ESPN Home Run Tracker Analysis: The 2016 HR/FB Downsiders

Three years ago, I conducted an exhaustive study of ESPN Home Run Tracker data. At that time, it was the primary tool I used to validate a batter’s power, before we got into the sexy new batted ball distance, and then combined that with standard deviation of distance and average absolute angle. The short story is I found that hitters with an unusually high percentage of “Just Enough” (JE) homers saw their HR/FB rates decline the following year, significantly more than the rest of the player population. On the other hand, those who hit a high percentage of “No Doubt” (ND) homers maintained their HR/FB rate much better than the rest of the group.

What’s interesting is that now we have two sources of HR/FB rate validation — this analysis and my xHR/FB rate. The hope is that they agree on the names, but I don’t know yet. So I will be cross-referencing the names I list here and discussing any disagreements. Refresh your memory with the list of downsiders produced by the xHR/FB rate equation.

For the first time, I lumped in “Lucky” homers with the “Just Enough” variety for a clearer picture of fortune. Here are the relevant definitions straight from the source:

“Just Enough” home run – Means the ball cleared the fence by less than 10 vertical feet, OR that it landed less than one fence height past the fence. These are the ones that barely made it over the fence.

“No Doubt” home run – Means the ball cleared the fence by at least 20 vertical feet AND landed at least 50 feet past the fence. These are the really deep blasts.

“Lucky” home run – A home run that would not have cleared the fence if it has been struck on a 70-degree, calm day.

Yesterday, I looked at the hitters with upside. Today, I’ll finish the analysis by taking a look at the hitters whose high JE + Lucky % suggests some major downside. The thinking here is that an errant gust of wind here and there or a millimeter difference in where the bat meets the ball may have assisted some balls to fly over the wall that may not happen again. I only included players who hit at least 15 homers. The average JE + Lucky % was 36.5% for the group.

The JE+Lucky % HR/FB Downsiders
Name JE Lucky JE+Lucky Total HR HR/FB JE+Lucky %
Billy Butler 10 1 11 15 10.8% 73.3%
Brandon Crawford 11 4 15 21 16.2% 71.4%
Ryan Howard 11 5 16 23 18.9% 69.6%
Brandon Belt 9 3 12 18 13.6% 66.7%
Neil Walker 8 2 10 16 9.9% 62.5%
David Peralta 8 2 10 17 17.7% 58.8%
Kyle Schwarber 7 2 9 16 24.2% 56.3%
Wilmer Flores 8 1 9 16 10.3% 56.3%
Evan Gattis 12 3 15 27 16.0% 55.6%
Miguel Sano 8 2 10 18 26.5% 55.6%

Aaaaaand Billy Butler continues to prove that his 2012 home run outburst was a complete fluke. The acquisition of Khris Davis may cut into Butler’s playing time a bit, so he’s just AL-Only material, and not even good material at that.

Well this is certainly interesting. Brandon Crawford’s HR/FB rate nearly tripled to easily set a new career high. The knee jerk reaction is to shout “FLUKE!!!!” and his sky high JE+Lucky % would validate those concerns. HOWEVER! His batted ball distance shot up 27 feet to over 300 feet! And his xHR/FB rate was actually 19.7%. That is completely opposite of what Home Run Tracker suggests. When at odds, I usually side with xHR/FB rate. Guess we’ll have to just wait and see.

…and Ryan Howard might officially be platooned. It’s about time! Also, xHR/FB rate agrees here. I wouldn’t touch him, though I doubt anyone is too excited to roster him.

Brandon Belt is another that we see a discrepancy between Home Run Tracker and xHR/FB. Belt’s xHR/FB rate has actually risen every season and hit a new career high at 17.1%. Obviously, the park is holding him back, and the deep right field wall may be resulting in an inflated JE number. It’s one of the shortcomings of using this method. I don’t think he has the downside his appearance here might suggest, nor the upside of xHR/FB, which fails to account for park factors.

Is it just me, or is Neil Walker super boring? Getting out of Pittsburgh will help, so he should avoid any potential downside.

Well damn, did anyone see a .380 wOBA coming from the bat of David Peralta? Did anyone even have a clue who David Peralta was before spring training? I sure didn’t. His xHR/FB rate validates his actual rate, which means that once again, the two methods are enjoying a heated argument. If he declines, I doubt that renders this method better — it’s simply because he probably just isn’t this good and will have trouble repeating that type of power.

Uh oh, not everyone’s favorite catcher eligible outfielder Kyle Schwarber! His batted ball distance was a robust 307.5 feet. But do you really expect him to sustain a mid-20% HR/FB rate? I think he’s a rather easy call to suffer some regression.

I hope Wilmer Flores isn’t too upset about being relegated to a reserve role. At least he won’t be as embarrassed if his HR/FB rate slips, because no one will really notice it if it comes in just 200-300 at-bats.

Finally, Evan Gattis is another where xHR/FB rate agrees. His distance was down 10 feet, but playing half his games at Minute Maid Park helped, as its home run boosting ways resulted in an 18.8% home HR/FB rate versus a 13.5% mark in away games. I say he remains rather neutral this year.

First Schwarber, now Miguel Sano?! His xHR/FB rate was relatively close to his actual, which is rather important considering his actual was a lofty 26.5%. But just like Schwarber, it’s just the laws of regression. Rookie gets first taste of Majors and puts on power show. League adjusts and he experiences some regression the following year. It’s more prudent to expect some decline than think he will post another top five HR/FB rate this year.





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.

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Michael
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Michael

I don’t know that David Peralta should go through much power regression next year, if any.