The chart below is filtered for starters only. Not surprisingly, all of the pitchers here are relievers, given that their samples are smaller. These are pitchers who may have been lucky or unlucky in turning their whiffs into strikeouts, or are possibly the grouping of pitchers who manage to change their K% around a particular whiff ability. Taking this one step further and using our (admittedly rough) xK%, we can identify some outliers. You can see the graph below for actual K% and Whiff/Swing% with the trend line that roughly denotes our "expected K%" or xK%. Using the formula our regression spits out for using Whiff/Swing to predict K%, we can develop an "Expected K%" in very rough terms that is K%=.007502+(.85006*Whiff%). 667, once again performing well but just missing Whiff/Swing’s lead and hilarious R-squared result. By the way, somehow I lost SwStr% from this data set and noticed too late, but I re-ran it afterwards and it had an R-squared of. Here we can predict even more of the variance in strikeout rate over the longer-term with our factors, and Whiff/Swing is even more significant, explaining 69% (haha, 69) of the variance. When I looked at all of the years from 2007-2012, the same story holds. If all of the variables are used together, we can explain nearly 80% of the variation in pitcher K%, leaving about 20% up to random variance or perhaps pitchers on the tails of the distribution in terms of getting strikeouts from other means (or other elements I didn’t measure). The chart below shows the results for the regression comparing K% to each indicator. It doesn’t sit right with me how low the R-squareds are for these other statistics, so perhaps I erred somewhere, but it’s possible that pitchers can manage strikeouts regardless of their repertoire or overall locating abilities, so long as they have swing-and-miss stuff. Resultsįor the single year regression for 2012 pitchers, Whiff/Swing performed the strongest of any of the indicators that I looked at. Matt’s analysis focused more on predicting next-year strikeout rates (his findings were that once a baseline K% is established, SwStr% doesn't tell you too much else), but my aim was simply to explain the anatomy of strikeouts (that is, this is descriptive, not predictive, for now). I’m a bit rusty on my regression analysis, but this Baseball Prospectus piece from Matt Swartz from a few seasons back seems to confirm my findings that swinging strike rates are highly correlated with strikeouts. I understand that for a strikeout analysis, I perhaps should have included all pitchers, and I can do that in the future if there is a compelling case for it, but for now those were the cut off points I used. Later, when I discuss the data for 2007-2012 (this is as far back as Dan Brooks’ excellent PitchFX work goes), I used 200 innings pitched as the cut-off. Using FanGraphs’ custom leaderboards and Baseball Prospectus' Whiff/Swing, I ran regressions for 2012 for K% against percentage of fastballs thrown (FA%), percentage of sliders thrown (SL%), average fastball velocity (vFA), overall strike rate (Strike%), overall swing rate (Swing%), first strike rate (F-Strike%), Horizontal and Vertical pitch movement (H Mov and V Mov, respectively), walk rate (BB%) and finally, SwStr% and Whiff/Swing.įor 2012, I used 40 innings pitched as the cut-off (or approximately 500 pitches). Since Whiff/Swing is new (well, the leaderboards are) and performed slightly better than SwStr% at explaining K% variation, when I refer to whiffs in this article, I’ll be referring to BP’s version.įor clarification, SwStr% is the percentage of total pitches a batter swings at and misses, while Whiff/Swing is the percentage of total swings a batter misses on. It seems that no statistic, sabermetric or otherwise, can effectively explain variance in K% (the percentage of plate appearances that end in a strikeout)…except for Swinging Strike % (from Fangraphs) and Whiff/Swing (from Baseball Prospectus). Still, when I ran the numbers for 2012 and the period from 2007-2012, I was stunned at just how extreme the results were. Of course, this isn’t exactly brain wrinkling, since your only ways to finish a strikeout are with a swinging or a looking strike, and the only ways to get strikes are swings, fouls, or looks. There may be many ways to skin a cat, train a fly, kill a man, etc, but it appears that there is only one solid and reliable way to strike batters out – to make them whiff on pitches.
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