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Abstract:
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Bunting is down in Major League Baseball recently. This decline is generally attributed to simply looking at the change in expected runs for typical bunting scenarios. However, run expectancy varies greatly from batter to batter and across various potential bunting scenarios. We aim to better understand the heterogeneous treatment effect of bunting. Heterogeneity comes with respect to different game scenarios (runners on which bases, number of outs, inning, score difference etc) and types of hitters (OPS, speed, bunting ability, etc). We estimate the effect of bunting among those who bunted using Bayesian Additive Regression Trees (BART) as well as propensity score methods (matching and inverse weighting). We show that there are certain scenarios where bunting is advantageous even if the overall change in run expectancy is negative.
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