Omar Gonzalez

P FCL Yankees

Age 22 RHP 6'4" / 175 lbs ROK peak 0.2 · 2028

Overview

ROKPROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2025	20	ROKNYY	11	10	47	3.86	4.60	22.3%	15.7%	—	K 15.2% · BB 19.7% · FIP 5.26
2026	21	ROKNYY	9	7	36	5.30	4.28	23.1%	7.7%	—	K 15.7% · BB 9.6% · FIP 5.95
2027PROJ	22	MLBNYY	—	—	0	6.04	6.04	15.5%	17.2%	—
2028PROJ49% MLB	23	MLBNYY	4	4	22	5.72	5.62	17.3%	15.4%	-0.1
2029PROJ70% MLB	24	MLBNYY	6	6	31	5.47	5.37	18.3%	14.2%	0.0

Next 3 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff35Control20Command25

Career WAR0.0 WAR median (P10 0.0 / P90 0.1, comp-based) · star 1% · didn't stick 96%

CompsKyle Larsen (age 21) · Danny Flatt (age 20) · Jackson Fristoe (age 22)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 148140vR 132

ERA6.04

FIP6.04

K%15.5%

BB%17.2%

K-BB%-1.8%

Proj IP45

Value2027 · WAR

Runs saved vs league avg · 45 IP

Grades · Contract

Scouting Grades

STUF3535

CTRL2020

CMD2525

K†3045

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)
2022	17	ROK MLE	DSL NYY Bombers	6	5	20	76	36	9	0	83%	3.4	32.2%	14.8%	17.4%	0.0%	0.44	2.25	—
2022	17	ROK RAW	DSL NYY Bombers	6	5	20	76	36	9	0	83%	3.4	47.4%	11.8%	35.5%	0.0%	0.44	0.89	—
2023	18	ROK MLE	DSL NYY Bombers	4	4	11	56	20	11	1	100%	2.8	24.3%	24.6%	-0.3%	1.7%	8.18	5.10	—
2023	18	ROK RAW	DSL NYY Bombers	4	4	11	56	20	11	1	100%	2.8	35.7%	19.6%	16.1%	1.8%	8.18	3.65	—
2024	19	ROK MLE	FCL Yankees	10	4	23	103	21	19	2	40%	2.3	13.9%	23.1%	-9.2%	2.4%	6.94	6.16	—
2024	19	ROK MLE	FCL Yankees	10	4	23	103	21	19	2	40%	2.3	13.9%	23.1%	-9.2%	2.4%	6.94	6.16	—
2024	19	ROK RAW	FCL Yankees	10	4	23	103	21	19	2	40%	2.3	20.4%	18.4%	1.9%	1.9%	6.94	4.86	—
2025	20	ROK MLE	FCL Yankees	11	10	47	197	44	31	5	91%	4.2	15.2%	19.7%	-4.5%	1.7%	3.86	5.26	—
2025	20	ROK MLE	FCL Yankees	11	10	47	197	44	31	5	91%	4.2	15.2%	19.7%	-4.5%	1.7%	3.86	5.26	—
2025	20	ROK RAW	FCL Yankees	11	10	47	197	44	31	5	91%	4.2	22.3%	15.7%	6.6%	2.5%	3.86	4.60	—
2026	21	ROK MLE	FCL Yankees	9	7	36	156	36	12	6	78%	4.0	15.7%	9.6%	6.1%	5.4%	5.30	5.95	—
2026	21	ROK MLE	FCL Yankees	9	7	36	156	36	12	6	78%	4.0	15.7%	9.6%	6.1%	5.4%	5.30	5.95	—
2026	21	ROK RAW	FCL Yankees	9	7	36	156	36	12	6	78%	4.0	23.1%	7.7%	15.4%	3.8%	5.30	4.28	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	22	MLB	NYY	—	—	0	—	—	—	—	—	—	15.5%	17.2%	-1.8%	3.1%	6.04	6.04	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	23	MLB MLE	NYY	8	8	45	188	33	29	6	100%	5.6	17.3%	15.4%	1.9%	3.0%	5.72	5.62	-0.1
2029	24	MLB MLE	NYY	8	8	45	188	35	27	6	100%	5.6	18.3%	14.2%	4.1%	3.0%	5.47	5.37	+0.0
2030	25	MLB MLE	NYY	5	5	30	127	24	17	4	100%	6.0	19.0%	13.5%	5.5%	3.0%	5.30	5.22	+0.1
2031	26	MLB MLE	NYY	5	5	30	126	24	17	4	100%	6.0	19.3%	13.4%	5.9%	3.0%	5.21	5.17	+0.1
2032	27	MLB MLE	NYY	5	5	29	123	24	17	4	100%	5.8	19.3%	13.5%	5.8%	3.0%	5.21	5.21	+0.1
2028	23	MLB CTX	NYY	4	4	22	93	16	14	3	100%	5.5	17.3%	15.4%	1.9%	3.0%	5.72	5.62	-0.1
2029	24	MLB CTX	NYY	6	6	31	131	24	19	4	100%	5.2	18.3%	14.2%	4.1%	3.0%	5.47	5.37	+0.0
2030	25	MLB CTX	NYY	4	4	24	101	19	14	3	100%	6.0	19.0%	13.5%	5.5%	3.0%	5.30	5.22	+0.0
2031	26	MLB CTX	NYY	4	4	26	110	21	15	3	100%	6.5	19.3%	13.4%	5.9%	3.0%	5.21	5.17	+0.1
2032	27	MLB CTX	NYY	4	4	26	110	21	15	3	100%	6.5	19.3%	13.5%	5.8%	3.0%	5.21	5.21	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		23	23	130	548	102	76	16	100%	5.6	18.7%	14.0%	4.7%	3.0%	5.38	4.93	+0.7
/ 200 IP	—	RAW		35	35	200	844	157	117	25	100%	5.8	18.7%	14.0%	4.7%	3.0%	5.38	4.93	+1.1
Career	—	MLE		40	30	137	588	107	102	15	75%	3.4	18.2%	17.4%	0.7%	2.6%	4.60	5.19	+0.1
/ 200 IP	—	MLE		58	44	200	858	156	150	22	75%	3.4	18.2%	17.4%	0.7%	2.6%	4.60	5.19	+0.1

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 ROKFCL Yankees · 36 IPFIP 4.24 → 5.95 (+1.71)K% 23.1% → 15.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.08%	—	×0.680	15.69%
BB% (unintentional) /BF	7.69%	—	×1.250	9.62%
HR / BF /BF	3.85%	÷1.000	×1.400	5.38%

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 4.24 → 5.95, 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 ROKFCL Yankees · 47 IPFIP 4.60 → 5.26 (+0.66)K% 22.3% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.34%	—	×0.680	15.19%
BB% (unintentional) /BF	15.74%	—	×1.250	19.67%
HR / BF /BF	2.54%	÷2.059	×1.400	1.73%

Result: FIP 4.60 → 5.26, 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).

2024 ROKFCL Yankees · 23 IPFIP 4.78 → 6.16 (+1.38)K% 20.4% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.39%	—	×0.680	13.86%
BB% (unintentional) /BF	18.45%	—	×1.250	23.06%
HR / BF /BF	1.94%	÷1.142	×1.400	2.38%

Result: FIP 4.78 → 6.16, 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 ROKDSL NYY Bombers · 11 IPFIP 3.55 → 5.10 (+1.54)K% 35.7% → 24.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.71%	—	×0.680	24.29%
BB% (unintentional) /BF	19.64%	—	×1.250	24.55%
HR / BF /BF	1.79%	÷1.463	×1.400	1.71%

Result: FIP 3.55 → 5.10, 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 ROKDSL NYY Bombers · 20 IPFIP 0.60 → 2.25 (+1.65)K% 47.4% → 32.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	47.37%	—	×0.680	32.21%
BB% (unintentional) /BF	11.84%	—	×1.250	14.80%
HR / BF /BF	0.00%	÷1.608	×1.400	0.00%

Result: FIP 0.60 → 2.25, 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
Omar Gonzalez (OGILVIE)	—	—	6.04	15.5%	17.2%	3.1%	—	5.43	18.1%	3.6%	119	-0.61	—	—	—
Kyle Larsen R 2024 · 5.54 FIP	21	SP-	5.54	15.8%	17.9%	2.8%	.274	4.95	19.9%	6.2%	83	-0.59	4.83	143	90
Danny Flatt R 2024 · 6.12 FIP	20	SP-	6.12	14.5%	15.7%	4.1%	.304	5.81	13.0%	-1.9%	76	-0.31	5.37	122	86
Jackson Fristoe R 2023 · 6.07 FIP	22	SP-	6.07	17.0%	24.2%	2.5%	.258	6.12	15.3%	-2.9%	112	+0.05	4.81	139	85
Rubén Menes R 2024 · 5.62 FIP	22	SP-	5.62	15.8%	15.2%	3.5%	.235	4.77	15.6%	2.4%	81	-0.85	3.39	112	83
Luis Mateo R 2015 · 5.17 FIP	22	SP-	5.17	11.4%	14.7%	2.1%	.315	5.67	15.0%	-1.3%	55	+0.50	4.52	112	83
Jefferson Jean R 2024 · 6.45 FIP	19	SP-	6.45	18.3%	21.5%	4.0%	.311	4.79	16.9%	-0.3%	87	-1.66	4.79	132	83
Fernando Baez R 2015 · 6.56 FIP	23	SP-	6.56	18.6%	18.8%	4.8%	.322	4.51	26.9%	7.8%	94	-2.05	3.45	129	82
Luis Mora R 2016 · 5.78 FIP	21	SP-	5.78	16.3%	15.4%	3.8%	.303	5.27	23.9%	1.2%	43	-0.51	3.95	119	82
Trey Riley R 2019 · 5.78 FIP	21	SP-	5.78	10.6%	19.0%	2.1%	.344	5.78	17.4%	5.2%	74	-0.00	4.35	227	82
Holt Jones R 2022 · 6.63 FIP	23	SP	6.63	17.9%	19.5%	4.7%	.296	4.64	18.4%	0.8%	147	-1.99	4.21	254	81
Marco Jimenez R 2022 · 5.27 FIP	22	SP-	5.27	19.6%	17.7%	2.9%	.372	3.89	18.3%	6.7%	148	-1.38	3.36	183	81
Carlos Marmol R 2006 · 5.89 FIP	23	SP-	5.89	16.6%	16.0%	3.9%	.252	3.03	31.5%	18.0%	231	-2.86	1.59	608	81
Luis Martinez-Gomez R 2024 · 5.22 FIP	21	SP-	5.22	13.9%	15.4%	2.5%	.237	4.69	20.8%	6.1%	105	-0.53	3.76	133	80
Zach Linginfelter R 2021 · 5.53 FIP	24	SP-	5.53	19.1%	16.0%	3.7%	.295	5.01	18.8%	-0.6%	46	-0.52	3.62	109	80
Ryan Harvey R 2023 · 5.20 FIP	22	SP	5.20	16.7%	13.1%	3.4%	.294	4.50	17.0%	5.9%	87	-0.70	4.08	133	80
Wills Montgomerie R 2018 · 5.10 FIP	23	SP-	5.10	19.3%	19.1%	2.2%	.275	4.51	20.1%	6.0%	105	-0.59	4.51	153	80
Kevin Gowdy R 2019 · 5.31 FIP	21	SP-	5.31	11.1%	17.0%	1.8%	.321	5.50	16.3%	6.0%	130	+0.19	3.11	333	80
J.P. Massey R 2023 · 4.95 FIP	23	SP	4.95	18.2%	16.0%	2.5%	.259	5.12	15.9%	0.6%	115	+0.17	4.72	195	79
Camilo Sanchez R 2024 · 5.26 FIP	21	SP-	5.26	15.2%	14.0%	3.1%	.289	7.17	13.8%	-0.2%	19	+1.91	3.57	117	79
Mason Pelio R 2023 · 6.38 FIP	23	SP-	6.38	13.3%	17.4%	4.0%	.255	5.88	9.0%	-12.8%	26	-0.50	5.88	122	79

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 1,234 reliever careers (754 from 1990-2025 + 480 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	1.1	37	17.9%	14.7%	1.16	5.54
Matt Anderson ('05)	1.3	51	19.3%	13.5%	1.05	5.19
Richie Lewis ('98)	1.5	59	18.1%	14.1%	1.38	4.88
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*
Brian Bruney ('12)	1.3	43	21.2%	15.8%	0.94	4.68
Dwayne Henry ('84-'95, -1 WAR*)	1.7	67	23.8%*	14.2%*	0.90*	5.02*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*