Author Archive: Mike Podhorzer

Finally, it’s time for the main event! After weeks discussing the history and research, correlations, the xHR/FB v4.0 equation itself, and various xHR/FB rate components, we now set our sights toward 2021. Today, I will identify and discuss a handful of fantasy relevant names that underperformed their xHR/FB rates most significantly. Remember that this doesn’t automatically mean we should be projecting a higher HR/FB rate this season. But perhaps rather than take the hitter’s actual HR/FB rate at face value, we should substitute our xHR/FB rate mark when reviewing his historical marks and making a 2021 projection.

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Last week, I used my xHR/FB v4.0 equation to share the names of the hitters who either enjoyed a HR/FB rate surge from 2019 or posted a surprise mark in 2020 after not playing in 2019. The wrinkle is that these players all posted xHR/FB rates that validated the HR/FB rate spikes. Today, let’s discuss hitters on the opposite end of the spectrum — those that suffered a surprise decline in HR/FB rate that was confirmed as a legit falloff by xHR/FB rate.

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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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