Estimating the effect of plate discipline using a causal inference framework: an application of the G-computation algorithm

Offensive performance in baseball depends on a number of correlated factors: the pitches the batter faces, the batter’s choice to swing, and the batter’s hitting ability. Recently a renewed focus on the effect of plate discipline on batter performance has emerged. Plate discipline has traditionally been summarized as the proportion of pitches inside and outside of the strike zone a player swings at; however, there have been few metrics proposed to assess the effect of plate discipline directly on batters’ outcomes. In this paper, we focus on estimating a batter’s performance if he were able to adopt a different plate discipline. Because we wish to assess the effect of a counterfactual plate discipline, we use a potential outcome framework and show how the G-computation algorithm can be used to isolate the effect of plate discipline separately from a batter’s hitting ability or the types of pitches the batter faces. As an example, we implement our approach using data collected with the PITCHf/x system over the 2012–2014 seasons to identify the improvement Starlin Castro would expect to see in offensive performance were he able to adopt Andrew McCutchen’s plate discipline. We estimate that had Castro adopted McCutchen’s discipline his batting average, on-base percentage, and slugging percentage would have increased 0.017 (se = 0.004), 0.040 (se = 0.006), and 0.028 (se = 0.008), respectively.