Brandon Woodruff

P Milwaukee Brewers

Age 34 RHP 6'4" / 241 lbs MLB Svc 8.0 · Ctrl thru 2027 peak 1.2 · 2027

Overview

MLBSP40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	30	MIX3 levels	14	14	79	2.40	3.07	28.6%	5.7%	2.3	K 29.2% · BB 5.9% · FIP 3.24
2025	32	MIX3 levels	22	22	107	3.04	2.96	27.6%	6.1%	3.2	K 26.2% · BB 6.0% · FIP 3.01
2026	33	MIX3 levels	11	11	54	3.48	3.06	27.4%	5.7%	1.6	K 27.0% · BB 5.2% · FIP 2.98
2027PROJ	34	MLBMIL	9	9	54	3.38	3.50	27.2%	5.7%	1.3
2028PROJ	35	MLBMIL	8	8	49	3.74	3.92	26.2%	6.3%	0.9
2029PROJ	36	MLBMIL	7	7	43	4.08	4.30	25.2%	6.9%	0.6

Last 3 seasons WAR 7.1 · Next 3 projected WAR 2.8Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service8.936First FA2028Options3NTC

2027Ext / FA-deal$14M

Skills · percentileStatcast 2026

xwOBA vs90

K%76

FB Velo12

CompsJose Oyervides (age 34) · Josh Lowey (age 34) · Orlando Hernandez (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9082vR 74

ERA3.38

FIP3.50

K%27.2%

BB%5.7%

K-BB%21.5%

Proj IP54

Value2027 · WAR

Runs saved vs league avg · 54 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1291.8

Whiff%

p3823.6%

p7627.0%

xwOBA vs

p900.263

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

Status FA

MLB Debut 2017

Service 8.936 yrs.days

Team Control Through 2027

First FA Year 2028

NTCOPT

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$14M	+1.3	$10M	-$3M
Totals		$14M		$10M	-$3M

Options · projected decision
2027	MUTUAL OPT	$13.5M / $0.0M buyout	DECLINE
2028	MUTUAL OPT	$20.0M / $10.0M buyout	DECLINE
2029	MUTUAL OPT	$13.5M / $0.0M buyout	DECLINE

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	22	A+ MLE	Brevard County Manatees	21	19	110	457	71	33	2	90%	5.2	12.0%	8.3%	3.7%	0.6%	3.45	3.48	—
2015	22	A+ RAW	Brevard County Manatees	21	19	110	457	71	33	2	90%	5.2	15.5%	7.2%	8.3%	0.4%	3.45	2.94	—
2016	23	MIX MLE	2 levels	28	28	158	633	173	40	6	100%	5.6	21.8%	7.0%	14.8%	1.0%	2.68	2.68	—
2016	23	MIX RAW	2 levels	28	28	158	633	173	40	6	100%	5.6	27.3%	6.3%	21.0%	0.9%	2.68	2.16	—
2017	24	MIX MLE	3 levels	25	25	120	518	103	38	13	100%	4.8	18.4%	7.5%	10.9%	2.8%	4.49	4.03	+2.0
2017	24	MIX RAW	3 levels	25	25	120	518	103	38	13	100%	4.8	19.9%	7.3%	12.5%	2.5%	4.49	3.74	+2.4
2018	25	MIX MLE	2 levels	36	21	114	486	115	46	12	58%	3.2	22.0%	9.8%	12.2%	2.4%	3.88	3.80	+2.2
2018	25	MIX RAW	2 levels	36	21	114	486	115	46	12	58%	3.2	23.7%	9.5%	14.2%	2.5%	3.88	3.66	+2.4
2019	26	MLB MLE	Milwaukee Brewers	22	22	122	493	143	30	12	100%	5.5	29.0%	6.1%	22.9%	2.3%	3.62	2.69	+4.2
2019	26	MLB RAW	Milwaukee Brewers	22	22	122	493	143	30	12	100%	5.5	29.0%	6.1%	22.9%	2.4%	3.62	2.77	+4.1
2020	27	MLB MLE	Milwaukee Brewers	13	13	74	293	91	18	9	100%	5.7	31.1%	6.1%	24.9%	2.9%	3.05	2.86	+2.4
2020	27	MLB RAW	Milwaukee Brewers	13	13	74	293	91	18	9	100%	5.7	31.1%	6.1%	24.9%	3.1%	3.05	2.95	+2.3
2021	28	MLB MLE	Milwaukee Brewers	30	30	179	708	211	43	18	100%	6.0	29.8%	6.1%	23.7%	2.4%	2.56	2.68	+6.3
2021	28	MLB RAW	Milwaukee Brewers	30	30	179	708	211	43	18	100%	6.0	29.8%	6.1%	23.7%	2.5%	2.56	2.77	+6.0
2022	29	MIX MLE	3 levels	29	29	161	649	204	44	19	100%	5.6	30.6%	6.8%	23.9%	2.8%	3.02	2.89	+5.1
2022	29	MIX RAW	3 levels	29	29	161	649	204	44	19	100%	5.6	31.4%	6.8%	24.7%	2.9%	3.02	2.92	+5.0
2023	30	MIX MLE	3 levels	14	14	79	297	85	17	9	100%	5.6	29.2%	5.9%	23.3%	3.4%	2.40	3.24	+2.1
2023	30	MIX RAW	3 levels	14	14	79	297	85	17	9	100%	5.6	28.6%	5.7%	22.9%	3.0%	2.40	3.07	+2.3
2025	32	MIX MLE	3 levels	22	22	107	427	118	26	11	100%	4.8	26.2%	6.0%	20.2%	2.5%	3.04	3.01	+3.2
2025	32	MIX MLE	3 levels	22	22	107	427	118	26	11	100%	4.8	26.2%	6.0%	20.2%	2.5%	3.04	3.01	+3.2
2025	32	MIX RAW	3 levels	22	22	107	427	118	26	11	100%	4.8	27.6%	6.1%	21.5%	2.6%	3.04	2.96	+3.2
2026	33	MIX MLE	3 levels	11	11	54	212	58	12	6	100%	4.9	27.0%	5.2%	21.8%	2.7%	3.48	2.98	+1.6
2026	33	MIX MLE	3 levels	11	11	54	212	58	12	6	100%	4.9	27.0%	5.2%	21.8%	2.7%	3.48	2.98	+1.6
2026	33	MIX RAW	3 levels	11	11	54	212	58	12	6	100%	4.9	27.4%	5.7%	21.7%	2.8%	3.48	3.06	+1.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			151	136	790	3158	918	198	89	90%	5.2	29.1%	6.3%	22.8%	2.8%	3.10	2.99	+23.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	MIL	9	9	54	228	62	13	7	100%	6.0	27.2%	5.7%	21.5%	2.9%	3.38	3.50	+1.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	MIL	8	8	49	206	54	13	7	100%	6.1	26.2%	6.3%	20.0%	3.2%	3.65	3.83	+0.9
2029	36	MLB MLE	MIL	7	7	43	183	46	13	7	100%	6.2	25.2%	6.9%	18.4%	3.6%	3.98	4.20	+0.6
2028	35	MLB CTX	MIL	8	8	49	207	54	13	7	100%	6.1	26.2%	6.3%	20.0%	3.4%	3.74	3.92	+0.9
2029	36	MLB CTX	MIL	7	7	43	182	46	12	7	100%	6.1	25.2%	6.9%	18.4%	3.8%	4.08	4.30	+0.6
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		166	151	883	3547	1018	223	103	91%	5.3	28.7%	6.3%	22.4%	2.9%	3.18	3.07	+25.7
/ 200 IP	—	RAW		38	34	200	804	231	51	23	91%	5.3	28.7%	6.3%	22.4%	2.9%	3.18	3.07	+5.8
Career	—	MLE		251	234	1277	5173	1295	358	117	93%	5.1	25.0%	6.9%	18.1%	2.3%	3.21	3.10	+25.2
/ 200 IP	—	MLE		39	37	200	810	203	56	18	93%	5.1	25.0%	6.9%	18.1%	2.3%	3.21	3.10	+5.7

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 A+Wisconsin Timber Rattlers · 5 IPFIP 3.90 → 5.20 (+1.30)K% 28.6% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.770	22.00%
BB% (unintentional) /BF	4.76%	—	×1.150	5.48%
HR / BF /BF	4.76%	÷1.000	×1.250	5.95%

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 100).

Result: FIP 3.90 → 5.20, 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 AAANashville Sounds · 25 IPFIP 3.26 → 3.41 (+0.15)K% 21.7% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.70%	—	×0.860	18.66%
BB% (unintentional) /BF	7.55%	—	×0.900	6.79%
HR / BF /BF	1.89%	÷1.111	×1.100	1.87%

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 122).

Result: FIP 3.26 → 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).

2025 A+Wisconsin Timber Rattlers · 17 IPFIP 2.31 → 2.79 (+0.48)K% 18.8% → 14.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.75%	—	×0.770	14.44%
BB% (unintentional) /BF	6.25%	—	×1.150	7.19%
HR / BF /BF	0.00%	÷1.246	×1.250	0.00%

Result: FIP 2.31 → 2.79, 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 A+Wisconsin Timber Rattlers · 7 IPFIP 0.74 → 1.28 (+0.54)K% 28.0% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.00%	—	×0.770	21.56%
BB% (unintentional) /BF	0.00%	—	×1.150	0.00%
HR / BF /BF	0.00%	÷1.033	×1.250	0.00%

Result: FIP 0.74 → 1.28, 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 AAAColorado Springs Sky Sox · 71 IPFIP 3.97 → 4.23 (+0.26)K% 21.9% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.94%	—	×0.880	19.30%
BB% (unintentional) /BF	10.32%	—	×1.050	10.84%
HR / BF /BF	2.58%	÷1.124	×1.100	2.52%

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 125).

Result: FIP 3.97 → 4.23, 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).

2017 AAAColorado Springs Sky Sox · 75 IPFIP 3.57 → 4.08 (+0.51)K% 21.5% → 19.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.54%	—	×0.880	18.95%
BB% (unintentional) /BF	7.38%	—	×1.050	7.75%
HR / BF /BF	2.46%	÷0.933	×1.100	2.90%

Result: FIP 3.57 → 4.08, 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 AABiloxi Shuckers · 114 IPFIP 2.12 → 2.62 (+0.50)K% 27.1% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.13%	—	×0.810	21.98%
BB% (unintentional) /BF	6.56%	—	×1.100	7.22%
HR / BF /BF	0.88%	÷1.243	×1.200	0.85%

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 149).

Result: FIP 2.12 → 2.62, 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 · 44 IPFIP 2.09 → 2.83 (+0.74)K% 27.8% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.84%	—	×0.770	21.44%
BB% (unintentional) /BF	5.68%	—	×1.150	6.53%
HR / BF /BF	1.14%	÷1.093	×1.250	1.30%

Result: FIP 2.09 → 2.83, 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).

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
Brandon Woodruff (OGILVIE)	—	—	—	—	—	2.9%	—	4.00	25.8%	19.2%	92	+0.50	—	—	—
Jose Oyervides R 2016 · 3.09 FIP	34	SP	3.09	24.9%	8.2%	1.9%	.318	5.55	17.0%	7.8%	195	+2.46	3.01	407	67
Josh Lowey R 2019 · 3.45 FIP	34	SP	3.45	22.4%	6.8%	2.5%	.303	—	—	—	—	—	2.22	426	64
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	56
Koji Uehara R 2009 · 3.52 FIP	34	SP	3.52	17.2%	3.9%	2.5%	.300	2.55	33.5%	30.6%	145	-0.97	1.28	481	54
Hiroki Kuroda R 2009 · 3.54 FIP	34	SP	3.54	17.9%	4.7%	2.5%	.270	3.43	19.1%	14.2%	618	-0.11	2.96	1,319	53
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	50
Jorge Martinez R 2019 · 3.71 FIP	34	SP	3.71	16.3%	5.7%	2.3%	.348	—	—	—	—	—	3.53	214	47
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	47
Jonathan Albaladejo R 2017 · 3.21 FIP	34	SP	3.21	14.2%	3.3%	1.6%	.341	—	—	—	—	—	2.30	173	46
Jonathan Castellanos R 2016 · 3.40 FIP	34	SP	3.40	15.2%	7.4%	1.2%	.314	5.04	9.4%	0.3%	174	+1.64	3.40	394	46
Casey Lawrence R 2022 · 4.55 FIP	34	SP-	4.55	18.6%	3.9%	4.6%	.237	4.99	13.4%	8.1%	414	+0.44	3.57	1,374	43
Yoanner Negrin R 2017 · 3.15 FIP	33	SP	3.15	15.6%	6.0%	1.1%	.277	3.87	17.5%	9.4%	148	+0.72	3.15	485	43
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	41
Logan Duran R 2016 · 3.77 FIP	34	SP	3.77	15.3%	8.9%	1.5%	.299	3.66	15.5%	6.6%	29	-0.11	3.42	187	40
Jon Leicester R 2015 · 3.76 FIP	36	SP	3.76	12.3%	5.9%	1.7%	.333	—	—	—	—	—	3.76	113	40
Chris Smith R 2015 · 3.79 FIP	34	SP	3.79	20.0%	8.1%	2.5%	.306	4.41	18.4%	10.2%	285	+0.62	3.79	477	39
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	38
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	37
Mitch Talbot R 2018 · 3.82 FIP	34	SP	3.82	13.7%	5.6%	2.1%	.281	5.01	13.2%	8.9%	59	+1.19	3.82	395	37
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	37

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	4.7	78	28.6%	6.3%	1.12	3.18
Bruce Sutter ('76-'88, 22 WAR*)	5.2	95	28.5%*	7.3%*	1.12*	3.14*
Julio Urías ('23)	3.6	102	24.3%	6.6%	1.05	3.11
Alejandro Peña ('81-'96, 15 WAR*)	5.3	81	24.7%*	7.5%*	0.96*	3.61*
Jeff Montgomery ('87-'99, 19 WAR*)	4.3	72	26.2%*	7.6%*	1.01*	3.16*
Eric Gagne ('08)	3.2	80	27.0%	8.5%	1.06	3.47
Sergio Romo ('22)	3.6	52	27.0%	6.1%	1.06	3.21