The 2022 Pod Projections are now available and include over 550 player forecasts! 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.
Yesterday, I compared my starting pitcher ERA Pod Projections to Steamer and discussed what was driving the gap. Let’s now review the pitchers I’m projecting for a worse ERA than Steamer. Remember in yesterday’s article I mentioned that my ERA projections are lower in aggregate, so the gaps are much smaller here. The important part is the ERA relative to the projection set, so these are larger differences than the absolute gaps would indicate.
After comparing my Pod Projections to Steamer for bother home runs and stolen bases, let’s now flip over to starting pitchers. This comparison is far easier, as it’s a ratio stat and therefore won’t need to be converted to the same PA scale. So let’s find out which starting pitchers I am forecasting for an ERA that is most below what Steamer is forecasting.
The Relief Pitcher Episode of the Beat the Shift Podcast – a baseball podcast for fantasy baseball players.
Guests: Greg Jewett & Eric Samulski
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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.
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.
Earlier this offseason I examined the accuracy of several projections. I’m going to give my recommendations today on the best way to balance those findings between getting the best results and keeping the process simple. I’m going to focus on playing time and stats for pitchers and hitters. While there could a way to weigh every single stat of every projection, it’s just a waste of time in my opinion. The best answer is to aggregate the best options.
I know some people will want a more in-depth answer while the following will be too much for others. Some projections, like ATC, are already trying to perfect the mix and still fall short of a straight average. The cause for the disconnect is that some of the stand-alone projections are constantly improving. What may be the best projection mix in one season is suboptimal in the next. I’m willing to have 95% of the projection accuracy and instead spend my time looking for information that the projections might have missed. Read the rest of this entry »
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.
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.
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.