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2012 Pod Pitcher Projections: Matt Moore

Posted By Mike Podhorzer On February 16, 2012 @ 12:00 am In Projecting X,Projections,Starting Pitchers | 10 Comments

In an effort to boost my post views and comment total, I went for the obvious to analyze my projections for next. Given the fact that Matt Moore has recently appeared on several 2013 Hall of Fame ballots, I thought it would be timely to determine just how worthy of a vote he may be. I present to you America’s Next Great Pitcherâ„¢. And yes, I have dibs on royalties if that becomes a future reality show.

Previous Pitcher Projection Articles:
Introduction
Michael Pineda
Jeremy Hellickson
Homer Bailey

IP: 180. He threw 174.1 innings last year between the minors and majors, including the post-season. He will probably be on an innings limit, so it is highly doubtful he reaches 200 innings.

LOB%: 74%. For a rookie with no history to use, I kept his LOB% projection the same as what my expected LOB% formula spit out. As a reminder, the league average LOB% last year among starters was 71.6%. My formula takes the pitcher’s skills into account, so although we lump it into the luck metric category, better pitchers do typically post a better LOB%.

GB%/LD%/FB%: 42%/19%/39%. This represent only a tick below the league average ground ball rate. In the minors, he consistently posted ground ball rates in the low 40% range, while in his minuscule 9.1 innings sample with the Rays last year, he posted a 42.9% mark.

HR/FB%: 10%. Unless the pitcher calls an extreme ballpark home, a rookie will always get a 10% projection from me for this metric.

BABIP: .300. Like HR/FB ratio, there has to be a compelling reason to project a rookie for anything other than around a league average BABIP.

BB/9: 3.1. He posted a 3.1 mark in Triple-A last season and a 2.5 mark in Double-A. His Major League Equivalents (MLEs) translated those rates to an overall 2.5 between the two levels, as league and park factors apparently increased walks, which were then adjusted downward for the translation. However, as recently as 2010, Moore’s control was only so-so, as he posted a 3.8 mark in High-A, and that followed a 5.1 mark in Single-A the prior year. It is clear his control has improved a great deal, but I felt most comfortable projecting an increase over last year’s MLE.

K/9: 9.8. I probably had the most trouble projecting his strikeout rate. Last year, his MLE K/9 was an amazing 10.7. He has actually never posted a strikeout rate below 11.5 at any stop in his professional career. That is insane. It was in only 9.1 innings, but his SwStk% was 14.2%! The leading starter last year (Michael Pineda) in SwStk% was at just 11.8%. So why then did I project a sub-10.0 K/9? I simply could not bring myself to project a potentially major league leading strikeout rate from a rookie. Zack Greinke’s K/9 is going to drop, so Brandon Morrow really is the only competition I would say for the K/9 title. It clearly appears that Moore has the skills to do it, but I think it would be foolish to actually project such a feat at this point.

With all the metrics I project manually completed, it is time to unveil my full projected stat line and compare it to the other projection systems.

System IP W ERA WHIP SO K/9 BB/9 GB%/LD%/FB% LOB% BABIP HR/FB
Pod 180 13 3.27 1.18 196 9.8 3.1 42%/19%/39% 74.1% 0.300 10%
RotoChamp 160 13 3.21 1.19 183 10.3 2.8 ??? ?? 0.314 ??
Fans (27) 167 12 3.20 1.15 191 10.3 3.0 ??? 77.5% 0.300 ??

It is pretty crazy to see the ERA projections so close. For the most part, all the numbers projected by RotoChamp and the Fans are relatively close and look reasonable. The only number that jumps out at me is RotoChamp’s BABIP projection. As I pointed out in the Jeremy Hellickson article, it seems like something wacky is going on with their BABIP projecting methodology. I am going to guess the projection was influenced by the ridiculously small sample last year when Moore’s BABIP was .381. We all know that mark is meaningless, but I cannot think of any other reason to project a BABIP 8% higher than last year’s league average.


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