On Friday, it was reported that Eddie Rosario, career Minnesota Twin, had signed a one-year contract with the division rival Indians. Is there a strong perception about either of these parks and their affects on offense? I don’t think so, unless you’re super familiar with park factors. However, just because it’s more difficult to guess off the top of our head like Yankee Stadium’s home run boosting power, doesn’t mean there’s no change in factors. So let’s consult them and see how Rosario’s offense might be affected.

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Today, I finish reviewing my hitter Pod Projections versus Steamer projections comparisons, ending with the stolen base downside guys. These are the hitters who I forecasted a significantly higher PA/SB rate than Steamer. Let’s see how they ended up performing.

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Today, we review the last of my hitting projections comparisons between my 2020 Pod Projections and Steamer. We move along to stolen base, where like I did for home runs, I turned it into a ratio, this time of plate appearances. That way playing time forecasts won’t influence the comparison. We begin with my review of the stolen base upside guys. Do note that I seem to be consistently lower on stolen base forecasts than Steamer, so this list isn’t very long.

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Yesterday, I reviewed my home run upside list, where I compared my Pod Projections to Steamer using AB/HR rate. Today, let’s now review my home run downside list.

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Toward the end of last February, about a month before many of us went into lockdown and the season was delayed indefinitely, I compared my home run Pod Projections to Steamer. I didn’t want playing time differences to influence the list, so rather than straight home run forecasts, I calculated the respective AB/HR rates and compared those. Then for fun, I calculated what a 600 at-bat home run projection would look like at each of the projections’ AB/HR marks. Let’s begin by reviewing the upside guys, with a caveat that some of my projections may have changed slightly since publishing, and, of course, the small sample size of the season means we’re both likely going to end up being very wrong on a lot of these names.

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On Tuesday, it was reported that George Springer agreed to sign with the Blue Jays, finally marking the first big free agent signing of the offseason. Springer has spent his entire career in Houston, where has surprisingly posted a lower wOBA than in away parks. Let’s check out the park factors to see if the change in home park might affect his results.

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The Padres are at it again, this time participating in a three-team trade that sees them acquiring starting pitcher Joe Musgrove. Musgrove has been a popular sleeper for the past couple of seasons, despite having never posted an ERA below 4.00 or a strikeout rate above 22%. He did accomplish both over 39.2 innings in 2020, though, but obviously such a tiny sample size doesn’t mean a whole lot. His career SIERA is a bit lower than his ERA, offering hope for better and a velocity surge at times also excited many. He’s been a tease so far, so will his new home park give him a better chance of breaking out? Let’s consult the park factors.

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Last week, I reviewed my starting pitcher K% surgers list, so today, I’ll review my starting pitcher K% decliners list, which was assembled using my pitcher xK% equation. Strikeout rates tend to bounce around throughout the season, so it’s pretty silly to be evaluating the accuracy of this list considering the pitchers only made 11 or 12 starts. But it’s all we have, so let’s get to it.

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Let’s move over to the pitching side where I’ll start my 2020 forecast reviews with strikeout rate, or K%. As a reminder, there is never, ever, ever a reason to evaluate K/9 instead of K%. A denominator based on outs is at risk of being heavily influenced by BABIP, walks issues, field errors, HR/FB rate, etc, because higher numbers in those metrics extend innings and result in additional batters faced, giving the pitcher more opportunities to strike out a batter, even though the denominator has remained the same. That can’t happen when your denominator is total batters faced, like in K%, as more batters faced in an inning will reduce K%, as it should, as opposed to having no effect on K/9.

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Today, I finish up reviewing my 2020 Forecast BABIP lists with the BABIP decliners. Once again, I used my xBABIP equation to identify the hitters who most outperformed their xBABIP marks in 2019. Now let’s see how they performed over the short 2020 season.

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