Sports coaches today have access to a growing amount of information that describes the performance of their players. Methods such as data mining have become increasingly useful tools to deal with the analytical demands of these high volumes of data. In this paper, we present a sports data mining approach using a combination of sequential association rule mining and clustering to extract useful information from a database of more than 400 high level beach volleyball games gathered at FIVB events in the years from 2013 to 2016 for both men and women. We regard each rally as a sequence of transactions including the tactical behaviours of the players. Use cases of our approach are shown by its application on the aggregated data for both genders and by analyzing the sequential patterns of a single player. Results indicate that sequential rule mining in conjunction with clustering can be a useful tool to reveal interesting patterns in beach volleyball performance data.
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