Dedniel Núñez

P New York Mets

Age 31 RHP 6'2" / 180 lbs MLB Svc 3.0 · Ctrl thru 2029 peak 0.5 · 2027

Overview

MLBRP40-MANArb-1FA 2030

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	28	MIX2 levels	39	0	49	2.02	1.81	36.3%	6.3%	1.6	K 34.8% · BB 6.1% · FIP 1.80
2025	29	MIX2 levels	29	2	29	4.08	3.90	24.6%	12.3%	0.2	K 20.3% · BB 10.1% · FIP 3.83
2026	30	MIX5 levels	31	1	32	3.66	3.48	26.4%	3.9%	0.4	K 27.8% · BB 0.0% · FIP 3.81
2027PROJ	31	MLBNYM	45	0	47	3.00	3.01	31.2%	2.0%	0.9
2028PROJ85% MLB	32	MLBNYM	44	0	46	3.28	3.39	29.6%	3.1%	0.7
2029PROJ88% MLB	33	MLBNYM	41	0	43	3.58	3.73	28.3%	4.0%	0.4

Last 3 seasons WAR 2.3 · Next 3 projected WAR 2.0Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2029Service3.430First FA2030

2027Arb-1$3M2028Arb-2$3M2029Arb-3$4M

Skills · percentileStatcast 2026

K%80

FB Velo79

xwOBA vs70

Form3.32 ERA (proj 3.00) · K 27.8% (proj 31.2%) · BB 0.0% (proj 2.0%) · last 90d · 19 IP through Oct 27

CompsAquilino López (age 30) · Jason Motte (age 30) · John Brebbia (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9082vR 74

ERA3.00

FIP3.01

K%31.2%

BB%2.0%

K-BB%29.2%

Proj IP58

Value2027 · WAR

Runs saved vs league avg · 58 IP

━ projected WAR━ paid WAR ($/8M) surplus deficit━ post-control+$6M ctrl surplus

Grades · Statcast · Contract

Scouting Grades

STUF7070

CTRL6060

CMD8080

K†7070

Statcast Percentiles

FB Velo

p7996.6

Whiff%

p9036.6%

p8027.8%

xwOBA vs

p700.293

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status Arb-1

MLB Debut 2024

Service 3.430 yrs.days

Team Control Through 2029

First FA Year 2030

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Arb-1	$3M	+0.9	$4M	+$0.7M
2028	Arb-2	$3M	+0.7	$3M	+$0.0M (cut)
2029	Arb-3	$4M	+0.4	$1M	+$0.0M (cut)
Totals		$3M		$8M	+$0.7M

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)
2017	21	ROK MLE	GCL Mets	10	8	45	203	46	16	3	80%	4.5	15.4%	9.9%	5.6%	2.1%	5.24	4.18	—
2017	21	ROK RAW	GCL Mets	10	8	45	203	46	16	3	80%	4.5	22.7%	7.9%	14.8%	1.5%	5.24	2.99	—
2018	22	ROK MLE	Kingsport Mets	11	7	40	173	36	16	2	64%	3.7	14.2%	11.6%	2.6%	1.6%	3.79	4.26	—
2018	22	ROK RAW	Kingsport Mets	11	7	40	173	36	16	2	64%	3.7	20.8%	9.2%	11.6%	1.2%	3.79	3.15	—
2019	23	MIX MLE	2 levels	16	15	80	338	94	23	5	94%	5.0	21.1%	7.9%	13.3%	1.8%	4.39	3.30	—
2019	23	MIX RAW	2 levels	16	15	80	338	94	23	5	94%	5.0	27.8%	6.8%	21.0%	1.5%	4.39	2.42	—
2022	26	MIX MLE	2 levels	26	0	33	152	49	13	4	0%	1.3	25.8%	10.6%	15.2%	4.0%	3.00	4.44	—
2022	26	MIX RAW	2 levels	26	0	33	152	49	13	4	0%	1.3	32.2%	8.6%	23.7%	2.6%	3.00	2.89	—
2023	27	MIX MLE	2 levels	40	0	57	261	70	30	9	0%	1.4	22.6%	10.6%	12.1%	3.9%	5.53	4.65	—
2023	27	MIX RAW	2 levels	40	0	57	261	70	30	9	0%	1.4	26.8%	11.5%	15.3%	3.4%	5.53	4.28	—
2024	28	MIX MLE	2 levels	39	0	49	190	69	12	3	0%	1.3	34.8%	6.1%	28.7%	1.6%	2.02	1.80	+1.6
2024	28	MIX MLE	2 levels	39	0	49	190	69	12	3	0%	1.3	34.8%	6.1%	28.7%	1.6%	2.02	1.80	+1.6
2024	28	MIX RAW	2 levels	39	0	49	190	69	12	3	0%	1.3	36.3%	6.3%	30.0%	1.6%	2.02	1.81	+1.6
2025	29	MIX MLE	2 levels	29	2	29	130	32	16	3	7%	1.0	20.3%	10.1%	10.2%	2.1%	4.08	3.83	+0.2
2025	29	MIX MLE	2 levels	29	2	29	130	32	16	3	7%	1.0	20.3%	10.1%	10.2%	2.1%	4.08	3.83	+0.2
2025	29	MIX RAW	2 levels	29	2	29	130	32	16	3	7%	1.0	24.6%	12.3%	12.3%	2.3%	4.08	3.90	+0.2
2026	30	MIX MLE	5 levels	31	1	32	129	34	5	5	3%	1.0	27.8%	0.0%	27.8%	5.6%	3.66	3.81	+0.3
2026	30	MIX MLE	5 levels	31	1	32	129	34	5	5	3%	1.0	27.8%	0.0%	27.8%	5.6%	3.66	3.81	+0.3
2026	30	MIX RAW	5 levels	31	1	32	129	34	5	5	3%	1.0	26.4%	3.9%	22.5%	3.9%	3.66	3.48	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			53	0	64	248	79	13	8	0%	1.2	31.9%	5.2%	26.6%	3.2%	2.97	2.86	+1.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	NYM	45	0	47	198	62	4	7	0%	1.0	31.2%	2.0%	29.2%	3.5%	3.00	3.01	+0.9
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	NYM	51	0	54	227	67	7	8	0%	1.1	29.6%	3.1%	26.5%	3.7%	3.29	3.40	+0.8
2029	33	MLB MLE	NYM	47	0	49	208	59	8	8	0%	1.0	28.3%	4.0%	24.3%	3.9%	3.59	3.74	+0.5
2030	34	MLB MLE	NYM	41	0	43	182	49	9	8	0%	1.1	27.2%	4.8%	22.4%	4.2%	3.90	4.08	+0.2
2028	32	MLB CTX	NYM	44	0	46	194	58	6	7	0%	1.0	29.6%	3.1%	26.5%	3.7%	3.28	3.39	+0.7
2029	33	MLB CTX	NYM	41	0	43	182	51	7	7	0%	1.0	28.3%	4.0%	24.3%	3.9%	3.58	3.73	+0.4
2030	34	MLB CTX	NYM	34	0	36	152	41	7	6	0%	1.1	27.2%	4.8%	22.4%	4.2%	3.89	4.07	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		172	0	189	777	229	34	29	0%	1.1	29.5%	4.3%	25.2%	3.7%	3.36	3.17	+3.1
/ 200 IP	—	RAW		182	0	200	822	243	36	31	0%	1.1	29.5%	4.3%	25.2%	3.7%	3.36	3.17	+3.3
Career	—	MLE		202	33	365	1576	354	135	42	16%	1.8	22.4%	8.6%	13.9%	2.7%	4.07	3.76	+2.7
/ 200 IP	—	MLE		111	18	200	864	194	74	23	16%	1.8	22.4%	8.6%	13.9%	2.7%	4.07	3.76	+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 2.75 → 2.87 (+0.12)K% 25.0% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.860	21.50%
BB% (unintentional) /BF	13.89%	—	×0.900	12.50%
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 2.75 → 2.87, 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 · 19 IPFIP 3.76 → 3.83 (+0.07)K% 23.6% → 20.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.60%	—	×0.860	20.29%
BB% (unintentional) /BF	11.24%	—	×0.900	10.11%
HR / BF /BF	2.25%	÷1.167	×1.100	2.12%

Result: FIP 3.76 → 3.83, 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 · 14 IPFIP 1.03 → 1.36 (+0.34)K% 38.2% → 32.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.18%	—	×0.860	32.84%
BB% (unintentional) /BF	9.09%	—	×0.900	8.18%
HR / BF /BF	0.00%	÷1.059	×1.100	0.00%

Result: FIP 1.03 → 1.36, 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).

2023 AAASyracuse Mets · 40 IPFIP 5.01 → 5.34 (+0.33)K% 24.4% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.37%	—	×0.860	20.95%
BB% (unintentional) /BF	13.71%	—	×0.900	12.34%
HR / BF /BF	4.06%	÷1.003	×1.100	4.45%

Result: FIP 5.01 → 5.34, 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).

2023 AABinghamton Rumble Ponies · 17 IPFIP 1.65 → 2.51 (+0.87)K% 34.4% → 27.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.38%	—	×0.810	27.84%
BB% (unintentional) /BF	4.69%	—	×1.100	5.16%
HR / BF /BF	1.56%	÷0.925	×1.200	2.03%

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 85).

Result: FIP 1.65 → 2.51, 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 AABinghamton Rumble Ponies · 28 IPFIP 3.26 → 4.44 (+1.19)K% 31.9% → 25.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.85%	—	×0.810	25.80%
BB% (unintentional) /BF	9.63%	—	×1.100	10.59%
HR / BF /BF	2.96%	÷0.895	×1.200	3.97%

Result: FIP 3.26 → 4.44, 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+St. Lucie Mets · 58 IPFIP 2.72 → 3.41 (+0.69)K% 24.1% → 18.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.11%	—	×0.770	18.57%
BB% (unintentional) /BF	7.91%	—	×1.150	9.09%
HR / BF /BF	1.19%	÷1.122	×1.250	1.32%

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 124).

Result: FIP 2.72 → 3.41, 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).

2019 AColumbia Fireflies · 22 IPFIP 1.56 → 2.97 (+1.41)K% 38.8% → 28.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.82%	—	×0.740	28.73%
BB% (unintentional) /BF	3.53%	—	×1.180	4.16%
HR / BF /BF	2.35%	÷0.949	×1.300	3.22%

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 90).

Result: FIP 1.56 → 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
Dedniel Núñez (OGILVIE)	—	—	3.01	31.2%	2.0%	3.5%	—	3.72	28.4%	24.5%	146	+0.71	—	—	—
Aquilino López R 2005 · 3.10 FIP	30	SU	3.10	30.6%	8.3%	2.8%	.333	3.89	16.2%	10.5%	96	+0.79	3.10	207	67
Jason Motte R 2012 · 3.13 FIP	30	SU	3.13	30.8%	5.7%	3.5%	.238	4.34	16.2%	11.5%	73	+1.21	2.22	387	67
John Brebbia R 2021 · 3.53 FIP	31	SU	3.53	30.9%	4.9%	4.4%	.326	3.72	24.3%	17.5%	162	+0.19	2.89	576	65
Seunghwan Oh R 2016 · 1.87 FIP	34	SU	1.87	32.9%	4.8%	1.7%	.263	3.63	24.1%	19.6%	146	+1.76	1.87	226	64
Andrew Kittredge R 2021 · 2.64 FIP	31	SU	2.64	27.3%	3.5%	2.5%	.255	3.98	20.9%	15.7%	116	+1.34	2.47	691	64
C.C. Lee R 2018 · 3.03 FIP	31	SU	3.03	34.7%	9.1%	3.1%	.275	—	—	—	—	—	2.59	149	63
Josh Lueke R 2015 · 2.29 FIP	30	SU	2.29	30.0%	4.1%	2.2%	.264	—	—	—	—	—	2.29	191	63
Josh Fields R 2017 · 3.98 FIP	31	SU	3.98	26.9%	6.3%	4.3%	.216	5.01	20.5%	12.9%	66	+1.03	1.80	330	63
Akinori Otsuka R 2004 · 2.61 FIP	32	SU	2.61	27.9%	6.4%	1.9%	.251	2.78	20.3%	13.4%	155	+0.17	2.39	232	63
Nate Jones R 2016 · 2.57 FIP	30	SU	2.57	29.2%	4.4%	2.5%	.234	4.09	23.4%	12.5%	30	+1.52	2.57	307	61
Jean Machi R 2013 · 2.16 FIP	31	SU	2.16	24.2%	4.3%	1.0%	.295	4.06	18.5%	11.5%	186	+1.90	2.16	306	61
Shawn Kelley R 2015 · 2.23 FIP	31	SU	2.23	30.7%	5.4%	1.9%	.291	4.09	29.3%	24.4%	133	+1.86	2.23	452	61
Jim Henderson R 2014 · 4.13 FIP	31	SU	4.13	34.0%	6.0%	5.7%	.407	4.88	21.6%	11.3%	65	+0.75	1.86	167	60
Gregory Infante R 2018 · 4.07 FIP	31	SU	4.07	22.0%	4.6%	4.1%	.291	—	—	—	—	—	2.97	230	59
Jason Adam R 2023 · 3.40 FIP	31	SU	3.40	31.1%	9.0%	3.2%	.222	2.99	25.0%	17.7%	173	-0.41	2.42	396	59
Chris Martin R 2018 · 3.04 FIP	32	SU	3.04	20.9%	1.7%	2.7%	.311	2.92	25.7%	23.1%	117	-0.12	1.76	431	59
Paul Sewald R 2021 · 2.82 FIP	31	SU	2.82	39.4%	7.2%	3.9%	.244	3.55	29.8%	22.9%	164	+0.73	2.71	611	59
Rafael Martín R 2015 · 2.94 FIP	31	SU	2.94	30.5%	7.1%	2.8%	.285	3.99	20.4%	12.9%	112	+1.05	2.94	227	58
Jay Jackson R 2019 · 2.80 FIP	31	SU	2.80	32.4%	9.1%	2.3%	.272	3.59	29.4%	19.9%	41	+0.79	2.36	261	58
Tommy Nance R 2022 · 3.28 FIP	31	SU	3.28	29.1%	9.2%	2.7%	.345	2.75	23.4%	18.0%	118	-0.53	2.08	360	58

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	1.3	54	29.8%	3.9%	1.42	3.30
Koji Uehara ('17)	2.4	53	30.7%	4.2%	1.12	2.66
Giovanny Gallegos ('24)	1.7	56	29.6%	6.6%	1.18	3.49
Chad Green ('25)	2.5	62	30.0%	6.4%	1.42	3.43
Luis DeLeon ('81-'89, 4 WAR*)	1.7	67	25.0%*	5.7%*	1.69*	3.69*
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*