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In baseball, every play on the field is quantitatively evaluated and has aneffect on individual and team strategies. The weighted on base average (wOBA)is well known as a measure of an batter's hitting contribution. However, thismeasure ignores the game situation, such as the runners on base, which coachesand batters are known to consider when employing multiple hitting strategies,yet, the effectiveness of these strategies is unknown. This is probably because(1) we cannot obtain the batter's strategy and (2) it is difficult to estimatethe effect of the strategies. Here, we propose a new method for estimating theeffect using counterfactual batting simulation. To this end, we propose a deeplearning model that transforms batting ability when batting strategy ischanged. This method can estimate the effects of various strategies, which hasbeen traditionally difficult with actual game data. We found that, when theswitching cost of batting strategies can be ignored, the use of differentstrategies increased runs. When the switching cost is considered, theconditions for increasing runs were limited. Our validation results suggestthat our simulation could clarify the effect of using multiple battingstrategies.

Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball