Last week, I reviewed my hitter Pod Projections vs Steamer projections comparisons. Let’s now move along to the starting pitchers and ERA. As a reminder from my original post:

Though Steamer is the best pitching projection system out there, it struggles on pitchers that have shown consistent BABIP and HR/FB rate suppression skills and deficiencies, as what usually works for the majority of pitchers — projecting a heavy dose of regression to the MLB mean — means it misses on those uncommon exceptions. I use Statcast’s xBABIP now for my projections, so I’m not afraid to forecast a mark that strays from the league average. However, I certainly still include some regression as we don’t always have enough batted balls in a pitcher’s history for that mark to stabilize.

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A whopping eight years ago, I shared the hitter xK% metric I developed using a couple of our plate discipline metrics. It was quite good, using only three variables, but still had a strong R-squared of 0.81. Since then, I haven’t discussed it all that much, but still use it to help formulate my Pod Projections. However, I have actually been using an updated version that I had never shared and it’s even better. The comments on my recent xwOBA articles inspired me to finally reveal the latest and greatest version of the hitter xK% metric.

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On Monday, I shared the names of eight pitchers whose Pod Projected ERA is significantly lower than Steamer. Today, let’s flip to the ERA downside names. Remember that in aggregate, Pod ERA projections are lower than Steamer, so the gap between ERA forecasts below are a lot smaller than on the upside list. Since it’s really relative projections and calculated dollar values that matter (we care how the projections compare to the player pool, not whether the pitcher is projected for a 3.00 ERA vs a 14.00 ERA), try to ignore the small degree Pod’s ERA is higher than Steamer and remember these are the largest outliers, so if put on the same ERA scale, the difference would be greater.

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This week, I finish up the Pod vs Steamer series that pits my Pod Projections against the Steamer projections. Today, we move on to pitchers, where I’ll compare the ERA forecasts from each of the systems and identify those pitchers I am projecting for significantly better ERA marks. Though Steamer is the best pitching projection system out there, it struggles on pitchers that have shown consistent BABIP and HR/FB rate suppression skills and deficiencies, as what usually works for the majority of pitchers — projecting a heavy dose of regression to the MLB mean — means it misses on those uncommon exceptions. I use Statcast’s xBABIP now for my projections, so I’m not afraid to forecast a mark that strays from the league average. However, I certainly still include some regression as we don’t always have enough batted balls in a pitcher’s history for that mark to stabilize.

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Yesterday, I compared my Pod Projections in the stolen base category to Steamer and identified five hitters I am forecasting for a meaningfully higher stolen base total. Today, let’s now look at the hitters I’m projecting for fewer stolen bases than Steamer. I’ll only highlight the fantasy relevant names as there are a number projected for limited playing time that aren’t worth discussing.

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The Pod Projection process sharing continues! The 2021 forecasts are now available and include nearly 600 player lines. As usual, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.

2021 Pod Projection Index
Ha-seong Kim
Trent Grisham

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Only two months after publishing the first one of this year, it’s time to get forecasting again with another 2021 Pod Projection! The 2021 forecasts are now available and include nearly 600 player lines. As usual, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.

2021 Pod Projection Index
Ha-seong Kim

Today, I’ll analyze 2020 fantasy breakout, Padres outfielder Trent Grisham. A 2019 minor league performance spike between Double-A and Triple-A put him on fantasy owners’ radars, but his MLB debut that year was a mixed bag. Still, he entered the 2020 season as a trendy sleeper, and he certainly delivered on those hopes by going 10/10 over the short season and easily outearning his cost. Now, fantasy owners aren’t entirely sure how to value him. His NFBC ADP since Feb 1 sits around 71, but he’s gone as early as pick 46 and as late as 119. Clearly, there’s little agreement on his 2021 value, which isn’t too surprising given the limited MLB sample we have to evaluate. So let’s go metric by metric, discussing and projecting each, and ultimately calculating a full projection line, which will be compared to the rest of the forecasts shared on his player page.

Plate Appearances: 631

Grisham spent the majority of his time batting leadoff last season. When he didn’t hit leadoff, he typically batted second. So unless he endures an extended slump, expect him to hit at or near the top of the order. My projected PAs assume he hits leadoff, but I didn’t give him full credit considering the slight risk he takes a seat against some left-handers.

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At last, it’s finally time to unmask xHR/FB v4.0! If you want a refresher on how we got here, review my xHR/FB history and v4.0 research and then check out the correlations of a variety of metrics that may or may not predict HR/FB rate.

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Yesterday, I shared the history of my xHR/FB rate equation and the first pieces of research on my journey toward developing Version 4.0. Today, I’ll discuss a myriad of correlations for a myriad of metrics and how those calculations helped me determine which would win a spot in my final equation. Fun!

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If it’s really true that Chicks Dig the Long Ball, then how do they feel about the nerds trying to figure out who will hit those long balls and how many of them they will hit? As fantasy owners, the home run is the ultimate result of a hitter’s plate appearance. It counts for a homer, obviously, but also a run scored, at least one run batted in, and a 1.000 batting average. Unfortunately, a hitter can’t also steal a base while rounding the bags on his trot home, but contributions in four of five categories in just one plate appearance seems good enough. Because of the value of a home run, accurately projecting them is one of the keys to a fantasy championship. Luckily, I’ve spent six years trying to do just that.

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