*The 2021 Pod Projections are now available!*
Now that my xHR/FB rate v4.0 equation has been revealed, let’s dive into the components of the equation and get to know each one of them. We’ll start with Std Dev of Dist FB+LD (SDD), which is the standard deviation of the batter’s fly balls and line drives. This is important because just knowing the average distance of those batted balls isn’t enough. A batter who alternates 400 foot blasts with 200 foot blasts is going to record a much greater HR/FB rate than the batter with consistent 300 foot shots (this batter likely owns a 0% HR/FB rate). Yet, both hitters will post the same average distance of 300 feet. So we need to differentiate between these two hitters, and SDD is how we do it.
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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.
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!
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.
Every season, I hope a superstar joins the Rockies, or one of their better hitters gets shipped out or signs elsewhere. It simply hasn’t happened very often, but it’s fun to see how the most unique park effects in baseball influences or has influenced the hitter’s results. We now get another chance to learn about the Coors Effect. This time with Nolan Arenado, who was just traded to the Cardinals. The challenge here is that Arenado played through a shoulder injury that ultimately resulted in an injured list stay. We don’t know exactly how long it affected him and can’t possibly quantify its exact effects. So if he improves significantly from last year’s .308 wOBA (and he certainly should), how many are going to conveniently ignore his health and claim the Coors Effect is a myth? Anywho, it’s something to remember, so let’s now compare the park factors for each park and how Arenado’s projection should be affected.
Yesterday, I reviewed my starting pitcher ERA upside guys, comparing my Pod Projections with Steamer. Today, I’ll review the downside guys.
We have completed our reviews of Pod Projections versus Steamer on the hitting side, so now let’s move on over to pitching. I only published two pieces, comparing our ERA projections. Today, I’ll recap the names included on my ERA upside list. Given that even a full season ERA reflects a good deal of good and/or bad fortune, only 60 games and around 12 starts means ERAs landed all over the place, sometimes far off from consensus forecasts. So it’ll be fun to see where we projected these pitchers and where their ERAs actually settled.
On Friday, it was reported that Eddie Rosario, career Minnesota Twin, had signed a one-year contract with the division rival Indians. Is there a strong perception about either of these parks and their affects on offense? I don’t think so, unless you’re super familiar with park factors. However, just because it’s more difficult to guess off the top of our head like Yankee Stadium’s home run boosting power, doesn’t mean there’s no change in factors. So let’s consult them and see how Rosario’s offense might be affected.
Today, I finish reviewing my hitter Pod Projections versus Steamer projections comparisons, ending with the stolen base downside guys. These are the hitters who I forecasted a significantly higher PA/SB rate than Steamer. Let’s see how they ended up performing.
Today, we review the last of my hitting projections comparisons between my 2020 Pod Projections and Steamer. We move along to stolen base, where like I did for home runs, I turned it into a ratio, this time of plate appearances. That way playing time forecasts won’t influence the comparison. We begin with my review of the stolen base upside guys. Do note that I seem to be consistently lower on stolen base forecasts than Steamer, so this list isn’t very long.