On Tuesday night, the competitors of the LABR Mixed drafted virtually congregated for our annual early/mid-February 15-team draft. Drafts this early are tough, because although in some respects it benefits the prepared and the more highly skilled, there are also many unknowns at this point of the offseason, that once known, could alter a player’s or multiple players’ values, perhaps significantly. In addition, all this additional time between the draft and opening day means more opportunity for injuries to decimate your roster before the season begins! So as a result, and although I’m not sure if this is even mathematically possible, it feels like the early draft time requires both more skill and more luck. Anyway, let’s get to the draft results. The full draft could be found here.

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Now that we’re through understanding the xHR/FB v4.0 equations and its components, let’s finally use it to evaluate past performance and help forecast 2021 performance. Today, I’ll share a list of names who enjoyed a breakout HR/FB rate in 2020 and their xHR/FB rate validated that surge (including surprising marks from players who didn’t play in 2019). Over small samples, luck plays a greater role, so knowing which spikes were real, based on the underlying skills displayed, is more important than ever when looking toward 2021.

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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. 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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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.

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