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This paper describes a fast hand strength estimation mod-el for the game of Gin Rummy. The algorithm is computationally inexpensive, and it incorporates not only cards in the player’s hand but also cards known to be in the opponent’s hand, cards in the discard pile, and the current game stage. This algorithm is used in conjunction with counterfactual regret (CFR) minimization to develop a gin rummy bot. CFR strategies were developed for the knocking strategies. The hand strength estimation algorithm was used to select a discard that balances the goals of maximizing the utility of the player’s hand and minimizing the likelihood that a card will be useful to the opponent. A study of the parameterization of this estimation algorithm demonstrates the soundness of approach as well as good performance under a wide range of parameter values.