We demonstrate the use of playtraces and playtrace clustering to identify strategies and card synergies in deck building card games, using Dominion as an example. We analyse two different types of playtraces generated from both online human play and a variety of AI agents. Firstly we examine playtraces defined by tracking card counts by round in a player's deck, and secondly utilising N-Grams generated from player actions. We investigate playtrace clustering using both the L knorm and Jensen-Shannon distance measures, in-conjunction with K-Means, K-Medoids and DBSCAN algorithms. We show that distinct playtrace clusters can arise from different strategies, and that playtraces and cluster centroids provide a means to identify both longer term strategies and also short-term tactics involving particular card combinations. Additionally, we use a restricted play framework to increase the variation in strategies and tactics explored by the AI agent. We conclude by highlighting how the game agnostic N-Gram based approach might be used to explore strategy spaces in tabletop games more broadly.