Understanding event sequences is a vital side of game analytics, since it is related to many participant modeling questions. This paper introduces a method for analyzing event sequences by detecting contrasting motifs; the intention is to find subsequences which might be significantly extra similar to at least one set of sequences vs. other sets. Compared to current methods, our technique is scalable and capable of dealing with long event sequences. We utilized our proposed sequence mining method to research player behavior in Minecraft, a multiplayer on-line game that helps many forms of player collaboration. As a sandbox sport, it gives gamers with a large amount of flexibility in deciding how to complete duties; this lack of objective-orientation makes the problem of analyzing Minecraft occasion sequences extra difficult than occasion sequences from extra structured games. Utilizing our strategy, we had been in a position to discover distinction motifs for many participant actions, despite variability in how different players completed the identical tasks. Moreover, PROXIMAL'S BLOG explored how the level of participant collaboration affects the contrast motifs. Although this paper focuses on purposes inside Minecraft, our device, which we've got made publicly out there along with our dataset, can be utilized on any set of recreation event sequences.