Why? Because a strikeout rate of 10% is 2X worse than 5% and 2X better than 20%. Thus the unit between 5% to 10% should be the same as 10% to 20%. I suspect that may eliminate the heterodescosity.

The bigger problem, however, may be the variance in this correlation. A high R^2 indicates a linear relationship. But if the variance is high, the predictability is more problematic, which is why the precision is lacking. By eye, I’m estimating a 1/2 log unit (10X) spread on the data. In other words, with a 10% swinging strikeout rate, the K-rate appears to show a 95% Confidence Interval from 12% to 24%. a 12% K-rate is a whole lot different from a 24% K-rate.

]]>Also your wife tolerates this? Does she have a sister…

]]>Statcorner.com has the ClStk% (called strike percentage) stat.

]]>Just a thought, anyways.

]]>I have a feeling that the type of pitch getting the swinging strikes will be a significant variable, with the change-up leading to the higher variance.

]]>Also, the philosophy of featuring a change-up as a strikeout pitch could have something to do with it. Obviously Liriano’s best pitch is his slider, but I believe (I haven’t looked this up) he threw more changeups this year.

]]>