Authors

Andrea Sibley

Document Type

Article

Publication Date

Spring 2019

Abstract

OverwatchTM is a video game published by Blizzard Entertainment R where two teams comprised of six people each compete against one another to accomplish a specific goal. The goal of each game is dependent on which map is being played. The maps are divided into four categories: Assault, Escort, Control, and Hybrid. A data set comprised of 3000 games of competitive OverwatchTM is used to determine how likely a team is to win their match. The factors used to determine the likelihood of winning are the map type and the skill ranking for each team. The data set is pre-processed by standardizing and encoding the data through Python. After the data is encoded, 80% of the data is divided into a training set and 20% of the data is divided into a testing set. Classification algorithms are tested against the data to determine which classifying method returns the highest accuracy. After using the training set, the Bagging Classifier shows the highest accuracy when compared to the testing set.

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