![]() (Fast pointed me to this video - it’s a great primer on approach angle for the uninitiated.) Now is as good a time as any to (re-)introduce the topic of approach angle, which Ethan Moore, momentarily of Prospects365 but now freshly and deservedly of the Twins’ R&D department, discusses wonderfully here. This, at least for me, inspires a question: why? Why does this happen? It’s not enough (again, at least for me) to accept that pitchers are trained to throw (and/or that hitters are trained to expect) fastball high, non-fastballs low, or that it’s “just the way it is.” Maybe it’s what is done typically - it’s an inarguable fact, observable in hundreds of thousands of lines of Statcast data - but it doesn’t explain how or why it came to be. At the top edge of the zone, fastballs generate not only as many (or more) whiffs as non-fastballs but also continue to show efficacy way, way above the zone where other pitches no longer do. Allow me to draw your attention back to the top and bottom of the zone, circled in purple:Īt the bottom edge of the zone, fastballs generate about half as many whiffs per swing as non-fastballs. ![]() I also know its answer is not so simple, either, because the curves are not quite shaped exactly the same. ![]() But, on average, is it possible that pitch height is the great differentiator and, therefore, also the great equalizer? It’s not as ridiculous a question as it may seem. There are so many other factors to pitch effectiveness (and effective pitching, which aren’t the same!). Unsurprisingly, on average, in most areas of the zone, fastballs generate whiffs nearly or just as effectively as non-fastballs. If I were a true clickbait expert, I might say “these results will shock you.” (Also: singles in your area.) Here are whiffs per swing (I’ll call this Whiff%) plotted against pitch height for each pitch class: A quick Statcast query can tell me that, for example, sliders generated swinging strikes nearly 7 percentage points more often than four-seamers. I’ll be frank: the question seemed like a waste of time. How does each pitch class fare on swings alone? Isolating swings would measure the true swinging-strike efficacy of each pitch class by not giving or taking away credit for swing frequency. Generally, each pitch class has a distinct swing frequency profile and, thus, a distinct swinging strike profile. The logical next step was to pair the two graphs together. At its bottom edge, hitters are half as likely to swing at fastballs as they are at non-fastballs at its top edge, twice as likely. The next graph all but affirmed my intuition:Īlthough the peaks of the bell curves cluster near the heart of the zone, we can see distinct differences in swing rate by pitch class at the thresholds of the strike zone. Similarly (but inversely), non-fastballs would generate more swinging strikes down low instead of up high. As such, I anticipated that fastballs probably induce more swinging strikes up high than down low simply because hitters swing more frequently at high fastballs. Regardless of efficacy, more swings will afford more chances for swinging strikes. But as I considered the matter further, the importance of swing frequency (Swing%) to SwStr% became clear (both use all pitches as a denominator). ![]() This finding is mildly interesting in and of itself. If we zoom out and consider the question at the macro level, independent of context (what’s the average swinging strike rate for all fastballs by pitch height?), we can see that fastballs generate more swinging strikes up in the zone, a phenomenon our own Jeff Zimmerman touched upon here. ![]()
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