Devin Williams

P New York Mets

Age 32 RHP 6'2" / 193 lbs MLB peak 0.6 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MIX3 levels	26	26	1.05	1.58	42.7%	11.7%	0.9	K 43.2% · BB 11.4% · FIP 1.49
2025	30	MLBNYY	67	62	4.79	2.41	34.7%	9.3%	1.6
2026	31	MLBNYM	44	41	4.43	2.98	28.3%	10.9%	0.8
2027PROJ	32	MLBNYM	45	47	3.44	3.31	31.7%	9.8%	0.7
2028PROJ95% MLB	33	MLBNYM	40	42	3.53	3.59	30.8%	10.2%	0.5
2029PROJ90% MLB	34	MLBNYM	32	34	3.75	3.90	29.9%	10.7%	0.3

Last 3 seasons WAR 3.3 · 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 vs85

K%83

FB Velo41

CompsJay Jackson (age 31) · Spencer Patton (age 33) · Kazuhiro Sasaki (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9183vR 75

ERA3.44

FIP3.31

K%31.7%

BB%9.8%

K-BB%21.9%

Proj IP47

Value2027 · WAR

Runs saved vs league avg · 47 IP

Grades · Statcast · Contract

Scouting Grades

STUF7575

CTRL4040

CMD7575

K†7070

Statcast Percentiles

FB Velo

p4193.9

Whiff%

p9034.4%

p8328.3%

xwOBA vs

p850.278

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	Wisconsin Timber Rattlers	22	13	89	379	89	35	3	59%	4.0	17.4%	10.9%	6.5%	0.8%	3.44	3.44	—
2015	20	A RAW	Wisconsin Timber Rattlers	22	13	89	379	89	35	3	59%	4.0	23.5%	9.2%	14.2%	0.8%	3.44	2.72	—
2016	21	MIX MLE	2 levels	22	12	97	418	94	46	6	55%	4.4	16.8%	12.9%	3.9%	1.6%	3.79	4.20	—
2016	21	MIX RAW	2 levels	22	12	97	418	94	46	6	55%	4.4	22.5%	11.0%	11.5%	1.4%	3.79	3.39	—
2018	23	A+ MLE	Carolina Mudcats	14	14	34	164	35	22	2	100%	2.4	16.4%	15.4%	1.0%	1.5%	5.82	4.50	—
2018	23	A+ RAW	Carolina Mudcats	14	14	34	164	35	22	2	100%	2.4	21.3%	13.4%	7.9%	1.2%	5.82	3.75	—
2019	24	MIX MLE	3 levels	47	0	71	300	96	36	5	0%	1.5	26.3%	13.2%	13.1%	1.9%	2.55	3.59	+0.8
2019	24	MIX RAW	3 levels	47	0	71	300	96	36	5	0%	1.5	32.0%	12.0%	20.0%	1.7%	2.55	2.83	+1.5
2020	25	MLB MLE	Milwaukee Brewers	22	0	27	100	53	9	1	0%	1.2	53.0%	9.0%	44.0%	1.0%	0.33	0.29	+1.4
2020	25	MLB RAW	Milwaukee Brewers	22	0	27	100	53	9	1	0%	1.2	53.0%	9.0%	44.0%	1.0%	0.33	0.66	+1.3
2021	26	MLB MLE	Milwaukee Brewers	58	0	54	226	87	28	5	0%	0.9	38.5%	12.4%	26.1%	2.1%	2.50	2.58	+1.3
2021	26	MLB RAW	Milwaukee Brewers	58	0	54	226	87	28	5	0%	0.9	38.5%	12.4%	26.1%	2.2%	2.50	2.64	+1.2
2022	27	MLB MLE	Milwaukee Brewers	65	0	61	240	96	29	2	0%	0.9	40.0%	12.1%	27.9%	0.8%	1.93	1.69	+2.1
2022	27	MLB RAW	Milwaukee Brewers	65	0	61	240	96	29	2	0%	0.9	40.0%	12.1%	27.9%	0.8%	1.93	1.80	+2.0
2023	28	MLB MLE	Milwaukee Brewers	61	0	59	231	87	27	4	0%	1.0	37.7%	11.7%	26.0%	1.6%	1.53	2.31	+1.6
2023	28	MLB RAW	Milwaukee Brewers	61	0	59	231	87	27	4	0%	1.0	37.7%	11.7%	26.0%	1.7%	1.53	2.40	+1.5
2024	29	MIX MLE	3 levels	26	0	26	103	44	12	1	0%	1.0	43.2%	11.4%	31.8%	1.1%	1.05	1.49	+0.9
2024	29	MIX MLE	3 levels	26	0	26	103	44	12	1	0%	1.0	43.2%	11.4%	31.8%	1.1%	1.05	1.49	+0.9
2024	29	MIX RAW	3 levels	26	0	26	103	44	12	1	0%	1.0	42.7%	11.7%	31.1%	1.0%	1.05	1.58	+0.9
2025	30	MLB MLE	New York Yankees	67	0	62	259	90	24	5	0%	0.9	34.7%	9.3%	25.5%	1.8%	4.79	2.33	+1.6
2025	30	MLB MLE	New York Yankees	67	0	62	259	90	24	5	0%	0.9	34.7%	9.3%	25.5%	1.8%	4.79	2.33	+1.6
2025	30	MLB RAW	New York Yankees	67	0	62	259	90	24	5	0%	0.9	34.7%	9.3%	25.5%	1.9%	4.79	2.41	+1.6
2026	31	MLB MLE	New York Mets	44	0	41	184	52	20	3	0%	0.9	28.3%	10.9%	17.4%	1.6%	4.43	2.99	+0.8
2026	31	MLB MLE	New York Mets	44	0	41	184	52	20	3	0%	0.9	28.3%	10.9%	17.4%	1.6%	4.43	2.99	+0.8
2026	31	MLB RAW	New York Mets	44	0	41	184	52	20	3	0%	0.9	28.3%	10.9%	17.4%	1.6%	4.43	2.98	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			352	0	338	1395	517	153	23	0%	1.0	37.1%	11.0%	26.1%	1.6%	2.69	2.28	+9.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	NYM	45	0	47	198	63	19	5	0%	1.0	31.7%	9.8%	21.9%	2.3%	3.44	3.31	+0.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	NYM	43	0	45	189	58	19	5	0%	1.0	30.8%	10.2%	20.6%	2.6%	3.53	3.59	+0.5
2029	34	MLB MLE	NYM	35	0	37	157	47	17	5	0%	1.1	29.9%	10.7%	19.2%	2.9%	3.75	3.90	+0.3
2028	33	MLB CTX	NYM	40	0	42	177	55	18	5	0%	1.1	30.8%	10.2%	20.6%	2.6%	3.53	3.59	+0.5
2029	34	MLB CTX	NYM	32	0	34	144	43	15	4	0%	1.1	29.9%	10.7%	19.2%	2.9%	3.75	3.90	+0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		425	0	414	1716	615	187	32	0%	1.0	35.8%	10.9%	25.0%	1.8%	2.86	2.47	+10.4
/ 200 IP	—	RAW		205	0	200	828	297	90	15	0%	1.0	35.8%	10.9%	25.0%	1.8%	2.86	2.47	+5.0
Career	—	MLE		448	39	620	2604	751	309	38	9%	1.4	28.9%	11.9%	17.0%	1.5%	3.08	2.97	+10.0
/ 200 IP	—	MLE		145	13	200	840	243	100	12	9%	1.4	28.9%	11.9%	17.0%	1.5%	3.08	2.97	+4.8

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.

2019 AABiloxi Shuckers · 53 IPFIP 2.60 → 3.46 (+0.86)K% 34.5% → 28.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.55%	—	×0.810	27.98%
BB% (unintentional) /BF	13.18%	—	×1.100	14.50%
HR / BF /BF	1.36%	÷1.018	×1.200	1.61%

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 104).

Result: FIP 2.60 → 3.46, 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 A+Carolina Mudcats · 34 IPFIP 3.67 → 4.50 (+0.83)K% 21.3% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.34%	—	×0.770	16.43%
BB% (unintentional) /BF	13.41%	—	×1.150	15.43%
HR / BF /BF	1.22%	÷1.010	×1.250	1.51%

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 102).

Result: FIP 3.67 → 4.50, 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 AWisconsin Timber Rattlers · 72 IPFIP 3.18 → 4.05 (+0.87)K% 24.1% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.10%	—	×0.740	17.84%
BB% (unintentional) /BF	11.07%	—	×1.180	13.07%
HR / BF /BF	1.30%	÷1.161	×1.300	1.46%

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 132).

Result: FIP 3.18 → 4.05, 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).

2016 A+Brevard County Manatees · 25 IPFIP 3.94 → 4.63 (+0.70)K% 18.0% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.02%	—	×0.770	13.87%
BB% (unintentional) /BF	10.81%	—	×1.150	12.43%
HR / BF /BF	1.80%	÷1.093	×1.250	2.06%

Result: FIP 3.94 → 4.63, 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 AWisconsin Timber Rattlers · 89 IPFIP 2.72 → 3.44 (+0.72)K% 23.5% → 17.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.48%	—	×0.740	17.38%
BB% (unintentional) /BF	9.23%	—	×1.180	10.90%
HR / BF /BF	0.79%	÷1.331	×1.300	0.77%

Result: FIP 2.72 → 3.44, 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).

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
Devin Williams (OGILVIE)	—	—	3.31	31.7%	9.8%	2.3%	—	3.73	30.4%	20.0%	82	+0.42	—	—	—
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	77
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	75
Kazuhiro Sasaki R 2000 · 3.93 FIP	32	SU	3.93	29.4%	9.8%	3.8%	.212	2.88	24.9%	19.3%	161	-1.05	2.76	223	74
Ryan Tepera R 2020 · 3.15 FIP	32	SU	3.15	34.8%	13.5%	2.3%	.341	3.29	25.2%	18.0%	129	+0.14	2.35	445	74
C.C. Lee R 2018 · 3.03 FIP	31	SU	3.03	34.7%	9.1%	3.1%	.275	—	—	—	—	—	2.59	149	73
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	72
Chaz Roe R 2019 · 3.05 FIP	32	SU	3.05	28.4%	12.7%	1.4%	.348	—	—	—	—	—	1.72	279	70
Ryan Brasier R 2020 · 2.97 FIP	32	SU	2.97	27.3%	9.1%	1.9%	.324	3.30	23.4%	17.1%	134	+0.33	2.31	426	70
Sergio Santos R 2015 · 4.17 FIP	32	SU	4.17	24.7%	8.2%	3.9%	.283	—	—	—	—	—	1.36	178	68
Dale Thayer R 2013 · 3.62 FIP	32	SU	3.62	23.7%	7.4%	2.9%	.287	3.85	20.6%	15.0%	103	+0.23	2.75	239	68
Jose Veras R 2013 · 3.51 FIP	32	SU	3.51	23.6%	8.3%	2.5%	.234	4.28	21.9%	10.2%	65	+0.77	3.37	422	67
Matt Miller R 2004 · 2.13 FIP	32	SU	2.13	24.3%	6.6%	0.4%	.265	3.47	19.2%	10.5%	45	+1.34	2.13	101	67
Brendan Donnelly R 2003 · 2.23 FIP	32	SU	2.23	25.7%	7.5%	0.7%	.261	3.85	22.6%	14.9%	171	+1.62	2.23	385	66
Steve Delabar R 2015 · 3.74 FIP	32	SU	3.74	25.0%	9.9%	2.7%	.246	4.62	20.4%	4.8%	24	+0.88	2.43	229	66
Julio Santana R 2005 · 3.70 FIP	32	SU	3.70	27.7%	8.5%	3.4%	.262	—	—	—	—	—	3.70	471	66
Grant Balfour R 2010 · 2.59 FIP	32	SU	2.59	25.2%	6.8%	1.4%	.270	3.50	25.6%	16.7%	199	+0.91	2.04	533	66
George Kontos R 2017 · 3.71 FIP	32	SU	3.71	25.2%	6.5%	3.5%	.287	4.41	14.7%	8.6%	97	+0.70	2.56	421	66
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	65
Chris Hatcher R 2017 · 4.31 FIP	32	SU	4.31	24.5%	7.4%	4.3%	.291	5.32	18.0%	8.4%	36	+1.01	2.51	246	65
Leonel Campos R 2017 · 3.70 FIP	30	SU	3.70	25.7%	11.8%	2.3%	.241	—	—	—	—	—	2.62	168	64

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	2.3	47	35.3%	11.0%	0.74	2.93
Greg Holland ('22)	2.7	55	29.8%	10.6%	0.71	3.14
David Robertson ('25)	4.5	64	31.9%	9.9%	0.82	2.93
Mike Gonzalez ('03-'13, 7 WAR*)	2.2	49	29.1%*	10.7%*	0.94*	3.18*
Bryan Harvey ('87-'94, 12 WAR*)	1.9	64	37.8%*	8.9%*	0.94*	2.59*
Rob Dibble ('88-'95, 10 WAR*)	2.4	68	41.6%*	12.1%*	0.76*	3.34*
Tom Henke ('82-'95, 25 WAR*)	3.9	66	35.9%*	7.7%*	0.93*	2.72*