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Developing a Model to Predict Match Outcomes in League of Legends

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League of Legends is a popular multiplayer online battle arena (MOBA) developed by Riot Games. Each team consists of 5 players who each control a single character (champion) which they select at the beginning of each game. In order to win the match, a team has to destroy the Nexus…

League of Legends is a popular multiplayer online battle arena (MOBA) developed by Riot Games. Each team consists of 5 players who each control a single character (champion) which they select at the beginning of each game. In order to win the match, a team has to destroy the Nexus (the central structure) in the opponent's base. League of Legends has grown rapidly since its release in 2009 and has over 70 million registered players. Several community websites have been created that track the performance of players and show detailed statistics for just about every aspect of the game. This project focuses on exploring the applicability of predictive analytics within League of Legends, by predicting the outcome of any given ranked match at the start of the game. It resulted in a model with accuracy of 58% using decision trees. An additional contribution of the project is a solution to a data collection anomaly that has biased previous studies.
ContributorsNeumann, Alexander (Author) / Clark, Joseph (Thesis director) / Simon, Alan (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
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The Making of ASU Biodesign Clinical Testing Laboratory (ABCTL): Information Technology

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As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the…

As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets<br/>identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL)<br/>among business, communications, management/training, law, and clinical analysis. The first<br/>chapter of this manuscript covers the background of clinical laboratory automation and details<br/>the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The<br/>second chapter discusses the usability and efficiency of key information technology systems of<br/>the ABCTL. The third chapter explains the role of quality control and data management within<br/>ABCTL’s use of information technology. The fourth chapter highlights the importance of data<br/>modeling and 10 best practices when responding to future public health emergencies.
ContributorsKandan, Mani (Co-author) / Leung, Michael (Co-author) / Woo, Sabrina (Co-author) / Knox, Garrett (Co-author) / Compton, Carolyn (Thesis director) / Dudley, Sean (Committee member) / Computer Science and Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05