GgViz: Accelerating Large-Scale Esports Game Analysis
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Game review is crucial for teams, players and media staff in sports. Despite its importance, game review is work-intensive and hard to scale. Recent advances in sports data collection have introduced systems that couple video with clustering techniques to allow for users to query sports situations of interest through sketching. However, due to data limitations, as well as differences in the sport itself, esports has seen a dearth of such systems. In this paper, we leverage emerging data for Counter-Strike: Global Offensive (CSGO) to develop ggViz, a novel visual analytics system that allows users to query a large esports data set for similar plays by drawing situations of interest. Along with ggViz, we also present a performant retrieval algorithm that can easily scale to hundreds of millions of game situations. We demonstrate ggViz's utility through detailed cases studies and interviews with staff from professional esports teams.
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