Washington Nationals v St Louis Cardinals - Game Two

Predicting baseball is hard y’all.

I say this not because it’s not obvious, but because I’m scanning my various leagues. Even in a great year for me, I have a few teams that just straight up stink.

Providing some solace in times like this is the occasional daily fantasy game. All I have to do is pick a few players for the night and try to beat people in a one-off. And, since it’s one night, there’s little doubt as to the role of chance. It’s hard to crow too loud when you got Alex Rios for his six-hit night when he’s never done that and he was the first to do it this year.

If you like daily fantasy, by the way, you can take me on in a battle this week. Free to enter, $300 on the line, all you have to do is pick a player from eight different tiers over at Draftstreet using this link, then watch the stats that night to see who won. Game on!

In any case, you have to try to win by all means necessary, says the man who’s been typing through pain because of a pickup basketball injury sustained while launching himself towards a meaningless steal in a meaningless game.

So, maybe in the context of trying to beat me this week, here’s a tool you can use. Jeff Zimmerman and Bill Petti developed Edge% to try and quantify pitcher command. That’s a tough thing to do, given that to really do it well, you might want to track where the glove was set up (where the pitcher was supposed to throw it) and where the ball ended up. Instead, Petti and Zimmerman just tracked the ability for a pitcher to get a ‘pitcher’s strike,’ or to hit the sliver on the edges of the strike zone that cause batters fits.

From the research, you might see that this metric sometimes helps explain a pitcher that is outperforming their peripherals. High Edge% pitchers have high strand rates, high ground ball rates, and lower walk rates and home run per fly ball rates — ostensibly because they are ‘pitching to contact’ (their contact rates are higher), but they are getting batters to hit pitchers’ pitches. Pitchers’ pitches shouldn’t end up in the air or over the wall as much.

Here are your top 15 pitches this year, sorted by Edge% (minimum 750 pitches):

Pitcher Edge% FIP ERA K% BB% GB% HR/FB
Brandon McCarthy 21.2% 3.76 5.00 13.5% 3.5% 42.7% 9.6%
Justin Masterson 21.1% 3.42 3.78 24.5% 9.2% 57.1% 11.0%
Justin Wilson 20.5% 3.43 1.98 21.5% 9.7% 54.7% 7.9%
Jordan Zimmermann 20.4% 3.19 2.57 18.0% 3.6% 49.1% 9.1%
Mike Pelfrey 20.3% 4.19 5.63 11.8% 7.1% 45.2% 6.9%
Doug Fister 20.3% 3.30 4.07 18.1% 4.1% 56.1% 9.8%
David Price 20.1% 3.59 4.18 21.8% 4.8% 48.1% 12.9%
Derek Holland 20.0% 2.86 3.19 23.0% 6.5% 40.2% 6.6%
Kevin Correia 20.0% 4.63 4.19 12.5% 4.7% 46.8% 13.7%
Alex Cobb 19.9% 3.54 3.01 22.2% 6.7% 57.1% 17.6%
Bronson Arroyo 19.5% 4.36 3.55 13.7% 4.6% 43.2% 11.6%
Zach McAllister 19.5% 4.22 3.43 15.9% 7.8% 38.4% 8.2%
Vance Worley 19.4% 5.55 7.21 10.7% 6.4% 47.1% 15.5%
Hector Santiago 19.4% 4.20 3.49 24.0% 11.1% 36.2% 10.3%
Scott Kazmir 19.2% 4.59 4.74 21.5% 7.9% 41.4% 15.1%

Jordan Zimmermann leaps off the page. With a mediocre strikeout rate and an ERA that’s much smaller than his FIP, he seems like a candidate for regression. But this stat suggests that perhaps his low-ish home run per fly ball rate is sustainable. And that he might be able to find a way to sustain a low batting average on balls in play. And if you’ve been wondering how Alex Cobb and Bronson Arroyo do it despite bad fastballs and middling FIPs, this might be a reason. Can Justin Masterson actually keep it up?? The magic is gone for Vance Worley, but at least he’s still hitting the edges.

And here’s the bottom 15 in Edge%. If you’re hoping for positive regression from some pitchers, and they are on this list, you might want to reconsider.

Pitcher Edge% FIP ERA K% BB% GB% HR/FB
Jake Westbrook 13.8% 4.17 2.78 9.4% 9.1% 62.6% 7.1%
Esmil Rogers 13.8% 4.03 3.84 15.1% 7.2% 44.8% 10.0%
Scott Diamond 13.9% 5.08 5.52 11.1% 5.5% 46.8% 14.4%
Francisco Liriano 14.0% 2.66 2.20 25.4% 9.3% 51.1% 6.8%
Jason Marquis 14.2% 5.72 3.79 14.8% 13.5% 53.8% 19.8%
Edwin Jackson 14.3% 3.88 5.50 19.4% 8.5% 52.4% 11.5%
Joe Saunders 14.3% 4.52 4.51 12.1% 6.7% 50.1% 12.6%
Yu Darvish 14.5% 3.23 3.02 32.5% 8.5% 43.9% 15.2%
Tim Hudson 14.5% 3.70 4.03 17.2% 6.5% 54.0% 10.4%
Wily Peralta 14.6% 4.39 4.82 13.0% 9.0% 52.8% 11.4%
Zack Greinke 14.7% 3.90 3.91 18.2% 8.0% 40.2% 9.2%
Ervin Santana 14.7% 3.92 2.90 20.0% 5.1% 47.6% 13.6%
Aaron Harang 14.7% 4.16 4.92 19.1% 3.6% 37.5% 12.0%
Justin Grimm 14.8% 4.88 5.88 16.9% 7.5% 44.4% 15.0%
Lucas Harrell 14.8% 5.35 5.06 13.9% 11.4% 53.2% 16.5%

Oof. Well, we knew Francisco Liriano was wild. And to some extent Yu Darvish. But there’s Ervin Santana, throwing only a fastball and slider, and outperforming his FIP by a full run. And maybe Edwin Jackson deserves some of the bad luck coming his way. Maybe Tim Hudson and Aaron Harang have lost some command in their age. And whoo boy is this just another reason to get out in front of that Jason Marquis regression.

One last wrinkle. Though the analysis is old, many of the personnel is the same: Jeff Zimmerman once did umpire projections. If you can find the umpires playing on Friday, you might be able to pair the right umpire with the right command-heavy pitcher to hit the bonus.

Good luck!

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