Gabe Speier

P Seattle Mariners

Age 32 LHP 5'11" / 200 lbs MLB peak 0.6 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MIX3 levels	45	1	40	4.08	2.95	25.7%	10.8%	0.8	K 25.5% · BB 10.7% · FIP 3.00
2025	30	MLBSEA	76	0	62	2.61	1.99	33.7%	4.1%	1.9
2026	31	MIX3 levels	56	1	48	3.38	3.77	24.6%	6.5%	0.4	K 23.7% · BB 6.8% · FIP 3.45
2027PROJ	32	MLBSEA	50	0	52	3.26	3.35	27.8%	5.3%	0.8
2028PROJ95% MLB	33	MLBSEA	42	0	44	3.48	3.63	26.9%	5.7%	0.5
2029PROJ90% MLB	34	MLBSEA	32	0	34	3.75	3.93	26.0%	6.2%	0.3

Last 3 seasons WAR 3.1 · Next 3 projected WAR 1.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs65

FB Velo62

K%58

Form4.67 ERA (proj 3.26) · K 27.4% (proj 27.8%) · BB 6.8% (proj 5.3%) · last 90d · 17 IP through Oct 27

CompsMatt Thornton (age 32) · Ricardo Rincon (age 32) · Pedro Feliciano (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8088vR 96

ERA3.26

FIP3.35

K%27.8%

BB%5.3%

K-BB%22.5%

Proj IP52

Value2027 · WAR

Runs saved vs league avg · 52 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL5555

CMD7575

K†6565

Statcast Percentiles

FB Velo

p6295.4

Whiff%

p4424.5%

p5823.7%

xwOBA vs

p650.297

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	A MLE	West Michigan Whitecaps	33	0	44	181	36	12	0	0%	1.3	14.7%	7.8%	6.9%	0.0%	2.86	2.85	—
2015	20	A RAW	West Michigan Whitecaps	33	0	44	181	36	12	0	0%	1.3	19.9%	6.6%	13.3%	0.0%	2.86	2.28	—
2016	21	MIX MLE	4 levels	39	0	58	245	49	20	3	0%	1.5	14.9%	9.1%	5.8%	1.5%	2.62	3.81	—
2016	21	MIX RAW	4 levels	39	0	58	245	49	20	3	0%	1.5	20.0%	8.2%	11.8%	1.2%	2.62	3.12	—
2017	22	AA MLE	Jackson Generals	36	4	69	308	50	30	4	11%	1.9	13.1%	10.7%	2.4%	1.6%	4.30	4.22	—
2017	22	AA RAW	Jackson Generals	36	4	69	308	50	30	4	11%	1.9	16.2%	9.7%	6.5%	1.3%	4.30	3.71	—
2018	23	AA MLE	Northwest Arkansas Naturals	46	0	61	276	48	19	3	0%	1.3	14.1%	7.6%	6.5%	1.3%	3.39	3.58	—
2018	23	AA RAW	Northwest Arkansas Naturals	46	0	61	276	48	19	3	0%	1.3	17.4%	6.9%	10.5%	1.1%	3.39	3.10	—
2019	24	MIX MLE	3 levels	56	0	70	310	83	28	14	0%	1.2	22.5%	8.5%	14.0%	4.8%	4.78	4.94	-0.3
2019	24	MIX RAW	3 levels	56	0	70	310	83	28	14	0%	1.2	26.8%	9.0%	17.7%	4.5%	4.78	4.54	+0.0
2020	25	MLB MLE	Kansas City Royals	8	0	6	30	6	4	1	0%	0.7	20.0%	13.3%	6.7%	3.3%	7.94	4.93	-0.0
2020	25	MLB RAW	Kansas City Royals	8	0	6	30	6	4	1	0%	0.7	20.0%	13.3%	6.7%	3.3%	7.94	5.39	-0.1
2021	26	MIX MLE	2 levels	52	0	53	224	62	7	5	0%	1.0	26.3%	3.8%	22.4%	2.8%	2.72	2.91	+1.0
2021	26	MIX RAW	2 levels	52	0	53	224	62	7	5	0%	1.0	27.7%	3.1%	24.6%	2.2%	2.72	2.38	+1.4
2022	27	MIX MLE	3 levels	48	1	47	226	50	16	13	2%	1.0	19.8%	7.5%	12.3%	6.5%	9.19	5.95	-0.8
2022	27	MIX RAW	3 levels	48	1	47	226	50	16	13	2%	1.0	22.1%	7.1%	15.0%	5.8%	9.19	5.59	-0.6
2023	28	MLB MLE	Seattle Mariners	69	0	55	216	64	7	7	0%	0.8	29.6%	3.2%	26.4%	3.3%	3.79	2.83	+1.1
2023	28	MLB RAW	Seattle Mariners	69	0	55	216	64	7	7	0%	0.8	29.6%	3.2%	26.4%	3.2%	3.79	2.81	+1.1
2024	29	MIX MLE	3 levels	45	1	40	167	43	18	2	2%	0.9	25.5%	10.7%	14.8%	1.3%	4.08	3.00	+0.7
2024	29	MIX MLE	3 levels	45	1	40	167	43	18	2	2%	0.9	25.5%	10.7%	14.8%	1.3%	4.08	3.00	+0.7
2024	29	MIX RAW	3 levels	45	1	40	167	43	18	2	2%	0.9	25.7%	10.8%	15.0%	1.2%	4.08	2.95	+0.8
2025	30	MLB MLE	Seattle Mariners	76	0	62	243	82	10	5	0%	0.8	33.7%	4.1%	29.6%	2.1%	2.61	1.93	+2.0
2025	30	MLB MLE	Seattle Mariners	76	0	62	243	82	10	5	0%	0.8	33.7%	4.1%	29.6%	2.1%	2.61	1.93	+2.0
2025	30	MLB RAW	Seattle Mariners	76	0	62	243	82	10	5	0%	0.8	33.7%	4.1%	29.6%	2.1%	2.61	1.99	+1.9
2026	31	MIX MLE	3 levels	56	1	48	199	49	13	7	2%	0.9	23.7%	6.8%	16.8%	2.7%	3.38	3.45	+0.6
2026	31	MIX MLE	3 levels	56	1	48	199	49	13	7	2%	0.9	23.7%	6.8%	16.8%	2.7%	3.38	3.45	+0.6
2026	31	MIX RAW	3 levels	56	1	48	199	49	13	7	2%	0.9	24.6%	6.5%	18.1%	3.5%	3.38	3.77	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			269	1	226	926	259	58	24	0%	0.8	28.0%	6.3%	21.7%	2.6%	3.50	2.96	+4.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	SEA	50	0	52	220	61	12	6	0%	1.0	27.8%	5.3%	22.5%	2.8%	3.26	3.35	+0.8
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	SEA	44	0	46	195	53	11	6	0%	1.1	26.9%	5.7%	21.2%	3.1%	3.48	3.63	+0.5
2029	34	MLB MLE	SEA	36	0	38	161	42	10	5	0%	1.1	26.0%	6.2%	19.8%	3.4%	3.75	3.93	+0.3
2028	33	MLB CTX	SEA	42	0	44	186	50	11	6	0%	1.0	26.9%	5.7%	21.2%	3.1%	3.48	3.63	+0.5
2029	34	MLB CTX	SEA	32	0	34	144	37	9	5	0%	1.1	26.0%	6.2%	19.8%	3.4%	3.75	3.93	+0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		344	1	305	1257	347	78	35	0%	0.9	27.6%	6.2%	21.4%	2.8%	3.52	3.07	+5.4
/ 200 IP	—	RAW		226	1	200	825	228	51	23	0%	0.9	27.6%	6.2%	21.4%	2.8%	3.52	3.07	+3.5
Career	—	MLE		564	7	612	2625	558	195	69	1%	1.1	21.2%	7.4%	13.8%	2.6%	3.97	3.69	+5.1
/ 200 IP	—	MLE		184	2	200	858	182	64	23	1%	1.1	21.2%	7.4%	13.8%	2.6%	3.97	3.69	+3.3

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.

2024 AAATacoma Rainiers · 15 IPFIP 2.74 → 2.84 (+0.09)K% 16.9% → 14.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.95%	—	×0.860	14.58%
BB% (unintentional) /BF	8.47%	—	×0.900	7.63%
HR / BF /BF	0.00%	÷1.142	×1.100	0.00%

Factors used: pitcher AAA level factors (Triple-A 2023+ robo-zone set: walks improve on promotion) — K ×0.86, BB ×0.90, 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 128).

Result: FIP 2.74 → 2.84, 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).

2022 AAAOmaha Storm Chasers · 27 IPFIP 6.24 → 7.02 (+0.78)K% 23.4% → 20.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.45%	—	×0.880	20.63%
BB% (unintentional) /BF	7.59%	—	×1.050	7.97%
HR / BF /BF	7.59%	÷0.984	×1.100	8.48%

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 97).

Result: FIP 6.24 → 7.02, 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 AAAOmaha Storm Chasers · 45 IPFIP 2.48 → 2.91 (+0.43)K% 29.8% → 26.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.84%	—	×0.880	26.26%
BB% (unintentional) /BF	3.66%	—	×1.050	3.85%
HR / BF /BF	2.62%	÷1.027	×1.100	2.80%

Result: FIP 2.48 → 2.91, 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 AAAOmaha Storm Chasers · 40 IPFIP 5.00 → 5.54 (+0.54)K% 25.6% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.57%	—	×0.880	22.50%
BB% (unintentional) /BF	7.39%	—	×1.050	7.76%
HR / BF /BF	5.68%	÷1.023	×1.100	6.11%

Result: FIP 5.00 → 5.54, 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 AANorthwest Arkansas Naturals · 22 IPFIP 2.97 → 3.89 (+0.92)K% 27.7% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.72%	—	×0.810	22.46%
BB% (unintentional) /BF	8.91%	—	×1.100	9.80%
HR / BF /BF	1.98%	÷0.901	×1.200	2.64%

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 80).

Result: FIP 2.97 → 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).

2018 AANorthwest Arkansas Naturals · 61 IPFIP 3.10 → 3.58 (+0.48)K% 17.4% → 14.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.39%	—	×0.810	14.09%
BB% (unintentional) /BF	6.88%	—	×1.100	7.57%
HR / BF /BF	1.09%	÷1.008	×1.200	1.29%

Result: FIP 3.10 → 3.58, 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 AAJackson Generals · 69 IPFIP 3.68 → 4.22 (+0.54)K% 16.2% → 13.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.23%	—	×0.810	13.15%
BB% (unintentional) /BF	9.74%	—	×1.100	10.71%
HR / BF /BF	1.30%	÷0.985	×1.200	1.58%

Result: FIP 3.68 → 4.22, 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 AKane County Cougars · 28 IPFIP 2.12 → 2.97 (+0.85)K% 25.6% → 19.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.62%	—	×0.740	18.96%
BB% (unintentional) /BF	5.79%	—	×1.180	6.83%
HR / BF /BF	0.83%	÷0.975	×1.300	1.10%

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 95).

Result: FIP 2.12 → 2.97, 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
Gabe Speier (OGILVIE)	—	—	3.35	27.8%	5.3%	2.8%	—	3.77	26.5%	20.6%	84	+0.42	—	—	—
Matt Thornton L 2009 · 2.30 FIP	32	SU	2.30	29.9%	6.2%	1.7%	.293	2.35	25.8%	20.1%	185	+0.05	1.71	663	74
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	72
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	72
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	68
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	66
Grant Dayton L 2020 · 3.75 FIP	32	SU	3.75	27.4%	8.5%	3.4%	.254	3.84	23.2%	13.8%	62	+0.09	1.63	277	66
Jeff Ibarra L 2019 · 2.85 FIP	31	SU	2.85	20.6%	3.4%	1.9%	.258	—	—	—	—	—	2.85	101	66
Brady Feigl L 2024 · 4.12 FIP	33	SU	4.12	23.7%	4.7%	4.4%	.312	3.70	22.2%	14.9%	14	-0.42	2.69	212	64
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	62
Mike Zagurski L 2017 · 2.94 FIP	34	SU	2.94	31.2%	6.8%	2.9%	.294	3.12	30.4%	21.1%	45	+0.18	2.94	120	61
Cory Luebke L 2016 · 3.88 FIP	31	SU	3.88	26.4%	9.1%	3.4%	.354	—	—	—	—	—	2.80	216	60
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	58
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	57
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	56
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	56
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	56
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	56
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	55
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	55
Jeremy Bleich L 2019 · 4.41 FIP	32	SW	4.41	20.1%	7.0%	3.9%	.360	—	—	—	—	—	3.20	241	55

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	1.8	52	27.6%	6.6%	1.08	3.49
Giovanny Gallegos ('24)	1.7	56	29.6%	6.6%	1.18	3.49
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Josh Fields ('18)	1.4	46	27.5%	8.0%	0.98	3.71
Héctor Rondón ('20)	2.2	54	24.2%	7.4%	1.03	3.49
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Joe Sambito ('76-'87, 8 WAR*)	3.1	70	26.8%*	7.5%*	1.06*	3.65*