The Z Files: How the Universal DH Affects Pitchers

The Z Files: How the Universal DH Affects Pitchers

This article is part of our The Z Files series.

There's been a lot of talk about the impact of a universal designated hitter, but mostly on the hitting side of the ledger. Lately however, the effect on pitchers, especially those of the Senior Circuit variety, has taken the spotlight.

There are two primary questions:

1. Are there specific hurlers earmarked for a greater of lesser adjustment relative to the average?

2. How do rankings adjust when facing a National League designated hitter is factored in?

Let's starter with the former, since it's necessary to investigate the latter. Data from the last three seasons will be considered. To be included in the study, a pitcher needed to face the equivalent of five innings against pitchers. There are 38 pitchers who satisfied that criteria each of the prior three seasons.

If it can be demonstrated individual pitchers' performance against pitchers each season is similar, then outlying 2019 numbers can be predictive of 2020 performance including a universal DH. That is, if a hurler's skills facing a pitcher were significantly better than the league average increase, he'll be hurt more by facing a designated hitter.

If it's predictive, skills derived from numbers with and without facing pitchers should be consistently better or worse than league average over the three-year span. Correlation will be used to test this, comparing 2017 to 2018, 2018 to 2019 and 2017 to 2019. If there is correlation, the results can be considered predictive. Random correlation suggests the skills exhibit variance, so trusting 2019 data in 2020 projections

There's been a lot of talk about the impact of a universal designated hitter, but mostly on the hitting side of the ledger. Lately however, the effect on pitchers, especially those of the Senior Circuit variety, has taken the spotlight.

There are two primary questions:

1. Are there specific hurlers earmarked for a greater of lesser adjustment relative to the average?

2. How do rankings adjust when facing a National League designated hitter is factored in?

Let's starter with the former, since it's necessary to investigate the latter. Data from the last three seasons will be considered. To be included in the study, a pitcher needed to face the equivalent of five innings against pitchers. There are 38 pitchers who satisfied that criteria each of the prior three seasons.

If it can be demonstrated individual pitchers' performance against pitchers each season is similar, then outlying 2019 numbers can be predictive of 2020 performance including a universal DH. That is, if a hurler's skills facing a pitcher were significantly better than the league average increase, he'll be hurt more by facing a designated hitter.

If it's predictive, skills derived from numbers with and without facing pitchers should be consistently better or worse than league average over the three-year span. Correlation will be used to test this, comparing 2017 to 2018, 2018 to 2019 and 2017 to 2019. If there is correlation, the results can be considered predictive. Random correlation suggests the skills exhibit variance, so trusting 2019 data in 2020 projections is ill advised.

Several factors were analyzed by percent difference between facing everyone and excluding pitchers: expected ERA, actual ERA, K/9, BB/9, HR/9 and Hits/9. Here are the results:

 

'17 to '18

'18 to '19

'17 to '19

Expected ERA

0.33

0.12

-0.02

Actual ERA

-0.11

-0.03

-0.14

K/9

-0.27

0.17

-0.09

BB/9

0.27

0.17

-0.20

HR/9

-0.06

0.08

-0.01

Hits/9

0.28

0.17

-0.03

If you're hoping to identify specific pitchers to target or avoid, there isn't much on which to hang your hat. It's probably best to discount the 2017 to 2019 data, since more organic skills changes are more likely to have occurred over a two-year span than only one. Still, even with those changes, if data facing a pitcher were useful, it would be reflected.

It's fair to wonder if there's correlation with expected ERA (calculated using a personal method). Even if there is, it's masked in the haze of actual ERA. Still, most projections regress towards expected ERA, so it's worth further investigation.

When conducting studies of this nature, sample size is a concern. Basing a conclusion on just 38 data points is sketchy, though Bill James taught us extreme results in small samples can be real, and the above correlations are extreme. As such, in lieu of looking at individual pitchers, the yearly data will be parsed into rolling segments, with overlap at each section. The pitchers qualifying for the study each season will be sorted by K/9, BB/9, HR/9 and Hits/9. The percentage skills difference will again be the specific metric investigated.

Each season's qualified pitchers were ranked in order of the above stats. Each season was parsed into six data points with 50 percent overlap. For example, a data set of 105 would average places 1-30, 15-45, 30-60, 45-75, 60-90 and 75-105. The three seasons had a sample between 95 and 102.

As an example, the qualified pitchers were sorted by K/9. The percentage difference between overall K/9 and K/9 against non-pitchers was calculated, along with the percentage difference between ERA with and without facing pitchers. The corresponding rolling averages were determined. The data was resorted by BB/9, then HR/9, then Hits/9, for all three seasons.

I'll spare you the array of tables that mostly demonstrate a whole boatload of randomness, but there's some evidence pitchers already giving up a high number of hits will be less hurt by facing a designated hitter. Keep in mind, the data is crunched on a percentage basis, so each extra hit results in a lower percentage increase. Still, it's interesting to note there is a class of pitchers less susceptible to facing a designated hitter.

This result corroborates some recent work by colleague and the brainchild of ZIPS, Dan Szymborski, published on Fangraphs. Szymborski identified groundball pitchers as less vulnerable to extra damage via the designated hitter. Because the BABIP (batting average on balls in play) on grounders is higher than flyball BABIP, groundball pitchers tend to allow more hits anyway, consistent with the above findings.

As far as the first question posed above is concerned, the data strongly points towards lumping performance facing pitchers in with BvP (batter versus pitcher), streaks, slumps, first/second half players, clutch, etc. There are likely examples where each of the above is real. The problem is, individuals aren't identifiable.

Obviously, anyone is free to impose a narrative, self-validating why a specific batter owns a certain pitcher, is a second half hitter, or folds under pressure. In this instance, it's your prerogative to target or avoid a pitcher based on 2019's numbers against pitchers. However, the data contends you have a 50/50 shot at being right.

While it would have made great fodder to now thumbnail pitchers helped or hurt most by the universal DH, it's not happening. On the other hand, this facilitates addressing the second question above, since performance adjustments can be applied in the aggregate, thus generating adjusted pitching projections.

Please keep in mind eliminating National League pitchers from lineups is only one necessary adjustment. Once schedules and venues are clarified, those changes will need to be incorporated as well. However, that information is still unknown, so the ensuing discussion incorporates my current projections, assuming a 162-game, standard schedule. In some cases, playing time expectations need to be refined, so consider the following more of a rough illustration than something that can be used to generate a personal draft strategy.

There are a few ways to estimate the effect of National League pitchers no longer strutting to the plate. One way is to assume the numbers in the American League carry over, with the concurrent premise National League designated hitters will produce similarly to their American League counterparts. The Junior Circuit isn't setting the bar very high (.252/.339/.467 last season), so it's plausible. Another is to use the difference in performance between National League pitchers when they do and don't face pitchers. The reason I have elected to use this pathway is that stripping out the designated hitters from the American League and pitchers hitting from the National League renders stronger National League lineups. That is, if the National League designated hitters match their cross-league brethren, more runs will be scored by the Senior Circuit. This method isn't perfect, as it presumes the National League designated hitters will perform as average hitters, but that's not implausible.

For what it's worth, American League pitchers also take a hit, as they're now likely facing more National League lineups, which in the aggregate are superior to what they would have faced during interleague play in a standard season. Under the proposed 82-game schedule, each team would play 30 interleague contests, a much higher proportion than in the standard 162-game schedule.

With that as a backdrop, here are my personal adjusted rankings for a 15-team mixed league. Again, they're subject to change once the official 2020 plan is announced and differ from the site's rankings.

PlayerLGTMNEWOLD
Gerrit ColeANYY12
Justin VerlanderAHOU23
Jacob deGromNNYM31
Max ScherzerNWAS44
Zack GreinkeAHOU59
Josh HaderNMIL610
Walker BuehlerNLAD75
Charlie MortonATAM813
Jack FlahertyNSTL96
Mike ClevingerACLE1014
Shane BieberACLE1112
Clayton KershawNLAD128
Blake SnellATAM1316
Lucas GiolitoACHW1417
Stephen StrasburgNWAS157
Kirby YatesNSDP1615
Chris PaddackNSDP1711
Liam HendriksAOAK1820
Hyun-Jin RyuATOR1926
Roberto OsunaAHOU2024
Corey KluberATEX2125
Jose BerriosAMIN2232
Tyler GlasnowATAM2327
Carlos CarrascoACLE2439
Frankie MontasAOAK2544
Mike MinorATEX2636
Taylor RogersAMIN2733
Nick AndersonATAM2834
Kenley JansenNLAD2940
Mike SorokaNATL3018
Brandon WoodruffNMIL3121
Aroldis ChapmanANYY3238
Aaron NolaNPHI3319
James PaxtonANYY3442
Will SmithNATL3537
Sean ManaeaAOAK3641
Eduardo RodriguezABOS3749
Jesus LuzardoAOAK3848
Luis CastilloNCIN3923
Shohei OhtaniALAA4050
Brad HandACLE4146
Lance LynnATEX4253
Patrick CorbinNWAS4322
Giovanny GallegosNSTL4443
Ken GilesATOR4557
Kyle HendricksNCHC4629
Brandon WorkmanABOS4752
Sonny GrayNCIN4828
Hansel RoblesALAA4954
Yu DarvishNCHC5031
Yonny ChirinosATAM5160
Ryan YarbroughATAM5255
Carlos MartinezNSTL5335
Jose UrquidyAHOU5466
Raisel IglesiasNCIN5562
Brendan McKayATAM56128
Edwin DiazNNYM5770
Julio TeheranALAA5887
Masahiro TanakaANYY5974
Seth LugoNNYM6076
Trevor BauerNCIN6130
Lance McCullers Jr.AHOU6286
Dellin BetancesNNYM6375
Jose LeclercATEX6477
Ryan PresslyAHOU6581
Matthew BoydADET6683
Zack WheelerNPHI6745
Hector NerisNPHI6871
Alex ColomeACHW6978
Kenta MaedaAMIN7079
Archie BradleyNARI7163
Michael PinedaAMIN7293
Joe MusgroveNPIT7351
Sean DoolittleNWAS74100
Madison BumgarnerNARI7547
Zach PlesacACLE76134
Adrian HouserNMIL7758
Scott ObergNCOL7894
Jake OdorizziAMIN7995
John MeansABAL8098
Emilio PaganNSDP81102
Marco GonzalesASEA8296
Dinelson LametNSDP8359
Nate PearsonATOR84104
Chase AndersonATOR85110
A.J. PukAOAK86109
Dylan BundyALAA8791
Chad GreenANYY88105
Zac GallenNARI8956
Dallas KeuchelACHW90124
Ross StriplingNLAD91101
Julio UriasNLAD9267
Marcus StromanNNYM9364
Max FriedNATL9461
Aaron CivaleACLE95130
Tony WatsonNSFG96171
Keone KelaNPIT97106
Kevin GinkelNARI98121
John BrebbiaNSTL99149
Brent SuterNMIL100107
Luke WeaverNARI10184
David PriceNLAD10272
Craig KimbrelNCHC10392
Ian KennedyAKCR104108
Yusmeiro PetitAOAK105117
Anthony DeSclafaniNCIN10688
Pedro BaezNLAD107116
Chris BassittAOAK10897
Mike FiersAOAK109120
Will HarrisNWAS110115
Joe JimenezADET111123
Joey LucchesiNSDP11265
Robert StephensonNCIN113132
Yusei KikuchiASEA114119
Jordan MontgomeryANYY115129
Reynaldo LopezACHW116131
Miles MikolasNSTL11780
Matt ShoemakerATOR118125
Robbie RayNARI11969
Caleb SmithNMIA12073
Brad KellerAKCR121144
Tommy KahnleANYY122133
German MarquezNCOL12368
Adam OttavinoANYY124137
Mike FoltynewiczNATL12585
Rich HillAMIN126135
Andrew HeaneyALAA127112
Nathan EovaldiABOS128167
Mychal GivensABAL129138
Kwang-Hyun KimNSTL130146
Rick PorcelloNNYM13182
Domingo GermanANYY132140
Blake TreinenNLAD133152
Tyler ClippardAMIN134147
Diego CastilloATAM135148

It's important to remember the performance delta between players is greatest at the top. In terms of expected earnings, a $4 or $5 difference results in just a spot or two drop at the top of the rankings, around 10 spots for those in the 25-35 range, growing to 15 and then 20 places later on.

None of this is perfect. We have no idea how the National League designated hitters will perform. Furthermore, all the data analyzed includes the usual strategy of working around the lower part of the order to get to the pitcher, which won't be happening. Still, there's ample information to gauge the effect of the universal designated hitters on pitching rankings, and in some cases it's significant. Upper and mid-range American League starters are far more desirable than in previous seasons, as evidenced by this table showing how many AL pitchers are included in each group:

SP Rank

NEW

OLD

1-30

19

13

31-60

17

14

61-90

14

16

Total

50

43

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ABOUT THE AUTHOR
Todd Zola
Todd has been writing about fantasy baseball since 1997. He won NL Tout Wars and Mixed LABR in 2016 as well as a multi-time league winner in the National Fantasy Baseball Championship. Todd is now setting his sights even higher: The Rotowire Staff League. Lord Zola, as he's known in the industry, won the 2013 FSWA Fantasy Baseball Article of the Year award and was named the 2017 FSWA Fantasy Baseball Writer of the Year. Todd is a five-time FSWA awards finalist.
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