In two-strike counts, it sits at 15 percent. In non-two-strike counts it drops down to 10.7 percent. The league-average swinging-strike percentage comes in at 12 percent. Getting swinging strikes late in a count (lets say two-strikes) is more important than getting strikes early in a count (non-two-strikes). What causes pitchers to over/under perform their strikeout numbers? The first thing I thought of was an inefficient distribution of swinging strikes. This bring us to the hypothesizing stage. 96), leaving a small amount of outliers that include: Cole (+3.1 percent), Frankie Montas (+2.8 percent), Derek Holland (-3.9 percent), and Homer Bailey (-3.5 percent). Using pitchers from this season as the sample, the correlation between actual strikeout percentage and Podhorzer’s expected strikeout percentage are pretty fantastic (r =. All the strike/strike type components also have high correlations, with swinging strike rate being the most stable skill.” “So in my data set, the YoY correlation of xK% was slightly higher than K%, both of which are pretty high. Using metrics such as strike percentage, looking strike percentage, swinging strike percentage, and foul strike percentage, he was able to develop an expected strikeout percentage number that correlates to actual strikeout percentage better season-to-season better than actual strikeout percentage. Luckily, there has been past research to try and improve the predictors of strikeout percentage, such as this piece from Mike Podhorzer over at RotoGraphs. There are other factors that go into a strikeout outside of swinging-strikes. While this analysis is useful, there are multiple caveats to it. As for the top under-performers, there’s Sandy Alcantara (-3.2 percent), Brett Anderson (-2.1 percent), Zack Godley (-1.8 percent), Nick Margevicius (-1.6 percent), and Griffin Canning (-1.6 percent). Among the top over-performers are Chris Sale (+10.4 percent), Brad Peacock (+10.0 percent), Gerrit Cole (+9.7 percent), Rich Hill (+9.7 percent), and Brandon Woodruff (+9.4 percent). The formula for our “expected” strikeout percentage ended up being swinging-strike percentage multiplied by a coefficient of 1.77.įinding the differentials between the expected strikeout percentage and actual strikeout percentage, we have the outliers. In simpler terms, turning swinging-strike percentage numbers into strikeout percentage numbers. The strong correlation between swinging-strike percentage and strikeout percentage gave the opportunity of performing linear regression analysis. But that brings us the second question that almost always comes up when examining correlation levels: who are the outliers? 81), giving us the obvious answer of yes, more swinging strikes results in more strikeouts. Examining an extended sample from this season tells us that the correlation is pretty strong (r =. This piece started with a questioning of the correlation between swings and misses (SwStr%) and strikeouts (K%). By conventional wisdom, this means that the more swings and misses a pitcher gets, the more strikeouts they’ll get. Generally, reduced to a single pitch, these things can factor into a pitcher generating a swing-and-miss. Command, control, velocity, movement, deception, entropy. For a pitcher, there are so many factors that go into getting strikeouts.
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