While many expert fantasy players have been drafting and posting their rosters and drafting and posting their rosters and… you get the point…, you may be waiting out the lockout before you schedule your draft. If that’s the case, you’re probably exhausted from all the fantasy content that has been pumped out into the meta-verse and at this point, probably have your mind made up on a few players. Let’s put that theory to the test and let’s see if you can guess who’s who based on only their counting stat projections. This is my second installment of the choose your player game and here are the rules:

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Yesterday, I began my annual Pod vs Steamer series by pitting my Pod Projections against Steamer in home run forecasts, highlighting those players I was more optimistic on. Rather than compare raw home run totals that are highly influenced by at-bat projections that may differ significantly, I put both projections on the same scale, 600 at-bats. That way we are comparing the home run skill forecasts with no influence from differences in playing time expectations.

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Every year, I pit my Pod Projections (now available!) against the Steamer projections in various categories. Today, I’m going to continue the annual smackdowns by calculating AB/HR rates and then extrapolating them over 600 at-bats. At that point, I’ll compare how many home runs each system is forecasting, given a 600 at-bat projection. I’ll start by sharing the names of hitters Pod is projecting for significantly more home runs than Steamer.

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It’s been a longer wait than in the past, but it’s finally Pod Projections time! The 2022 forecasts are now available and include nearly 550 player lines. As usual in my Pod Projection posts, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.

Today, I’ll analyze former top overall prospect, Wander Franco. He made his eagerly anticipated debut last season and was as solid as expected, despite being just 20 years old. While a 14 homer and four stolen base pace over a full season certainly didn’t thrill fantasy owners, he posted a .348 wOBA and managed to maintain his sterling contact ability by striking out just 12% of the time. That’s mightily impressive for a rookie who wasn’t even of legal drinking age yet.

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Here is a tale of two tweets.

The first one tries to see if fantasy managers would consider rostering a great pitcher in every regard except they would have a 5.00 ERA.

Would you consider rostering an elite pitcher in all aspects (e.g. Woodruff) expect they would guarantee to have a 5.00 ERA (think Andrew Heaney HR issues but worse)?

— Jeff Zimmerman (@jeffwzimmerman) February 21, 2022

And now the same options (I see the steam vs. stream mistake, my bad!) but for a hitter who is projected to have a .220 AVG.

Would you consider rostering a hitter pitcher in all aspects (e.g. Kyle Tucker) expect they would be guaranteed to have a .220 AVG?

Assume a 5×5 Roto league.

— Jeff Zimmerman (@jeffwzimmerman) February 22, 2022

The results are a stark difference. The deal is that a .220 AVG and a 5.00 ERA will hurt a roto team the same amount. While it may not be obvious, a little math might help. To determine the effects, I took the Standings Gain Points equations from 15-team redraft leagues from The Process (a great resource, you should buy it).

AVG SGP = ((1669+H)/(6525+AB)-0.256)/0.0012

ERA SGP = (((489+ER)*9)/(1122+IP)-3.92)/(-0.0566)

These formulas determine how much a fantasy team would move up and down the standings based on the rate stat. The volume does matter since it’s worse to add 150 innings of a 5.00 ERA to a team than just 20 innings. Read the rest of this entry »

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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