  This manuscript uses machine learning techniques to exploit baseball
pitchers' decision making, so-called "Baseball IQ," by modeling the at-bat
information, pitch selection and counts, as a Markov Decision Process (MDP).
Each state of the MDP models the pitcher's current pitch selection in a
Markovian fashion, conditional on the information immediately prior to making
the current pitch. This includes the count prior to the previous pitch, his
ensuing pitch selection, the batter's ensuing action and the result of the
pitch.
