A hierarchical Bayesian model of pitch framing

Since the advent of high-resolution pitch tracking data (PITCHf/x), many in the sabermetrics community have attempted to quantify a Major League Baseball catcher’s ability to “frame” a pitch (i.e. increase the chance that a pitch is a called as a strike). Especially in the last 3 years, there has been an explosion of interest in the “art of pitch framing” in the popular press as well as signs that teams are considering framing when making roster decisions. We introduce a Bayesian hierarchical model to estimate each umpire’s probability of calling a strike, adjusting for the pitch participants, pitch location, and contextual information like the count. Using our model, we can estimate each catcher’s effect on an umpire’s chance of calling a strike. We are then able translate these estimated effects into average runs saved across a season. We also introduce a new metric, analogous to Jensen, Shirley, and Wyner’s Spatially Aggregate Fielding Evaluation metric, which provides a more honest assessment of the impact of framing.