ggViz: Accelerating Large-Scale Esports Game Analysis
This addresses the problem of inefficient game review for esports professionals, though it is incremental as it adapts existing sports analytics methods to esports.
The authors tackled the lack of sketch-based querying systems for esports analysis by developing ggViz, a visual analytics tool for CSGO that enables users to query large datasets using game state sketches, resulting in a system validated through expert interviews and case studies.
While esports organizations are increasingly adopting practices of conventional sports teams, such as dedicated analysts and data-driven decision-making, video-based game review is still the primary mode of game analysis. In conventional sports, advances in data collection have introduced systems that allow for sketch-based querying of game situations. 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 player tracking data for Counter-Strike: Global Offensive (CSGO) to develop ggViz, a visual analytics system that allows users to query a large esports data set through game state sketches to find similar game states. Users are guided to game states of interest using win probability charts and round icons, and can summarize collections of states through heatmaps. We motivate our design through interviews with esports experts to especially address the issue of game review. We demonstrate ggViz's utility through detailed case studies and expert interviews with coaches, managers, and analysts from professional esports teams.