ggViz: Accelerating Large-Scale Esports Game Analysis

Peter Xenopoulos, João Rulff, Claudio Silva

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number238
JournalProceedings of the ACM on Human-Computer Interaction
StatePublished - Oct 29 2022


  • esports
  • game analytics
  • sports play retrieval

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Human-Computer Interaction
  • Computer Networks and Communications


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