Yesterday, I compared batter HR/FB rates to my xHR/FB rates to identify nine hitters who posted xHR/FB rates significantly higher than their actual marks, suggesting upside this season if they are able to maintain those underlying skills. Today, I’ll check in on those hitters on the opposite end of the spectrum, with a list of six batters who overperformed their xHR/FB rates most. If the underlying skills driving the xHR/FB rates stick in 2019, these hitters are at serious risk of HR/FB rate regression.

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While my xHR/FB rate equation wasn’t developed to serve as a next season projection, you could certainly use it as a historical guide, just the way you would normally review actual HR/FB rate. That’s precisely what I do to formulate my Pod Projections. One of the various ways to utilize xHR/FB rate is to compare the hitter’s mark to his actual mark to determine how “real” the result was. Today, I’ll identify and discuss 9 hitters who posted xHR/FB rates significantly higher than their actual marks, suggesting upside this season if they are able to maintain those underlying skills.

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We humans love to categorize and label things. Are you an extrovert or introvert? Does your favorite band play rock or pop music? Is that movie you saw last night a drama or romcom? Are pancakes a breakfast food or the best dinner ever? So it’s no surprise that this infatuation with ensuring everything fits into a box has spread to fantasy baseball. But I just don’t get it.

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Yesterday, I discussed 10 hitters with big HR/FB rates whose marks were actually validated by their xHR/FB rates. Comparing HR/FB rate to xHR/FB rate helps guide my 2019 Pod Projections. Now let’s flip it and check on the hitters who posted surprisingly low HR/FB rates, but that were actually validated by low xHR/FB rates.

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One of the first steps you must take en route to completing a player projection is determining if the previous season’s performance was “for real”. We all use historical statistics as our baseline for future forecasts, and the Pod Projections are no different. How do we come up with a home run projection? There are a bunch of components driving that projection, one of which is the hitter’s HR/FB rate. We could use my xHR/FB rate equation to look back and help determine whether a hitter’s actual HR/FB rate was real. So let’s begin with the guys who posted high HR/FB rates that xHR/FB completely supported. Though the validation doesn’t automatically mean a repeat is in the cards, there’s certainly better odds than if the metric suggested great fortune was primarily behind the mark.

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Alas, it’s Pod Projections time, as the 2019 forecasts are now available! As usual, I’ll dive into my projection methodology (detailed in Projecting X 2.0) by sharing my process on several hitters and pitchers.

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On Tuesday, the Blue Jays signed Freddy Galvis to a one-year contract. This could be a signal that the Jays have given up on Devon Travis at second base, who was terrible both offensively and defensively last season, which would push Lourdes Gurriel Jr. to second, opening up shortstop for Galvis. Amazingly, Galvis has now been an every day player for four straight seasons, and yet has never posted a wOBA exceeding .298. Will a move to Toronto, playing half his games at the Rogers Centre, be the spark he needs to finally get that wOBA over .300? Let’s check the park factors.

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Yesterday, I identified and discussed five hitters who made surprise appearances near the top of the average fly ball distance leaderboard. Today, I’ll talk bottom dwellers. Let’s find out which surprising hitters found themselves bringing up the rear. Like I did with the leaders, I won’t include the hitter if he also appeared on the surprise barrels per true fly ball laggard list as well.

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About a week ago, I shared the surprising hitters who finished amid the top tier in barrels per true fly ball rate, a metric I created that acts as one of the primary components of my xHR/FB rate equation. Another major component of the equation is average fly ball distance (Avg FB Dist), which isn’t typically discussed, as it’s not on the default Statcast leaderboard. So let’s find out which surprising hitters, who hit at least 30 fly balls, finished near the top in the metric. I also decided not to include any hitters that also appeared on the Brls/TFB surprise leaders list.

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On Saturday, A.J. Pollock signed a four-year deal with the Dodgers, after spending his entire career with the Diamondbacks. Before the introduction of the humidor last season, Chase Field (Diamondbacks) was one of the most hitter friendly parks in baseball. But the humidor changed that as intended. So now we have a real park factor battle between his old home and his new Dodger Stadium home. Since the FanGraphs 2018 park factors haven’t been published yet, I will share a comparison from another source. Unfortunately, this source is missing strikeout and walk factors, which could certainly have an effect.

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