Yesterday, I unveiled the best pitcher expected walk percentage equation yet. By simply looking at the percentage of total pitches that are thrown for a strike and the rate at which the strikes thrown are put into play, we can get a pretty good idea of what a pitcher’s walk percentage should be. I was literally in the middle of typing up today’s post putting the equation to work on 2013 data to get an idea of which pitchers should have a higher or lower BB% when another light bulb went off.
Felix Hernandez had appeared on the list of pitchers with a higher xBB% than BB%. In checking out his Str% and I/Str trends on Baseball Reference, the explanation was that his I/Str was at a career low which was increasing his xBB%. Obviously, if a batter fails to put a strike in play, the at-bat continues and the opportunity for a base on balls still exists. However, a low I/Str also illustrates a pitcher’s dominance. If a batter is unable to put a pitcher’s strikes into play, you would assume this pitcher has a high strikeout percentage. In Felix Hernandez’s case, his strikeout percentage sits at a career high. No wonder his I/Str is at a career low, batters are striking out rather than putting the ball in play!
So now I’m thinking that a pitcher’s strikeout percentage must have some impact on his walk rate. Back I went to my data set, bringing in K% and adding that variable to the other two already part of the initial equation. The resulting R-squared improved by a meaningful amount, and better yet was the effect on Felix Hernandez’s xBB%. But first, let’s check out the regression graph.
xBB% = 0.7598 + (-0.7300 * Str%) + (-0.5729 * I/Str) + (-0.2341 * K%)
While it’s usually not a good idea to add another variable for the heck of it, I think the addition of strikeout percentage is necessary. Under the old equation, Hernandez’s xBB% was 8.1%, while under the new one, it is just 7.2%. That’s a huge difference and I feel much more comfortable with the latter. That would be right in line with his career walk percentage. Also important to note is that his Str% is actually lower than his previous three seasons, which is the likely explanation behind his expected walk percentage not being even better.
If you recall, the R-squared from yesterday’s equation was 0.73, so this update provides a small, but meaningful gain. As usual, it still appears that there is work to be done. But I am happier with this equation than yesterday’s, so next week I could get back to the task I initially started and use the equation to look at 2013 data.
Mike Podhorzer is the 2015 Fantasy Sports Writers Association Baseball Writer of the Year. He produces player projections using his own forecasting system and is the author of the eBook Projecting X 2.0: How to Forecast Baseball Player Performance, which teaches you how to project players yourself. His projections helped him win the inaugural 2013 Tout Wars mixed draft league. Follow Mike on Twitter @MikePodhorzer and contact him via email.