Every year, I pit my Pod Projections 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. Many of these players figure to be part-timers, so consider them sleepers in deeper leagues.

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The Pod Projection process sharing continues! The 2020 forecasts are now available and include over 500 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.

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Finally, it’s Pod Projections time! The 2020 forecasts are now available and include over 500 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.

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Yesterday, I used my pitcher xK% equation to highlight four starting pitchers whose underlying skills suggested a dramatically higher strikeout rate than actually posted, hinting at upside this year if the pitcher could maintain those skills. Today, let’s talk about the starting pitchers who most outperformed their xK% marks, heightening the risk of a decline this season, absent an improvement in underlying skills.

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Three years ago, I shared an updated version of my pitcher xK% metric, which correlated strongly with actual strikeout rate, given its 0.931 R-squared. While some of my other xMetrics I calculate and then use them to serve as a historical guide to assist in my Pod Projection forecasts, I actually project the underlying components of xK% myself and the vast majority of the time, keep the projected strikeout rate those components spit out. There are instances where I do change the forecast though, as some pitchers have a history of outperforming or underperforming their xK% marks, for whatever reason. Anyhow, let’s discuss four starting pitchers who posted xK% marks above their actual marks, hinting at some upside this year.

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Last week, I used my hitter xBABIP equation to identify and discuss 8 hitters who could enjoy significant BABIP spikes this season, if they maintained the underlying skills driving those marks. Today, I’ll talk about the other side of the coin, those hitters whose xBABIP marks suggests serious downside this season, unless they improve their underlying skills.

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Welcome to the RotoGraphs Matchmaker Service! No, I cannot find you a date. However, we could hopefully facilitate the marriage of league owner with leagueless owner. If you are seeking an owner to fill your fantasy league or are the owner hoping to be seeked to join that unfilled league, this is your new home. In the comments, please advertise your league openings or your availability and desire to join a league. To make things easier, it would be helpful to include the details of the league you’re seeking to fill or prefer to join in the following format:

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Two years ago, I introduced you to the latest iteration of my hitter xBABIP equation, this time incorporating the effects of defensive shifts. Let’s find out which fantasy relevant hitters most underperformed their xBABIP marks in 2019, suggesting the potential for dramatic upside in 2020…if the hitter maintains those underlying skills.

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In what turned out to be quite soap operatic, the Red Sox and Dodgers finally complete a trade…though not exactly the original one that was tentatively accepted last week, which also included the Twins. The biggest name to move is of course Mookie Betts, who makes what was already an excellent Dodgers offense into one that is now laughably good. But for those who have already spent a first round pick on Betts, plan to keep him, or are wondering how to adjust his value after the move, the big question now is how might the switch in home park affect his performance. Let’s consult the park factors (2018) and find out!

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Last Tuesday, just four days into February, I participated in the earliest LABR draft ever. We always draft in February, but had to do so a week earlier than usual this year due to owner availability. Though I’m certainly not a fan of February drafts, at least it provides me the needed motivation to finish my first run of Pod Projections (now available!) that drive my player values. Without the forecasts and valuation spreadsheet, I’d be drafting blind, and that’s no blueprint for a Yoo-hoo shower. This actually marked my return to the league, as I was on vacation last year when the draft was scheduled.

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