Master three popular sports ranking methods and create winning March Madness brackets with mathematical precision. Learn from top professors in the field.
This course teaches you how to use math to predict outcomes in March Madness. You’ll learn about three sports ranking methods and how to create your own brackets. It’s all about making smart picks based on numbers, not just gut feelings!
Learn 3 popular sport ranking methods and how to create March Madness brackets with them. Let math make the picks!
A Faculty Project Course - Best Professors Teaching the World
Every year, people across the United States predict how the field of 65 teams will play in the Division I NCAA Men’s Basketball Tournament by filling out a tournament bracket for the postseason play. Not sure who to pick? Let math help you out!
In this course, you will learn three popular rating methods two of which are also used by the Bowl Championship Series, the organization that determines which college football teams are invited to which bowl games. The first method is simple winning percentage.  The other two methods are the Colley Method and the Massey Method, each of which computes a ranking by solving a system of linear equations. We also learn how to adapt the methods to take late season momentum into account. This allows you to create your very own mathematically-produced brackets for March Madness by writing your own code or using the software provided with this course.Â
From this course, you will learn math driven methods that have led Dr. Chartier and his students to place in the top 97% of 4.6 million brackets submitted to ESPN! Â See more:
Math Improves March Madness PredictionsÂ
By the end of the course, you will be able to rank sports teams using 3 popular sports ranking methods and create brackets for March Madness.
In this course, you will learn how to rank using winning percentage, the Colley method, and the Massey method, and how to adapt each ranking method to integrate momentum.
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Tim Chartier is an Associate Professor of Mathematics at Davidson College. He is a recipient of a national teaching award from the Mathematical Association of America. Published by Princeton University Press, Tim coauthored Numerical Methods: Design, Analysis, and Computer Implementation of Algorithms with Anne Greenbaum. As a researcher, Tim has worked with both Lawrence Livermore and Los Alamos National Laboratories...
Very nice
Good
It was good by the way it's very informative
it is interesting
Instructor was fine, but I thought this would be more advanced.
I never studied linear algebra in college, so I thought that I would try this course. I you learn better by applying math to tangible problems, I recommend this course.
This course seems to have been published about six years ago. With the increase interest in data science in that period and the readily available software packages, it would be nice to see some updates.
Amazing
Nice comfortable pace; brief lectures; lecture slides contain interesting images and few words
Erklärung verständlich, gut rübergebracht.
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Bethany D. M.
I am taking this to do my own march madness bracket research on the womens side of the sports world