Baseball is back. On February 14, pitchers and catchers reported to Spring Training, and the reports of players being in the Best Shape Of Their Life have started pouring in. Absent from these festivities: the reigning National League Cy Young winner, who as of this writing is still unemployed. Part of that is certainly teams biding their time to maximize their leverage, but part of it may be that Blake Snell is one of the more difficult pitchers in baseball to project. Leaving aside his health record for a second, there’s some evidence of a weak foundation underlying his success. Others have written extensively about the concerns behind Snell’s league-leading 2.25 ERA last season, and they center around his propensity for giving hitters a free base. Consider how our Pitch Quality metrics, which value a pitcher based solely on the characteristics of his pitches and not on results, rank him among qualified starters: While his Stuff Quality ranks 13th among qualified starters, his Overall Pitch Quality—which takes into account both Stuff and Locations—ranks 36th. Put simply, he’s not hitting good enough locations to maximize the value of his stuff.
Where analysts begin to disagree is in what’s driving such seemingly poor location of his pitches. While many attribute his walks to poor command, others have argued they are by design: a worthwhile by-product of targeting the edges of the zone and being unwilling to give in and risk hard contact. The truth is likely some combination of both, but it’s difficult to disentangle the two without knowing where Blake was targeting with each pitch. Luckily for us, a presentation at last year’s Saber Seminar by Scott Powers and Vicente Iglesias offered a roadmap for doing just that. They used what’s known as a Hierarchical Model to estimate the average and standard deviation of a pitcher’s pitch locations and then simulate new locations using these model estimates. Our methodology differs slightly from theirs, and though it may sound complicated, the general approach is intuitive and easy to follow.
We start by modeling the expected horizontal and vertical location of each pitch based on the type of pitch it was, the count in which it was thrown, the handedness of the batter and pitcher, and the identity of the batter. Next, we adjust these expected locations based on the general tendencies of the pitcher.[1]
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