Author Archive: Mike Podhorzer

It’s xBABIP week and on Monday, I unveiled the latest incarnation of my equation, this time incorporating shift data. Then yesterday, I analyzed leaguewide shift data trends and unearthed some interesting tidbits. Today, it’s finally time to talk some names. We’ll begin by looking at the players that enjoyed the biggest gains using the new xBABIP equation versus Alex Chamberlain’s 2015 version that I had been using as my primary go-to, and also the biggest losers.

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Yesterday, I unveiled the newest incarnation of the xBABIP equation, this time tacking on a shift-related component. The defensive shift has been all the rage these past couple of years as teams are utilizing data more and more for any incremental advantage they could find. Finally with the Splits Leaderboard, we have all the data at our disposal to dive into. Let’s jump in, shall we?

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For years now, we have attempted to better understand the seems-impossible-to-predict metric we all know and love, batting average on balls in play, or BABIP for us nerds who like acronyms. As far back as 2008, we have tried, tried, and tried again to come up with an xBABIP equation strong enough to play with the other xMetrics. Two years ago, I developed what was at the time, the best we had. Then, we were given a gift in the form of data collected by Baseball Info Solutions, some of which Alex Chamberlain used to improve my equation and make it easier to calculate. Then Andrew Perpetua developed a Statcast driven xBABIP. Finally, Alex updated his equation by tacking on a seasonal constant.

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On Monday, I introduced my new Statcast fueled batter xHR/FB rate, adjusted for home park, and then yesterday, I shared a list of six batters with significant HR/FB rate upside for 2017, given the gap between their 2016 xHR/FB rate and actual HR/FB rate. Today I’ll discuss 10 hitters with major downside.

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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.

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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.

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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.

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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.

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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.

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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.

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