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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On Saturday, it was reported that Kyle Schwarber had agreed to a one-year contract with the Nationals. After spending six seasons with the Cubs, Schwarber now finds himself with a new home park for the first time. Let’s consult the park factors to see how the change might impact his performance.

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It’s another blockbuster! Last Thursday, the Indians traded Francisco Lindor and Carlos Carrasco to the Mets. Today, I’ll focus on just Lindor and consult the park factors to determine how the team switch might affect his performance.

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Let’s move along to reviewing my 2020 BABIP surgers list. I used my xBABIP equation and identified the fantasy relevant names who most underperformed that mark. While merely underperforming your xBABIP doesn’t automatically mean a BABIP spike is forthcoming, I’d say the odds are pretty high for the biggest underperformers. Also important is the hitter maintains the underlying skills driving that xBABIP. If his skills falter, then of course his BABIP isn’t going to rise to meet the previous year xBABIP. Since BABIP is heavily influenced by luck, a shortened season means even more randomness than usual. Remembering that, let’s see how they ended up performing.

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Yesterday I continued my review of 2020 preseason articles by recapping my HR/FB rate surgers list. Today, I’ll review the flip side, the hitters that appeared on my HR/FB rate decliners list. The list was compiled using my xHR/FB rate, and then I identified the fantasy relevant hitters with the most significant overperformance. Let’s see how they ended up doing over the shortened season.

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