Nearly three years ago, I unveiled my original xHR/FB rate. Back then, Statcast was in its infancy, and wasn’t installed in all 30 stadiums until the upcoming season. As such, my original equation used metrics that Jeff Zimmerman provided me from scraped Gameday data, I believe. The equation was solid enough, producing a 0.649 adjusted R-squared. Clearly, there was more work to be done, but sadly, the data required to make improvements simply wasn’t available.

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Nearly a year ago, I introduced the newest version of my xHR/FB rate, this time using the new Statcast metric, Barrels per Batted Ball Event (Brls/BBE). Along with Brls/BBE, I included two additional components — pulled and opposite field fly ball percentage, which I just lumped together for the second factor in the equation (oh, and also a park factor adjustment).

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With three seasons in the Statcast Leaderboard books, it’s time to dive in to its short digital history. While the various metrics published on the site are sure to provide a better understanding of what drives a handful of statistics, I’m most interested in what fuels home runs. The leaderboard shares a number of interesting metrics, several of which would intuitively correlate with HR/FB rates. Given the leaguewide home run surge — for the second straight year, HR/FB rate hit a new high since 2002, the first season FanGraphs has batted ball data — we might find some answers on the virtual pages of Statcast’s records.

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It’s time to unveil the list of 10 fantasy relevant hitters whose 2017 xHR/FB rates most exceeded their actual HR/FB rate marks. I use my Statcast-fueled xHR/FB rate equation to determine which hitters could enjoy a spike in HR/FB rate in 2018. Of course, this all assumes the hitter posts similar marks in the components of the xHR/FB rate metric — Brls/BBE, along with fly ball pull and opposite field percentages. All xHR/FB rates are the park-adjusted versions.

This list isn’t necessarily a sleeper list, unless your leaguemates are expecting a 2018 HR/FB rate like 2017 for the player. Rather, it’s simply a list of hitters with a better than average chance of enjoying an increase in HR/FB rate.

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Let’s shift back to the bottom of the Statcast leaderboard and discuss seven names you might be surprised to find down there. The surprise won’t be driven by a bunch of guys who just posted 20% HR/FB rates yet don’t have the barrels to explain such results, but rather one of “whoa, his power really did dry up in 2017, what happened?!”.

In 2017, league average Brls/BBE was 6.4%, while the median mark was 5.3%. The median is much lower because it includes the many hitters who recorded few plate appearances and batted ball events, a group which likely posted lower Brls/BBE marks. After all, more power equals more playing time, all else being equal. The average is weighted and so the 437 batted ball events from Giancarlo Stanton, who posted a 17.4% Brls/BBE is going to pull up that mark, but ends up counting the same as the 16 batted ball events from Brett Eibner, who posted a slightly better 18.8% Brls/BBE.

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Let’s continue diving into the Statcast leaderboard, sticking with my favorite metric, Brls/BBE (barrels per batted ball event). While it’s no surprise to find Aaron Judge atop the leaderboard, followed by Joey Gallo, and J.D. Martinez, there are other names that do surprise and provide actionable information. So let’s peruse the top 50 hitters (there are 540 players on the list, including pitchers, as the board simply includes everyone with at least 30 batted ball events) sorted by Brls/BBE and discuss six legit sleepers for home runs in 2018.

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Yesterday, I shared 19 hitters whose Brls/BBE marks spiked from 2016 to 2017. A surge in this metric usually coincides with a jump in HR/FB rate, and sure enough, 18 of the 19 batters did enjoy an increase. Now let’s check in on the hitters who suffered a decline in their Brls/BBE marks. We shall assume that each of their HR/FB rates also declined. Was this actually the case?

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Towards the end of 2016, one of the most exciting new metrics was introduced using Statcast data — Barrels, which are “well-struck ball[s] where the combination of exit velocity and launch angle generally leads to a minimum .500 batting average and 1.500 slugging percentage”. But counting stats aren’t exactly the best when evaluating performance, as playing time factors in, and we don’t want that. Luckily, Baseball Savant also provides us with a “batted ball event (BBE)” number, and then generously does the math for us, calculating a Barrels per Batted Ball Event (Brls/BBE), which I then use for my analysis. Given that Brls/BBE is a pure measure of power, it stands to reason that we want to see our favorite power surgers enjoy major growth in the metric from one year to the next, in order to validate that increased output. So let’s check in on the Brls/BBE surgers from 2016 to 2017.

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Yesterday, the Giants decided that Pablo Sandoval wasn’t actually the answer at third base for them in 2018 and traded for Rays-lifer Evan Longoria. Now, Longoria’s chances of rebounding in the home run category have just gone splat. While he has posted HR/FB rates in the mid-to-high teens for most of his career, that mark dipped just below 11% in 2014 and 2015, before rebounding back into the mid-teens in 2016, en route to a career high homer total. The rebound was short-lived though, as his HR/FB rate fell back down to just 10.5% — a career low — this season. Given that he’s not so totally over the hill and he has shown strong power skills as recently as that 2016 season, you had to have figured some sort of dead cat bounce. But now, that bounce is far less likely to occur.

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In my Monday post discussing how park factors may affect Zack Cozart’s performance, I responded to a comment opining that “the Angels IF defense is going to be insane” with the following:

The offense should be above average and that defense and upgraded offense means potentially serious bumps to Angels starting pitchers.

I said that without actually doing any research on how the Angels defense performed in 2017 or looking into the batted ball distributions of the pitchers and how that matches with the strengths of the fielding unit. So let’s do that now.

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