2017 Pod Projections: Lance McCullers

The Pod Projections are back! My projections are based on the methodology shared in my eBook Projecting X 2.0, and the process continues to evolve and improve.

A couple of months ago, I received my first Pod Projection request from a commenter, and that request was for Astros starting pitcher Lance McCullers. The 23-year-old made his Houston debut in 2015, as he made 22 starts and posted an impressive 3.22 ERA with excellent underlying skills. Unfortunately, he followed up that freshman effort by finding himself on the disabled list for what amounted to about half the season. He dealt with both shoulder and elbow issues, which limited him to just 14 starts. Although his control deserted him, he still posted strong skills, en route to an identical ERA as 2015. Now, he’s the newest member of my 2017 LABR Mixed Draft squad, so let’s find out what I projected his 2017 results to look like.

IP: 150 (26 games started)

Shoulder and elbow injuries, oh my. McCullers was limited to just 89 total innings last year and his all-time high in a season is just 157.2, set in 2015. Obviously, when trying to project the innings (or even plate appearances on the hitting side) of someone who has dealt with health issues, it’s really just a shot in the dark. He could throw 190 and make me look silly, spend half the season on the DL again and make me look too optimistic, or get babied by the Astros to ensure his health and hit 150 on the dot.

K%: 27.4%

McCullers boosted his strikeout rate from 24.8% in 2015 to 30.1% last season, backed by a surge in SwStk% from 9.8% to 13.0%. The explanation is easy — he pumped up his already high usage of his curve ball from 36% to nearly 50%, almost all of which came at the expense of his fastball. And his curve was awesome (20.9% SwStk% in 2016). For the vast majority of pitchers, more curves and fewer fastballs is going to pump up strikeout rate. His xK% marks were right in line, coming in at 24% and then 29%. I can’t imagine he throws 50% curves again, which would push his strikeout rate back down, but the whiff rate on both his fastball and changeup could rebound, offsetting some of the strikeout rate loss he might suffer if he throws fewer curves.

BB%: 9.8%

Interestingly, McCullers threw nearly the same rate of strikes from 2015 to 2016 (actually, his 2016 mark was slightly higher), yet his walk rate spiked from 8.3% to 12.8%! His xBB% does validate the jump somewhat, all of which was due to a dramatic increase in the rate of 3-0 counts he ran. Control is the one skill young pitchers could improve in a hurry, and this projection is just barely below his career average at this point.

GB%/LD%/FB%: 51% / 21.5% / 27.5%

This is essentially his career average, as the additional curves, plus more grounders from his fastball, resulted in his ground ball rate rocketing from 46.5% to 57.3%. The increased grounder rate on his fastball probably won’t last, but all those curves should keep his ground ball rate well above the league average of 44.7%.

HR/FB%: 12.0%

Over a sample of just 206.2 innings, I almost completely ignore actual HR/FB rate up to this point. HR/FB rate doesn’t even stabilize until 400 fly balls, and he’s at just 150 for his career! Minute Maid Park boosts home runs by 10%, and although the league average HR/FB rate last season was 12.8%, I figure some regression there and that McCullers will end up close to it. With a 10% career mark, I’m at least allowing for a tiny possibility there’s some HR/FB rate suppression skills here.

BABIP: .303

Lots of grounders, a slightly higher than average LD%, and few pop-ups usually result in a higher than league average BABIP. Not .383 high, of course, like he posted last year, but certainly worse than the league, which posted a .298 mark last season. The Astros infield is projected to be about average, so that’s no help.

Below is my final projected pitching line, along with the other systems for comparison:

For a 23-year-old coming off injury issues with just 206.2 MLB innings to his name, we are in amazing agreement on his 2017 performance line. Check out how close the ERA projections from Pod, Fans, and ZiPS are!

Because of the surprising similarities, there’s not a whole lot to say here. Though, the projected .327 BABIP by ZiPS is a perfect example of why I vastly prefer Steamer for pitching projections. ZiPS simply doesn’t regress toward league average enough and takes actual results in the luck metrics like gospel. Steamer knows better. If the system forecasted a more reasonable BABIP, it would have easily been the most bullish projection.