The Z Files: Shifting Pitchers to Target

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Updated on February 27, 2023 9:16AM EST

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.

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

	Pitcher	No. GB	GB%	BABIP v LHB	GB BABIP v LHB	%SHIFT LHB
1	Tony Gonsolin	267	45.1%	0.202	0.167	83.8%
2	Luis Castillo	302	40.9%	0.255	0.219	83.3%
3	Nathan Eovaldi	176	43.3%	0.264	0.213	79.5%
4	Lance Lynn	289	42.1%	0.277	0.250	76.1%
5	Paolo Espino	176	45.3%	0.320	0.226	73.9%
6	Johnny Cueto	288	42.5%	0.302	0.184	71.7%
7	Joe Musgrove	403	44.2%	0.256	0.173	70.9%
8	Michael Lorenzen	187	55.0%	0.272	0.219	65.8%
9	Mitch White	177	43.2%	0.279	0.160	65.7%
10	Glenn Otto	276	43.8%	0.219	0.143	64.1%
11	Marcus Stroman	285	55.9%	0.309	0.202	63.5%
12	Erick Fedde	279	40.6%	0.289	0.208	63.4%
13	Jon Gray	250	45.8%	0.300	0.219	63.2%
14	Alex Cobb	302	63.2%	0.345	0.288	62.6%
15	Ross Stripling	216	40.9%	0.256	0.183	61.8%
16	Noah Syndergaard	244	45.4%	0.289	0.148	61.7%
17	Zach Thompson	220	41.6%	0.257	0.169	61.7%
18	Sandy Alcantara	480	52.8%	0.230	0.139	61.4%
19	Dane Dunning	333	50.0%	0.314	0.208	61.3%
20	Shohei Ohtani	318	45.3%	0.308	0.173	61.3%
21	Pablo Lopez	394	48.2%	0.282	0.160	60.8%
22	Luis Severino	174	40.8%	0.198	0.170	60.3%
23	Paul Blackburn	241	42.5%	0.305	0.103	60.2%
24	George Kirby	284	43.8%	0.277	0.284	58.1%
25	Zac Gallen	338	53.8%	0.186	0.126	58.0%
26	Tyler Mahle	255	41.0%	0.245	0.254	57.6%
27	Logan Webb	399	62.2%	0.318	0.244	57.5%
28	Frankie Montas	290	49.2%	0.297	0.209	57.5%
29	Aaron Nola	390	41.5%	0.262	0.235	57.3%
30	Adam Wainwright	379	46.2%	0.298	0.233	55.4%
31	Corbin Burnes	407	53.8%	0.274	0.163	54.9%
32	James Kaprielian	295	41.6%	0.256	0.218	54.6%
33	Shane Bieber	355	50.4%	0.284	0.214	53.5%
34	Zach Davies	253	46.8%	0.231	0.162	53.0%
35	Carlos Carrasco	284	51.3%	0.304	0.183	52.5%
36	Kyle Gibson	322	46.2%	0.289	0.176	51.2%
37	Kevin Gausman	291	40.7%	0.317	0.282	51.2%
38	Tyler Wells	184	41.2%	0.217	0.151	51.1%
39	Merrill Kelly	372	42.0%	0.257	0.144	50.7%
40	Zack Greinke	241	52.4%	0.299	0.231	50.6%
41	Kyle Bradish	175	54.8%	0.288	0.200	49.7%
42	Graham Ashcraft	212	61.6%	0.278	0.230	49.5%
43	Brady Singer	306	44.1%	0.295	0.278	48.1%
44	Brad Keller	247	50.6%	0.263	0.241	47.2%
45	Dakota Hudson	274	58.3%	0.301	0.227	45.8%
46	Mitch Keller	322	52.8%	0.322	0.250	45.0%

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
1	Dodgers	52.2	42	70.5	0.232	0.217
2	Astros	50.4	25.4	82.1	0.244	0.217
3	Blue Jays	50.3	42.9	60	0.259	0.225
4	Mariners	45.3	33.8	63.3	0.257	0.237
5	Marlins	43.9	36.1	55.5	0.248	0.238
6	Twins	43.8	25.7	70.1	0.259	0.243
7	White Sox	38.4	18.2	63.4	0.259	0.230
8	Cubs	38	22.2	60.2	0.233	0.216
9	Mets	37.7	31.4	46.9	0.269	0.252
10	Tigers	37	19.3	66.5	0.279	0.242
11	Rangers	35.8	21.2	62.6	0.256	0.229
12	Red Sox	35.3	15	70.7	0.270	0.237
13	Angels	35.2	19.9	62.6	0.260	0.240
14	Nationals	32.8	12.7	60.8	0.264	0.244
15	Athletics	32.1	19.7	52.5	0.255	0.228
16	Giants	31.8	16.7	55.9	0.250	0.225
17	Pirates	31.3	16.2	52.1	0.244	0.238
18	Braves	29.6	22.8	38.6	0.254	0.233
19	Reds	29.5	10.1	55.3	0.256	0.244
20	Cardinals	27.9	17.4	44.3	0.243	0.224
21	Diamondbacks	27.9	14.7	49.5	0.256	0.223
22	Phillies	27.3	15.5	45.3	0.269	0.257
23	Rays	27.3	20.1	42.3	0.257	0.229
24	Brewers	26.8	14.6	47.4	0.243	0.229
25	Royals	26.3	13	47.7	0.272	0.252
26	Yankees	25.8	10.4	53.1	0.256	0.233
27	Padres	24.6	5	54.7	0.268	0.238
28	Orioles	23.5	10	46.3	0.254	0.236
29	Guardians	22.6	5.9	45.6	0.239	0.226
30	Rockies	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.