UC Irvine

The growing prominence of esports and competitive gaming has not only attracted a global audience but captured the curiosity of academic researchers. With increased research interest, the necessity for tools to collect contextual game data has subsequently risen significantly. Currently, a combination of generalized software solutions such as keyloggers, screen recorders, and audio recorders can be used to gather artifacts regarding an individual player. However, these solutions are inadequate when used to support research questions concerning player interactions in a multiplayer context. To address this problem, I introduce CapturEsports, a software tool to facilitate research in esports by supporting the capture and organization of multiple streams of player data. Focusing on League of Legends developed by Riot Games as a proof of concept, CapturEsports enables researchers to capture screen video recordings, voice communication, keystrokes, and in-game contextual events (such as player kills, buildings destroyed, and item purchases) for multiple players simultaneously, thereby facilitating analysis of team behavior. CapturEsports performs data collection and provides access to the data remotely, allowing studies to be conducted in a distributed manner. In this thesis, I present the motivation behind CapturEsports, an examination of alternative software tools, and a detailed description of the tool I developed. I also discuss the primary design decisions and underlying requirements, the overall architecture and organization of data structures, and a preliminary evaluation and analysis of CapturEsports as an esports research application.