Edwin Díaz

P Los Angeles Dodgers

Age 33 RHP 6'3" / 165 lbs MLB peak 0.9 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MIX2 levels	56	0	56	3.40	2.74	39.0%	9.4%	1.2	K 38.9% · BB 9.3% · FIP 2.77
2025	31	MLBNYM	62	0	66	1.63	1.88	38.0%	8.1%	2.1
2026	32	MIX4 levels	33	1	29	8.16	3.34	30.1%	12.6%	0.4	K 29.2% · BB 9.4% · FIP 3.47
2027PROJ	33	MLBLAD	48	0	50	3.24	3.05	35.3%	8.6%	0.9
2028PROJ	34	MLBLAD	44	0	46	3.31	3.36	34.4%	9.1%	0.7
2029PROJ	35	MLBLAD	39	0	41	3.54	3.69	33.4%	9.7%	0.4

Last 3 seasons WAR 3.8 · Next 3 projected WAR 2.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%88

FB Velo78

xwOBA vs36

CompsBrendan Donnelly (age 33) · Michael Kohn (age 33) · Fernando Cruz (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9183vR 75

ERA3.24

FIP3.05

K%35.3%

BB%8.6%

K-BB%26.6%

Proj IP50

Value2027 · WAR

Runs saved vs league avg · 50 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p7896.5

Whiff%

p4724.9%

p8829.2%

xwOBA vs

p360.328

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	21	MIX MLE	2 levels	27	27	141	584	145	46	8	100%	5.2	19.8%	8.7%	11.1%	1.8%	3.82	3.49	—
2015	21	MIX RAW	2 levels	27	27	141	584	145	46	8	100%	5.2	24.8%	7.9%	17.0%	1.4%	3.82	2.76	—
2016	22	MIX MLE	2 levels	65	6	92	379	142	20	8	9%	1.4	34.8%	5.5%	29.3%	2.3%	2.53	2.12	+2.7
2016	22	MIX RAW	2 levels	65	6	92	379	142	20	8	9%	1.4	37.5%	5.3%	32.2%	2.1%	2.53	1.80	+3.1
2017	23	MLB MLE	Seattle Mariners	66	0	66	278	89	30	10	0%	1.0	32.0%	10.8%	21.2%	3.7%	3.27	3.79	+0.6
2017	23	MLB RAW	Seattle Mariners	66	0	66	278	89	30	10	0%	1.0	32.0%	10.8%	21.2%	3.6%	3.27	3.74	+0.6
2018	24	MLB MLE	Seattle Mariners	73	0	73	280	124	17	5	0%	1.0	44.3%	6.1%	38.2%	1.8%	1.96	1.13	+3.0
2018	24	MLB RAW	Seattle Mariners	73	0	73	280	124	17	5	0%	1.0	44.3%	6.1%	38.2%	1.8%	1.96	1.30	+2.9
2019	25	MLB MLE	New York Mets	66	0	58	254	99	19	15	0%	0.9	39.0%	7.5%	31.5%	5.9%	5.59	4.01	+0.4
2019	25	MLB RAW	New York Mets	66	0	58	254	99	19	15	0%	0.9	39.0%	7.5%	31.5%	5.9%	5.59	4.03	+0.4
2020	26	MLB MLE	New York Mets	26	0	26	110	50	14	2	0%	1.0	45.5%	12.7%	32.7%	1.8%	1.75	1.87	+0.8
2020	26	MLB RAW	New York Mets	26	0	26	110	50	14	2	0%	1.0	45.5%	12.7%	32.7%	1.8%	1.75	1.85	+0.8
2021	27	MLB MLE	New York Mets	63	0	63	257	89	22	3	0%	1.0	34.6%	8.6%	26.1%	1.2%	3.45	1.90	+2.0
2021	27	MLB RAW	New York Mets	63	0	63	257	89	22	3	0%	1.0	34.6%	8.6%	26.1%	1.2%	3.45	1.94	+2.0
2022	28	MLB MLE	New York Mets	61	0	62	235	118	17	3	0%	1.0	50.2%	7.2%	43.0%	1.3%	1.31	0.48	+3.1
2022	28	MLB RAW	New York Mets	61	0	62	235	118	17	3	0%	1.0	50.2%	7.2%	43.0%	1.3%	1.31	0.75	+2.9
2024	30	MIX MLE	2 levels	56	0	56	223	87	21	7	0%	1.0	38.9%	9.3%	29.6%	3.2%	3.40	2.77	+1.2
2024	30	MIX MLE	2 levels	56	0	56	223	87	21	7	0%	1.0	38.9%	9.3%	29.6%	3.2%	3.40	2.77	+1.2
2024	30	MIX RAW	2 levels	56	0	56	223	87	21	7	0%	1.0	39.0%	9.4%	29.6%	3.1%	3.40	2.74	+1.2
2025	31	MLB MLE	New York Mets	62	0	66	258	98	21	4	0%	1.1	38.0%	8.1%	29.8%	1.6%	1.63	1.78	+2.2
2025	31	MLB MLE	New York Mets	62	0	66	258	98	21	4	0%	1.1	38.0%	8.1%	29.8%	1.6%	1.63	1.78	+2.2
2025	31	MLB RAW	New York Mets	62	0	66	258	98	21	4	0%	1.1	38.0%	8.1%	29.8%	1.6%	1.63	1.88	+2.1
2026	32	MIX MLE	4 levels	33	1	29	143	43	18	3	3%	0.9	29.2%	9.4%	19.8%	3.0%	8.16	3.47	+0.4
2026	32	MIX MLE	4 levels	33	1	29	143	43	18	3	3%	0.9	29.2%	9.4%	19.8%	3.0%	8.16	3.47	+0.4
2026	32	MIX RAW	4 levels	33	1	29	143	43	18	3	3%	0.9	30.1%	12.6%	17.5%	2.1%	8.16	3.34	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			541	0	538	2201	867	182	57	0%	1.0	39.4%	8.3%	31.1%	2.6%	3.03	2.27	+14.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	LAD	48	0	50	211	74	18	6	0%	1.0	35.3%	8.6%	26.6%	2.7%	3.24	3.05	+0.9
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	LAD	43	0	46	192	66	18	6	0%	1.1	34.4%	9.1%	25.2%	3.0%	3.31	3.36	+0.7
2029	35	MLB MLE	LAD	39	0	41	172	58	17	6	0%	1.0	33.4%	9.7%	23.7%	3.3%	3.54	3.69	+0.4
2028	34	MLB CTX	LAD	44	0	46	194	67	18	6	0%	1.0	34.4%	9.1%	25.2%	3.0%	3.31	3.36	+0.7
2029	35	MLB CTX	LAD	39	0	41	173	58	17	6	0%	1.1	33.4%	9.7%	23.7%	3.3%	3.54	3.69	+0.4
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		623	0	624	2565	991	216	68	0%	1.0	38.6%	8.4%	30.2%	2.7%	3.08	2.37	+16.3
/ 200 IP	—	RAW		200	0	200	822	317	69	22	0%	1.0	38.6%	8.4%	30.2%	2.7%	3.08	2.37	+5.2
Career	—	MLE		598	34	732	3001	1043	246	73	6%	1.2	34.8%	8.2%	26.6%	2.4%	3.18	2.54	+15.8
/ 200 IP	—	MLE		163	9	200	820	285	67	20	6%	1.2	34.8%	8.2%	26.6%	2.4%	3.18	2.54	+5.1

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 AAAOklahoma City Comets · 6 IPFIP 4.09 → 4.07 (−0.02)K% 23.5% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.53%	—	×0.860	20.24%
BB% (unintentional) /BF	23.53%	—	×0.900	21.18%
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 4.09 → 4.07, 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 AAJackson Generals · 41 IPFIP 1.85 → 2.65 (+0.80)K% 33.3% → 27.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.810	27.00%
BB% (unintentional) /BF	4.32%	—	×1.100	4.75%
HR / BF /BF	1.85%	÷0.992	×1.200	2.24%

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 98).

Result: FIP 1.85 → 2.65, 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 AAJackson Generals · 104 IPFIP 2.81 → 3.51 (+0.69)K% 23.3% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.25%	—	×0.810	18.83%
BB% (unintentional) /BF	8.35%	—	×1.100	9.19%
HR / BF /BF	1.13%	÷0.890	×1.200	1.52%

Result: FIP 2.81 → 3.51, 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).

2015 A+Bakersfield Blaze · 37 IPFIP 2.56 → 3.45 (+0.89)K% 29.8% → 22.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.79%	—	×0.770	22.94%
BB% (unintentional) /BF	6.38%	—	×1.150	7.34%
HR / BF /BF	2.13%	÷1.077	×1.250	2.47%

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 2.56 → 3.45, 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).

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
Edwin Díaz (OGILVIE)	—	—	—	—	—	2.7%	—	3.52	33.9%	24.5%	86	+0.47	—	—	—
Brendan Donnelly R 2004 · 3.05 FIP	33	SU	3.05	32.6%	8.7%	2.9%	.302	3.88	19.0%	11.8%	150	+0.83	2.23	385	72
Michael Kohn R 2019 · 3.34 FIP	33	SU	3.34	36.1%	8.4%	4.0%	.244	—	—	—	—	—	3.32	133	66
Fernando Cruz R 2023 · 2.50 FIP	33	SU	2.50	35.1%	10.0%	2.0%	.313	3.21	34.3%	22.3%	169	+0.71	2.50	371	65
Joe Nelson R 2008 · 3.08 FIP	33	SU	3.08	26.1%	7.8%	2.2%	.252	5.35	19.8%	5.5%	40	+2.27	3.08	139	64
Chris Martin R 2019 · 3.14 FIP	33	SU	3.14	30.1%	2.3%	4.2%	.314	2.35	27.1%	24.7%	117	-0.79	1.76	431	63
James Hoyt R 2020 · 2.74 FIP	33	SU	2.74	32.3%	11.3%	1.7%	.235	6.83	17.8%	6.0%	35	+4.09	1.61	312	63
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	61
Spencer Patton R 2021 · 3.04 FIP	33	SU	3.04	27.9%	8.7%	2.2%	.305	4.97	21.2%	10.4%	88	+1.93	2.79	239	60
Marcos Mateo R 2015 · 3.12 FIP	31	SU	3.12	28.0%	8.0%	2.5%	.247	—	—	—	—	—	3.12	104	58
Mike Adams R 2011 · 2.35 FIP	33	SU	2.35	26.7%	4.3%	1.7%	.210	3.58	21.5%	13.3%	96	+1.23	1.36	405	56
Huascar Brazobán R 2023 · 3.52 FIP	33	SU	3.52	25.3%	11.3%	2.1%	.304	3.54	21.9%	11.8%	195	+0.02	3.08	370	56
Carlos Fisher R 2016 · 2.48 FIP	33	SU	2.48	26.5%	10.0%	0.6%	.352	3.91	18.1%	5.2%	44	+1.43	2.48	245	56
Jay Jackson R 2021 · 4.14 FIP	33	SU	4.14	31.1%	13.3%	3.6%	.255	4.48	26.4%	20.6%	105	+0.34	2.36	261	54
Tony Barnette R 2017 · 3.56 FIP	33	SU	3.56	22.6%	7.1%	2.7%	.335	2.61	25.0%	20.2%	26	-0.95	2.61	144	54
Blake Parker R 2018 · 4.12 FIP	33	SU	4.12	25.4%	6.5%	4.3%	.290	4.12	24.9%	16.5%	121	+0.00	2.52	406	54
Kazuhiro Sasaki R 2001 · 2.79 FIP	33	SU	2.79	23.8%	3.4%	2.3%	.228	2.95	25.8%	18.5%	94	+0.16	2.76	223	54
Dale Thayer R 2014 · 3.56 FIP	33	SU	3.56	23.4%	4.9%	3.3%	.243	4.34	15.8%	8.8%	38	+0.78	2.75	239	54
Jason Urquidez R 2016 · 2.39 FIP	33	SU	2.39	22.6%	5.3%	1.0%	.328	1.25	28.2%	24.0%	46	-1.14	1.25	123	53
Vinny Nittoli R 2024 · 3.96 FIP	33	SU	3.96	29.1%	8.5%	4.1%	.328	4.34	22.2%	14.6%	39	+0.38	3.66	331	52
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	50

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	3.4	56	38.3%	8.8%	1.03	3.09
Tom Henke ('82-'95, 25 WAR*)	3.9	66	35.9%*	7.7%*	0.93*	2.72*
Bryan Harvey ('87-'94, 12 WAR*)	1.9	64	37.8%*	8.9%*	0.94*	2.59*
David Robertson ('25)	4.5	64	31.9%	9.9%	0.82	2.93
Billy Wagner ('10)	4.5	65	33.2%	8.3%	0.82	2.31
Ken Giles ('22)	1.8	59	33.3%	7.8%	0.71	2.71
Rob Dibble ('88-'95, 10 WAR*)	2.4	68	41.6%*	12.1%*	0.76*	3.34*