On Monday, I introduced my new Statcast fueled batter xHR/FB rate, adjusted for home park. Let’s finally get to the part you have been eagerly awaiting — the potential 2017 home run surgers. These are the guys whose xHR/FB rates far exceeded their actual HR/FB rates.
Yesterday, I introduced you to the new Statcast fueled xHR/FB rate equation I developed during my recent xMetric frenzy. It’s simple to use, requiring just a couple of variables, and its components are easily accessible. So now armed with all the data that led to the equation, let’s dive in, explore, and have a little fun.
Nearly three years ago, I developed and published the original batter xHR/FB rate equation. While I used it during the season to analyze players, it was unfortunately behind the FG+ pay wall and shrouded in mystery. Then almost exactly two years ago, I unmasked the equation and shared it with the entire world. The equation used three components compiled by Jeff Zimmerman and did a fairly solid job of estimating what a hitter’s HR/FB rate should have been (adjusted R-squared of 0.649). Sadly, the data fueling the equation is no longer available, so naturally I decided to create a new equation. A Statcast charged one.
Yesterday, I share an updated version of Alex Chamberlain’s pitcher xBB% equation and used it to identify the fantasy relevant pitchers whose walk rates should improve this season. Today, I’ll check in on the other side of the coin, those starting pitchers whose xBB% was well above their actual BB% in 2016. This group will find it challenging to fend off the regression monster this year without throwing more strikes.
About three and a half years ago, I shared the bestest starting pitcher xBB% formula yet. Since I mentioned to you recently that I have been on an xEquation binge, I updated that bestest xBB% one too, of course. But as I was working on it with an additional variable, I realized that Alex Chamberlain had literally done the exact same thing about two years ago. That same thing was adding the 3-0% metric from Baseball-Reference.com, which is the percentage of plate appearances in which a 3-0 count is seen. So rather than take credit for developing a better version of my original xBB% metric, I’m now simply updating the coefficients of Alex’s equation.
Last week, I reintroduced my xK% equation, this time, with updated coefficients. The equation’s components were the exact same, so there was nothing new or exciting to report. However, on the following day, I published the top 10 over/ underperformers from 2011 to 2016 as a fun little exercise to learn who has broken the model. I then attempted to figure out if there was a common theme among the over/underperformers and after performing some additional research and calculations, settled on a possible explanation — changeups are bad for strikeouts. Turns out, I was actually onto something and that something was already discovered two years ago.
Let’s finish up new xK% equation week with a look at which starting pitchers gained and lost the most with the new coefficients versus the old ones. Though all of the coefficients increased, while the intercept is now a higher negative number, the L/Str and F/Str coefficients increased more dramatically than those for Str% and S/Str. So, one would imagine that a pitcher relying more on looking and foul strikes, as opposed to swinging, would get a relative boost using the new equation.
Yesterday I continued new xK% equation week by discussing the 10 pitchers that have overperformed and underperformed the metric the most since 2011. While I calculated the group averages, pulled in fastball velocity, and most frequently used secondary pitch, the sample size was far too tiny to conclude anything. So at the request of commenter JUICEMANE, I have decided to do a larger study in an attempt to explain why some pitchers consistently over- or underperform the xK% equation. Do the players within each group on either side have anything in common with their groupmates?
Yesterday I rolled out an updated version of my pitcher xK% equation, which estimates what a pitcher’s strikeout rate “should be” given various strike and strike type metrics found at Baseball-Reference.com. With my data set, I put together a table calculating historical averages during the time period (2011-2016) I compiled data for. I’ll share the top 10 pitchers that have outperformed and underperformed their xK% (so if a pitcher outperformed by 2% in 2011 and 3% in 2012, I’ll be looking at the total of 5%, rather than the average of 2.5%), and we’ll try to figure out what, if anything, the pitchers in each group have in common with each other. So let the fun begin!
The best equation I have ever developed from an adjusted R-squared perspective is pitcher xK%. That equation yielded a 0.913 R-squared, so it didn’t exactly need any tweaking. However, I was on an xEquation rush recently and decided to develop new ones, as well as update old ones. So we’ll begin with xK% and dedicate my week’s posts to looking at the new version of the metric.
