*The 2021 Pod Projections are now available!*

Last week, I unveiled my newest xHR/FB rate equation, v4.0. Since, I have been diving into each of the components to better understand the context, what’s good, and what’s bad. Yesterday, we had some Barrel FB% fun, so today, let’s move along to Barrel LD%. While I’ve used Barrel FB% often in the past, I have never used Barrel LD%. It’s a good thing I investigated the metric, because it actually correlates slightly better year-to-year than Barrel FB%, though it correlates with HR/FB rate a bit less so.

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*The 2021 Pod Projections are now available!*

Last week, I unveiled my newest xHR/FB rate equation, v4.0. Since, I have been diving into each of the components to better understand the context, what’s good, and what’s bad. Today, let’s move along to Barrel FB%. Barrels is my most favorite metric developed by the Statcast crew because it combines exit velocity (EV) with launch angle (LA). All I need to know is whether a ball was classified as a barrel or not and I will know whether there’s a good chance it went for a homer or stayed in the park. The rate is a percentage of those batted balls with the optimal combination of EV and LA, and it is far superior than just looking at average exit velocity and launch angle.

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*The 2021 Pod Projections are now available!*

Last week, I unveiled my newest xHR/FB rate equation, v4.0. On Thursday, I dove into Std Dev of Dist FB+LD, which is one of the components of the equation and not a metric that is typically discussed because the values need to be calculated manually. Today, let’s move along to Avg Dist FB+LD, which is a much more familiar metric. It’s simply the average distance of a batter’s fly balls and line drives. Naturally, all else being equal, the higher the average, the greater the HR/FB rate.

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*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.

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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!

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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.

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Yesterday, I reviewed my home run upside list, where I compared my Pod Projections to Steamer using AB/HR rate. Today, let’s now review my home run downside list.

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Last week, I reviewed my starting pitcher K% surgers list, so today, I’ll review my starting pitcher K% decliners list, which was assembled using my pitcher xK% equation. Strikeout rates tend to bounce around throughout the season, so it’s pretty silly to be evaluating the accuracy of this list considering the pitchers only made 11 or 12 starts. But it’s all we have, so let’s get to it.

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Let’s move over to the pitching side where I’ll start my 2020 forecast reviews with strikeout rate, or K%. As a reminder, there is never, ever, ever a reason to evaluate K/9 instead of K%. A denominator based on outs is at risk of being heavily influenced by BABIP, walks issues, field errors, HR/FB rate, etc, because higher numbers in those metrics extend innings and result in additional batters faced, giving the pitcher more opportunities to strike out a batter, even though the denominator has remained the same. That can’t happen when your denominator is total batters faced, like in K%, as more batters faced in an inning will reduce K%, as it should, as opposed to having no effect on K/9.

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