Gregory Soto

P Pittsburgh Pirates

Age 32 LHP 6'1" / 247 lbs MLB peak 0.7 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR
2024	29	MLBBAL	66	53	4.42	2.99	26.6%	9.8%	1.0
2025	30	MLBNYM	70	60	4.18	2.79	25.1%	8.2%	1.3
2026	31	MLBPIT	69	67	3.63	2.92	27.7%	9.5%	1.3
2027PROJ	32	MLBPIT	54	57	3.74	3.50	27.8%	9.2%	0.7
2028PROJ	33	MLBPIT	43	45	3.76	3.78	26.9%	9.6%	0.4
2029PROJ	34	MLBPIT	31	33	3.96	4.09	26.0%	10.1%	0.2

Last 3 seasons WAR 3.6 · Next 3 projected WAR 1.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs90

FB Velo87

K%80

Form3.63 ERA (proj 3.74) · K 27.5% (proj 27.8%) · BB 10.8% (proj 9.2%) · last 90d · 22 IP through Oct 27

CompsHideki Okajima (age 32) · Pedro Feliciano (age 32) · George Sherrill (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 7684vR 92

ERA3.74

FIP3.50

K%27.8%

BB%9.2%

K-BB%18.6%

Proj IP57

Value2027 · WAR

Runs saved vs league avg · 57 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p8797.1

Whiff%

p6327.3%

p8027.7%

xwOBA vs

p900.270

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 Tigers	9	5	37	166	40	25	0	56%	4.1	16.4%	18.8%	-2.4%	0.0%	2.19	4.10	—
2015	20	ROK RAW	GCL Tigers	9	5	37	166	40	25	0	56%	4.1	24.1%	15.1%	9.0%	0.0%	2.19	2.96	—
2017	22	MIX MLE	2 levels	23	23	124	518	144	65	4	100%	5.4	20.7%	14.7%	6.0%	1.0%	2.25	3.78	—
2017	22	MIX RAW	2 levels	23	23	124	518	144	65	4	100%	5.4	27.8%	12.5%	15.3%	0.8%	2.25	2.77	—
2018	23	A+ MLE	Lakeland Flying Tigers	25	23	113	517	115	70	4	92%	4.5	17.1%	15.6%	1.6%	0.8%	4.45	4.04	—
2018	23	A+ RAW	Lakeland Flying Tigers	25	23	113	517	115	70	4	92%	4.5	22.2%	13.5%	8.7%	0.8%	4.45	3.38	—
2019	24	MIX MLE	4 levels	43	16	98	457	92	51	13	37%	2.3	18.5%	11.4%	7.1%	3.1%	5.58	4.70	-0.2
2019	24	MIX RAW	4 levels	43	16	98	457	92	51	13	37%	2.3	20.1%	11.2%	9.0%	2.8%	5.58	4.50	+0.1
2020	25	MLB MLE	Detroit Tigers	27	0	23	98	29	13	2	0%	0.9	29.6%	13.3%	16.3%	2.1%	4.30	3.45	+0.3
2020	25	MLB RAW	Detroit Tigers	27	0	23	98	29	13	2	0%	0.9	29.6%	13.3%	16.3%	2.0%	4.30	3.40	+0.3
2021	26	MLB MLE	Detroit Tigers	62	0	64	276	76	38	7	0%	1.0	27.5%	13.8%	13.8%	2.6%	3.39	3.97	+0.4
2021	26	MLB RAW	Detroit Tigers	62	0	64	276	76	38	7	0%	1.0	27.5%	13.8%	13.8%	2.5%	3.39	3.93	+0.5
2022	27	MLB MLE	Detroit Tigers	64	0	60	263	60	33	2	0%	0.9	22.8%	12.5%	10.3%	0.8%	3.28	3.20	+1.0
2022	27	MLB RAW	Detroit Tigers	64	0	60	263	60	33	2	0%	0.9	22.8%	12.5%	10.3%	0.8%	3.28	3.18	+1.0
2023	28	MLB MLE	Philadelphia Phillies	69	0	60	250	65	22	6	0%	0.9	26.0%	8.8%	17.2%	2.2%	4.62	3.25	+0.9
2023	28	MLB RAW	Philadelphia Phillies	69	0	60	250	65	22	6	0%	0.9	26.0%	8.8%	17.2%	2.4%	4.62	3.33	+0.9
2024	29	MLB MLE	Baltimore Orioles	66	0	53	244	65	24	4	0%	0.8	26.6%	9.8%	16.8%	1.7%	4.42	3.00	+1.0
2024	29	MLB MLE	Baltimore Orioles	66	0	53	244	65	24	4	0%	0.8	26.6%	9.8%	16.8%	1.7%	4.42	3.00	+1.0
2024	29	MLB RAW	Baltimore Orioles	66	0	53	244	65	24	4	0%	0.8	26.6%	9.8%	16.8%	1.6%	4.42	2.99	+1.0
2025	30	MLB MLE	New York Mets	70	0	60	279	70	23	4	0%	0.9	25.1%	8.2%	16.8%	1.4%	4.18	2.81	+1.3
2025	30	MLB MLE	New York Mets	70	0	60	279	70	23	4	0%	0.9	25.1%	8.2%	16.8%	1.4%	4.18	2.81	+1.3
2025	30	MLB RAW	New York Mets	70	0	60	279	70	23	4	0%	0.9	25.1%	8.2%	16.8%	1.4%	4.18	2.79	+1.3
2026	31	MLB MLE	Pittsburgh Pirates	69	0	67	285	79	27	5	0%	1.0	27.7%	9.5%	18.2%	1.9%	3.63	3.00	+1.2
2026	31	MLB MLE	Pittsburgh Pirates	69	0	67	285	79	27	5	0%	1.0	27.7%	9.5%	18.2%	1.9%	3.63	3.00	+1.2
2026	31	MLB RAW	Pittsburgh Pirates	69	0	67	285	79	27	5	0%	1.0	27.7%	9.5%	18.2%	1.8%	3.63	2.92	+1.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			460	7	445	1971	489	212	39	2%	1.0	24.8%	10.8%	14.1%	2.0%	4.16	3.47	+5.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	PIT	54	0	57	241	67	22	5	0%	1.1	27.8%	9.2%	18.6%	2.2%	3.74	3.50	+0.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	PIT	42	0	45	188	51	18	5	0%	1.1	26.9%	9.6%	17.3%	2.5%	3.76	3.78	+0.4
2029	34	MLB MLE	PIT	32	0	33	140	36	14	4	0%	1.0	26.0%	10.1%	15.9%	2.8%	3.96	4.09	+0.2
2030	35	MLB MLE	PIT	21	0	22	95	24	10	3	0%	1.1	25.1%	10.7%	14.4%	3.1%	4.24	4.42	+0.0
2028	33	MLB CTX	PIT	43	0	45	190	51	18	5	0%	1.0	26.9%	9.6%	17.3%	2.5%	3.76	3.78	+0.4
2029	34	MLB CTX	PIT	31	0	33	139	36	14	4	0%	1.1	26.0%	10.1%	15.9%	2.8%	3.96	4.09	+0.2
2030	35	MLB CTX	PIT	21	0	22	93	23	10	3	0%	1.0	25.1%	10.7%	14.4%	3.1%	4.24	4.42	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		555	7	545	2394	600	254	50	1%	1.0	25.1%	10.6%	14.4%	2.1%	4.12	3.49	+6.9
/ 200 IP	—	RAW		204	3	200	878	220	93	18	1%	1.0	25.1%	10.6%	14.4%	2.1%	4.12	3.49	+2.5
Career	—	MLE		527	67	760	3353	752	420	54	13%	1.4	22.4%	12.5%	9.9%	1.6%	3.86	3.68	+6.3
/ 200 IP	—	MLE		139	18	200	882	198	111	14	13%	1.4	22.4%	12.5%	9.9%	1.6%	3.86	3.68	+2.3

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.

2019 AAAToledo Mud Hens · 23 IPFIP 3.29 → 3.89 (+0.59)K% 27.5% → 24.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.52%	—	×0.880	24.22%
BB% (unintentional) /BF	11.93%	—	×1.050	12.52%
HR / BF /BF	1.83%	÷0.888	×1.100	2.27%

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 78).

Result: FIP 3.29 → 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).

2019 AAErie SeaWolves · 13 IPFIP 4.19 → 4.50 (+0.30)K% 22.2% → 18.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.22%	—	×0.810	18.00%
BB% (unintentional) /BF	7.41%	—	×1.100	8.15%
HR / BF /BF	3.70%	÷1.294	×1.200	3.44%

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 159).

Result: FIP 4.19 → 4.50, 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 A+Lakeland Flying Tigers · 113 IPFIP 3.36 → 4.04 (+0.68)K% 22.2% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.24%	—	×0.770	17.13%
BB% (unintentional) /BF	13.54%	—	×1.150	15.57%
HR / BF /BF	0.77%	÷1.277	×1.250	0.76%

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 155).

Result: FIP 3.36 → 4.04, 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 AWest Michigan Whitecaps · 96 IPFIP 2.78 → 3.93 (+1.15)K% 28.7% → 21.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.71%	—	×0.740	21.25%
BB% (unintentional) /BF	13.37%	—	×1.180	15.77%
HR / BF /BF	0.74%	÷0.849	×1.300	1.14%

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 70).

Result: FIP 2.78 → 3.93, 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+Lakeland Flying Tigers · 28 IPFIP 2.73 → 3.25 (+0.52)K% 24.6% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.56%	—	×0.770	18.91%
BB% (unintentional) /BF	9.65%	—	×1.150	11.10%
HR / BF /BF	0.88%	÷1.747	×1.250	0.63%

Result: FIP 2.73 → 3.25, 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 Tigers · 37 IPFIP 2.97 → 4.10 (+1.13)K% 24.1% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.10%	—	×0.680	16.39%
BB% (unintentional) /BF	15.06%	—	×1.250	18.83%
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 2.97 → 4.10, 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
Gregory Soto (OGILVIE)	—	—	—	—	—	2.2%	—	4.03	26.2%	16.2%	100	+0.53	—	—	—
Hideki Okajima L 2008 · 3.49 FIP	32	SU	3.49	23.3%	8.5%	2.3%	.253	4.09	18.4%	11.4%	107	+0.60	3.05	238	73
Pedro Feliciano L 2009 · 3.36 FIP	32	SU	3.36	24.4%	5.8%	2.9%	.272	2.69	20.0%	11.4%	63	-0.67	2.69	378	69
George Sherrill L 2009 · 2.95 FIP	32	SU	2.95	21.6%	7.1%	1.4%	.249	3.79	19.7%	10.4%	72	+0.84	2.74	323	67
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	67
Mike Myers L 2001 · 3.22 FIP	32	SU	3.22	21.3%	10.1%	1.2%	.263	4.20	15.7%	5.5%	116	+0.98	3.22	533	66
Ricardo Rincon L 2002 · 2.80 FIP	32	SU	2.80	22.1%	4.5%	1.8%	.270	4.19	17.7%	7.4%	137	+1.39	2.72	436	65
Juan Pablo Perez L 2013 · 3.68 FIP	34	SU	3.68	25.2%	11.5%	2.3%	.250	3.68	26.5%	11.2%	51	-0.00	1.75	104	64
John Bale L 2007 · 2.49 FIP	33	SU	2.49	23.5%	8.4%	0.6%	.364	3.78	15.2%	6.5%	55	+1.29	2.49	168	64
Clay Rapada L 2012 · 3.00 FIP	31	SU	3.00	24.5%	10.3%	1.2%	.273	3.84	14.3%	6.3%	44	+0.84	3.00	120	63
Cory Luebke L 2016 · 3.88 FIP	31	SU	3.88	26.4%	9.1%	3.4%	.354	—	—	—	—	—	2.80	216	63
Hisanori Takahashi L 2010 · 3.50 FIP	35	SW	3.50	22.1%	7.0%	2.5%	.292	3.81	21.1%	15.0%	118	+0.31	3.50	240	62
Cam Booser L 2024 · 3.11 FIP	32	SU	3.11	26.2%	8.0%	2.2%	.338	3.94	27.0%	19.2%	107	+0.83	2.76	314	60
Doug Creek L 2001 · 4.48 FIP	32	SU	4.48	23.7%	15.8%	2.5%	.272	4.99	20.3%	7.6%	69	+0.51	4.48	277	60
Caleb Thielbar L 2019 · 3.05 FIP	32	SU	3.05	25.9%	5.3%	2.7%	.337	2.71	30.2%	22.6%	143	-0.34	2.22	603	59
Randy Flores L 2007 · 2.72 FIP	32	SU	2.72	18.6%	5.9%	0.8%	.365	4.78	16.1%	6.0%	69	+2.06	2.51	250	59
Heriberto Ruelas L 2015 · 2.95 FIP	33	SU	2.95	19.1%	7.3%	1.0%	.352	3.57	16.2%	8.5%	89	+0.62	2.95	117	58
Scott Downs L 2008 · 2.88 FIP	32	SU	2.88	19.7%	6.9%	1.0%	.243	2.98	19.1%	13.8%	162	+0.10	2.68	748	58
Zach Phillips L 2019 · 3.67 FIP	32	SU	3.67	19.1%	8.5%	2.0%	.355	—	—	—	—	—	2.33	202	58
Jeremy Bleich L 2019 · 4.41 FIP	32	SW	4.41	20.1%	7.0%	3.9%	.360	—	—	—	—	—	3.20	241	58
Kelly Wunsch L 2003 · 3.36 FIP	31	SU	3.36	20.6%	13.1%	0.6%	.152	3.82	21.0%	9.6%	24	+0.46	3.36	175	57

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	3.0	50	25.3%	10.9%	0.82	4.09
John Axford ('21)	2.6	58	25.6%	11.3%	0.86	3.90
Marc Rzepczynski ('18)	2.2	48	21.6%	10.8%	0.75	3.89
Adam Ottavino ('25)	3.7	62	27.2%	10.4%	0.87	3.48
Mike Fetters ('89-'04, 6 WAR*)	3.6	55	20.3%*	10.4%*	0.83*	3.70*
Jeff Parrett ('86-'96, 6 WAR*)	3.6	72	24.8%*	11.0%*	1.02*	4.07*
Tim Stoddard ('75-'89, 4 WAR*)	3.6	66	26.2%*	11.2%*	1.14*	4.24*