Michael Gomez

P Memphis Redbirds

Age 30 RHP 6'3" / 210 lbs AAA

Overview

AAAPROSPECT

MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	AAATB	39	0	56	5.01	3.01	27.8%	9.0%	—	K 23.9% · BB 8.1% · FIP 3.27
2025	28	AAASTL	23	2	34	6.88	5.28	18.5%	14.2%	—	K 15.9% · BB 12.8% · FIP 5.52
2027PROJ	30	MLBSTL	2	0	2	4.85	4.85	19.7%	10.6%	-0.0
2028PROJ37% MLB	31	MLBSTL	6	0	6	4.91	4.99	19.7%	10.8%	-0.0

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsWirfin Obispo (age 30) · Jim Miller (age 30) · Joe Nelson (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 125117vR 109

ERA4.85

FIP4.85

K%19.7%

BB%10.6%

K-BB%9.0%

Proj IP23

Value2027 · WAR

Runs saved vs league avg · 23 IP

Grades · Contract

Scouting Grades

K†4545

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)
2018	21	ROK MLE	GCL Phillies West	6	0	8	36	5	5	0	0%	1.3	13.9%	13.9%	0.0%	0.0%	3.52	3.69	—
2018	21	ROK RAW	GCL Phillies West	6	0	8	36	5	5	0	0%	1.3	13.9%	13.9%	0.0%	0.0%	3.52	3.75	—
2019	22	MIX MLE	2 levels	15	0	27	102	28	7	1	0%	1.8	22.6%	9.2%	13.4%	1.8%	1.01	3.32	—
2019	22	MIX RAW	2 levels	15	0	27	102	28	7	1	0%	1.8	27.5%	6.9%	20.6%	1.0%	1.01	2.27	—
2021	24	AA MLE	Somerset Patriots	31	0	49	208	50	14	4	0%	1.6	19.5%	7.4%	12.1%	2.2%	3.12	3.60	—
2021	24	AA RAW	Somerset Patriots	31	0	49	208	50	14	4	0%	1.6	24.0%	6.7%	17.3%	1.9%	3.12	2.98	—
2022	25	MIX MLE	2 levels	46	0	65	303	59	35	4	0%	1.4	16.6%	11.4%	5.2%	1.5%	4.55	3.98	—
2022	25	MIX RAW	2 levels	46	0	65	303	59	35	4	0%	1.4	19.5%	11.6%	7.9%	1.3%	4.55	3.70	—
2023	26	AAA MLE	Scranton/Wilkes-Barre RailRiders	47	0	68	313	61	43	7	0%	1.4	16.8%	12.4%	4.4%	2.4%	5.45	4.58	—
2023	26	AAA RAW	Scranton/Wilkes-Barre RailRiders	47	0	68	313	61	43	7	0%	1.4	19.5%	13.7%	5.8%	2.2%	5.45	4.55	—
2024	27	AAA MLE	Durham Bulls	39	0	56	245	68	22	5	0%	1.4	23.9%	8.1%	15.8%	2.1%	5.01	3.27	—
2024	27	AAA MLE	Durham Bulls	39	0	56	245	68	22	5	0%	1.4	23.9%	8.1%	15.8%	2.1%	5.01	3.27	—
2024	27	AAA RAW	Durham Bulls	39	0	56	245	68	22	5	0%	1.4	27.8%	9.0%	18.8%	2.0%	5.01	3.01	—
2025	28	AAA MLE	Memphis Redbirds	23	2	34	162	30	23	5	9%	1.5	15.9%	12.8%	3.1%	3.9%	6.88	5.52	—
2025	28	AAA MLE	Memphis Redbirds	23	2	34	162	30	23	5	9%	1.5	15.9%	12.8%	3.1%	3.9%	6.88	5.52	—
2025	28	AAA RAW	Memphis Redbirds	23	2	34	162	30	23	5	9%	1.5	18.5%	14.2%	4.3%	3.1%	6.88	5.28	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	STL	2	0	2	8	2	1	0	0%	1.0	19.7%	10.6%	9.0%	3.1%	4.85	4.85	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	STL	17	0	18	74	15	8	2	0%	1.0	19.7%	10.8%	8.8%	3.3%	4.91	4.99	-0.1
2028	31	MLB CTX	STL	6	0	6	25	5	3	1	0%	1.0	19.7%	10.8%	8.8%	3.3%	4.91	4.99	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		6	0	6	27	5	3	1	0%	1.1	19.7%	10.8%	8.8%	3.3%	4.91	4.61	-0.0
/ 200 IP	—	RAW		190	0	200	844	155	93	31	0%	1.1	19.7%	10.8%	8.8%	3.3%	4.91	4.61	-0.2
Career	—	MLE		207	2	306	1369	256	143	30	1%	1.5	18.7%	10.5%	8.2%	2.2%	4.53	4.06	-0.1
/ 200 IP	—	MLE		135	1	200	895	167	94	20	1%	1.5	18.7%	10.5%	8.2%	2.2%	4.53	4.06	-1.0

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 AAAMemphis Redbirds · 34 IPFIP 5.03 → 5.52 (+0.49)K% 18.5% → 15.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.52%	—	×0.860	15.93%
BB% (unintentional) /BF	14.20%	—	×0.900	12.78%
HR / BF /BF	3.09%	÷0.869	×1.100	3.91%

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 5.03 → 5.52, 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 AAADurham Bulls · 56 IPFIP 3.01 → 3.27 (+0.26)K% 27.8% → 23.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.76%	—	×0.860	23.87%
BB% (unintentional) /BF	8.98%	—	×0.900	8.08%
HR / BF /BF	2.04%	÷1.061	×1.100	2.12%

Result: FIP 3.01 → 3.27, 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 AAAScranton/Wilkes-Barre RailRiders · 68 IPFIP 4.42 → 4.58 (+0.16)K% 19.5% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.49%	—	×0.860	16.76%
BB% (unintentional) /BF	13.74%	—	×0.900	12.36%
HR / BF /BF	2.24%	÷1.017	×1.100	2.42%

Result: FIP 4.42 → 4.58, 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).

2022 AAAScranton/Wilkes-Barre RailRiders · 61 IPFIP 3.65 → 3.98 (+0.33)K% 18.9% → 16.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.88%	—	×0.880	16.62%
BB% (unintentional) /BF	10.84%	—	×1.050	11.38%
HR / BF /BF	1.40%	÷1.006	×1.100	1.53%

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 101).

Result: FIP 3.65 → 3.98, 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).

2021 AASomerset Patriots · 49 IPFIP 2.98 → 3.60 (+0.62)K% 24.0% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.04%	—	×0.810	19.47%
BB% (unintentional) /BF	6.73%	—	×1.100	7.40%
HR / BF /BF	1.92%	÷1.053	×1.200	2.19%

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 111).

Result: FIP 2.98 → 3.60, 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 A+Clearwater Threshers · 18 IPFIP 2.37 → 3.32 (+0.95)K% 29.3% → 22.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.33%	—	×0.770	22.59%
BB% (unintentional) /BF	8.00%	—	×1.150	9.20%
HR / BF /BF	1.33%	÷0.949	×1.250	1.76%

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 90).

Result: FIP 2.37 → 3.32, 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 ALakewood BlueClaws · 8 IPFIP 1.69 → 2.26 (+0.57)K% 22.2% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.22%	—	×0.740	16.44%
BB% (unintentional) /BF	3.70%	—	×1.180	4.37%
HR / BF /BF	0.00%	÷0.809	×1.300	0.00%

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 62).

Result: FIP 1.69 → 2.26, 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).

2018 ROKGCL Phillies West · 8 IPFIP 3.69 → 4.50 (+0.82)K% 13.9% → 9.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.89%	—	×0.680	9.44%
BB% (unintentional) /BF	13.89%	—	×1.250	17.36%
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 3.69 → 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).

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
Michael Gomez (OGILVIE)	—	—	4.85	19.7%	10.6%	3.1%	—	4.99	19.7%	8.8%	18	+0.14	—	—	—
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	91
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	88
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	85
Dustin Molleken R 2015 · 3.74 FIP	30	SU	3.74	20.4%	11.7%	1.6%	.287	3.90	20.6%	8.4%	77	+0.16	3.38	129	85
Esteban Haro R 2016 · 4.55 FIP	30	SU	4.55	17.1%	10.2%	2.9%	.338	3.83	11.6%	6.3%	75	-0.72	3.76	164	84
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	84
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	82
Erik Davis R 2017 · 4.25 FIP	30	SU	4.25	22.2%	11.1%	3.0%	.349	3.52	17.4%	8.5%	65	-0.73	3.05	227	79
Scott Atchison R 2007 · 4.55 FIP	31	SU	4.55	19.1%	7.6%	3.8%	.297	4.59	16.2%	9.5%	60	+0.04	2.52	340	79
Carlos Fisher R 2011 · 4.33 FIP	28	SU	4.33	15.5%	9.1%	2.5%	.275	—	—	—	—	—	2.48	245	77
Mike Broadway R 2017 · 4.73 FIP	30	SU	4.73	18.3%	10.2%	3.4%	.245	4.65	23.0%	12.4%	49	-0.08	1.74	195	77
Francisco Mendoza R 2016 · 3.81 FIP	28	SU	3.81	17.9%	7.7%	2.3%	.344	—	—	—	—	—	3.13	120	76
Adrian Garza R 2017 · 4.47 FIP	29	SU	4.47	16.0%	7.0%	3.4%	.351	5.18	9.7%	4.1%	113	+0.71	3.94	229	76
Jose Manuel Lopez R 2015 · 5.21 FIP	30	SU	5.21	18.6%	7.6%	4.9%	.373	3.56	18.5%	9.3%	93	-1.65	3.50	170	76
Ivan Zavala R 2017 · 5.02 FIP	30	SU	5.02	12.8%	8.0%	3.6%	.286	5.15	12.7%	5.3%	61	+0.13	4.27	209	76
Ronald Peña R 2021 · 4.49 FIP	29	SU	4.49	16.5%	10.4%	2.7%	.288	—	—	—	—	—	3.72	187	75
Marc Magliaro R 2018 · 3.94 FIP	28	SU	3.94	19.6%	7.3%	2.9%	.307	—	—	—	—	—	3.94	125	75
Greg Burke R 2013 · 3.70 FIP	30	SU	3.70	17.9%	8.3%	1.9%	.357	3.61	22.1%	14.8%	62	-0.09	3.61	139	75
Ryan Doolittle R 2016 · 4.38 FIP	28	SU	4.38	13.9%	7.1%	2.8%	.269	—	—	—	—	—	4.03	101	74
Dalier Hinojosa R 2016 · 4.30 FIP	30	SU	4.30	18.7%	12.6%	2.2%	.329	—	—	—	—	—	3.41	104	74