Yesterday, I discussed six 2019 fantasy breakouts and concluded with a verdict as to whether I believed each was for real or would be a 2020 bust. Today, let’s flip to the 2019 busts.

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Yesterday, I discussed five players that aren’t generally thought of as gaining value due to the delayed start to the season. Today, I want to discuss players that stand to lose value because they were originally expected to enjoy a playing time boost replacing an injured player, but might no longer do so.

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The delayed start to the season is going to have a significant impact on player values. If you originally expected a player to miss the first two months of the season, now that player is looking like he’ll be ready for opening day. We all know the big names that gain value with the later start date — Justin Verlander, Aaron Judge, James Paxton, etc. Let’s ignore them and discuss some of the less expensive players expected to have a smaller impact. Though the impact is certainly smaller, that doesn’t mean there isn’t profit to be had.

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While moving the MLB season back was the best possible action with the given information, it leaves the fantasy game and analysts like me in a little bit of limbo. Today, I had planned on writing about closers or just do another addition of Mining the News. Also, I was supposed to be off to New York for my Tout Wars auction (moved online) and my NFBC Main Event draft (moved back). After both those drafts, it was to be a week of absorbing as much news as possible and then covering games that matter while scouring over lineups and pitching debuts. Now what?

At its core, fantasy baseball allows people to escape their real-world problems and that diversion has been taken away when they need it most. While many people will have more pressing matters as the COVID-19 spreads, it would have been nice for them to enjoy their favorite escape. And most other live entertainment is on hold for the time being. I don’t know what other options people have to keep themselves entertained but it might be a good time to spend some time with your family and learn a new skill or even read one of those “book” things.
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Yesterday, I compared my Pod Projected home runs per 600 at-bats to Steamer’s forecast to identify and discuss a slew of hitters my projections are far more bullish on in the dinger department. Today, I’ll flip to the opposite end, those hitters who Pod forecasts for fewer home runs than Steamer, given 600 at-bats.

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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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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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A little more than a year ago, Al Melchior had the brilliant and beautifully straightforward idea of investigating how strongly pretty much ever Statcast metric correlated with various traditional power metrics and compiling them in one post. He asked me to help out, which I was more than glad to do.

Recently, I saw folks talking about this again, and someone asked specifically about the 2019 season. I figured I could refresh the values from the original post quickly enough (certainly a lot more quickly than I did last time), and it would also help bring pertinent information to the fore for folks neck-deep in draft prep.

Spoiler alert: the results barely changed. But! I do feel more confident in this particular set of values, as I nerded out with programming instead of pulling dozens of different queries from the Baseball Savant search function and constantly getting frazzled.

OK, here’s the goods. For 2019 hitters:

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