Weak Contact and the National League Cy Young Race

The National League Cy Young race is an incredibly competitive one, and as Dave Cameron (who has a vote this year) broke down a few weeks ago, much of the differences between the candidates deals with run prevention in a team sense (RA/9-WAR and ERA) versus run prevention in a component sense (FIP, WAR). As a result, there has been considerable discussion on the concept of weak contact, and last week I looked at the role of the Cubs defense in the Chicago pitchers’ low BABIPs. Taking a small step further, let’s use the Statcast to look at weak and strong contact to determine if the Cy Young candidates in the National League have been helping out their defenses.

To whittle down the candidates, I found the pitchers who are among the National League’s top 10 both by WAR and RA/9-WAR — and then added Jose Fernandez, who just missed the second list. This is a list of those pitchers and their respective ERA, FIP and WAR marks.

National League Cy Young Candidates
Name ERA NL Rank FIP NL Rank WAR
Noah Syndergaard 2.63 3 2.34 1 6.1
Clayton Kershaw 1.73 1* 1.68 1* 6.1
Jose Fernandez 2.99 9 2.39 2 5.7
Max Scherzer 2.78 6 3.08 4 5.6
Johnny Cueto 2.86 7 3.06 3 4.9
Madison Bumgarner 2.57 4 3.12 5 4.9
Kyle Hendricks 2.06 1 3.27 6 4.1
Jon Lester 2.40 2 3.45 7 3.9
*Kershaw does not have enough innings to qualify

As you can see, the NL pitchers ranked first and second in ERA only rank sixth and seventh in FIP, which has led to discussions, particularly with regard to Kyle Hendricks, about how to evaluate such discrepancies when discussing a pitcher’s Cy Young candidacy. To examine the type of contact a pitcher is generating, ee can start with a simple look at average exit velocity. Here are the pitchers’ average exit-velocity numbers and MLB ranks, per Baseball Savant.

Exit Velocity of NL Cy Young Candidates
Avg Exit Velocity (mph) MLB Rank
Clayton Kershaw 87.1 6
Kyle Hendricks 87.3 9
Noah Syndergaard 87.5 12
Max Scherzer 87.7 13
Johnny Cueto 88.1 25
Jon Lester 88.3 30
Madison Bumgarner 89.1 60
Jose Fernandez 90.0 106

While the evidence isn’t overwhelming, there is some reason to think that a pitcher has some, if not a lot, of influence over exit velocity, with the bulk of the influence coming from the batter. Those arguing for Kyle Hendricks for the Cy Young would likely say there is a considerable effect and point to the very good exit-velocity numbers and very low BABIP he’s conceded as evidence. That said, Clayton Kershaw has an even better average exit velocity and his BABIP isn’t quite as low as Hendricks’. Which pitcher gets more credit?

We can further break down the exit-velocity numbers by ground balls versus balls in the air (fly balls and line drives), as seen below.

Exit Velocity of NL Cy Young Candidates by Batted-Ball Type
FB/LD EV (mph) MLB Rank GB EV (mph) MLB Rank
Jon Lester 90.1 5 88.0 95
Kyle Hendricks 90.4 11 85.8 36
Clayton Kershaw 90.5 12 84.8 16
Noah Syndergaard 91.2 23 85.0 19
Max Scherzer 91.6 30 85.7 33
Madison Bumgarner 91.7 32 87.3 73
Jose Fernandez 92.1 57 89.2 128
Johnny Cueto 92.4 65 85.3 30
SOURCE: Baseball Savant

Weaker fly balls are going to lead to a lot of outs, and that has certainly been a strength both for Lester and Hendricks, who lead the Cy Young hopefuls by that measure. One thing to note about this collection of pitchers, though: the Cubs and Giants are considered two of the best defenses in baseball. Perhaps unsurprisingly, half the pitchers here belong to one of those two clubs. The Marlins and Dodgers, meanwhile, likely possess above-average defenses, while the Mets are the only team represented above with a defense that grades out as a below-average one.

We know that launch angle plays a role in a batted-ball outcomes, and we can use that data to see how much of a pitcher’s batted balls typically become hits. Baseball Savant now carries expected batting average (xBA) on its site (which August used to illustrate the relative lack of luck being enjoyed by Zach Britton this season), which takes a batted ball’s exit velocity and launch angle and determines how often that play will be a hit. Thus, for many plays, we can make a determination if it is the pitcher helping out the defense or vice versa.

I took xBA and divided it into buckets. First, I took all the batted balls for which the xBA is under .200 to look at the sure outs, then went up to between .200 and .300, between .300 and .400, between .400 and .600 and above .600. There wee around 40,000 batted balls in the first group, around 10,000 each in the next two groups, around 15,000 in the group between .400 and .600, and more than 20,000 in the group at .600+. Here are the BABIPs for each of those groups.

BABIP for xBA buckets
under .200 0.064
.200-.300 0.226
.300-.400 0.339
.400-.600 0.481
.600+ 0.767
SOURCE: Baseball Savant

As you can see, the under-.200 bucket is indeed full of sure outs, as those balls become hits just one time out of 16 batted balls. The chart below shows how the Cy Young candidates have done with those easy outs, and show how many extra outs the pitchers have gained on those plays compared to the average on these types of plays.

NL Cy Young Candidates and the Easy Outs
BABIP H/PA Outs Gained/Lost
Kyle Hendricks 0.028 5 / 177 6.3
Johnny Cueto 0.034 7 / 204 6.1
Jon Lester 0.038 6 / 157 4.0
Noah Syndergaard 0.039 6 / 155 3.9
Max Scherzer 0.046 9 / 194 3.4
Jose Fernandez 0.045 5 / 112 2.2
Madison Bumgarner 0.063 13 / 206 0.2
Clayton Kershaw 0.067 8 / 119 -0.4
SOURCE: Baseball Savant

Almost all of these types of batted balls will get turned into outs, but even in these situations, Hendricks has been lucky. Whether you want to assign that luck to defense or simply the ball going to the right spot, Hendricks has gotten six more outs than the average pitcher would have recorded given roughly the same exit velocity and launch angle. Hendricks is certainly inducing weak contact, but he has fared even better than the contact profile would indicate.

By continuing this exercise in every bucket, we can determine how many outs have been gained or lost this season. The chart below shows all of the buckets as well as the total number outs gained or lost.

Outs Gained by on Expected Batting Average (xBA)
<.200 xBA .200-.300 .300-.400 .400-.600 >.600 xBA Outs Gained/Lost
Jon Lester 4.0 3.1 0.3 4.2 9.4 21.0
Johnny Cueto 6.1 -0.5 4.3 9.6 -5.6 13.9
Kyle Hendricks 6.3 4.5 1.6 2.9 -2.5 12.8
Clayton Kershaw -0.4 1.5 3.5 -0.3 2.1 6.4
Max Scherzer 3.4 -0.5 0.5 2.3 0.1 5.8
Jose Fernandez 2.2 0.8 -2.5 1.7 1.6 3.8
Madison Bumgarner 0.2 2.9 1.2 -3.0 0.8 2.1
Noah Syndergaard 3.9 -3.7 -1.4 -10.9 -3.9 -16.0
SOURCE: Baseball Savant

Because we have the expected batting average based on the contact profile (exit velocity and launch angle), it seems nearly impossible that these respective gains and losses are the product of some sort of pitcher skill. What we have done is neutralized a portion of the weak contact argument by showing how all pitchers with that same contact profile should perform. We can see above where the pitchers gain and lose the most. Jon Lester’s biggest gains have come on the sure outs. Johnny Cueto’s have come on the hard-hit balls that become hits half of the time, while Noah Syndergaard has had the opposite happen to him. Even among teammates, we have differing results, with Cueto benefiting a lot from factors beyond his control while Bumgarner failing to reap the same rewards.

The totals might not seem like much, but keep in mind that roughly one hit per start is going to equal around 50 points of BABIP at the end of the season. Taking these totals and adding them to their current BABIPs, we can see what the BABIP might be based on Statcast batted-ball profile.

NL Cy Young Candidates Adjusted BABIP
BABIP Outs Gained/Lost Adjusted BABIP Difference
Jon Lester .254 21.0 .299 -.045
Kyle Hendricks .242 12.8 .269 -.027
Johnny Cueto .281 13.9 .304 -.023
Clayton Kershaw .261 6.4 .282 -.021
Max Scherzer .248 5.8 .260 -.012
Jose Fernandez .337 3.8 .346 -.009
Madison Bumgarner .272 2.1 .276 -.004
Noah Syndergaard .328 -16.0 .292 .036

While Jon Lester makes the biggest jump by this methodology — and Hendricks appears a little behind him — Hendricks’ jump still places his adjusted BABIP at .269, a pretty low figure. If you’re inclined to believe that there’s a skill involved in suppressing BABIP — and that inducing weak contact plays a role in whether a ball lands for a hit or not — Hendricks’ number still allows support for that claim. It would be foolish not to entertain the considerable impact the Cubs’ defense has had on those batted balls — and clearly Hendricks’ league-leading ERA and low BABIP are not all his doing. However, he is inducing weaker contact than most, although not as weak as Max Scherzer, who has had the benefit/curse of having a lot of his hard hit balls clear the fence and not factor in.

And poor Noah Syndergaard, whose BABIP should be roughly 36 points lower, but apparently has not been helped much by his defense or luck. While he might have benefited from a low home-run rate (8.3% HR/FB) and a high LOB% (77.6%), that appears to have been balanced out by bad luck on balls in play. His very low 2.34 FIP and 2.63 ERA seem roughly where they should be, and it will be interesting to see if his lack of innings (174) holds him back for the Cy Young. The NL Cy Young race is likely to be a close one. It’s fair to look at weak contact if you think it helps, but it is important to look at how that weak contact happens and look at other pitchers in context to determine what kind of role the pitchers play in turning batted balls into outs.

We hoped you liked reading Weak Contact and the National League Cy Young Race by Craig Edwards!

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Craig Edwards can be found on twitter @craigjedwards.

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“However, he is inducing weaker contact than most, although not as weak as Max Scherzer, who has had the benefit/curse of having a lot of his hard hit balls clear the fence and not factor in.”

Why wouldn’t you include HR in this analysis when trying to determine who truly induces weak contact? I get that you’re looking at how much they’ve been helped by defense, so logically HR shouldn’t factor in, but if you’re trying to get a picture of how good a pitcher is at limiting damaging contact, BABIP can’t be everything.

What would it show if you included every HR allowed as a “hit in play” in the adjusted BABIP column? Who would come out ahead then?


I agree. This kind of analysis would greatly benefit from use of BACON rather than BABIP.



yet another thing that’s better with bacon

Shirtless Bartolo Colon

Bacon grease on your fingers also helps fastball movement….

(That’s what I’ve been told anyways)


I think BACON would be a good addition to this article.

I don’t know what the abbreviation would be, but I’d love to see an expected wOBA against (xwOBAA?) using a similar type of analysis. At the very least I think it would be interesting too see how it stacks up against the existing DIPS in predictive value and what data we might need from statcast to improve it.