Alex Cobb

P Toledo Mud Hens

Age 39 RHP 6'3" / 205 lbs AAA peak 0.6 · 2027

Overview

AAASP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	35	MIX2 levels	29	29	155	3.83	3.69	21.2%	5.6%	3.2	K 20.3% · BB 5.7% · FIP 3.86
2024	36	MIX4 levels	10	10	39	3.20	2.69	24.5%	7.5%	1.3	K 16.1% · BB 4.8% · FIP 3.24
2025	37	MIX2 levels	10	7	21	1.74	3.58	28.6%	17.9%	—	K 28.6% · BB 17.9% · FIP 3.60
2026	38	No MLB games played
2027PROJ	39	MLBDET	—	—	0	4.28	4.26	23.3%	8.9%	—
2028PROJ	40	MLBDET	5	5	30	4.53	4.69	22.2%	9.6%	0.2

Last 3 seasons WAR 4.5 · Next 2 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

FB Velo45

xwOBA vs21

K%10

CompsFrancisco Campos (age 42) · Walter Silva (age 39) · Yasutomo Kubo (age 38)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114106vR 98

ERA4.28

FIP4.26

K%23.3%

BB%8.9%

K-BB%14.4%

Proj IP40

Value2027 · WAR

Runs saved vs league avg · 40 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p4594.2

Whiff%

p1017.1%

p1016.1%

xwOBA vs

p210.346

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)
2011	23	MLB MLE	Tampa Bay Rays	9	9	53	224	37	20	3	100%	5.9	16.5%	8.9%	7.6%	1.4%	3.42	3.59	+1.2
2011	23	MLB RAW	Tampa Bay Rays	9	9	53	224	37	20	3	100%	5.9	16.5%	8.9%	7.6%	1.3%	3.42	3.57	+1.2
2012	24	MLB MLE	Tampa Bay Rays	23	23	136	569	106	38	11	100%	5.9	18.6%	6.7%	12.0%	2.0%	4.03	3.46	+3.2
2012	24	MLB RAW	Tampa Bay Rays	23	23	136	569	106	38	11	100%	5.9	18.6%	6.7%	12.0%	1.9%	4.03	3.43	+3.3
2013	25	MLB MLE	Tampa Bay Rays	22	22	143	578	134	41	13	100%	6.5	23.2%	7.1%	16.1%	2.3%	2.76	3.31	+3.7
2013	25	MLB RAW	Tampa Bay Rays	22	22	143	578	134	41	13	100%	6.5	23.2%	7.1%	16.1%	2.2%	2.76	3.27	+3.8
2014	26	MLB MLE	Tampa Bay Rays	27	27	166	681	149	46	11	100%	6.2	21.9%	6.8%	15.1%	1.7%	2.87	3.02	+5.0
2014	26	MLB RAW	Tampa Bay Rays	27	27	166	681	149	46	11	100%	6.2	21.9%	6.8%	15.1%	1.6%	2.87	3.00	+5.0
2016	28	MIX MLE	3 levels	13	13	44	207	31	15	8	100%	3.4	14.0%	6.9%	7.1%	4.5%	7.63	5.26	+0.1
2016	28	MIX RAW	3 levels	13	13	44	207	31	15	8	100%	3.4	15.0%	7.2%	7.7%	3.9%	7.63	5.09	+0.2
2017	29	MLB MLE	Tampa Bay Rays	29	29	179	742	128	42	22	100%	6.2	17.3%	5.7%	11.6%	3.0%	3.66	4.03	+2.9
2017	29	MLB RAW	Tampa Bay Rays	29	29	179	742	128	42	22	100%	6.2	17.3%	5.7%	11.6%	3.0%	3.66	3.97	+3.1
2018	30	MLB MLE	Baltimore Orioles	28	28	152	661	102	38	24	100%	5.4	15.4%	5.7%	9.7%	3.3%	4.90	4.32	+1.9
2018	30	MLB RAW	Baltimore Orioles	28	28	152	661	102	38	24	100%	5.4	15.4%	5.7%	9.7%	3.6%	4.90	4.56	+1.5
2019	31	MLB MLE	Baltimore Orioles	3	3	12	60	8	2	9	100%	4.1	13.3%	3.3%	10.0%	13.5%	10.95	9.83	-0.6
2019	31	MLB RAW	Baltimore Orioles	3	3	12	60	8	2	9	100%	4.1	13.3%	3.3%	10.0%	15.0%	10.95	11.78	-0.9
2020	32	MLB MLE	Baltimore Orioles	10	10	52	226	38	18	8	100%	5.2	16.8%	8.0%	8.8%	3.2%	4.30	4.44	+0.6
2020	32	MLB RAW	Baltimore Orioles	10	10	52	226	38	18	8	100%	5.2	16.8%	8.0%	8.8%	3.5%	4.30	4.67	+0.4
2021	33	MLB MLE	Los Angeles Angels	18	18	93	393	98	33	5	100%	5.2	24.9%	8.4%	16.5%	1.2%	3.76	2.74	+3.1
2021	33	MLB RAW	Los Angeles Angels	18	18	93	393	98	33	5	100%	5.2	24.9%	8.4%	16.5%	1.3%	3.76	2.76	+3.1
2022	34	MLB MLE	San Francisco Giants	28	28	150	631	151	43	9	100%	5.3	23.9%	6.8%	17.1%	1.5%	3.73	2.79	+4.9
2022	34	MLB RAW	San Francisco Giants	28	28	150	631	151	43	9	100%	5.3	23.9%	6.8%	17.1%	1.4%	3.73	2.73	+5.1
2023	35	MIX MLE	2 levels	29	29	155	660	140	37	20	100%	5.3	20.3%	5.7%	14.6%	3.2%	3.83	3.86	+2.9
2023	35	MIX RAW	2 levels	29	29	155	660	140	37	20	100%	5.3	21.2%	5.6%	15.6%	3.0%	3.83	3.69	+3.2
2024	36	MIX MLE	4 levels	10	10	39	159	39	12	2	100%	3.9	16.1%	4.8%	11.3%	1.6%	3.20	3.24	+1.0
2024	36	MIX MLE	4 levels	10	10	39	159	39	12	2	100%	3.9	16.1%	4.8%	11.3%	1.6%	3.20	3.24	+1.0
2024	36	MIX RAW	4 levels	10	10	39	159	39	12	2	100%	3.9	24.5%	7.5%	17.0%	1.3%	3.20	2.69	+1.3
2025	37	MIX MLE	2 levels	10	7	21	84	24	15	1	70%	2.1	28.6%	17.9%	10.7%	1.2%	1.74	3.60	—
2025	37	MIX MLE	2 levels	10	7	21	84	24	15	1	70%	2.1	28.6%	17.9%	10.7%	1.2%	1.74	3.60	—
2025	37	MIX RAW	2 levels	10	7	21	84	24	15	1	70%	2.1	28.6%	17.9%	10.7%	1.2%	1.74	3.58	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			233	233	1328	5577	1108	368	140	100%	5.7	19.9%	6.6%	13.3%	2.5%	3.84	3.63	+28.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	39	MLB	DET	—	—	0	—	—	—	—	—	—	23.3%	8.9%	14.4%	3.0%	4.28	4.26	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	40	MLB MLE	DET	5	5	30	126	28	12	4	100%	6.0	22.2%	9.6%	12.6%	3.4%	4.53	4.69	+0.2
2028	40	MLB CTX	DET	5	5	30	127	28	12	4	100%	6.0	22.2%	9.6%	12.6%	3.4%	4.53	4.69	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		238	238	1358	5703	1136	380	144	100%	5.7	19.9%	6.7%	13.3%	2.5%	3.85	3.65	+28.8
/ 200 IP	—	RAW		35	35	200	840	167	56	21	100%	5.7	19.9%	6.7%	13.3%	2.5%	3.85	3.65	+4.2
Career	—	MLE		259	256	1397	5875	1163	396	147	99%	5.4	19.8%	6.7%	13.1%	2.5%	3.84	3.66	+28.7
/ 200 IP	—	MLE		37	37	200	841	167	57	21	99%	5.4	19.8%	6.7%	13.1%	2.5%	3.84	3.66	+4.2

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.

2025 AAAToledo Mud Hens · 10 IPFIP 4.08 → 4.06 (−0.02)K% 23.3% → 20.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.26%	—	×0.860	20.00%
BB% (unintentional) /BF	23.26%	—	×0.900	20.93%
HR / BF /BF	0.00%	÷0.872	×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 74).

Result: FIP 4.08 → 4.06, 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+West Michigan Whitecaps · 11 IPFIP 3.10 → 4.63 (+1.53)K% 34.1% → 26.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.15%	—	×0.770	26.29%
BB% (unintentional) /BF	12.20%	—	×1.150	14.02%
HR / BF /BF	2.44%	÷0.850	×1.250	3.59%

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 70).

Result: FIP 3.10 → 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).

2024 ASan Jose Giants · 10 IPFIP 2.32 → 3.67 (+1.35)K% 34.7% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.69%	—	×0.740	25.67%
BB% (unintentional) /BF	8.16%	—	×1.180	9.63%
HR / BF /BF	2.04%	÷0.959	×1.300	2.77%

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 92).

Result: FIP 2.32 → 3.67, 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).

2024 AAAColumbus Clippers · 10 IPFIP 2.01 → 2.21 (+0.20)K% 25.7% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.71%	—	×0.860	22.11%
BB% (unintentional) /BF	8.57%	—	×0.900	7.71%
HR / BF /BF	0.00%	÷1.060	×1.100	0.00%

Result: FIP 2.01 → 2.21, 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 AAADurham Bulls · 15 IPFIP 5.07 → 5.12 (+0.05)K% 13.7% → 12.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.70%	—	×0.880	12.05%
BB% (unintentional) /BF	6.85%	—	×1.050	7.19%
HR / BF /BF	4.11%	÷1.167	×1.100	3.87%

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 133).

Result: FIP 5.07 → 5.12, 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).

2016 A+Charlotte Stone Crabs · 7 IPFIP 2.96 → 3.47 (+0.51)K% 16.7% → 12.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.770	12.83%
BB% (unintentional) /BF	10.00%	—	×1.150	11.50%
HR / BF /BF	0.00%	÷1.142	×1.250	0.00%

Result: FIP 2.96 → 3.47, 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).

	Profile at Match Age	Next 3 Yrs	Career
Alex Cobb (OGILVIE)	—	—	—	—	—	3.0%	—	4.69	22.2%	12.6%	30	+0.43	—	—	—
Francisco Campos R 2015 · 4.77 FIP	42	SP	4.77	11.5%	7.9%	3.0%	.303	4.08	12.2%	7.0%	197	-0.69	4.06	332	54
Walter Silva R 2016 · 4.22 FIP	39	SP	4.22	10.3%	5.5%	2.4%	.294	4.35	10.0%	1.8%	235	+0.13	3.67	453	52
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	51
Pablo Ortega R 2016 · 4.01 FIP	39	SP	4.01	8.9%	4.8%	1.9%	.262	4.49	13.3%	7.1%	148	+0.48	4.01	375	46
Lorenzo Barcelo R 2016 · 3.48 FIP	38	SP	3.48	12.8%	1.1%	2.4%	.329	—	—	—	—	—	3.14	259	39
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	38
Mauricio Lara L 2017 · 3.70 FIP	38	SP	3.70	12.0%	6.3%	1.5%	.301	4.96	12.3%	5.5%	111	+1.26	3.62	444	26
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	25

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	7.0	107	20.0%	7.0%	0.98	3.86
Alex Wood ('24)	6.3	114	22.1%	7.1%	0.91	3.78
Brandon McCarthy ('18)	6.1	94	17.6%	6.3%	1.07	4.20
Roger Nelson ('67-'76, 10 WAR*)	3.2	106	19.5%*	7.1%*	0.98*	3.88*
Jaime García ('18)	5.7	126	19.2%	7.7%	0.87	3.85
Bruce Kison ('71-'85, 16 WAR*)	9.0	121	20.4%*	8.5%*	1.10*	4.16*
Ray Washburn ('61-'70, 12 WAR*)	6.0	121	17.6%*	7.5%*	1.17*	4.17*

			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
Alex Cobb (OGILVIE)	—	—	—	—	—	3.0%	—	4.69	22.2%	12.6%	30	+0.43	—	—	—
Francisco Campos R 2015 · 4.77 FIP	42	SP	4.77	11.5%	7.9%	3.0%	.303	4.08	12.2%	7.0%	197	-0.69	4.06	332	54
Walter Silva R 2016 · 4.22 FIP	39	SP	4.22	10.3%	5.5%	2.4%	.294	4.35	10.0%	1.8%	235	+0.13	3.67	453	52
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	51
Pablo Ortega R 2016 · 4.01 FIP	39	SP	4.01	8.9%	4.8%	1.9%	.262	4.49	13.3%	7.1%	148	+0.48	4.01	375	46
Lorenzo Barcelo R 2016 · 3.48 FIP	38	SP	3.48	12.8%	1.1%	2.4%	.329	—	—	—	—	—	3.14	259	39
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	38
Mauricio Lara L 2017 · 3.70 FIP	38	SP	3.70	12.0%	6.3%	1.5%	.301	4.96	12.3%	5.5%	111	+1.26	3.62	444	26
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	25

Alex Cobb

Contract

Career Stats + Projections

How the minor-league lines were translated

Career Path Comps

Career Comps