The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X, and the process continues to evolve and improve.

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Yesterday, I discussed four pitchers whose xBB% marks were well below their actual BB% marks. Given their penchant for throwing strikes, they look like good bets to improve their control this season. Today, I’ll check in on five starting pitchers who posted walk rates below their xBB% marks. A jump in walk rate could be in their futures, which would result in a higher ERA and WHIP, all else being equal.

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Along with the xK% formula I devised and updated last year, I also developed an xBB% equation. Unfortunately, it isn’t as good as the expected strikeout rate formula, as our community has really struggled to determine how the various underlying skill metrics should interact to result in an expected walk rate. That said, my version is still the best I’ve seen, so it’s better than nothing. But there are seemingly consistent underperformers and overperformers, so don’t take a pitcher’s xBB% as gospel.

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Yesterday, I shared with you the names of a group of starting pitchers whose xK% was much higher than his actual K%. So today I’m going to check in on the opposite end of the spectrum, those starters whose xK% was well below their actual K% marks. You might call this group your bust candidates. Well, that is if people are paying for a repeat of their 2014 strikeout rates of course.

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Last year, I shared my updated xK% equation, which blends a pitcher’s overall strike percentage with his called, swinging and foul strike rates to produce an expected strikeout rate. While its wonderfully high adjusted R-squared tells us how well it works, it’s even better used when dealing with a small number of innings since the metric uses pitches thrown, greatly alleviating sample size issues. It’s therefore a huge help when projecting young starting pitchers for my Pod Projections who were up in the Majors for just a grande sized cup of coffee.

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At the end of October, I asked a very serious, important question — Is There Any Hope For Mike Moustakas? I was quite negative, which is something that is hard not to come away feeling when staring at Mike Moustakas‘ statistical record. But then upon typing yesterday’s ESPN Home Run Tracker Analysis: The Upsiders and finding his name appear, I discovered a positive, and then another one and then another one. Behold, reasons for optimism! These new discoveries are tempting me to up my Moustakas forecast in my 2015 Pod Projections (available now!).

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Last week, I dug into the 2014 ESPN Home Run Tracker data and identified six hitters who applying the findings of my previous analysis are candidates for HR/FB rate downside this season. Today it’s time to take a look on the other side of the coin, those hitters who appear to have some serious upside. This time I’ll check in on the bottom of the “Just Enough” percentage (JE%) leaderboard. The thinking here is that an errant gust of wind here and there or a millimeter difference in where the bat meets the ball could push balls that had been fly outs to head just over the wall.

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Two years ago, I conducted an exhaustive study of ESPN Home Run Tracker data. At that time, it was the primary tool I used to validate a batter’s power, before we got into the sexy new batted ball distance, and then combined that with SDD and angle. The short story is I found that hitters with an unusually high percentage of “Just Enough” (JE) homers saw their HR/FB rates decline the following year, significantly more than the rest of the player population. On the other hand, those who hit a high percentage of “No Doubt” (ND) homers maintained their HR/FB rate much better than the rest of the group.

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On Monday, I unveiled the xHR/FB rate equation I devised a year ago. Yesterday, I began my look back at 2014, discussing a selection of hitters whose xHR/FB rates suggest serious HR/FB rate upside this year. Today, I finish my look back, this time at the overachievers. These are the bust candidates, at least when it comes to home runs, assuming of course they sustain similar batted ball distances, average absolute angles and standard deviation of distances (SDD). Since my formula ignores home ballpark which absolutely plays a major role, I will mention it as a possible explanation for such overperformance as warranted.

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Yesterday, I unveiled the xHR/FB rate equation I devised a year ago. So today I’ll begin my look back at 2014 and discuss a selection of hitters whose xHR/FB rates suggest serious HR/FB rate upside this year, assuming of course they sustain similar batted ball distances, average absolute angles and standard deviation of distances (SDD). Since my formula ignores home ballpark which absolutely plays a major role, I will mention it as a possible explanation for such underperformance as warranted.

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