Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball
This work addresses a specific challenge for baseball coaches and analysts by providing a method to estimate strategy effects, though it is incremental in applying simulation techniques to this domain.
The paper tackled the problem of evaluating the effectiveness of different hitting strategies in baseball, which is difficult due to unobservable batter strategies and complex game situations, and found that using multiple strategies can increase runs when switching costs are ignored, but benefits are limited when costs are considered.
In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and 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 estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. To this end, we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs were limited. Our validation results suggest that our simulation could clarify the effect of using multiple batting strategies.