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The Video game-Crowdsourcing model to recollect data motivates people to participate by entertaining them. Research showed that the solutions players make in this model are competitive against experts in the area. Yet, the studies in the area focus on mimicking people's behavior, including their mistakes. Therefore, we use a Video game-Crowdsourcing to model a problem of interest to find strategies for it. To describe matches from the video game we created, we designed a representation that simplifies the discovery of strategies. Our experimentation compares high score matches against low score ones to find the best behaviors. We used 13 matches played by us to validate the methodology with a known strategy for the problem. Then, we applied it with matches from players. The results suggest that extracting subsequences is a process to find strategies and that we can use them to design algorithms to improve current algorithmic solutions for that problem.