Kodai Senga

P New York Mets

Age 34 RHP 6'1" / 202 lbs MLB peak 0.3 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	31	MIX3 levels	6	6	19	3.72	3.36	29.6%	11.1%	0.5	K 14.9% · BB 13.8% · FIP 4.69
2025	32	MIX3 levels	25	25	127	3.27	3.86	23.1%	10.9%	2.3	K 22.6% · BB 11.4% · FIP 4.01
2026	33	MIX4 levels	35	12	79	6.58	5.02	25.3%	12.9%	-0.4	K 26.5% · BB 14.5% · FIP 5.42
2027PROJ	34	MLBNYM	8	8	51	4.80	4.97	25.1%	13.1%	0.2
2028PROJ95% MLB	35	MLBNYM	7	7	42	5.10	5.31	24.2%	13.7%	0.0
2029PROJ86% MLB	36	MLBNYM	4	4	25	5.44	5.67	23.1%	14.3%	-0.1

Last 3 seasons WAR 2.4 · Next 3 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo75

K%73

xwOBA vs17

Form4.35 ERA (proj 4.80) · K 29.2% (proj 25.1%) · BB 12.4% (proj 13.1%) ▲ last 90d · 21 IP through Oct 27

CompsGuillermo Moscoso (age 34) · Jarrett Grube (age 34) · Tim Adleman (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 129121vR 113

ERA4.80

FIP4.97

K%25.1%

BB%13.1%

K-BB%12.0%

Proj IP51

Value2027 · WAR

Runs saved vs league avg · 51 IP

Grades · Statcast · Contract

Scouting Grades

STUF5555

CTRL3535

CMD4040

K†6060

Statcast Percentiles

FB Velo

p7596.3

Whiff%

p7429.1%

p7326.5%

xwOBA vs

p170.351

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)
2023	30	MLB MLE	New York Mets	29	29	166	694	202	77	17	100%	5.7	29.1%	11.1%	18.0%	2.5%	2.98	3.40	+4.1
2023	30	MLB RAW	New York Mets	29	29	166	694	202	77	17	100%	5.7	29.1%	11.1%	18.0%	2.4%	2.98	3.39	+4.1
2024	31	MIX MLE	3 levels	6	6	19	81	24	9	2	100%	3.2	14.9%	13.8%	1.0%	2.0%	3.72	4.69	+0.2
2024	31	MIX MLE	3 levels	6	6	19	81	24	9	2	100%	3.2	14.9%	13.8%	1.0%	2.0%	3.72	4.69	+0.2
2024	31	MIX RAW	3 levels	6	6	19	81	24	9	2	100%	3.2	29.6%	11.1%	18.5%	2.5%	3.72	3.36	+0.5
2025	32	MIX MLE	3 levels	25	25	127	542	125	59	13	100%	5.1	22.6%	11.4%	11.2%	2.5%	3.27	4.01	+2.1
2025	32	MIX MLE	3 levels	25	25	127	542	125	59	13	100%	5.1	22.6%	11.4%	11.2%	2.5%	3.27	4.01	+2.1
2025	32	MIX RAW	3 levels	25	25	127	542	125	59	13	100%	5.1	23.1%	10.9%	12.2%	2.4%	3.27	3.86	+2.3
2026	33	MIX MLE	4 levels	35	12	79	363	92	47	15	34%	2.3	26.5%	14.5%	12.0%	5.0%	6.58	5.42	-0.8
2026	33	MIX MLE	4 levels	35	12	79	363	92	47	15	34%	2.3	26.5%	14.5%	12.0%	5.0%	6.58	5.42	-0.8
2026	33	MIX RAW	4 levels	35	12	79	363	92	47	15	34%	2.3	25.3%	12.9%	12.4%	4.1%	6.58	5.02	-0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			83	60	346	1479	395	174	44	72%	4.2	26.7%	11.8%	14.9%	3.0%	3.77	3.98	+5.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	NYM	8	8	51	215	54	28	8	100%	6.4	25.1%	13.1%	12.0%	3.5%	4.80	4.97	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	NYM	7	7	44	187	45	26	7	100%	6.3	24.2%	13.7%	10.5%	3.9%	5.10	5.31	+0.0
2029	36	MLB MLE	NYM	5	5	29	122	28	17	5	100%	5.8	23.1%	14.3%	8.9%	4.3%	5.44	5.67	-0.1
2030	37	MLB MLE	NYM	3	3	16	68	15	10	3	100%	5.4	22.1%	14.9%	7.1%	4.7%	5.82	6.07	-0.1
2028	35	MLB CTX	NYM	7	7	42	177	43	24	7	100%	6.0	24.2%	13.7%	10.5%	3.9%	5.10	5.31	+0.0
2029	36	MLB CTX	NYM	4	4	25	106	24	15	4	100%	6.2	23.1%	14.3%	8.9%	4.3%	5.44	5.67	-0.1
2030	37	MLB CTX	NYM	2	2	13	55	12	8	3	100%	6.5	22.1%	14.9%	7.1%	4.7%	5.82	6.07	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		96	73	426	1815	474	221	58	76%	4.4	26.1%	12.2%	13.9%	3.2%	4.06	4.19	+6.1
/ 200 IP	—	RAW		45	34	200	852	223	104	27	76%	4.4	26.1%	12.2%	13.9%	3.2%	4.06	4.19	+2.9
Career	—	MLE		95	72	392	1680	433	203	50	76%	4.1	25.8%	12.1%	13.7%	3.0%	3.84	4.09	+5.6
/ 200 IP	—	MLE		49	37	200	858	221	103	26	76%	4.1	25.8%	12.1%	13.7%	3.0%	3.84	4.09	+2.6

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.

2026 AAASyracuse Mets · 9 IPFIP 2.33 → 2.49 (+0.15)K% 22.7% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.73%	—	×0.860	19.55%
BB% (unintentional) /BF	9.09%	—	×0.900	8.18%
HR / BF /BF	0.00%	÷1.000	×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 100).

Result: FIP 2.33 → 2.49, 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).

2026 AABinghamton Rumble Ponies · 6 IPFIP 4.31 → 5.27 (+0.97)K% 23.8% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.81%	—	×0.810	19.29%
BB% (unintentional) /BF	4.76%	—	×1.100	5.24%
HR / BF /BF	4.76%	÷1.000	×1.200	5.71%

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 100).

Result: FIP 4.31 → 5.27, 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 · 10 IPFIP 1.15 → 1.45 (+0.30)K% 30.8% → 26.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.77%	—	×0.860	26.46%
BB% (unintentional) /BF	5.13%	—	×0.900	4.62%
HR / BF /BF	0.00%	÷1.167	×1.100	0.00%

Result: FIP 1.15 → 1.45, 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 AABinghamton Rumble Ponies · 4 IPFIP 5.31 → 6.29 (+0.98)K% 19.0% → 15.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.05%	—	×0.810	15.43%
BB% (unintentional) /BF	9.52%	—	×1.100	10.48%
HR / BF /BF	4.76%	÷0.990	×1.200	5.77%

Result: FIP 5.31 → 6.29, 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).

2024 AAASyracuse Mets · 11 IPFIP 4.64 → 4.69 (+0.05)K% 17.3% → 14.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.31%	—	×0.860	14.88%
BB% (unintentional) /BF	15.38%	—	×0.900	13.85%
HR / BF /BF	1.92%	÷1.059	×1.100	2.00%

Result: FIP 4.64 → 4.69, 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
Kodai Senga (OGILVIE)	—	—	4.97	25.1%	13.1%	3.5%	—	5.56	23.5%	9.4%	89	+0.59	—	—	—
Guillermo Moscoso R 2018 · 5.07 FIP	34	SP-	5.07	18.2%	8.4%	4.5%	.318	4.35	12.6%	5.9%	104	-0.72	3.56	486	57
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	57
Tim Adleman R 2022 · 5.69 FIP	34	SP-	5.69	17.5%	8.6%	5.4%	.275	—	—	—	—	—	3.63	605	53
Nelson Figueroa R 2008 · 3.94 FIP	34	SW	3.94	17.1%	11.8%	1.4%	.306	3.96	17.5%	9.9%	192	+0.02	3.71	499	53
Orlando Hernandez R 2004 · 3.77 FIP	34	SP	3.77	23.4%	10.0%	2.5%	.278	4.34	20.5%	11.7%	438	+0.57	3.35	1,315	52
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	46
Josh Lindblom R 2021 · 3.79 FIP	34	SP-	3.79	22.7%	6.9%	3.2%	.319	5.07	20.8%	12.0%	133	+1.28	1.83	480	46
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	45
Paolo Espino R 2021 · 4.32 FIP	34	SP-	4.32	20.2%	5.1%	4.2%	.277	4.81	16.6%	10.9%	260	+0.49	3.42	933	45
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	45
Rolando Valdez R 2019 · 5.36 FIP	33	SP-	5.36	11.7%	7.6%	4.2%	.360	—	—	—	—	—	3.06	258	45
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	44
Juan Omar Espinoza R 2016 · 4.84 FIP	32	SP-	4.84	8.4%	9.2%	2.4%	.348	—	—	—	—	—	4.81	104	43
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	43
Zach Neal R 2023 · 5.98 FIP	34	SW	5.98	15.6%	6.0%	6.3%	.291	—	—	—	—	—	4.24	732	40
Juan Delgadillo R 2017 · 4.47 FIP	34	SP	4.47	10.9%	6.2%	2.7%	.311	—	—	—	—	—	4.47	143	40
Yohan Pino R 2017 · 5.33 FIP	33	SP-	5.33	15.2%	3.6%	5.6%	.296	—	—	—	—	—	3.84	193	40
Walter Silva R 2009 · 5.72 FIP	32	SP	5.72	9.1%	12.4%	3.3%	.330	—	—	—	—	—	3.67	453	39
David Buchanan R 2024 · 4.26 FIP	35	SP-	4.26	14.7%	6.5%	2.9%	.337	5.56	12.2%	4.1%	29	+1.30	3.88	560	39
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	39

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	2.4	70	25.9%	12.3%	1.26	4.16
Danny Frisella ('67-'76, 4 WAR*)	3.0	68	24.8%*	11.0%*	1.25*	3.99*
Lance McCullers ('85-'92, 5 WAR*)	2.6	88	25.7%*	11.1%*	1.13*	3.70*
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Tony Cingrani ('18)	1.7	56	25.8%	11.3%	1.26	4.01
Brandon Workman ('21)	1.6	55	24.3%	11.1%	1.04	4.02
Michael Kopech ('25)	2.1	103	25.8%	12.5%	1.45	4.14