Date of Award

2016

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Statistics

Department

Statistics

College

Science and Mathematics

Faculty Advisor

Magel, Rhonda

Description

Soccer is considered the most popular sport on earth and applying statistical models to analyze small soccer data has been of a keen interest to modern researchers. Statistical modeling of soccer data also provides guidance and assistance to stakeholders. The goal of this paper is to establish a consistent statistical approach to help in the prediction of future World Cup championships. Ordinary least squares regression is used to develop models which predict goal margin of games and logistic regression is used to develop models which estimate the probability of a team winning the game. Discriminant Analysis was also used to determine which variables significantly influence individual game wins. The Fisher classification procedure allows for interpretability while providing a robust approach to classifying the 32 contestants of the 2014 World Cup using the previous data from 2006 and 2010 World Cup Championships.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/25808

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