But linear weights does exactly what you are talking about. It essentially looks at play-by-play data and figures out what every event's effecton runs scored is, based on different situations. It takes the weighted average of the effects of an event, based on the 24 base/out states (no on, 1 out, runner 1st, no out, etc...) and uses an average value of each event: LINK Comparing the relative negative value of a K and an out, you can see it varies depending on a sitation. For instance, with 2 out they are equal for obvious reasons. With a runner at third and less than 2 out, it worse to K than any other out. With a runner at 1st it's actually BETTER to K, since it's the ground out dp isn't possible then. ANYWAY, taking a weighted average of all those events based on what situations occur most often (no one on and no outs is weighted the highest, for instance) what are the values of each? A K is worth -.31 outs, every other out is worth -.299. Hardly a difference at all. In conclusion, while Branyan's K's especially hurt with runners on and less than 2 out, his K's are hardly worse in an average situation.