Yesterday, I reviewed the list of hitters who might benefit from an increased rate of pulled flied balls heading into the season. Today, I’ll recap the hitters who already hit a ton of fly balls in 2017, but posted below average Brls/True FB and Avg FB Dist marks. The thinking here is they needed every bit of that FB Pull% to knock balls over the fence, considering they were below average in the other two metrics. Let’s see how they performed.

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In early February, I essentially listed hitters who have posted low fly ball pull percentages. However, I added two important additional filters to finalize the list — above average Brls/True FB and Avg FB Dist. The idea here is that they already have the raw power to hit their balls out of the park, but they just need to hit those balls more to the pull side where the fences are closer. These are the guys I considered potentially benefiting most from more pulled flies. Let’s find out whether they did indeed increase their FB Pull% and if that jump also fueled a spike in HR/FB rate.

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Yesterday, I reviewed my preseason list of nine potential strikeout rate surgers. Today, let’s find out how the 10 pitchers I identified as potential strikeout rate decliners actually performed.

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In mid-February, I used my pitcher xK% equation to highlight nine pitchers that may have strikeout rate upside for 2018. The important thing to remember is that xK% is not a projection and isn’t meant to be forward-looking. Rather, it’s descriptive, or backward looking, the same way you might think about FIP/xFIP/SIERA. I have found many pitchers who consistently over/underperform their xK% marks for whatever reason, so I never blindly use previous xK% marks as my next season forecast. That said, let’s find out how the pitchers with the greatest positive divergence between 2017 K% and 2017 xK% performed in 2018.

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Let’s wrap up my 2018 Pod Projection reviews, ending with Tommy Pham. A surprise breakout in 2017, we didn’t truly know what we were going to get in 2018. A move to Tampa mid-season didn’t exactly benefit his offense as he merely went from one pitcher friendly park to another. Let’s find out how he ended up performing compared to my projection.

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Shohei Ohtani was one of the most hyped rookies in recent memory. Fueling the excitement was the fact that he came over from Japan and he was a two-way player, both pitching and hitting, and doing each well…very well. Obviously, attempting to translate performance from a foreign league to MLB is difficult, and then you have to actually forecast the upcoming season, which is far tougher than a regular run of the mill projection for an MLB veteran.

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Let’s return to reviewing my preseason Pod Projections, this time with Luis Castillo, a popular sleeper choice and breakout candidate, and for good reason. My original writeup is here.

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Yesterday, I reviewed my stolen base upside guys, after comparing my Pod Projections to Steamer projections in the preseason. Today, I’ll review my downside guys. Let’s see how they performed.

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Let’s continue reviewing my Pod vs Steamer series, pitting my preseason Pod Projections against the Steamer forecasts. Today, I’ll recap my stolen base upside picks. I used PA/SB as my ratio to account for any differences in playing time projections. Let’s see how the group performed.

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After just about a week of the season, I published a list of eight hitters whose early fly ball rates were significantly higher than their 2017 marks. I dubbed these guys as potential fly ball revolution participators. Let’s find out if they were able to maintain their FB% gains and whether it led to a home run surge.

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