@article{oai:kyutech.repo.nii.ac.jp:00006086,
 author = {Kira,  Akifumi and Inakawa,  Keisuke and Fujita, Toshiharu and 藤田, 敏治},
 issue = {2},
 journal = {日本オペレーションズ・リサーチ学会論文誌},
 month = {Apr},
 note = {In this paper, baseball is formulated as a finite non-zero-sum Markov game with approximately 6.45 million states. We give an effective dynamic programming algorithm which computes equilibrium strategies and the equilibrium winning percentages for both teams in less than 2 second per game. Optimal decision making can be found depending on the situation—for example, for the batting team, whether batting for a hit, stealing a base or sacrifice bunting will maximize their win percentage, or for the fielding team, whether to pitch to or intentionally walk a batter, yields optimal results. Based on this model, we discuss whether the last-batting team has an advantage. In addition, we compute the optimal batting order, in consideration of the decision making in a game.},
 pages = {64--82},
 title = {A Dynamic Programming Algorithm for Optimizing Baseball Strategies},
 volume = {62},
 year = {2019},
 yomi = {フジタ, トシハル}
}