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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Though it might feel like he’s older, Asdrubal Cabrera will be entering just his age 33 season. It will happen while in a Rangers uniform, as he signed a one-year deal with the club on Tuesday. This makes it his sixth team in six seasons. Despite moving all around the country, he has remained pretty consistent offensively. How might the move to Globe Life Park (GLP) affect his stable performance? Let’s check the park factors, comparing GLP to Citi Field (Mets), as he spent the majority of last season in New York.

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We all knew that Sonny Gray’s days as a New York Yankee had come to an end, and finally, the Reds acquired him and immediately signed him to a three-year extension. Moving to the National League is a good thing. Will the park switch improve his chances to rebound as well? Let’s check the 2017 park factors.

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Yesterday, I identified and discussed six hitters who appeared near the top of my barrels per true fly ball (Brls/TFB) leaderboard. As a reminder, Brls/TFB is one of the primary components of my xHR/FB rate equation and is calculated by taking the Statcast Barrels count and dividing by the batter’s fly balls minus infield flies. Today, let’s check in on the hitters who surprisingly ranked closer to the bottom of the leaderboard, rather than the top.

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Two weeks ago, I discussed the hitters who enjoyed surges and suffered declines in their barrels per true fly ball (Brls/TFB) rates, a metric that’s one of the primary components of my xHR/FB rate equation. Today, let’s identify and discuss some surprises at or near the top of the Brls/TFB leaderboard. Some of these hitters did post high HR/FB rate, so those hitters might not be so surprising as Brls/TFB leaders, but rather the rankign validates the success.

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