Understanding event sequences is an important aspect of game analytics, since it is relevant to many player modeling questions. This paper introduces a method for analyzing event sequences by detecting contrasting motifs; the aim is to discover subsequences that are significantly more similar to one set of sequences vs. other sets. Compared to existing methods, our technique is scalable and capable of handling long event sequences. We applied our proposed sequence mining approach to analyze player behavior in Minecraft, a multiplayer online game that supports many forms of player collaboration. As a sandbox game, it provides players with a large amount of flexibility in deciding how to complete tasks; this lack of goal-orientation makes the problem of analyzing Minecraft event sequences more challenging than event sequences from more structured games. Using our approach, we were able to discover contrast motifs for many player actions, despite variability in how different players accomplished the same tasks. Furthermore, we explored how the level of player collaboration affects the contrast motifs. Although this paper focuses on applications within Minecraft, our tool, which we have made publicly available along with our dataset, can be used on any set of game event sequences.