Dustin May

P Milwaukee Brewers

Age 29 RHP 6'6" / 180 lbs MLB Svc 7.0 · Ctrl thru 2027 peak 1.7 · 2027

Overview

MLBSP40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2023	25	MLBLAD	9	9	48	2.62	2.95	18.2%	8.6%	1.5
2025	27	MLBBOS	25	23	132	4.96	4.51	21.1%	9.1%	1.4
2026	28	MLBMIL	31	31	146	4.70	3.40	23.2%	8.1%	3.6
2027PROJ	29	MLBMIL	18	18	111	4.37	4.12	23.0%	8.4%	1.7
2028PROJ	30	MLBMIL	15	15	92	4.42	4.41	22.3%	8.7%	1.2
2029PROJ	31	MLBMIL	12	12	75	4.56	4.65	21.5%	9.0%	0.7

Last 3 seasons WAR 6.4 · Next 3 projected WAR 3.6Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service7.343First FA2028Options2

2027Ext / FA-deal$14M

Skills · percentileStatcast 2026

FB Velo84

K%55

xwOBA vs40

Form5.48 ERA (proj 4.37) · K 26.8% (proj 23.0%) · BB 8.2% (proj 8.4%) ▲ last 60d · 21 IP through Oct 27

CompsChase Anderson (age 29) · Tylor Megill (age 29) · Tom Koehler (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 112104vR 96

ERA4.37

FIP4.12

K%23.0%

BB%8.4%

K-BB%14.6%

Proj IP111

Value2027 · WAR

Runs saved vs league avg · 111 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p8496.9

Whiff%

p3423.0%

p5523.2%

xwOBA vs

p400.323

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

Status FA

MLB Debut 2019

Service 7.343 yrs.days

Team Control Through 2027

First FA Year 2028

OPT

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$14M	+1.7	$14M	+$0.0M
Totals		$14M		$14M	+$0.0M

Options · projected decision
2027	MUTUAL OPT	$13.6M / $0.0M buyout	DECLINE
2028	MUTUAL OPT	$20.0M / $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)
2016	18	ROK MLE	AZL Dodgers	10	6	30	135	34	4	0	60%	3.0	17.1%	3.7%	13.4%	0.0%	3.86	2.12	—
2016	18	ROK RAW	AZL Dodgers	10	6	30	135	34	4	0	60%	3.0	25.2%	3.0%	22.2%	0.0%	3.86	1.25	—
2017	19	MIX MLE	2 levels	25	24	134	566	128	27	8	96%	5.4	15.9%	5.8%	10.1%	2.0%	3.63	3.62	—
2017	19	MIX RAW	2 levels	25	24	134	566	128	27	8	96%	5.4	22.6%	4.8%	17.8%	1.4%	3.63	2.57	—
2018	20	MIX MLE	2 levels	23	23	133	552	122	29	9	100%	5.8	17.2%	5.9%	11.3%	1.9%	3.39	3.45	—
2018	20	MIX RAW	2 levels	23	23	133	552	122	29	9	100%	5.8	22.1%	5.3%	16.8%	1.6%	3.39	2.80	—
2019	21	MIX MLE	3 levels	34	24	141	588	142	34	7	71%	4.2	20.9%	6.2%	14.7%	1.4%	3.44	2.92	+4.3
2019	21	MIX RAW	3 levels	34	24	141	588	142	34	7	71%	4.2	24.1%	5.8%	18.4%	1.2%	3.44	2.46	+5.2
2020	22	MLB MLE	Los Angeles Dodgers	12	10	56	224	44	16	9	83%	4.7	19.6%	7.1%	12.5%	3.9%	2.57	4.46	+0.6
2020	22	MLB RAW	Los Angeles Dodgers	12	10	56	224	44	16	9	83%	4.7	19.6%	7.1%	12.5%	4.0%	2.57	4.47	+0.6
2021	23	MLB MLE	Los Angeles Dodgers	5	5	23	93	35	6	4	100%	4.6	37.6%	6.5%	31.2%	4.1%	2.74	3.00	+0.7
2021	23	MLB RAW	Los Angeles Dodgers	5	5	23	93	35	6	4	100%	4.6	37.6%	6.5%	31.2%	4.3%	2.74	3.10	+0.7
2022	24	MIX MLE	3 levels	12	12	51	213	65	20	5	100%	4.2	28.2%	9.9%	18.3%	2.5%	3.35	3.32	+1.3
2022	24	MIX RAW	3 levels	12	12	51	213	65	20	5	100%	4.2	30.5%	9.4%	21.1%	2.3%	3.35	3.00	+1.5
2023	25	MLB MLE	Los Angeles Dodgers	9	9	48	187	34	16	1	100%	5.3	18.2%	8.6%	9.6%	0.5%	2.62	2.93	+1.5
2023	25	MLB RAW	Los Angeles Dodgers	9	9	48	187	34	16	1	100%	5.3	18.2%	8.6%	9.6%	0.5%	2.62	2.95	+1.5
2025	27	MLB MLE	Boston Red Sox	25	23	132	584	123	53	21	92%	5.3	21.1%	9.1%	12.0%	3.7%	4.96	4.50	+1.4
2025	27	MLB MLE	Boston Red Sox	25	23	132	584	123	53	21	92%	5.3	21.1%	9.1%	12.0%	3.7%	4.96	4.50	+1.4
2025	27	MLB RAW	Boston Red Sox	25	23	132	584	123	53	21	92%	5.3	21.1%	9.1%	12.0%	3.6%	4.96	4.51	+1.4
2026	28	MLB MLE	Milwaukee Brewers	31	31	146	621	144	50	14	100%	4.7	23.2%	8.1%	15.1%	2.1%	4.70	3.34	+3.7
2026	28	MLB MLE	Milwaukee Brewers	31	31	146	621	144	50	14	100%	4.7	23.2%	8.1%	15.1%	2.1%	4.70	3.34	+3.7
2026	28	MLB RAW	Milwaukee Brewers	31	31	146	621	144	50	14	100%	4.7	23.2%	8.1%	15.1%	2.3%	4.70	3.40	+3.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			102	88	470	1977	441	160	54	86%	4.6	22.3%	8.1%	14.2%	2.7%	4.12	3.74	+9.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	MIL	18	18	111	469	108	39	13	100%	6.2	23.0%	8.4%	14.6%	2.8%	4.37	4.12	+1.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	MIL	15	15	92	389	87	34	12	100%	6.1	22.3%	8.7%	13.6%	3.0%	4.34	4.33	+1.2
2029	31	MLB MLE	MIL	12	12	75	317	68	29	10	100%	6.2	21.5%	9.0%	12.5%	3.2%	4.47	4.56	+0.7
2030	32	MLB MLE	MIL	10	10	59	248	51	23	9	100%	5.9	20.7%	9.4%	11.3%	3.4%	4.68	4.81	+0.4
2031	33	MLB MLE	MIL	7	7	43	180	36	18	7	100%	6.1	19.9%	9.9%	10.0%	3.7%	4.93	5.09	+0.1
2028	30	MLB CTX	MIL	15	15	92	388	86	34	12	100%	6.1	22.3%	8.7%	13.6%	3.1%	4.42	4.41	+1.2
2029	31	MLB CTX	MIL	12	12	75	317	68	29	11	100%	6.2	21.5%	9.0%	12.5%	3.3%	4.56	4.65	+0.7
2030	32	MLB CTX	MIL	10	10	59	249	52	24	9	100%	5.9	20.7%	9.4%	11.3%	3.6%	4.77	4.90	+0.4
2031	33	MLB CTX	MIL	7	7	43	182	36	18	7	100%	6.1	19.9%	9.9%	10.0%	3.9%	5.03	5.19	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		147	133	738	3111	683	264	93	90%	5.0	22.0%	8.5%	13.5%	3.0%	4.31	3.96	+12.7
/ 200 IP	—	RAW		40	36	200	843	185	72	25	90%	5.0	22.0%	8.5%	13.5%	3.0%	4.31	3.96	+3.4
Career	—	MLE		186	167	894	3763	771	269	84	90%	4.8	20.5%	7.2%	13.3%	2.2%	3.78	3.51	+11.7
/ 200 IP	—	MLE		42	37	200	842	172	60	19	90%	4.8	20.5%	7.2%	13.3%	2.2%	3.78	3.51	+3.2

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.

2022 AAAOklahoma City Dodgers · 19 IPFIP 1.92 → 2.54 (+0.62)K% 41.8% → 36.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	41.77%	—	×0.880	36.76%
BB% (unintentional) /BF	7.59%	—	×1.050	7.97%
HR / BF /BF	2.53%	÷0.997	×1.100	2.79%

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 99).

Result: FIP 1.92 → 2.54, 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 AATulsa Drillers · 79 IPFIP 2.50 → 3.26 (+0.75)K% 25.8% → 20.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.83%	—	×0.810	20.92%
BB% (unintentional) /BF	6.01%	—	×1.100	6.61%
HR / BF /BF	1.50%	÷0.909	×1.200	1.98%

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 82).

Result: FIP 2.50 → 3.26, 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 AAAOklahoma City Dodgers · 27 IPFIP 2.32 → 2.59 (+0.26)K% 21.1% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.05%	—	×0.880	18.53%
BB% (unintentional) /BF	7.89%	—	×1.050	8.29%
HR / BF /BF	0.00%	÷1.073	×1.100	0.00%

Result: FIP 2.32 → 2.59, 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+Rancho Cucamonga Quakes · 98 IPFIP 2.89 → 3.64 (+0.75)K% 23.1% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.10%	—	×0.770	17.78%
BB% (unintentional) /BF	4.18%	—	×1.150	4.80%
HR / BF /BF	2.21%	÷1.058	×1.250	2.61%

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 112).

Result: FIP 2.89 → 3.64, 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 AATulsa Drillers · 34 IPFIP 2.52 → 2.93 (+0.41)K% 19.3% → 15.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.31%	—	×0.810	15.64%
BB% (unintentional) /BF	8.28%	—	×1.100	9.10%
HR / BF /BF	0.00%	÷1.147	×1.200	0.00%

Result: FIP 2.52 → 2.93, 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 AGreat Lakes Loons · 123 IPFIP 2.75 → 3.62 (+0.87)K% 21.5% → 15.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.52%	—	×0.740	15.93%
BB% (unintentional) /BF	4.95%	—	×1.180	5.84%
HR / BF /BF	1.52%	÷0.970	×1.300	2.04%

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 94).

Result: FIP 2.75 → 3.62, 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).

2017 A+Rancho Cucamonga Quakes · 11 IPFIP 0.32 → 1.08 (+0.76)K% 36.6% → 28.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.59%	—	×0.770	28.17%
BB% (unintentional) /BF	2.44%	—	×1.150	2.80%
HR / BF /BF	0.00%	÷1.085	×1.250	0.00%

Result: FIP 0.32 → 1.08, 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 ROKAZL Dodgers · 30 IPFIP 1.35 → 2.12 (+0.77)K% 25.2% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.19%	—	×0.680	17.13%
BB% (unintentional) /BF	2.96%	—	×1.250	3.70%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. 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 1.35 → 2.12, 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
Dustin May (OGILVIE)	—	—	—	—	—	2.8%	—	4.53	21.6%	12.6%	226	+0.41	—	—	—
Chase Anderson R 2017 · 3.30 FIP	29	SP	3.30	23.4%	7.0%	2.3%	.259	4.81	20.8%	12.5%	331	+1.51	3.30	1,260	71
Tylor Megill R 2024 · 3.33 FIP	29	SP	3.33	27.0%	9.7%	2.4%	.301	3.33	27.4%	16.4%	83	+0.00	3.02	603	66
Tom Koehler R 2015 · 4.41 FIP	29	SP	4.41	17.1%	9.2%	2.9%	.279	4.49	20.3%	10.8%	287	+0.08	3.76	822	64
Taylor Jungmann R 2017 · 3.88 FIP	27	SP-	3.88	18.8%	11.1%	1.8%	.279	—	—	—	—	—	3.59	435	63
Alexi Ogando R 2013 · 4.22 FIP	29	SP-	4.22	16.8%	9.3%	2.5%	.249	4.43	19.0%	8.4%	122	+0.21	2.83	538	63
Jose Samayoa R 2019 · 4.05 FIP	29	SP	4.05	19.4%	7.0%	3.1%	.314	—	—	—	—	—	3.44	262	62
Anthony DeSclafani R 2019 · 3.99 FIP	29	SP	3.99	24.0%	6.3%	3.9%	.268	3.98	21.1%	14.5%	220	-0.01	3.38	1,014	62
Claudio Vargas R 2007 · 4.76 FIP	29	SP-	4.76	17.7%	8.4%	3.8%	.307	4.10	16.8%	8.2%	98	-0.66	3.66	765	62
Jesse Hahn R 2017 · 3.66 FIP	28	SP	3.66	16.6%	9.7%	1.3%	.327	2.19	29.2%	16.9%	17	-1.47	2.19	499	61
Kenta Maeda R 2017 · 3.81 FIP	29	SP	3.81	25.1%	5.9%	3.8%	.273	3.24	28.7%	21.7%	346	-0.57	2.87	1,092	61
Kodai Senga R 2023 · 3.40 FIP	30	SP	3.40	29.1%	11.1%	2.5%	.274	4.52	23.4%	10.8%	186	+1.12	3.40	352	61
Mario Gonzalez R 2016 · 3.94 FIP	29	SP	3.94	17.9%	5.7%	2.9%	.276	4.53	14.1%	9.1%	104	+0.59	3.94	368	61
Josh Lowey R 2015 · 3.35 FIP	30	SP	3.35	21.1%	7.8%	1.9%	.311	2.30	26.7%	20.1%	231	-1.05	2.22	426	60
Jameson Taillon R 2021 · 4.09 FIP	29	SP	4.09	23.2%	7.1%	3.7%	.268	4.02	20.2%	15.0%	497	-0.07	3.12	1,401	60
Matt Buschmann R 2015 · 4.03 FIP	31	SP	4.03	17.2%	8.7%	2.3%	.282	4.84	12.8%	4.4%	142	+0.81	4.03	277	60
Chris Bassitt R 2018 · 3.49 FIP	29	SP-	3.49	20.5%	8.1%	2.0%	.307	3.64	23.5%	16.8%	364	+0.15	2.92	1,596	59
Matt Harvey R 2018 · 4.17 FIP	29	SP	4.17	19.8%	5.3%	3.8%	.294	4.74	16.9%	9.2%	222	+0.57	1.87	1,044	59
Matt Shoemaker R 2016 · 3.29 FIP	29	SP	3.29	21.4%	4.3%	2.6%	.310	4.27	22.4%	14.0%	137	+0.98	3.12	728	59
Victor Santos R 2006 · 4.42 FIP	29	SP-	4.42	15.5%	7.5%	3.1%	.347	5.78	16.9%	7.4%	63	+1.36	4.09	602	59
JC Ramírez R 2017 · 4.47 FIP	28	SP	4.47	16.9%	7.7%	3.3%	.287	5.48	12.2%	2.1%	41	+1.01	3.75	393	59

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	4.2	71	22.1%	8.6%	1.12	4.29
Joe Hesketh ('84-'94, 10 WAR*)	4.8	87	23.2%*	8.8%*	1.07*	3.95*
Brett Cecil ('18)	3.8	76	20.7%	8.3%	1.14	4.29
John Henry Johnson ('78-'87, 6 WAR*)	3.0	75	24.6%*	9.6%*	1.15*	4.15*
Roy Lee Jackson ('77-'86, 5 WAR*)	2.8	70	21.9%*	8.6%*	1.09*	3.99*
Rich Hill ('25)	7.1	101	23.9%	9.2%	1.12	4.02
Carlos Villanueva ('16)	5.0	91	20.4%	7.6%	1.25	4.31