Last Thursday, I used my new xBABIP equation to identify 10 fantasy relevant hitters whose xBABIP marks were significantly above their actual BABIP marks, suggesting serious BABIP upside in 2017. Today, I’ll make many of you sad with a list of names who are at risk of major BABIP regression this season, if they don’t improve their underlying skills by a massive degree. By no means do you want to avoid these names, you just simply don’t want to value them assuming their 2016 BABIP marks are actually sustainable. But since someone in your league probably does believe the 2016 BABIP is real, you probably won’t end up rostering them at a fair price.

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A week ago, I introduced the newest version of our ever evolving xBABIP equation, this time incorporating the much-needed shift data. Last Thursday, I identified the 10 fantasy relevant hitters with the greatest BABIP upside in 2017, given the gap between their 2016 BABIP and xBABIP. In the comments, I was asked if I could perform a retrospective analysis to see how the new equation would have done if I ran it heading into the 2016 season.

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Finally, after unmasking the newest version of xBABIP that accounts for shifts, it’s time to get to the names…you know, the kind of list you could actually use for your fantasy leagues this year! So let’s identify and discuss the fantasy relevant hitters whose xBABIP marks were significantly above their actual BABIP marks. These ten hitters should enjoy a BABIP rebound in 2017, assuming their BABIP-related skills remain stable.

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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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