Robinson Ortiz

P Tacoma Rainiers

Age 27 LHP 6'0" / 180 lbs AAA

Overview

AAA40-MANPROSPECT

ETA2028MLB%30%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	ALAA	6	0	11	0.82	4.19	6.4%	12.8%	—	K 6.4% · BB 12.8% · FIP 4.18
2025	25	MIX3 levels	48	1	59	2.73	3.17	28.3%	12.6%	—	K 22.8% · BB 13.4% · FIP 3.94
2026	26	AAASEA	43	0	49	5.69	4.10	27.2%	13.8%	—	K 23.4% · BB 12.5% · FIP 4.32
2027PROJ	27	MLBSEA	17	0	18	4.78	4.78	23.4%	13.6%	-0.0
2028PROJ55% MLB	28	MLBSEA	24	0	25	4.79	4.82	23.3%	13.3%	-0.1
2029PROJ68% MLB	29	MLBSEA	26	0	27	4.85	4.91	23.1%	13.2%	-0.1

Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 -0.3 / P90 1.0, comp-based) · star 0% · didn't stick 94%

CompsRyan Verdugo (age 28) · Aaron Brown (age 27) · Colton Johnson (age 26)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 108116vR 124

ERA4.78

FIP4.78

K%23.4%

BB%13.6%

K-BB%9.9%

Proj IP60

Value2027 · WAR

Runs saved vs league avg · 60 IP

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)
2017	17	ROK MLE	DSL Dodgers 1	11	11	37	151	35	5	0	100%	3.4	15.8%	4.1%	11.6%	0.0%	3.13	2.29	—
2017	17	ROK RAW	DSL Dodgers 1	11	11	37	151	35	5	0	100%	3.4	23.2%	3.3%	19.9%	0.0%	3.13	1.63	—
2018	18	ROK MLE	AZL Dodgers	11	9	32	136	42	12	2	82%	2.9	21.0%	11.0%	10.0%	2.1%	4.18	3.85	—
2018	18	ROK RAW	AZL Dodgers	11	9	32	136	42	12	2	82%	2.9	30.9%	8.8%	22.1%	1.5%	4.18	2.42	—
2019	19	A MLE	Great Lakes Loons	19	18	86	365	74	40	10	95%	4.5	15.0%	12.9%	2.1%	3.5%	4.59	5.39	—
2019	19	A RAW	Great Lakes Loons	19	18	86	365	74	40	10	95%	4.5	20.3%	11.0%	9.3%	2.7%	4.59	4.28	—
2021	21	A+ MLE	Great Lakes Loons	4	2	12	54	12	13	0	50%	2.9	17.1%	27.7%	-10.6%	0.0%	0.77	5.16	—
2021	21	A+ RAW	Great Lakes Loons	4	2	12	54	12	13	0	50%	2.9	22.2%	24.1%	-1.9%	0.0%	0.77	4.39	—
2024	24	A MLE	Rancho Cucamonga Quakes	6	0	11	47	3	6	0	0%	1.8	6.4%	12.8%	-6.4%	0.0%	0.82	4.18	—
2024	24	A MLE	Rancho Cucamonga Quakes	6	0	11	47	3	6	0	0%	1.8	6.4%	12.8%	-6.4%	0.0%	0.82	4.18	—
2024	24	A RAW	Rancho Cucamonga Quakes	6	0	11	47	3	6	0	0%	1.8	6.4%	12.8%	-6.4%	0.0%	0.82	4.19	—
2025	25	MIX MLE	3 levels	48	1	59	254	72	32	4	2%	1.2	22.8%	13.4%	9.4%	1.9%	2.73	3.94	—
2025	25	MIX MLE	3 levels	48	1	59	254	72	32	4	2%	1.2	22.8%	13.4%	9.4%	1.9%	2.73	3.94	—
2025	25	MIX RAW	3 levels	48	1	59	254	72	32	4	2%	1.2	28.3%	12.6%	15.7%	1.6%	2.73	3.17	—
2026	26	AAA MLE	Tacoma Rainiers	43	0	49	224	61	31	6	0%	1.1	23.4%	12.5%	11.0%	2.9%	5.69	4.32	—
2026	26	AAA MLE	Tacoma Rainiers	43	0	49	224	61	31	6	0%	1.1	23.4%	12.5%	11.0%	2.9%	5.69	4.32	—
2026	26	AAA RAW	Tacoma Rainiers	43	0	49	224	61	31	6	0%	1.1	27.2%	13.8%	13.4%	2.7%	5.69	4.10	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	SEA	17	0	18	76	18	10	2	0%	1.1	23.4%	13.6%	9.9%	2.8%	4.78	4.78	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	SEA	44	0	46	195	46	26	6	0%	1.1	23.3%	13.3%	10.0%	3.0%	4.79	4.82	-0.1
2029	29	MLB MLE	SEA	38	0	39	166	38	22	5	0%	1.0	23.1%	13.2%	9.9%	3.1%	4.85	4.91	-0.2
2028	28	MLB CTX	SEA	24	0	25	106	25	14	3	0%	1.0	23.3%	13.3%	10.0%	3.0%	4.79	4.82	-0.1
2029	29	MLB CTX	SEA	26	0	27	114	26	15	4	0%	1.0	23.1%	13.2%	9.9%	3.1%	4.85	4.91	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		50	0	52	220	51	29	7	0%	1.0	23.2%	13.2%	9.9%	3.0%	4.82	4.49	+0.0
/ 200 IP	—	RAW		190	0	200	844	196	112	27	0%	1.1	23.2%	13.2%	9.9%	3.0%	4.82	4.49	+0.1
Career	—	MLE		142	41	287	1231	230	151	27	29%	2.0	18.7%	12.3%	6.4%	2.2%	3.86	4.29	-0.3
/ 200 IP	—	MLE		99	29	200	858	160	106	19	29%	2.0	18.7%	12.3%	6.4%	2.2%	3.86	4.29	-0.7

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 AAATacoma Rainiers · 49 IPFIP 4.02 → 4.32 (+0.29)K% 27.2% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.23%	—	×0.860	23.42%
BB% (unintentional) /BF	13.84%	—	×0.900	12.46%
HR / BF /BF	2.68%	÷1.000	×1.100	2.95%

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 100).

Result: FIP 4.02 → 4.32, 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).

2025 A+Great Lakes Loons · 22 IPFIP 4.16 → 5.39 (+1.24)K% 28.0% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.00%	—	×0.770	21.56%
BB% (unintentional) /BF	14.00%	—	×1.150	16.10%
HR / BF /BF	3.00%	÷0.992	×1.250	3.78%

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 98).

Result: FIP 4.16 → 5.39, 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 AATulsa Drillers · 21 IPFIP 1.48 → 2.17 (+0.69)K% 34.9% → 28.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.88%	—	×0.810	28.26%
BB% (unintentional) /BF	10.47%	—	×1.100	11.51%
HR / BF /BF	0.00%	÷0.957	×1.200	0.00%

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 91).

Result: FIP 1.48 → 2.17, 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 AAAOklahoma City Comets · 16 IPFIP 3.85 → 4.04 (+0.20)K% 20.6% → 17.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.59%	—	×0.860	17.71%
BB% (unintentional) /BF	13.24%	—	×0.900	11.91%
HR / BF /BF	1.47%	÷0.956	×1.100	1.69%

Result: FIP 3.85 → 4.04, 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 ARancho Cucamonga Quakes · 11 IPFIP 4.18 → 4.61 (+0.43)K% 6.4% → 4.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	6.38%	—	×0.740	4.72%
BB% (unintentional) /BF	12.77%	—	×1.180	15.06%
HR / BF /BF	0.00%	÷1.072	×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 114).

Result: FIP 4.18 → 4.61, 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 A+Great Lakes Loons · 12 IPFIP 4.27 → 5.16 (+0.89)K% 22.2% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.22%	—	×0.770	17.11%
BB% (unintentional) /BF	24.07%	—	×1.150	27.69%
HR / BF /BF	0.00%	÷0.930	×1.250	0.00%

Result: FIP 4.27 → 5.16, 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).

2019 AGreat Lakes Loons · 86 IPFIP 4.28 → 5.39 (+1.11)K% 20.3% → 15.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.27%	—	×0.740	15.00%
BB% (unintentional) /BF	10.96%	—	×1.180	12.93%
HR / BF /BF	2.74%	÷1.018	×1.300	3.50%

Result: FIP 4.28 → 5.39, 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 ROKAZL Dodgers · 32 IPFIP 2.42 → 3.85 (+1.44)K% 30.9% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.88%	—	×0.680	21.00%
BB% (unintentional) /BF	8.82%	—	×1.250	11.03%
HR / BF /BF	1.47%	÷1.000	×1.400	2.06%

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 2.42 → 3.85, 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
Robinson Ortiz (OGILVIE)	—	—	—	—	—	2.8%	—	4.86	23.2%	9.9%	86	+0.08	—	—	—
Ryan Verdugo L 2015 · 3.61 FIP	28	SU	3.61	19.8%	10.8%	1.5%	.281	3.36	22.7%	17.3%	82	-0.25	3.36	153	80
Aaron Brown L 2019 · 4.04 FIP	27	SU	4.04	22.9%	13.5%	2.1%	.317	4.77	19.4%	6.7%	129	+0.73	4.04	269	80
Colton Johnson L 2024 · 3.92 FIP	26	SU	3.92	20.8%	11.0%	2.1%	.309	4.53	12.2%	-0.4%	48	+0.61	3.92	106	79
Juan Grullon L 2016 · 4.64 FIP	26	SU	4.64	19.8%	10.9%	3.3%	.305	—	—	—	—	—	4.64	134	78
Mitchell Tyranski L 2024 · 4.90 FIP	26	SU	4.90	21.0%	12.3%	3.7%	.234	3.46	20.5%	4.0%	57	-1.44	3.46	145	77
Tommy Vail L 2024 · 3.65 FIP	25	SW	3.65	27.5%	15.3%	1.7%	.237	5.19	19.5%	2.9%	99	+1.54	3.65	168	76
Matt Thornton L 2004 · 4.18 FIP	27	SU	4.18	20.3%	16.2%	1.4%	.304	4.18	21.8%	11.5%	167	-0.00	1.71	663	76
Mark Manfredi Sr. L 2024 · 4.60 FIP	24	SW	4.60	22.0%	14.9%	2.7%	.289	3.22	25.4%	11.6%	128	-1.38	2.91	204	76
Brad Kuntz L 2018 · 4.10 FIP	26	SU	4.10	20.9%	11.7%	2.4%	.352	—	—	—	—	—	2.35	197	75
Brendan Nail L 2023 · 4.36 FIP	27	SU	4.36	19.3%	10.5%	2.8%	.262	—	—	—	—	—	3.45	165	75
Robby Ahlstrom L 2024 · 3.89 FIP	25	SU	3.89	21.9%	11.6%	2.1%	.294	4.02	20.7%	11.1%	120	+0.13	3.79	332	75
Will Lamb L 2017 · 5.78 FIP	26	SU	5.78	20.0%	14.0%	4.7%	.322	—	—	—	—	—	3.64	178	74
Nick Hagadone L 2013 · 4.75 FIP	27	SU	4.75	22.6%	15.0%	3.0%	.253	3.34	25.9%	19.0%	51	-1.41	2.97	141	74
Jake Rice L 2024 · 3.75 FIP	27	SU	3.75	21.8%	13.8%	1.4%	.273	5.90	16.5%	0.9%	54	+2.15	3.16	199	74
Graeme Stinson L 2023 · 4.85 FIP	25	SU	4.85	19.5%	15.6%	2.6%	.302	—	—	—	—	—	3.56	146	73
Trey Jeans L 2022 · 5.09 FIP	26	SU	5.09	22.3%	16.5%	3.3%	.291	—	—	—	—	—	2.74	111	73
David Rollins L 2017 · 5.58 FIP	27	SU	5.58	18.0%	12.1%	4.5%	.260	5.68	15.9%	12.5%	20	+0.10	3.59	132	73
Stephen Randolph L 2003 · 4.83 FIP	29	SU	4.83	18.5%	14.8%	2.6%	.247	5.69	15.8%	-3.0%	82	+0.86	4.83	155	73
Andrew Magno L 2024 · 4.30 FIP	26	SU	4.30	21.7%	10.4%	3.1%	.341	3.41	21.7%	9.6%	119	-0.89	3.23	328	73
Will Gilbert L 2019 · 4.42 FIP	25	SU	4.42	19.9%	10.3%	3.1%	.306	—	—	—	—	—	2.92	141	73

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	0.7	48	23.3%	13.4%	1.14	4.80
John Hudek ('99)	1.0	40	23.0%	13.7%	1.30	4.43
Fred Lasher ('63-'71, -1 WAR*)	1.0	50	21.5%*	11.9%*	1.05*	4.68*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*
Armando Almanza ('05)	1.1	54	24.4%	14.3%	1.43	4.82
David Aardsma ('15)	1.7	48	23.1%	12.4%	1.09	4.27
Dwayne Henry ('84-'95, -1 WAR*)	1.7	67	23.8%*	14.2%*	0.90*	5.02*