The Z Files: Shifting Pitchers to Target

The Z Files: Shifting Pitchers to Target

This article is part of our The Z Files series.

There has been a lot of talk regarding how individual hitters, or at least a specific set of batters will be aided with MLB legislating the shift. There has been a lot less chatter regarding how pitching will be influenced.

To briefly review, starting in 2023, at least two fielders need to be situated on either side of the keystone, all with their cleats off the outfield grass (or turf). Non-conventional alignments are permitted, such as the third baseman where the shortstop normally plays and the shortstop guarding up the middle, but still to the left of the second base bag. Additionally, outfielders are free to set up anywhere, including the left fielder playing the rover position in right field, with the other two outfielders patrolling the rest of the space.

In 2022, shifts were deployed 55 percent of the time to lefty swingers with right-handed batters seeing the shift in 19.6 percent of their trips to the dish. Overall, 33.9 percent of all plate appearances were defensed via the shift.

As fantasy managers, we all want to know which players will be most affected, and by how much. Unfortunately, it's not that simple. Well, identifying players most likely to benefit is plausible. The extent, however, is just a guesstimate.

Some are frustrated by that last sentence. Don't we have all sorts of shift data? Can't we overlay park images? 

The objective is to determine how the outcome of a batted ball event with the shift would differ without it.

There has been a lot of talk regarding how individual hitters, or at least a specific set of batters will be aided with MLB legislating the shift. There has been a lot less chatter regarding how pitching will be influenced.

To briefly review, starting in 2023, at least two fielders need to be situated on either side of the keystone, all with their cleats off the outfield grass (or turf). Non-conventional alignments are permitted, such as the third baseman where the shortstop normally plays and the shortstop guarding up the middle, but still to the left of the second base bag. Additionally, outfielders are free to set up anywhere, including the left fielder playing the rover position in right field, with the other two outfielders patrolling the rest of the space.

In 2022, shifts were deployed 55 percent of the time to lefty swingers with right-handed batters seeing the shift in 19.6 percent of their trips to the dish. Overall, 33.9 percent of all plate appearances were defensed via the shift.

As fantasy managers, we all want to know which players will be most affected, and by how much. Unfortunately, it's not that simple. Well, identifying players most likely to benefit is plausible. The extent, however, is just a guesstimate.

Some are frustrated by that last sentence. Don't we have all sorts of shift data? Can't we overlay park images? 

The objective is to determine how the outcome of a batted ball event with the shift would differ without it. Here is the problem. There is no way to know where the defense would have been set without the shift. The result presumes knowing the defense deployed, not to mention the approach of the pitcher and batter may have been different. The exact outcome cannot be extracted with 100 percent certainty.

Calm down, everything will be okay. We don't know where an outlying BABIP will regress either. The same goes for HR/FB, LOB% and any other metric with an unknown (or luck) element. Mathematically, the way to handle the shift is the same as these other examples: regressing actual to expected.

Maybe this has been semantics all along, but instead of saying the result of a certain batted ball event would have been different, it's better to discern the probability of varying outcomes. Doing so unveils expected hits (xHits). Now we have an entity against which to regress, helping quantify the effect of the shift.

The news gets even better. The xHits metric on Statcast essentially achieves what we all desire: the probability of outcomes for a batted ball without the shift. Via research not presented here, using BABIP, it can be demonstrated the shift primarily influences groundballs from left-handed batters. If you look at the xHit leaderboard, measured by the biggest delta between expected and actual batting average, it's not a coincidence there is a preponderance of left-handed or switch-hitting batters at the top.

It's not perfect, but regressing actual hits to expected hits helps 2023 hitting projections account for the shift. What about pitching?

The chief difference between evaluating hitters and pitchers is some hitters were burdened by the shift in just about all of their at-bats, while the percentage of plate appearances a pitcher was backed by a shift is far more balanced, clustering around the 33.9 percent league average. Some teams shifted more, while right-handed pitchers probably faced more lefty swingers.

With a lower percentage of at-bats affected by the shift, the xHits metric for pitchers reflects more of the non-shift related variance. Many formulaic-driven projection systems will already regress to xHits, but there are likely some hurlers destined to give up a few more knocks in 2023.

The following table shows the BABIP (batting average on balls in play) of hard grounders since 2015, broken down by batter and pitcher handedness:

Season

ALL

LHB v RHP

LHB v LHP

RHB v LHP

RHB v RHP

2015

0.251

0.241

0.241

0.257

0.258

2016

0.254

0.243

0.233

0.266

0.262

2017

0.252

0.244

0.247

0.255

0.258

2018

0.247

0.233

0.239

0.255

0.256

2019

0.243

0.232

0.238

0.257

0.247

2020

0.236

0.210

0.219

0.254

0.255

2021

0.241

0.227

0.233

0.251

0.250

2022

0.240

0.219

0.226

0.251

0.253

It's clear the shift has aided both right-handers and southpaws when facing left-handed batters. The catch is volume, as displayed here via number of groundballs in each matchup:

Season

LHB v RHP

LHB v LHP

RHB v LHP

RHB v RHP

2015

18122

4482

10010

20695

2016

17087

3917

9425

20829

2017

16640

3919

9532

20624

2018

16119

4135

10157

19496

2019

15829

4105

9700

19624

2020

6117

1448

3428

6796

2021

15180

4472

10515

19194

2022

15715

3727

10037

20549

The class of hurlers most likely to be hurt by shift legislation is groundball righties. Before diving into some examples, similar breakdowns of outfield line drive and flyball data are far less conclusive, so it's best to focus on groundball data. For what it's worth, the same conclusion was derived for similar investigations with hitters.

Okay, here's the fun part. What follows is a list of starting pitchers most likely to have an increase in BABIP this season. This is beyond any natural regression they may be in line to receive. The filters are:

  • Right-handed pitcher
  • Minimum 40 innings pitched
  • Minimum 45 percent groundball rate
  • Shift deployed at least 45 percent of the time against left-handed batters
PitcherNo. GBGB%BABIP v LHBGB BABIP v LHB%SHIFT LHB
1Tony Gonsolin26745.1%0.2020.16783.8%
2Luis Castillo30240.9%0.2550.21983.3%
3Nathan Eovaldi17643.3%0.2640.21379.5%
4Lance Lynn28942.1%0.2770.25076.1%
5Paolo Espino17645.3%0.3200.22673.9%
6Johnny Cueto28842.5%0.3020.18471.7%
7Joe Musgrove40344.2%0.2560.17370.9%
8Michael Lorenzen18755.0%0.2720.21965.8%
9Mitch White17743.2%0.2790.16065.7%
10Glenn Otto27643.8%0.2190.14364.1%
11Marcus Stroman28555.9%0.3090.20263.5%
12Erick Fedde27940.6%0.2890.20863.4%
13Jon Gray25045.8%0.3000.21963.2%
14Alex Cobb30263.2%0.3450.28862.6%
15Ross Stripling21640.9%0.2560.18361.8%
16Noah Syndergaard24445.4%0.2890.14861.7%
17Zach Thompson22041.6%0.2570.16961.7%
18Sandy Alcantara48052.8%0.2300.13961.4%
19Dane Dunning33350.0%0.3140.20861.3%
20Shohei Ohtani31845.3%0.3080.17361.3%
21Pablo Lopez39448.2%0.2820.16060.8%
22Luis Severino17440.8%0.1980.17060.3%
23Paul Blackburn24142.5%0.3050.10360.2%
24George Kirby28443.8%0.2770.28458.1%
25Zac Gallen33853.8%0.1860.12658.0%
26Tyler Mahle25541.0%0.2450.25457.6%
27Logan Webb39962.2%0.3180.24457.5%
28Frankie Montas29049.2%0.2970.20957.5%
29Aaron Nola39041.5%0.2620.23557.3%
30Adam Wainwright37946.2%0.2980.23355.4%
31Corbin Burnes40753.8%0.2740.16354.9%
32James Kaprielian29541.6%0.2560.21854.6%
33Shane Bieber35550.4%0.2840.21453.5%
34Zach Davies25346.8%0.2310.16253.0%
35Carlos Carrasco28451.3%0.3040.18352.5%
36Kyle Gibson32246.2%0.2890.17651.2%
37Kevin Gausman29140.7%0.3170.28251.2%
38Tyler Wells18441.2%0.2170.15151.1%
39Merrill Kelly37242.0%0.2570.14450.7%
40Zack Greinke24152.4%0.2990.23150.6%
41Kyle Bradish17554.8%0.2880.20049.7%
42Graham Ashcraft21261.6%0.2780.23049.5%
43Brady Singer30644.1%0.2950.27848.1%
44Brad Keller24750.6%0.2630.24147.2%
45Dakota Hudson27458.3%0.3010.22745.8%
46Mitch Keller32252.8%0.3220.25045.0%

All of the data is when facing left-handed batters. Everything but the first BABIP column is specific to at-bats resulting in a grounder.

Personally, I don't feel determining an xBABIP is worthwhile, as there is too much variance and the Statcast xHit metric in essence already does it. However, there are some key names on this list.

  • In today's culture, it's probably more acceptable to tell a joke in a foreign accent than dare say anything negative regarding Sandy Alcantara, but his BABIP is likely to suffer, especially with Luis Arraez and Joey Wendle playing up the middle.
  • It's ironic that Shohei Ohtani, the batter, is slated to benefit from legislating the shift, but the pitcher version will likely find it a detriment.
  • Tony Gonsolin is still due a visit from the regression monster, but a good deal of his perceived "good luck" on batted balls was actually boosted by a high shift rate with an extremely low BABIP on left-handed groundballs.

An anecdotal blurb can be provided for each pitcher on the list. I chose these three to call attention to the quality of pitcher with which we're dealing, especially the first two. It's necessary to at least consider everyone listed is a candidate for a high BABIP this season.

Keep in mind every pitcher will likely surrender more hits due to the lack of a shift, so it's not just the group above. However, their risk factors are higher.

Another general approach is investigating which teams used the shift more last season. That said, a higher number may not be indicative of allowing more hits this season. The fielders can still be cleverly deployed, and they may simply be better defenders, so they may still generate a lower BABIP. Even so, knowing the teams being forced to adhere to the new rules more than others is useful when tracking the effect, understanding the variance associated with work of this nature.

Team

Overall Shift%%

Shift% vs. RHB

Shift% vs. LHB

RHB GB BABIP

LHB GB BABIP

1Dodgers

52.2

42

70.5

0.232

0.217

2Astros

50.4

25.4

82.1

0.244

0.217

3Blue Jays

50.3

42.9

60

0.259

0.225

4Mariners

45.3

33.8

63.3

0.257

0.237

5Marlins

43.9

36.1

55.5

0.248

0.238

6Twins

43.8

25.7

70.1

0.259

0.243

7White Sox

38.4

18.2

63.4

0.259

0.230

8Cubs

38

22.2

60.2

0.233

0.216

9Mets

37.7

31.4

46.9

0.269

0.252

10Tigers

37

19.3

66.5

0.279

0.242

11Rangers

35.8

21.2

62.6

0.256

0.229

12Red Sox

35.3

15

70.7

0.270

0.237

13Angels

35.2

19.9

62.6

0.260

0.240

14Nationals

32.8

12.7

60.8

0.264

0.244

15Athletics

32.1

19.7

52.5

0.255

0.228

16Giants

31.8

16.7

55.9

0.250

0.225

17Pirates

31.3

16.2

52.1

0.244

0.238

18Braves

29.6

22.8

38.6

0.254

0.233

19Reds

29.5

10.1

55.3

0.256

0.244

20Cardinals

27.9

17.4

44.3

0.243

0.224

21Diamondbacks

27.9

14.7

49.5

0.256

0.223

22Phillies

27.3

15.5

45.3

0.269

0.257

23Rays

27.3

20.1

42.3

0.257

0.229

24Brewers

26.8

14.6

47.4

0.243

0.229

25Royals

26.3

13

47.7

0.272

0.252

26Yankees

25.8

10.4

53.1

0.256

0.233

27Padres

24.6

5

54.7

0.268

0.238

28Orioles

23.5

10

46.3

0.254

0.236

29Guardians

22.6

5.9

45.6

0.239

0.226

30Rockies

18.5

11

27.8

0.246

0.233

Hmm. The table is displayed via greatest deployment of the shift, and the first four teams were all in the playoffs. Oh wait, seven of the bottom 13 clubs were in the playoffs and shifted the least. Never mind.

Reiterating that the noise in small samples will be deafening, it'll be interesting to track the component BABIP for the top teams. A way to reduce some of the variance is to group teams into subsets, maybe six groups of five teams, then gauging how each is faring in terms of component BABIP.

For those curious (like me), there was a very small correlation between the percentage of shifts versus LHB (and RHB) and the resulting BABIP.

LHB-0.19
RHB-0.16

The correlation is negative, indicating more shifting led to a slightly lower BABIP. Based on these numbers, one can argue if legislating the shift is even necessary, but that's a discussion for another day, in another setting. It's a reality, and hopefully you've been enlightened with respect to some significant starting pitchers who carry some cause for concern.

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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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