On Tuesday night, the competitors of the LABR Mixed draft virtually congregated for our annual mid-February 15-team draft. Drafts this early are challenging. On the one hand, the early timing benefits the prepared and the more highly skilled. On the other hand, there remains a great many unknowns that we need to make educated guesses for at best, and complete shots in the dark for at worst. In addition, and during a normal pre-season period, the extra time between the draft and opening day (versus leagues that draft a week to three before the seasons begins) means more opportunity for injuries to decimate your roster before the season even begins!

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Yesterday, I used my newest hitter xBABIP equation to discuss the batters whose actual BABIP marks most underperformed their xBABIP marks. Now let’s look at the overperformers, or those whose actual BABIP most exceeded their xBABIP marks.

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Nearly two weeks after introducing my newest hitter xBABIP equation, it’s time to unveil the list of underperformers. This is the group that most underperformed their xBABIP, which could result in undervaluation if your leaguemates are paying for a 2021 repeat, and not a 2022 rebound. Of course, remember that a higher 2021 xBABIP than actual BABIP is not a 2022 projection. However, if you’re using historical BABIP to forecast future BABIP, then I would highly advise you use xBABIP instead of actual BABIP as your historical marks, especially for hitters with a small sample of playing time. I’ll use a 75 ball in play minimum once again.

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Yesterday, I listed and discussed a handful of hitters whose actual 2021 BABIP marks were within 0.010 of league average, which normally wouldn’t make you think twice about its repeatability for the 2022 season. However, these hitters posted significantly higher xBABIP marks at least 0.020 higher than their actual marks. Let’s now flip over to the hitters who posted near-league average BABIP marks, but this time finished with xBABIP marks significantly below those BABIP marks.

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Today, we continue our exploration of my new hitter xBABIP equation by identifying hitters whose 2021 BABIPs were around the non-pitcher league average of .293, but whose xBABIPs were significantly different. When you see a BABIP of .380 or .220, that clearly raises red flags, with immediate reactions of decline, in the case of the former, or improvement, in the case of the latter, in the upcoming season. But no such reaction is triggered when you see a BABIP around the league average, right? However, just being around the league average doesn’t necessarily mean it’s legit. So today, let’s begin by discussing those hitters who posted BABIPs marks within .010 of league average (between .283 and .303), but xBABIP marks significantly higher. If your leaguemates are using 2021 BABIP to shape their 2022 hitter forecasts, these hitters’ batting average contributions could be undervalued.

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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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