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Activity Number: 562
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics in Sports
Abstract #317863
Title: Survive and Advariance
Author(s): Caitlin Phelps* and Nicholas Kapur and Marschall Furman
Companies: North Carolina State University and North Carolina State University and North Carolina State University
Keywords: Data Wrangling ; Machine Learning ; MCMC Sampling ; Variance ; Clustering ; Simulation

The NCAA March Madness Tournament garners America's attention for an entire month. Bracket challenges find their way into the workplace, home, and a variety of social scenes. A key component to winning these challenges is correctly identifying which underdog teams will eliminate nationally renowned programs. For years,"bracketologists" have attempted to create models that best make these predictions. Our project realizes that there is sufficient data to repeatedly simulate single games instead of simply creating models, which allows us to better quantify the uncertainty of the outcome and identify potentially powerful underdogs. We plan to synthesize data from multiple sources and update our simulations on a game-by-game basis. The goal is to use these simulations to divide teams into three categories: those that are both powerful and resistant to variation; those that are powerful, yet susceptible to losing in single game scenarios; and those that are underdogs, yet can rise to the occasion and win unexpectedly.

Authors who are presenting talks have a * after their name.

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