Seranthony Domínguez

P Seattle Mariners

Age 32 RHP 6'1" / 225 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR
2024	29	MLBBAL	63	59	4.45	4.51	26.7%	8.2%	0.0
2025	30	MLBTOR	67	63	3.16	3.24	30.3%	13.0%	1.0
2026	31	MLBSEA	67	62	3.63	4.29	25.0%	11.2%	0.2
2027PROJ	32	MLBSEA	51	54	4.09	4.29	27.9%	11.3%	0.2
2028PROJ	33	MLBSEA	38	40	4.38	4.57	27.0%	11.8%	-0.0
2029PROJ	34	MLBSEA	27	28	4.68	4.87	26.1%	12.2%	-0.1

Last 3 seasons WAR 1.2 · Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs67

K%65

Form2.81 ERA (proj 4.09) · K 20.6% (proj 27.9%) · BB 8.8% (proj 11.3%) ▼ last 90d · 26 IP through Oct 27

CompsJumbo Diaz (age 32) · Mike Broadway (age 31) · Jose Veras (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10799vR 91

ERA4.09

FIP4.29

K%27.9%

BB%11.3%

K-BB%16.6%

Proj IP54

Value2027 · WAR

Runs saved vs league avg · 54 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL3535

CMD5555

K†6565

Statcast Percentiles

FB Velo

p9097.3

Whiff%

p6727.9%

p6525.0%

xwOBA vs

p670.295

Contract

No contract data on file. Pre-MLB players and minor-league free agents do not yet have salary projections — entries are added at first 40-man roster placement.

Career Stats + Projections

Real MLB stats plus park-adjusted projections — this season (so far / rest / full), then future seasons and career totals.

show model inputsbasis chips: RAW = as recorded · CTX = with park/league context · A+C = actual + context combined · MLE = level/park-neutral

Methodology▸ how WAR & MLE are computed

Stats are park-neutral MLB equivalents. The "TEAM" column shows current org affiliation only — we do NOT project at that team's park.
Rates (K%, BB%, HR-BF) are park-stripped; MiLB stat lines are translated to their MLB-equivalent via per-level MLE factors.
WAR is computed from projected runs prevented vs a replacement pitcher (the marginal arm a team would call up if this pitcher were unavailable) — calibrated to the standard WAR scale.

Year	Age	Lvl	Team	G	GS	IP	BF	K	BB	HR	GS%	IP/G	K%	BB%	K-BB%	HR%	ERA	FIP	WAR
▸ Career — actual stats, one row per year (as recorded, no adjustments)
▸ Career — adjusted (each season translated to a park/league-neutral MLB-equivalent line; levels combined per year)
▸ Model inputs — neutral MLE (past 3 seasons + 2027 so far)
2015	20	ROK MLE	GCL Phillies	2	1	8	36	9	7	1	50%	3.8	25.0%	19.4%	5.6%	2.8%	2.35	4.98	—
2015	20	ROK RAW	GCL Phillies	2	1	8	36	9	7	1	50%	3.8	25.0%	19.4%	5.6%	2.8%	2.35	5.19	—
2016	21	A MLE	Lakewood BlueClaws	10	10	48	193	50	20	2	100%	4.8	19.2%	12.2%	6.9%	1.6%	2.42	3.89	—
2016	21	A RAW	Lakewood BlueClaws	10	10	48	193	50	20	2	100%	4.8	25.9%	10.4%	15.5%	1.0%	2.42	2.81	—
2017	22	MIX MLE	2 levels	17	15	68	285	82	34	6	88%	4.0	22.2%	13.3%	8.9%	2.7%	3.72	4.40	—
2017	22	MIX RAW	2 levels	17	15	68	285	82	34	6	88%	4.0	28.8%	11.9%	16.8%	2.1%	3.72	3.34	—
2018	23	MIX MLE	3 levels	64	0	75	292	95	23	4	0%	1.2	32.0%	8.7%	23.4%	1.6%	2.65	2.38	+2.0
2018	23	MIX RAW	3 levels	64	0	75	292	95	23	4	0%	1.2	32.5%	7.9%	24.7%	1.4%	2.65	2.18	+2.1
2019	24	MLB MLE	Philadelphia Phillies	27	0	25	110	29	12	3	0%	0.9	26.4%	10.9%	15.5%	2.5%	4.01	3.65	+0.3
2019	24	MLB RAW	Philadelphia Phillies	27	0	25	110	29	12	3	0%	0.9	26.4%	10.9%	15.5%	2.7%	4.01	3.79	+0.2
2021	26	MIX MLE	4 levels	20	3	21	104	24	12	5	15%	1.1	22.3%	13.3%	9.0%	1.8%	7.59	3.90	+0.2
2021	26	MIX RAW	4 levels	20	3	21	104	24	12	5	15%	1.1	23.1%	11.5%	11.5%	4.8%	7.59	5.58	-0.3
2022	27	MIX MLE	2 levels	55	0	52	211	62	23	4	0%	0.9	29.5%	10.1%	19.3%	1.8%	2.94	2.89	+1.0
2022	27	MIX RAW	2 levels	55	0	52	211	62	23	4	0%	0.9	29.4%	10.9%	18.5%	1.9%	2.94	3.04	+0.9
2023	28	MIX MLE	2 levels	59	0	52	232	53	23	7	0%	0.9	21.4%	9.8%	11.6%	2.9%	3.63	4.13	+0.3
2023	28	MIX RAW	2 levels	59	0	52	232	53	23	7	0%	0.9	22.8%	9.9%	12.9%	3.0%	3.63	4.14	+0.3
2024	29	MLB MLE	Baltimore Orioles	63	0	59	255	68	21	12	0%	0.9	26.7%	8.2%	18.4%	4.7%	4.45	4.49	+0.0
2024	29	MLB MLE	Baltimore Orioles	63	0	59	255	68	21	12	0%	0.9	26.7%	8.2%	18.4%	4.7%	4.45	4.49	+0.0
2024	29	MLB RAW	Baltimore Orioles	63	0	59	255	68	21	12	0%	0.9	26.7%	8.2%	18.4%	4.7%	4.45	4.51	+0.0
2025	30	MLB MLE	Toronto Blue Jays	67	0	63	261	79	34	5	0%	0.9	30.3%	13.0%	17.2%	1.9%	3.16	3.25	+1.0
2025	30	MLB MLE	Toronto Blue Jays	67	0	63	261	79	34	5	0%	0.9	30.3%	13.0%	17.2%	1.9%	3.16	3.25	+1.0
2025	30	MLB RAW	Toronto Blue Jays	67	0	63	261	79	34	5	0%	0.9	30.3%	13.0%	17.2%	1.9%	3.16	3.24	+1.0
2026	31	MLB MLE	Seattle Mariners	67	0	62	260	65	29	9	0%	0.9	25.0%	11.2%	13.8%	3.6%	3.63	4.35	+0.1
2026	31	MLB MLE	Seattle Mariners	67	0	62	260	65	29	9	0%	0.9	25.0%	11.2%	13.8%	3.6%	3.63	4.35	+0.1
2026	31	MLB RAW	Seattle Mariners	67	0	62	260	65	29	9	0%	0.9	25.0%	11.2%	13.8%	3.5%	3.63	4.29	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			389	0	368	1551	425	159	44	0%	0.9	27.4%	10.3%	17.2%	2.8%	3.52	3.64	+4.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	SEA	51	0	54	228	64	26	7	0%	1.1	27.9%	11.3%	16.6%	3.2%	4.09	4.29	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	SEA	38	0	40	168	45	20	6	0%	1.0	27.0%	11.8%	15.3%	3.4%	4.38	4.57	-0.0
2029	34	MLB MLE	SEA	26	0	28	117	31	14	4	0%	1.1	26.1%	12.2%	13.9%	3.7%	4.68	4.87	-0.1
2030	35	MLB MLE	SEA	17	0	18	74	19	10	3	0%	1.0	25.2%	12.8%	12.4%	4.1%	5.00	5.21	-0.1
2028	33	MLB CTX	SEA	38	0	40	169	46	20	6	0%	1.1	27.0%	11.8%	15.3%	3.4%	4.38	4.57	-0.0
2029	34	MLB CTX	SEA	27	0	28	118	31	14	4	0%	1.0	26.1%	12.2%	13.9%	3.7%	4.68	4.87	-0.1
2030	35	MLB CTX	SEA	17	0	18	76	19	10	3	0%	1.1	25.2%	12.8%	12.4%	4.1%	5.00	5.21	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		470	0	453	1910	520	203	57	0%	1.0	27.2%	10.6%	16.6%	3.0%	3.73	3.79	+4.1
/ 200 IP	—	RAW		207	0	200	843	230	90	25	0%	1.0	27.2%	10.6%	16.6%	3.0%	3.73	3.79	+1.8
Career	—	MLE		451	29	532	2239	579	248	58	6%	1.2	25.9%	11.1%	14.8%	2.6%	3.52	3.74	+3.7
/ 200 IP	—	MLE		170	11	200	842	218	93	22	6%	1.2	25.9%	11.1%	14.8%	2.6%	3.52	3.74	+1.6

How the minor-league lines were translated

One row per minor-league stint. Open a row to see each step: the raw rate, the park adjustment, the league/level factor, the age adjustment (hitters) and the resulting MLE rate. Factors are the same ones listed on the MLE Factors page.

2021 AAALehigh Valley IronPigs · 12 IPFIP 3.44 → 3.90 (+0.47)K% 25.4% → 22.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.40%	—	×0.880	22.35%
BB% (unintentional) /BF	12.70%	—	×1.050	13.33%
HR / BF /BF	1.59%	÷0.957	×1.100	1.82%

Factors used: pitcher AAA level factors — K ×0.88, BB ×1.05, HR ×1.10. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 91).

Result: FIP 3.44 → 3.90, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2021 AAReading Fightin Phils · 5 IPFIP 11.39 → 10.86 (−0.53)K% 10.7% → 8.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.71%	—	×0.810	8.68%
BB% (unintentional) /BF	10.71%	—	×1.100	11.79%
HR / BF /BF	14.29%	÷1.344	×1.200	12.76%

Factors used: pitcher AA level factors — K ×0.81, BB ×1.10, HR ×1.20. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 169).

Result: FIP 11.39 → 10.86, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2018 AAReading Fightin Phils · 13 IPFIP 0.51 → 1.16 (+0.64)K% 36.7% → 29.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.73%	—	×0.810	29.76%
BB% (unintentional) /BF	4.08%	—	×1.100	4.49%
HR / BF /BF	0.00%	÷1.164	×1.200	0.00%

Result: FIP 0.51 → 1.16, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2017 A+Clearwater Threshers · 62 IPFIP 3.39 → 4.40 (+1.01)K% 28.8% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.85%	—	×0.770	22.21%
BB% (unintentional) /BF	11.54%	—	×1.150	13.27%
HR / BF /BF	2.31%	÷1.058	×1.250	2.73%

Factors used: pitcher A+ level factors — K ×0.77, BB ×1.15, HR ×1.25. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 112).

Result: FIP 3.39 → 4.40, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2017 ROKGCL Phillies · 5 IPFIP 2.76 → 4.03 (+1.26)K% 28.0% → 19.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.00%	—	×0.680	19.04%
BB% (unintentional) /BF	16.00%	—	×1.250	20.00%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 100).

Result: FIP 2.76 → 4.03, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2016 ALakewood BlueClaws · 48 IPFIP 2.79 → 3.89 (+1.09)K% 25.9% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.91%	—	×0.740	19.17%
BB% (unintentional) /BF	10.36%	—	×1.180	12.23%
HR / BF /BF	1.04%	÷0.864	×1.300	1.56%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 73).

Result: FIP 2.79 → 3.89, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2015 ROKGCL Phillies · 8 IPFIP 4.98 → 6.88 (+1.90)K% 25.0% → 17.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.680	17.00%
BB% (unintentional) /BF	19.44%	—	×1.250	24.31%
HR / BF /BF	2.78%	÷1.000	×1.400	3.89%

Result: FIP 4.98 → 6.88, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

Career Path Comps

What happened to similar pitchers from this age · Y-axis: ERA+ (100 = league avg, 150 = elite, 80 = below) · Bands: P10–P90 outer, P25–P75 inner · Gold: OGILVIE projection

			Profile at Match Age					Next 3 Yrs				Career
Comp	Age	Role	FIP	K%	BB%	HR/BF	BABIP	FIP	K%	K-BB%	IP	ΔFIP	Low FIP	IP	Match
Seranthony Domínguez (OGILVIE)	—	—	4.29	27.9%	11.3%	3.2%	—	4.80	26.4%	14.2%	85	+0.51	—	—	—
Jumbo Diaz R 2016 · 3.85 FIP	32	SU	3.85	22.3%	9.2%	2.7%	.247	3.28	23.0%	13.8%	141	-0.57	2.80	319	78
Mike Broadway R 2018 · 4.65 FIP	31	SU	4.65	23.0%	10.6%	3.9%	.282	—	—	—	—	—	1.74	195	74
Jose Veras R 2013 · 3.51 FIP	32	SU	3.51	23.6%	8.3%	2.5%	.234	4.28	21.9%	10.2%	65	+0.77	3.37	422	73
Scot Shields R 2007 · 3.58 FIP	32	SU	3.58	24.1%	10.3%	2.2%	.271	4.26	20.3%	7.3%	127	+0.68	2.77	686	72
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	72
Chris Hatcher R 2017 · 4.31 FIP	32	SU	4.31	24.5%	7.4%	4.3%	.291	5.32	18.0%	8.4%	36	+1.01	2.51	246	71
Kazuhiro Sasaki R 2000 · 3.93 FIP	32	SU	3.93	29.4%	9.8%	3.8%	.212	2.88	24.9%	19.3%	161	-1.05	2.76	223	71
Steve Delabar R 2015 · 3.74 FIP	32	SU	3.74	25.0%	9.9%	2.7%	.246	4.62	20.4%	4.8%	24	+0.88	2.43	229	71
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	70
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	69
Jeff Fulchino R 2011 · 4.44 FIP	31	SU	4.44	20.4%	11.1%	3.0%	.302	—	—	—	—	—	3.36	178	68
Dale Thayer R 2013 · 3.62 FIP	32	SU	3.62	23.7%	7.4%	2.9%	.287	3.85	20.6%	15.0%	103	+0.23	2.75	239	67
Dan Winkler R 2022 · 3.87 FIP	32	SU	3.87	24.4%	14.0%	1.9%	.281	—	—	—	—	—	2.40	231	67
Huascar Brazobán R 2022 · 3.77 FIP	32	SU	3.77	28.5%	11.4%	3.0%	.275	3.49	23.3%	12.9%	187	-0.28	3.08	370	67
Sergio Santos R 2015 · 4.17 FIP	32	SU	4.17	24.7%	8.2%	3.9%	.283	—	—	—	—	—	1.36	178	66
Brian Sanches R 2011 · 4.35 FIP	32	SU	4.35	19.6%	11.1%	2.7%	.250	—	—	—	—	—	3.39	229	66
Josh Fields R 2018 · 3.48 FIP	32	SU	3.48	21.4%	6.5%	2.5%	.224	7.56	18.9%	9.5%	25	+4.08	1.80	330	66
Santiago Casilla R 2012 · 3.98 FIP	32	SU	3.98	20.2%	6.6%	3.2%	.246	3.23	21.6%	13.7%	166	-0.75	2.76	647	65
Nate Jones R 2018 · 4.09 FIP	32	SU	4.09	23.4%	10.9%	2.9%	.279	5.85	21.4%	13.8%	38	+1.76	2.57	307	65
Leonel Campos R 2017 · 3.70 FIP	30	SU	3.70	25.7%	11.8%	2.3%	.241	—	—	—	—	—	2.62	168	65

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 1,234 reliever careers (754 from 1990-2025 + 480 historical, 1901-2023), also matched on career-to-date at the same age. * Historical (Lahman) careers: rates era-adjusted to 2001-24 league levels; WAR is an OGILVIE proxy.

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	2.5	46	27.3%	11.0%	1.17	3.76
Brad Boxberger ('23)	2.4	43	28.7%	11.5%	1.19	3.50
Tony Sipp ('19)	2.5	46	25.2%	10.5%	1.32	3.72
Matt Barnes ('24)	2.3	52	29.0%	10.6%	1.02	4.21
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*
Tim Stoddard ('75-'89, 4 WAR*)	3.6	66	26.2%*	11.2%*	1.14*	4.24*
Rob Murphy ('85-'95, 6 WAR*)	3.1	69	25.2%*	9.2%*	1.07*	3.94*