022 NCAA Men's Basketball Tournament

Data and model files to accompany the Medium article on visualizing model components to understand uncertainty in statistical models.

Back to basics NCAA basketball pool software implemented in pure C.

A group project for BYU CS 478 to predict march madness game outcomes.

Package to let you easily represent and progress Bracket-style single elimination Tournaments, allowing you to focus on matchup logic and analysis without worrying about the boilerplate. Supports 2, 4, 8, 16, 32, 64...2^n starting teams.

NCAA web3 Athlete Profile(s)§§§

This is a statistical program designed to analyze data from past years in order to make an accurate prediction of the outcomes of this year's NCAA Tournament.

This project uses team ratings from KenPom.com to predict the winner of an NCAA Tournament bracket pool

The purpose of the ncaa_select_picks repository is two fold: (1) Provide Python package that those more experienced with Python may use to fill out NCAA Men's March Madness brackets who just want to fill out a bracket for fun and (2) Provide an easy to use Python notebook for anyone to use to develop their understanding of Python and algorithms.…

Simple script for making a March Madness bracket using smart but random coins

Tournament Pool and Bracket Tracker

ncaa basketball team level data with tourney outcomes

An R package to quickly obtain clean and tidy men's basketball play by play data.