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

TL;DR

This paper introduces a deep learning-based counterfactual simulation method to estimate the impact of different batting strategies in baseball, accounting for game situations and strategy switching costs.

Contribution

It presents a novel approach combining deep learning and counterfactual simulation to evaluate batting strategies, addressing limitations of traditional analysis.

Findings

Different strategies can increase runs when switching costs are ignored.

Strategy effectiveness depends on switching costs, limiting potential gains.

Simulation clarifies the impact of multiple batting strategies.

Abstract

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.