2022 Pod vs Steamer — HR Downside

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.

Let’s now review the hitters who I am forecasting for home run downside vs Steamer.

HR Downside
Name Pod AB/HR Steamer AB/HR Pod HR – 600 AB Steamer HR – 600 AB Diff
Oneil Cruz 27.0 19.9 22.2 30.2 -8.0
Vladimir Guerrero Jr. 14.7 12.3 40.9 48.7 -7.8
Giancarlo Stanton 15.8 13.3 37.9 45.0 -7.1
Orlando Arcia 38.7 27.6 15.5 21.7 -6.2
Spencer Torkelson 18.8 15.8 31.9 37.9 -6.1
Austin Barnes 44.3 30.8 13.6 19.5 -5.9
Josh Naylor 30.6 23.7 19.6 25.4 -5.7
Cam Gallagher 65.9 40.5 9.1 14.8 -5.7
Keibert Ruiz 26.2 21.0 22.9 28.6 -5.7
Juan Soto 16.4 14.4 36.7 41.8 -5.1

It figures that the top of my downside list is a top prospect who recorded just nine plate appearances in the Majors last year. Oneil Cruz recorded just 29 Triple-A plate appearances before his promotion to the Pirates, and I have to admit, there’s far more art than science when projecting players with little to no MLB experience.

However, it’s pretty clear here what’s driving the home run projection gap — the strikeout rate. Somehow, Cruz’s strikeout rate forecasts are all over the map from the various systems. They range from a low of just 21.2% to a high of 27.8%. Which system is forecasting that 21.2% mark? That’s right, Steamer! I have no idea why it’s so optimistic. That would be the second lowest strikeout rate of Cruz’s professional career, and the lowest since his 2016 debut in the Rookie league.

I’m at a 28% strikeout rate, which is a massive difference! It’s nearly identical to THE BAT and THE BAT X, though. Aside from the questions about his potential strikeout rate, he’ll need to prove he could hit at least a league average rate of fly balls. Given his power potential, he should certainly be trying to.

Gee, I’m low on Vladimir Guerrero Jr. again?! Oy vey. Once again, strikeout rate has a lot to do with it. I’m easily projecting the highest mark among all the projection systems. Why? Because he significantly outperformed his xK% last year. Given the short career so far, he hasn’t yet proven he is going to be a big outperformer each year. So I’m going to forecast some regression, though not entirely to his worse xK%. He also outperformed his xHR/FB rate by over 3% last year. I’m still projecting a slightly higher mark than he posted, but it’s possible the projection systems like Steamer are taking his actual mark more at face value than I am.

Ha, I feel like I’m also annually low on Giancarlo Stanton! Again, Steamer is tied for the lowest projected strikeout rate, while I’m just below ZiPS. But perhaps the bigger issue is that he outperformed his xHR/FB by a whopping 6.4%! That’s a career best outperformance for him, so it wouldn’t be right to blindly chalk that up to Yankee Stadium. This was the lowest xHR/FB rate he has posted going back to 2015. So I’m fairly sure Steamer and perhaps the rest of the projection systems are forecasting a higher HR/FB rate than my 25%. This is the type of hidden downside that could help you avoid a potential disappointment.

Ehhh, we’re all just kind of educationally guessing for Spencer Torkelson right? Clearly I guessed low! At the risk of sounding like a broken record, strikeout rate is a big factor here. Steamer is again the lowest on the totem pole, while I’m the only one over 23%. Torkelson has also posted high FB% marks, so it’s possible Steamer is assuming a high-40% mark, while I’m hedging a bit with a 43.5% forecast.

I hate to look pessimistic on Keibert Ruiz, because I’m truly a fan of his skill set. For the first time, I’m actually the most optimistic about his strikeout rate, so we could immediately rule that out as a reason why I’m lower on his home run total. It’s possible FB% has something to do with it given his recent history, but I hedged a bit and looked further back, as I just don’t think Ruiz has the power to be hitting fly balls well over 40% of the time. It would hurt his offense and continue keeping his BABIP down.

Juan Soto is another familiar name on this list, as I’m guessing his small sample power surge in 2020 is still weighing heavily in the projections. But Soto handily outperformed his xHR/FB rate last year, so my forecast would still be higher than his xHR/FB rates he has posted in three of his four seasons. While his age and accomplishments so far mean it would surprise no one if he posted another 30%+ HR/FB rate soon, his 2020 looks like the clear outlier right now.





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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jbgocubsmember
2 years ago

no one asked, but in my opinion your two columns (HR/AB and HR/600 AB) are somewhat redundant and it would actually be helpful to see one column with total volume – playing time factored in.

jbgocubsmember
2 years ago
Reply to  Mike Podhorzer

yes, which you can do either through HR/AB or HR/600ABs. having the volume/playing time stats would better accentuate the fact that some guy is “projected” to hit homers at a great rate even though he’s not expected to get much playing time. still redundant