Last week, I introduced the latest iteration of my ever-improving hitter xBABIP equation, by starting with Statcast’s implied xBABIP (SxBABIP) calculation and adding additional variables to my regression. As you could imagine, it has resulted in a Pod xBABIP (PxBABIP) that sometimes varies widely from SxBABIP. So yesterday, I shared a large group of hitters that PxBABIP was significantly higher for vs SxBABIP. The pattern was a speedy group who avoided pulling grounders into the shift and hit their grounders to the opposite field more frequently than the league. Today, let’s now check out the group of hitters whose PxBABIP is well below SxBABIP.

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Last week, I introduced the latest iteration of my ever-improving hitter xBABIP equation. This time, I decided to take advantage of Statcast’s implied xBABIP calculation, since it determines the hit probability of every batted ball. That’s beyond my abilities, so I figured I would use it as a base and build upon it. It proved successful. In my article, I noted several factors that are ignored in the Statcast equation, which I incorporated into my new equation, and in turn pumped up or pushed down many hitter’s xBABIP marks vs Statcast’s. So let’s now begin by reviewing the hitters whose Pod xBABIP marks are significantly higher than Statcast’s xBABIP.

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It’s been a looooooooong journey toward understanding what underlying skills drive a hitter’s BABIP ability. No matter how much understanding we have gained over the years, it has been a struggle to develop an equation that produced an R-squared much over 0.50. That’s not terrible, but when my hitter xHR/FB equation spits out an impressive 0.826 R-Squared, I continue to strive for better. I shared my last hitter xBABIP equation almost exactly five years ago, and since, I have yet to see a better one.

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Yesterday, I listed and discussed the hitters whose HR/FB rates most underperformed their xHR/FB rates. In that list were a couple of potentially undervalued gems to remember for your 2022 drafts and auctions. Let’s now flip to the overperformers, those whose actual HR/FB rates most exceeded their xHR/FB rates. This group might end up being overvalued if your leaguemates are buying them expecting their 2021 HR/FB rates to be repeated.

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Let’s finish things up in dissecting my hitter xHR/FB rate and its components by patrolling for potential sleepers during your 2022 drafts. We’re going straight to the xHR/FB rate underperformers this time and discussing the hitters whose actual HR/FB rates were most below that mark, using a minimum of 30 fly balls and line drives as defined by Statcast. While the higher xHR/FB rate is not a projection, it does suggest the hitter deserved significantly better, and that might not be accounted for in their various forecasts (though, it will be reflected in the Pod Projections, of course, when they are released).

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Last week, I listed and discussed the hitters whose HR/FB rate surges weren’t real. In other words, hitters who enjoyed HR/FB spikes that my xHR/FB rate equation didn’t believe in, or match. Today, we’ll now look at those hitters whose HR/FB rates declines, but their xHR/FB rates were significantly higher, possibly signifying some bad fortune. That could potentially result in these hitters being undervalued, or at least being underprojected for home runs.

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Today, we’re keeping with our dive into hitter xHR/FB rate, but instead of discussing those whose marks validated their actual HR/FB rates, it’s time to reveal what has been fake news. That is, which hitters enjoyed a HR/FB rate surge, but xHR/FB rate wasn’t buying it? Depending on your leaguemates, this group might end up being overvalued on draft day.

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Yesterday, I identified and discussed the hitters whose xHR/FB rates validated their HR/FB rate spikes. Today, let’s flip to the other side and check out the list of hitters whose HR/FB rates fell compared to 2020, and whose xHR/FB rate confirmed those results.

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Let’s finally move past the hitter xHR/FB rate variables and get into the equation’s output. Today, I’ll start by checking in on the hitters whose HR/FB rates spiked in 2021 that were real, meaning actually validated by their xHR/FB rates. In other words, the HR/FB rate surge was accompanied by a similar xHR/FB rate, lending more credence to the jump. A word of caution — just because the xHR/FB rate validated the HR/FB rate does not mean the hitter is going to sustain this new level, but it should certainly give us more confidence that he will than if his xHR/FB rate came up significantly short.

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Two weeks ago, I published a series of posts diving into average distance of fly balls and line drives (ADFBLD). Somehow, I forgot to actually complete the series by discussing the decliners versus 2020. I shared the surgers, so now let’s return to this xHR/FB equation variable by discussing the hitters who lost the most ADFBLD over a minimum of 30 flies + liners in both 2020 and 2021.

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