Yesterday, I continued my Pod Projections vs Steamer battle by pitting our stolen base projections against each other, and identifying those I forecasted for a higher total. Let’s now review the players I am projecting for fewer steals per 650 PA than Steamer.

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Today, I continue my Pod Projections vs Steamer battle, this time moving along to stolen bases. Similarly to the way I compared our home run forecasts, I’m going to calculate a PA/SB rate first and then extrapolate that projection over 650 plate appearances so we’re only comparing stolen base projections and playing time forecasts don’t factor in. I’ll begin with the players I’m projecting for more stolen bases, or the upside guys.

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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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The 2022 Pod Projections 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.

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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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The Understanding Statistics Episode of the Beat the Shift Podcast – a baseball podcast for fantasy baseball players.

Guest: Mike Podhorzer

Bar Mitzvah Talk

• Coke & Pepsi

Projections

• Pod Projections
• Manual vs. automated projections
• Where do automated projections fail / blind spots?

In-Season

• At what point in the season should you rely on in-season statistics vs. pre-season projections?
  • Francisco Lindor 2021

Understandings Statistics Section

• Hitting Statistics
  • BA
  • OBP
  • SLG
  • OPS
  • BB%
  • K%
  • HR/FB%
  • Barrel%
• How can you tell if a YOY increase in a statistic is real skill, or if it was a fluke?
• BABIP
• Using Statcast to identify pockets of value / undervalued players
• What in-season statistics should you use when setting waiver wire pickups for hitters?
• Pitching Statistics
  • ERA
  • WHIP
  • Swing Strike %
• How to adjust for a potential universal DH in 2022?
• Strand Rate (LOB%)
• ERA Estimators
  • FIP vs. xFIP vs. SIERA
    • Descriptive vs. Predictive
• What in-season statistics should you use when setting waiver wire pickups for pitchers?
  • K% is superior to K/9
  • K-BB% or K% – Which is more valuable to track in-season?
• What statistics should one track to tout relievers for saves in 2022?
• Scouting for saves in 2022
• Other important statistics to track
  • Plate discipline metrics
    • O-Swing%, Z-Swing%, O-Contact%, Z-Contact%, etc.

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Today, I share the final review of my pre-season Pod Projections posts. This time, we shift to a starting pitcher, Zach Plesac, whose original writeup is here. Plesac did post a sub-4.00 ERA during his 2019 debut, but it wasn’t backed by his skills, as he handily outperformed his ugly 5.13 SIERA. During the short 2020, he enjoyed a true breakout as his strikeout rate surged thanks to pitch mix changes. Let’s see how he did for an encore and how it compared to the projections.

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Let’s continue reviewing the Pod Projections I shared early in the year. Today, I’ll review my Trent Grisham forecast. You can find the original writeup here. Grisham enjoyed somewhat of a fantasy breakout during the short 2020 season, as he went 10/10 over 252 plate appearances, putting him on a 20+/20+ pace over a full season. We fantasy owners salivate over that power/speed potential. Let’s see how he followed up and compare it to my projections and the rest of the forecasts.

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As you probably already know, I manually project player performance each and every year, and make the forecasts available on my Pod Projections page. It’s a seriously time-consuming task, but the manual process gives me some advantages versus a computer system, so I continue to create them. Early in the year, I share a couple of my Pod Projections, the individual forecasted metrics, and an explanation of the process I follow to arrive at each number. This year, the first projection I shared was that of Ha-seong Kim, who had just signed a four year contract with the Padres after spending seven seasons in the KBO (Korean Baseball Organization). Projecting veteran baseball players is challenging enough, so you can imagine the added layer of difficulty when working on a forecast for a player coming over from a foreign league. Let’s find out how Kim performed compared to my projection and the two that were published in early January.

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