Corbin Burnes

P Arizona Diamondbacks

Age 32 RHP 6'3" / 245 lbs MLB Svc 8.0 · Ctrl thru 2031 peak 1.3 · 2027

Overview

MLBSP40-MANExtFA 2032

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MLBBAL	32	32	194	2.92	3.45	23.1%	6.1%	4.7
2025	30	MLBARI	11	11	64	2.66	3.72	23.8%	9.4%	1.3
2026	31	MIX2 levels	4	4	12	6.57	8.05	15.1%	7.5%	-0.4	K 15.1% · BB 7.5% · FIP 7.96
2027PROJ	32	MLBARI	13	13	82	3.79	4.02	24.3%	7.9%	1.3
2028PROJ	33	MLBARI	12	12	76	3.84	3.93	25.4%	7.5%	1.2
2029PROJ	34	MLBARI	11	11	69	4.07	4.23	24.5%	8.0%	0.9

Last 3 seasons WAR 5.6 · Next 3 projected WAR 3.4Projected rows: MLB playing time only, park and league context applied.

ContractCONFIRMED

Control thru2031Service8.285First FA2032

2027Ext$30M2028Ext$30M2029Ext$30M2030Ext$30M2031Ext$30M

CompsMatt Shoemaker (age 32) · Zack Segovia (age 32) · Elian Leyva (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 110101vR 93

ERA3.79

FIP4.02

K%24.3%

BB%7.9%

K-BB%16.4%

Proj IP82

Value2027 · WAR

Runs saved vs league avg · 82 IP

━ projected WAR━ paid WAR ($/8M) surplus deficit-$63M ctrl surplus

Contract

Contract CONFIRMED · hand-curated extension

Status FA

MLB Debut 2018

Service 8.285 yrs.days

Team Control Through 2031

First FA Year 2032

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$30M	+1.3	$10M	-$20M
2028	Ext	$30M	+1.2	$9M	-$18M
2029	Ext	$30M	+0.9	$3M	-$21M
2030	Ext	beyond projection horizon · no WAR estimate
2031	Ext	beyond projection horizon · no WAR estimate
Totals		$90M		$22M	-$59M

Underwater contract
Projected production over the remaining contract is worth -$59M less than the salary owed. Most often this reflects an aging-curve fade that was visible at signing — the team paid a premium for the early years.

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	21	MIX MLE	2 levels	12	6	36	146	41	18	1	50%	3.0	19.1%	15.7%	3.4%	0.9%	2.02	3.99	—
2016	21	MIX RAW	2 levels	12	6	36	146	41	18	1	50%	3.0	28.1%	12.3%	15.8%	0.7%	2.02	2.68	—
2017	22	MIX MLE	2 levels	26	26	146	560	140	36	3	100%	5.6	19.9%	7.2%	12.6%	0.5%	1.67	2.63	—
2017	22	MIX RAW	2 levels	26	26	146	560	140	36	3	100%	5.6	25.0%	6.4%	18.6%	0.5%	1.67	2.19	—
2018	23	MIX MLE	2 levels	49	13	117	495	116	40	11	27%	2.4	21.5%	8.4%	13.1%	2.2%	4.32	3.53	+1.4
2018	23	MIX RAW	2 levels	49	13	117	495	116	40	11	27%	2.4	23.4%	8.1%	15.4%	2.2%	4.32	3.37	+1.7
2019	24	MIX MLE	2 levels	40	11	71	339	95	29	19	28%	1.8	27.1%	8.7%	18.5%	5.4%	8.71	4.89	-0.3
2019	24	MIX RAW	2 levels	40	11	71	339	95	29	19	28%	1.8	28.0%	8.6%	19.5%	5.6%	8.71	5.12	-0.5
2020	25	MLB MLE	Milwaukee Brewers	12	9	60	240	88	24	2	75%	5.0	36.7%	10.0%	26.7%	0.8%	2.11	1.71	+2.9
2020	25	MLB RAW	Milwaukee Brewers	12	9	60	240	88	24	2	75%	5.0	36.7%	10.0%	26.7%	0.8%	2.11	1.79	+2.8
2021	26	MLB MLE	Milwaukee Brewers	28	28	167	657	234	34	7	100%	6.0	35.6%	5.2%	30.4%	1.0%	2.43	1.30	+9.3
2021	26	MLB RAW	Milwaukee Brewers	28	28	167	657	234	34	7	100%	6.0	35.6%	5.2%	30.4%	1.1%	2.43	1.45	+9.0
2022	27	MLB MLE	Milwaukee Brewers	33	33	202	797	243	51	23	100%	6.1	30.5%	6.4%	24.1%	2.7%	2.94	2.84	+6.6
2022	27	MLB RAW	Milwaukee Brewers	33	33	202	797	243	51	23	100%	6.1	30.5%	6.4%	24.1%	2.9%	2.94	2.93	+6.3
2023	28	MLB MLE	Milwaukee Brewers	32	32	194	784	200	66	22	100%	6.1	25.5%	8.4%	17.1%	2.7%	3.39	3.48	+4.6
2023	28	MLB RAW	Milwaukee Brewers	32	32	194	784	200	66	22	100%	6.1	25.5%	8.4%	17.1%	2.8%	3.39	3.53	+4.4
2024	29	MLB MLE	Baltimore Orioles	32	32	194	784	181	48	22	100%	6.1	23.1%	6.1%	17.0%	2.8%	2.92	3.48	+4.6
2024	29	MLB MLE	Baltimore Orioles	32	32	194	784	181	48	22	100%	6.1	23.1%	6.1%	17.0%	2.8%	2.92	3.48	+4.6
2024	29	MLB RAW	Baltimore Orioles	32	32	194	784	181	48	22	100%	6.1	23.1%	6.1%	17.0%	2.8%	2.92	3.45	+4.7
2025	30	MLB MLE	Arizona Diamondbacks	11	11	64	265	63	25	7	100%	5.8	23.8%	9.4%	14.3%	2.8%	2.66	3.82	+1.2
2025	30	MLB MLE	Arizona Diamondbacks	11	11	64	265	63	25	7	100%	5.8	23.8%	9.4%	14.3%	2.8%	2.66	3.82	+1.2
2025	30	MLB RAW	Arizona Diamondbacks	11	11	64	265	63	25	7	100%	5.8	23.8%	9.4%	14.3%	2.6%	2.66	3.72	+1.3
2026	31	MIX MLE	2 levels	4	4	12	53	8	4	5	100%	3.1	15.1%	7.5%	7.5%	9.4%	6.57	7.96	-0.4
2026	31	MIX MLE	2 levels	4	4	12	53	8	4	5	100%	3.1	15.1%	7.5%	7.5%	9.4%	6.57	7.96	-0.4
2026	31	MIX RAW	2 levels	4	4	12	53	8	4	5	100%	3.1	15.1%	7.5%	7.5%	9.4%	6.57	8.05	-0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			213	152	978	3961	1122	281	109	71%	4.6	28.3%	7.1%	21.2%	2.8%	3.20	3.12	+27.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	ARI	13	13	82	346	84	27	10	100%	6.3	24.3%	7.9%	16.4%	2.9%	3.79	4.02	+1.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	ARI	12	12	76	319	81	24	10	100%	6.3	25.4%	7.5%	17.9%	3.2%	3.94	4.03	+1.2
2029	34	MLB MLE	ARI	11	11	69	290	71	23	10	100%	6.3	24.5%	8.0%	16.5%	3.5%	4.18	4.34	+0.9
2028	33	MLB CTX	ARI	12	12	76	321	82	24	10	100%	6.3	25.4%	7.5%	17.9%	3.0%	3.84	3.93	+1.2
2029	34	MLB CTX	ARI	11	11	69	291	71	23	10	100%	6.3	24.5%	8.0%	16.5%	3.3%	4.07	4.23	+0.9
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		237	176	1123	4571	1274	328	128	74%	4.7	27.9%	7.2%	20.7%	2.8%	3.30	3.19	+30.5
/ 200 IP	—	RAW		42	31	200	814	227	58	23	74%	4.7	27.9%	7.2%	20.7%	2.8%	3.30	3.19	+5.4
Career	—	MLE		279	205	1263	5120	1354	386	119	73%	4.5	26.5%	7.5%	18.9%	2.3%	3.20	3.10	+29.6
/ 200 IP	—	MLE		44	32	200	811	215	61	19	73%	4.5	26.5%	7.5%	18.9%	2.3%	3.20	3.10	+5.3

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 AAASan Antonio Missions · 22 IPFIP 3.22 → 3.65 (+0.43)K% 24.0% → 21.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.04%	—	×0.880	21.15%
BB% (unintentional) /BF	8.65%	—	×1.050	9.09%
HR / BF /BF	1.92%	÷0.977	×1.100	2.17%

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 95).

Result: FIP 3.22 → 3.65, 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).

2018 AAAColorado Springs Sky Sox · 79 IPFIP 3.37 → 3.64 (+0.27)K% 23.6% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.62%	—	×0.880	20.78%
BB% (unintentional) /BF	9.04%	—	×1.050	9.49%
HR / BF /BF	2.04%	÷1.124	×1.100	2.00%

Result: FIP 3.37 → 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).

2017 AABiloxi Shuckers · 86 IPFIP 2.07 → 2.50 (+0.43)K% 24.9% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.93%	—	×0.810	20.19%
BB% (unintentional) /BF	5.93%	—	×1.100	6.53%
HR / BF /BF	0.59%	÷1.393	×1.200	0.51%

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 179).

Result: FIP 2.07 → 2.50, 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).

2017 A+Carolina Mudcats · 60 IPFIP 2.13 → 2.83 (+0.70)K% 25.1% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.11%	—	×0.770	19.34%
BB% (unintentional) /BF	7.17%	—	×1.150	8.25%
HR / BF /BF	0.45%	÷0.959	×1.250	0.58%

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 92).

Result: FIP 2.13 → 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).

2016 AWisconsin Timber Rattlers · 29 IPFIP 3.06 → 3.99 (+0.93)K% 25.8% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.83%	—	×0.740	19.12%
BB% (unintentional) /BF	13.33%	—	×1.180	15.73%
HR / BF /BF	0.83%	÷1.161	×1.300	0.93%

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 3.06 → 3.99, 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 Brewers · 7 IPFIP 0.83 → 2.11 (+1.28)K% 38.5% → 26.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.46%	—	×0.680	26.15%
BB% (unintentional) /BF	7.69%	—	×1.250	9.62%
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 0.83 → 2.11, 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
Corbin Burnes (OGILVIE)	—	—	—	—	—	2.9%	—	4.18	25.0%	17.2%	144	+0.16	—	—	—
Matt Shoemaker R 2019 · 3.80 FIP	32	SP	3.80	22.2%	8.3%	2.8%	.181	5.06	18.8%	11.1%	159	+1.26	3.12	728	53
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	51
Elian Leyva R 2019 · 4.30 FIP	30	SP-	4.30	17.0%	8.5%	2.9%	.323	—	—	—	—	—	3.10	158	50
Stan Spencer R 2000 · 4.42 FIP	30	SP	4.42	19.2%	8.7%	3.4%	.259	—	—	—	—	—	3.61	119	49
Hideki Irabu R 2001 · 3.78 FIP	32	SP	3.78	24.3%	4.1%	4.0%	.380	5.69	14.7%	7.8%	47	+1.91	3.78	514	48
Tyler Herron R 2017 · 4.02 FIP	30	SP	4.02	16.4%	8.7%	2.2%	.377	—	—	—	—	—	4.02	117	47
Mike Burns R 2009 · 4.90 FIP	31	SP-	4.90	17.2%	6.6%	4.4%	.307	—	—	—	—	—	3.10	104	47
Reinier Roibal R 2019 · 4.91 FIP	30	SP	4.91	17.5%	11.8%	3.3%	.426	—	—	—	—	—	3.11	133	47
Mitch Lively R 2017 · 3.47 FIP	31	SP-	3.47	17.0%	6.4%	1.8%	.304	—	—	—	—	—	2.39	158	46
Brandon Beachy R 2019 · 4.19 FIP	32	SP	4.19	17.4%	9.5%	2.5%	.315	—	—	—	—	—	1.97	364	46
Rob Wooten R 2017 · 5.15 FIP	32	SP	5.15	21.2%	4.9%	5.9%	.394	4.38	15.5%	12.5%	43	-0.77	2.48	253	46
Bryan Evans R 2019 · 3.89 FIP	32	SP	3.89	18.7%	10.4%	1.9%	.450	—	—	—	—	—	3.89	231	45
Chuck Smith R 2001 · 3.99 FIP	31	SP	3.99	18.4%	8.1%	2.6%	.289	—	—	—	—	—	3.08	211	45
Jesus Adrian Castillo R 2016 · 3.88 FIP	32	SP	3.88	12.8%	6.0%	2.0%	.245	4.27	13.6%	7.2%	58	+0.39	2.23	173	44
Danny Gutierrez R 2017 · 4.11 FIP	30	SP	4.11	15.1%	6.1%	2.8%	.362	—	—	—	—	—	3.34	239	44
Tim Adleman R 2019 · 4.30 FIP	31	SP	4.30	22.8%	7.8%	3.9%	.266	4.71	18.5%	11.1%	90	+0.41	3.63	605	44
Miles Mikolas R 2021 · 4.27 FIP	32	SP	4.27	16.4%	5.5%	3.4%	.269	4.09	17.3%	13.0%	575	-0.18	3.13	1,386	43
Dennis O'Grady R 2019 · 3.62 FIP	30	SP	3.62	17.2%	8.1%	1.7%	.344	—	—	—	—	—	3.62	157	43
Yohan Pino R 2015 · 4.13 FIP	31	SP-	4.13	17.8%	5.8%	3.3%	.312	5.33	15.2%	11.6%	35	+1.20	3.84	193	43
Hector Ambriz R 2016 · 3.63 FIP	32	SP	3.63	16.0%	6.4%	2.0%	.273	6.40	10.4%	2.9%	91	+2.77	2.24	259	42

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	6.0	110	27.6%	7.3%	1.10	3.34
Bruce Sutter ('76-'88, 22 WAR*)	5.2	95	28.5%*	7.3%*	1.12*	3.14*
Alejandro Peña ('81-'96, 15 WAR*)	5.3	81	24.7%*	7.5%*	0.96*	3.61*
Julio Urías ('23)	3.6	102	24.3%	6.6%	1.05	3.11
Rollie Fingers ('68-'85, 28 WAR*)	8.5	106	27.0%*	6.9%*	0.98*	3.55*
Eric Gagne ('08)	3.2	80	27.0%	8.5%	1.06	3.47
James Paxton ('24)	4.8	95	25.3%	7.9%	1.05	3.77