Since Emile Borel's study in 1938, the game of poker has resurfaced every decade as a test bed for research in mathematics, economics, game theory, and now a variety of computer science subfields. Poker is an excellent domain for AI research because it is a game of imperfect information and a game where opponent modeling can yield virtually unlimited complexity. Recent strides in poker research have produced computer programs that can outplay most intermediate players, but there is still a significant gap between computer programs and human experts due to the lack of accurate, purposeful opponent models. We present a method for constructing models of strategic deficiency, that is, an opponent model with an inherent roadmap for exploitation. In our model, a player using this method is able to outperform even the best static player when playing against a wide variety of opponents.