Understanding occasion sequences is an important facet of game analytics, since it's related to many player modeling questions. This paper introduces a way for analyzing event sequences by detecting contrasting motifs; the goal is to discover subsequences which are significantly extra related to one set of sequences vs. other sets. In comparison with current methods, our approach is scalable and capable of handling long event sequences. We utilized our proposed sequence mining method to investigate player conduct in Minecraft, a multiplayer on-line recreation that helps many types of player collaboration. As Top Minecraft Servers , it gives players with a large amount of flexibility in deciding how to complete tasks; this lack of purpose-orientation makes the issue of analyzing Minecraft occasion sequences extra difficult than occasion sequences from extra structured video games. Utilizing our approach, we have been in a position to find contrast motifs for many participant actions, despite variability in how completely different players accomplished the same duties. Furthermore, we explored how the extent of participant collaboration impacts the contrast motifs. Although this paper focuses on applications inside Minecraft, our instrument, which we have now made publicly obtainable along with our dataset, can be utilized on any set of sport event sequences.