Evaluating Predictors for the 2022 World Cup Using Decision Trees and Random Forests

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Authors
Morgen, Nicole
Advisor
Fasteen, Jodi
Cline, Kelly
Morris, Jeffrey
Editor
Date of Issue
2024
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Publisher
Citation
Series/Report No.
item.page.identifier
Title
Evaluating Predictors for the 2022 World Cup Using Decision Trees and Random Forests
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Type
Thesis
Description
Abstract
It is the goal of this project to accurately predict the results of the 2022 Qatar World Cup and develop a sophisticated model that can be applied to future World Cups. The FIFA 2022 World Cup was home to exciting upsets, devastating losses and unexpected results. FIFA rankings and past performance statistics were insufficient predictors for results, advancements and performances in the World Cup. In this project, machine learning algorithms and predictors beyond rankings will be used to predict the results of the Qatar 2022 World Cup. The FIFA ranking prediction method will serve as a baseline for accuracy. The two machine learning algorithms that will be considered for this project are decision trees and random forests, the latter of which can determine the validity of various parameters. The random forest algorithm created a more effective model for predicting the results of World Cup matches, exceeding the FIFA baseline accuracy by nearly 20% on individual games and by 6% in terms of team advancements. Some of the better predictors for World Cup matches were win proportions, FIFA points, the standard deviation in the age of players on the team and the average number of goals scored per game. While the random forest algorithm was a better predictor of games than both the FIFA baseline and decision tree models, it did not entirely accurately forecast the knockout bracket of the 2022 Qatar World Cup.
Sponsors
Degree Awarded
Bachelor's
Semester
Spring
Department
Mathematics