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.

翻译：游戏审查对于球队、球员和体育媒体工作人员至关重要。游戏审查尽管很重要,但审查是工作密集型的,而且很难推广。最近体育数据收集的进展引入了将视频与组合技术相结合的系统,以便用户通过草图查询感兴趣的体育情况。然而,由于数据有限,体育本身也存在差异,Esport公司发现缺少这种系统。在这份文件中,我们利用新兴数据来开发ggViz:全球进攻性(CSGO),这是一种新型视觉分析系统,让用户通过绘制感兴趣的情况来查询大型Eports数据集进行类似游戏。我们与ggViz公司一道,还提出了一种可以轻易推广到数亿个游戏情况的性能检索算法。我们通过详细的案例研究和与专业体育团队的工作人员的访谈来展示ggViz的实用性。