Sean Manaea

P New York Mets

Age 35 LHP 6'5" / 245 lbs MLB peak 1.1 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	32	MLBNYM	32	32	182	3.47	3.62	24.9%	8.5%	3.9
2025	33	MIX4 levels	21	17	79	5.79	3.73	27.9%	4.8%	1.6	K 28.5% · BB 4.6% · FIP 3.99
2026	34	MLBNYM	33	19	151	4.78	4.22	22.1%	6.7%	2.1
2027PROJ	35	MLBNYM	17	17	102	4.35	4.46	22.6%	6.7%	1.1
2028PROJ	36	MLBNYM	13	13	79	4.63	4.82	21.5%	7.3%	0.5
2029PROJ	37	MLBNYM	9	9	52	4.99	5.22	20.4%	8.0%	0.1

Last 3 seasons WAR 7.6 · Next 3 projected WAR 1.7Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%50

xwOBA vs32

FB Velo10

Form5.79 ERA (proj 4.35) · K 20.0% (proj 22.6%) · BB 3.3% (proj 6.7%) · last 60d · 28 IP through Oct 27

CompsDaniel Rodriguez (age 34) · Orlando Lara (age 34) · Tsuyoshi Wada (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 102110vR 119

ERA4.35

FIP4.46

K%22.6%

BB%6.7%

K-BB%15.8%

Proj IP102

Value2027 · WAR

Runs saved vs league avg · 102 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1091.3

Whiff%

p2822.2%

p5022.1%

xwOBA vs

p320.333

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	23	MIX MLE	3 levels	14	14	74	309	90	26	5	100%	5.3	23.2%	9.6%	13.6%	1.7%	2.66	3.28	—
2015	23	MIX RAW	3 levels	14	14	74	309	90	26	5	100%	5.3	29.1%	8.4%	20.7%	1.6%	2.66	2.60	—
2016	24	MIX MLE	3 levels	29	28	166	682	148	42	22	97%	5.7	21.4%	6.0%	15.3%	3.5%	3.63	3.97	+2.8
2016	24	MIX RAW	3 levels	29	28	166	682	148	42	22	97%	5.7	21.7%	6.2%	15.5%	3.2%	3.63	3.80	+3.2
2017	25	MIX MLE	2 levels	30	30	163	710	145	55	18	100%	5.4	20.2%	7.8%	12.4%	2.9%	4.32	3.95	+2.8
2017	25	MIX RAW	2 levels	30	30	163	710	145	55	18	100%	5.4	20.4%	7.7%	12.7%	2.5%	4.32	3.77	+3.2
2018	26	MLB MLE	Oakland Athletics	27	27	161	654	108	31	21	100%	6.0	16.5%	4.7%	11.8%	3.5%	3.59	4.24	+2.2
2018	26	MLB RAW	Oakland Athletics	27	27	161	654	108	31	21	100%	6.0	16.5%	4.7%	11.8%	3.2%	3.59	4.03	+2.6
2019	27	MIX MLE	3 levels	13	13	66	256	83	17	9	100%	5.1	31.7%	6.2%	25.5%	3.8%	3.14	3.32	+1.7
2019	27	MIX RAW	3 levels	13	13	66	256	83	17	9	100%	5.1	32.4%	6.6%	25.8%	3.5%	3.14	3.13	+1.9
2020	28	MLB MLE	Oakland Athletics	11	11	54	222	45	8	7	100%	4.9	20.3%	3.6%	16.7%	3.5%	4.50	3.75	+1.1
2020	28	MLB RAW	Oakland Athletics	11	11	54	222	45	8	7	100%	4.9	20.3%	3.6%	16.7%	3.2%	4.50	3.56	+1.2
2021	29	MLB MLE	Oakland Athletics	32	32	179	754	194	41	25	100%	5.6	25.7%	5.4%	20.3%	3.6%	3.91	3.62	+3.9
2021	29	MLB RAW	Oakland Athletics	32	32	179	754	194	41	25	100%	5.6	25.7%	5.4%	20.3%	3.3%	3.91	3.43	+4.3
2022	30	MLB MLE	San Diego Padres	30	28	158	671	156	49	29	93%	5.3	23.2%	7.3%	15.9%	4.2%	4.96	4.38	+1.9
2022	30	MLB RAW	San Diego Padres	30	28	158	671	156	49	29	93%	5.3	23.2%	7.3%	15.9%	4.3%	4.96	4.44	+1.8
2023	31	MLB MLE	San Francisco Giants	37	10	118	499	128	42	14	27%	3.2	25.7%	8.4%	17.2%	3.0%	4.44	3.66	+1.2
2023	31	MLB RAW	San Francisco Giants	37	10	118	499	128	42	14	27%	3.2	25.7%	8.4%	17.2%	2.8%	4.44	3.54	+1.4
2024	32	MLB MLE	New York Mets	32	32	182	738	184	63	21	100%	5.7	24.9%	8.5%	16.4%	2.9%	3.47	3.64	+3.9
2024	32	MLB MLE	New York Mets	32	32	182	738	184	63	21	100%	5.7	24.9%	8.5%	16.4%	2.9%	3.47	3.64	+3.9
2024	32	MLB RAW	New York Mets	32	32	182	738	184	63	21	100%	5.7	24.9%	8.5%	16.4%	2.8%	3.47	3.62	+3.9
2025	33	MIX MLE	4 levels	21	17	79	351	98	17	15	81%	3.8	28.5%	4.6%	24.0%	5.0%	5.79	3.99	+1.3
2025	33	MIX MLE	4 levels	21	17	79	351	98	17	15	81%	3.8	28.5%	4.6%	24.0%	5.0%	5.79	3.99	+1.3
2025	33	MIX RAW	4 levels	21	17	79	351	98	17	15	81%	3.8	27.9%	4.8%	23.1%	4.3%	5.79	3.73	+1.6
2026	34	MLB MLE	New York Mets	33	19	151	653	144	44	25	58%	4.6	22.1%	6.7%	15.3%	3.8%	4.78	4.20	+2.1
2026	34	MLB MLE	New York Mets	33	19	151	653	144	44	25	58%	4.6	22.1%	6.7%	15.3%	3.8%	4.78	4.20	+2.1
2026	34	MLB RAW	New York Mets	33	19	151	653	144	44	25	58%	4.6	22.1%	6.7%	15.3%	3.8%	4.78	4.22	+2.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			276	229	1396	5849	1328	387	196	83%	5.1	22.7%	6.6%	16.1%	3.4%	4.16	3.85	+25.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	NYM	17	17	102	431	97	29	16	100%	6.0	22.6%	6.7%	15.8%	3.7%	4.35	4.46	+1.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	NYM	13	13	79	333	72	24	14	100%	6.1	21.5%	7.3%	14.2%	4.1%	4.63	4.82	+0.5
2029	37	MLB MLE	NYM	9	9	52	221	45	18	10	100%	5.8	20.4%	8.0%	12.4%	4.5%	4.99	5.22	+0.1
2028	36	MLB CTX	NYM	13	13	79	334	72	24	14	100%	6.1	21.5%	7.3%	14.2%	4.1%	4.63	4.82	+0.5
2029	37	MLB CTX	NYM	9	9	52	220	45	18	10	100%	5.8	20.4%	8.0%	12.4%	4.5%	4.99	5.22	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		298	251	1527	6403	1445	429	219	84%	5.1	22.6%	6.7%	15.9%	3.4%	4.21	3.92	+26.9
/ 200 IP	—	RAW		39	33	200	839	189	56	29	84%	5.1	22.6%	6.7%	15.9%	3.4%	4.21	3.92	+3.5
Career	—	MLE		309	261	1551	6499	1501	436	224	84%	5.0	23.1%	6.7%	16.4%	3.5%	4.09	3.89	+26.4
/ 200 IP	—	MLE		40	34	200	838	194	56	29	84%	5.0	23.1%	6.7%	16.4%	3.5%	4.09	3.89	+3.5

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.

2025 A+Brooklyn Cyclones · 7 IPFIP 3.77 → 4.56 (+0.80)K% 21.1% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.05%	—	×0.770	16.21%
BB% (unintentional) /BF	7.89%	—	×1.150	9.08%
HR / BF /BF	2.63%	÷1.073	×1.250	3.07%

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 115).

Result: FIP 3.77 → 4.56, 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).

2025 AAASyracuse Mets · 9 IPFIP 2.11 → 2.39 (+0.29)K% 35.3% → 30.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.29%	—	×0.860	30.35%
BB% (unintentional) /BF	2.94%	—	×0.900	2.65%
HR / BF /BF	2.94%	÷1.167	×1.100	2.77%

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 133).

Result: FIP 2.11 → 2.39, 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 AAALas Vegas Aviators · 28 IPFIP 2.98 → 3.39 (+0.41)K% 41.0% → 36.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.95%	—	×0.880	36.04%
BB% (unintentional) /BF	5.71%	—	×1.050	6.00%
HR / BF /BF	4.76%	÷1.119	×1.100	4.68%

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 124).

Result: FIP 2.98 → 3.39, 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 A+Stockton Ports · 8 IPFIP 3.60 → 4.41 (+0.80)K% 23.8% → 18.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.81%	—	×0.770	18.33%
BB% (unintentional) /BF	9.52%	—	×1.150	10.95%
HR / BF /BF	2.38%	÷1.114	×1.250	2.67%

Result: FIP 3.60 → 4.41, 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 AAANashville Sounds · 18 IPFIP 2.12 → 2.66 (+0.54)K% 28.8% → 25.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.77%	—	×0.880	25.32%
BB% (unintentional) /BF	5.48%	—	×1.050	5.75%
HR / BF /BF	1.37%	÷0.856	×1.100	1.76%

Result: FIP 2.12 → 2.66, 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 AAMidland RockHounds · 50 IPFIP 2.92 → 3.75 (+0.84)K% 29.7% → 24.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.67%	—	×0.810	24.03%
BB% (unintentional) /BF	10.05%	—	×1.100	11.05%
HR / BF /BF	1.91%	÷0.982	×1.200	2.34%

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 96).

Result: FIP 2.92 → 3.75, 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 A+Wilmington Blue Rocks · 20 IPFIP 1.43 → 2.05 (+0.62)K% 27.2% → 20.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.16%	—	×0.770	20.91%
BB% (unintentional) /BF	4.94%	—	×1.150	5.68%
HR / BF /BF	0.00%	÷0.769	×1.250	0.00%

Result: FIP 1.43 → 2.05, 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
Sean Manaea (OGILVIE)	—	—	—	—	—	3.7%	—	4.98	21.1%	13.5%	131	+0.52	—	—	—
Daniel Rodriguez L 2019 · 4.77 FIP	34	SP	4.77	20.8%	7.8%	4.5%	.329	—	—	—	—	—	2.89	391	50
Orlando Lara L 2019 · 4.09 FIP	34	SP	4.09	22.0%	7.8%	3.4%	.329	—	—	—	—	—	3.85	426	49
Tsuyoshi Wada L 2015 · 3.66 FIP	34	SP	3.66	16.8%	7.1%	2.0%	.307	—	—	—	—	—	3.60	188	47
Kwang Hyun Kim L 2021 · 4.21 FIP	33	SP-	4.21	17.7%	8.2%	2.9%	.266	—	—	—	—	—	3.76	146	45
Mauricio Lara L 2015 · 3.62 FIP	36	SP	3.62	13.9%	6.9%	1.5%	.310	3.69	12.1%	5.1%	220	+0.07	3.62	444	41
Andrew Albers L 2021 · 4.60 FIP	35	SP	4.60	16.9%	3.7%	4.5%	.312	—	—	—	—	—	2.98	568	38
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	37
Marlon Arias L 2019 · 4.83 FIP	34	SP	4.83	19.8%	11.2%	3.6%	.273	—	—	—	—	—	3.73	154	37
Chris Narveson L 2017 · 4.59 FIP	35	SP	4.59	12.2%	10.4%	2.2%	.264	—	—	—	—	—	4.00	625	36
Arturo Lopez L 2017 · 4.34 FIP	34	SP	4.34	10.3%	5.4%	2.6%	.342	5.46	12.2%	5.4%	117	+1.12	4.34	445	36
Francisley Bueno L 2016 · 3.48 FIP	35	SP	3.48	15.0%	4.6%	1.9%	.312	4.44	11.7%	3.3%	58	+0.96	2.43	188	35
Jason Lane L 2015 · 5.56 FIP	38	SP	5.56	8.4%	6.2%	4.3%	.307	—	—	—	—	—	5.56	164	33
Oscar Rivera L 2016 · 4.10 FIP	35	SP	4.10	10.7%	7.2%	1.8%	.309	5.80	9.8%	2.0%	42	+1.70	3.68	237	31
Merrill Kelly R 2024 · 4.26 FIP	35	SP	4.26	21.0%	6.3%	3.9%	.271	4.39	19.4%	11.5%	346	+0.13	3.60	1,170	30
Masato Yoshii R 2000 · 5.32 FIP	35	SP	5.32	12.1%	6.5%	4.4%	.302	4.38	13.1%	8.0%	244	-0.94	4.15	757	28
Jorge Castillo L 2017 · 4.97 FIP	35	SP	4.97	11.2%	6.7%	3.6%	.337	4.91	19.6%	11.5%	119	-0.06	4.50	408	28
Hisashi Iwakuma R 2016 · 4.15 FIP	35	SP	4.15	17.6%	5.1%	3.4%	.307	6.31	12.5%	3.1%	31	+2.16	3.19	884	28
Tomas Solis L 2017 · 5.48 FIP	34	SP	5.48	11.3%	7.9%	4.2%	.322	6.44	8.9%	1.4%	57	+0.96	4.20	327	27
Jose Contreras R 2007 · 4.23 FIP	35	SP	4.23	13.2%	7.1%	2.5%	.318	3.76	17.4%	10.1%	309	-0.47	2.43	1,279	26
Kenshin Kawakami R 2010 · 4.06 FIP	35	SP	4.06	15.1%	5.6%	2.8%	.293	—	—	—	—	—	3.97	244	24

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	8.1	116	22.6%	6.8%	1.33	4.22
Mike Minor ('22)	7.0	128	21.6%	7.0%	1.30	4.25
Ian Kennedy ('23)	9.5	146	21.9%	8.0%	1.32	4.16
Drew Smyly ('24)	5.7	104	23.1%	7.7%	1.49	4.20
Don Robinson ('78-'92, 16 WAR*)	9.8	131	20.5%*	7.8%*	1.26*	4.41*
Marty Pattin ('68-'80, 21 WAR*)	10.2	157	19.9%*	6.9%*	1.34*	4.16*
Scott Bankhead ('86-'95, 10 WAR*)	4.5	100	21.2%*	7.3%*	1.36*	4.16*