Rafael Montero

P Scranton/Wilkes-Barre RailRiders

Age 36 RHP 6'0" / 193 lbs AAA

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	33	MIX2 levels	58	0	55	3.95	5.30	20.1%	12.0%	-0.5	K 18.6% · BB 11.6% · FIP 5.49
2025	34	MLBDET	59	0	60	4.48	4.00	22.9%	13.8%	0.4
2026	35	AAAScranton/Wilk…	28	1	27	5.67	3.54	25.8%	10.2%	—	K 22.2% · BB 9.1% · FIP 3.80
2027PROJ	36	MLBNYY	6	0	6	4.56	4.61	22.6%	11.6%	-0.0
2028PROJ	37	MLBNYY	25	0	26	4.83	5.01	21.5%	12.2%	-0.1
2029PROJ	38	MLBNYY	10	0	10	5.20	5.44	20.3%	12.9%	-0.1

Last 3 seasons WAR -0.1 · Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

xwOBA vs67

FB Velo58

K%54

CompsKeiichi Yabu (age 36) · Joe Nelson (age 34) · Juan Sandoval (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 111103vR 95

ERA4.56

FIP4.61

K%22.6%

BB%11.6%

K-BB%11.0%

Proj IP60

Value2027 · WAR

Runs saved vs league avg · 60 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p5895.1

Whiff%

p7629.3%

p5422.9%

xwOBA vs

p670.295

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)
2014	23	MLB MLE	New York Mets	10	8	44	194	42	23	8	80%	4.4	21.6%	11.9%	9.8%	4.1%	4.06	5.04	+0.2
2014	23	MLB RAW	New York Mets	10	8	44	194	42	23	8	80%	4.4	21.6%	11.9%	9.8%	4.1%	4.06	5.11	+0.1
2015	24	MIX MLE	4 levels	12	7	29	120	27	6	1	58%	2.4	22.5%	5.0%	17.5%	0.8%	3.77	2.29	+1.1
2015	24	MIX RAW	4 levels	12	7	29	120	27	6	1	58%	2.4	22.5%	5.0%	17.5%	0.8%	3.77	2.30	+1.1
2016	25	MIX MLE	3 levels	34	28	148	678	128	71	20	82%	4.4	16.6%	11.0%	5.5%	3.0%	5.66	4.76	+1.1
2016	25	MIX RAW	3 levels	34	28	148	678	128	71	20	82%	4.4	18.9%	10.5%	8.4%	2.9%	5.66	4.57	+1.4
2017	26	MIX MLE	2 levels	39	23	148	665	151	74	15	59%	3.8	22.0%	11.2%	10.8%	2.2%	4.93	3.88	+2.7
2017	26	MIX RAW	2 levels	39	23	148	665	151	74	15	59%	3.8	22.7%	11.1%	11.6%	2.3%	4.93	3.88	+2.7
2019	28	MIX MLE	4 levels	33	5	47	188	65	6	5	15%	1.4	30.1%	4.4%	25.7%	4.3%	2.85	3.45	+0.6
2019	28	MIX RAW	4 levels	33	5	47	188	65	6	5	15%	1.4	34.6%	3.2%	31.4%	2.7%	2.85	2.11	+1.4
2020	29	MLB MLE	Texas Rangers	17	0	18	72	19	6	2	0%	1.0	26.4%	8.3%	18.1%	2.6%	4.08	3.35	+0.3
2020	29	MLB RAW	Texas Rangers	17	0	18	72	19	6	2	0%	1.0	26.4%	8.3%	18.1%	2.8%	4.08	3.44	+0.2
2021	30	MLB MLE	Houston Astros	44	0	49	225	42	16	4	0%	1.1	18.7%	7.1%	11.6%	1.8%	6.39	3.43	+0.7
2021	30	MLB RAW	Houston Astros	44	0	49	225	42	16	4	0%	1.1	18.7%	7.1%	11.6%	1.8%	6.39	3.42	+0.7
2022	31	MLB MLE	Houston Astros	71	0	68	270	73	21	3	0%	1.0	27.0%	7.8%	19.3%	1.1%	2.37	2.43	+1.7
2022	31	MLB RAW	Houston Astros	71	0	68	270	73	21	3	0%	1.0	27.0%	7.8%	19.3%	1.1%	2.37	2.46	+1.7
2023	32	MLB MLE	Houston Astros	68	0	67	298	79	29	11	0%	1.0	26.5%	9.7%	16.8%	3.8%	5.08	4.18	+0.3
2023	32	MLB RAW	Houston Astros	68	0	67	298	79	29	11	0%	1.0	26.5%	9.7%	16.8%	3.7%	5.08	4.17	+0.3
2024	33	MIX MLE	2 levels	58	0	55	234	47	28	10	0%	0.9	18.6%	11.6%	7.1%	4.6%	3.95	5.49	-0.6
2024	33	MIX MLE	2 levels	58	0	55	234	47	28	10	0%	0.9	18.6%	11.6%	7.1%	4.6%	3.95	5.49	-0.6
2024	33	MIX RAW	2 levels	58	0	55	234	47	28	10	0%	0.9	20.1%	12.0%	8.1%	4.3%	3.95	5.30	-0.5
2025	34	MLB MLE	Detroit Tigers	59	0	60	253	58	35	5	0%	1.0	22.9%	13.8%	9.1%	2.1%	4.48	4.05	+0.4
2025	34	MLB MLE	Detroit Tigers	59	0	60	253	58	35	5	0%	1.0	22.9%	13.8%	9.1%	2.1%	4.48	4.05	+0.4
2025	34	MLB RAW	Detroit Tigers	59	0	60	253	58	35	5	0%	1.0	22.9%	13.8%	9.1%	2.0%	4.48	4.00	+0.4
2026	35	AAA MLE	Scranton/Wilkes-Barre RailRiders	28	1	27	128	33	13	3	4%	1.0	22.2%	9.1%	13.0%	2.6%	5.67	3.80	—
2026	35	AAA MLE	Scranton/Wilkes-Barre RailRiders	28	1	27	128	33	13	3	4%	1.0	22.2%	9.1%	13.0%	2.6%	5.67	3.80	—
2026	35	AAA RAW	Scranton/Wilkes-Barre RailRiders	28	1	27	128	33	13	3	4%	1.0	25.8%	10.2%	15.6%	2.3%	5.67	3.54	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			380	30	523	2278	517	233	62	8%	1.4	22.7%	10.2%	12.5%	2.7%	4.68	4.00	+3.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	NYY	6	0	6	25	6	3	1	0%	1.0	22.6%	11.6%	11.0%	2.9%	4.56	4.61	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	NYY	25	0	26	109	23	13	4	0%	1.0	21.5%	12.2%	9.3%	3.3%	4.83	5.01	-0.1
2029	38	MLB MLE	NYY	10	0	10	44	9	6	2	0%	1.0	20.3%	12.9%	7.4%	3.7%	5.20	5.44	-0.1
2028	37	MLB CTX	NYY	25	0	26	110	24	13	4	0%	1.0	21.5%	12.2%	9.3%	3.3%	4.83	5.01	-0.1
2029	38	MLB CTX	NYY	10	0	10	42	9	5	2	0%	1.0	20.3%	12.9%	7.4%	3.7%	5.20	5.44	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		414	30	559	2431	549	252	67	7%	1.3	22.6%	10.4%	12.2%	2.8%	4.70	4.02	+3.5
/ 200 IP	—	RAW		148	11	200	870	196	90	24	7%	1.3	22.6%	10.4%	12.2%	2.8%	4.70	4.02	+1.2
Career	—	MLE		473	72	761	3325	727	332	92	15%	1.6	21.9%	10.0%	11.9%	2.8%	4.62	4.04	+3.1
/ 200 IP	—	MLE		124	19	200	874	191	87	24	15%	1.6	21.9%	10.0%	11.9%	2.8%	4.62	4.04	+1.1

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 AAAScranton/Wilkes-Barre RailRiders · 27 IPFIP 3.50 → 3.80 (+0.30)K% 25.8% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.78%	—	×0.860	22.17%
BB% (unintentional) /BF	10.16%	—	×0.900	9.14%
HR / BF /BF	2.34%	÷1.000	×1.100	2.58%

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 3.50 → 3.80, 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 AAASugar Land Space Cowboys · 16 IPFIP 3.40 → 3.99 (+0.58)K% 34.3% → 29.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.29%	—	×0.860	29.49%
BB% (unintentional) /BF	12.86%	—	×0.900	11.57%
HR / BF /BF	2.86%	÷0.903	×1.100	3.48%

Result: FIP 3.40 → 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).

2019 AAFrisco RoughRiders · 9 IPFIP 0.57 → 1.13 (+0.56)K% 33.3% → 27.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.810	27.00%
BB% (unintentional) /BF	2.22%	—	×1.100	2.44%
HR / BF /BF	0.00%	÷1.208	×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 142).

Result: FIP 0.57 → 1.13, 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 ROKAZL Rangers · 7 IPFIP -1.31 → 0.10 (+1.41)K% 52.2% → 35.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	52.17%	—	×0.680	35.48%
BB% (unintentional) /BF	0.00%	—	×1.250	0.00%
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 -1.31 → 0.10, 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).

2017 AAALas Vegas 51s · 29 IPFIP 3.14 → 3.40 (+0.26)K% 32.2% → 28.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.17%	—	×0.880	28.31%
BB% (unintentional) /BF	10.43%	—	×1.050	10.96%
HR / BF /BF	2.61%	÷1.209	×1.100	2.37%

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 142).

Result: FIP 3.14 → 3.40, 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 AAALas Vegas 51s · 80 IPFIP 4.54 → 4.79 (+0.26)K% 17.7% → 15.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.66%	—	×0.880	15.54%
BB% (unintentional) /BF	9.61%	—	×1.050	10.09%
HR / BF /BF	3.12%	÷1.088	×1.100	3.15%

Result: FIP 4.54 → 4.79, 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 AABinghamton Mets · 49 IPFIP 3.71 → 4.27 (+0.56)K% 20.0% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.810	16.20%
BB% (unintentional) /BF	9.50%	—	×1.100	10.45%
HR / BF /BF	2.00%	÷1.085	×1.200	2.21%

Result: FIP 3.71 → 4.27, 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).

2015 AAALas Vegas 51s · 8 IPFIP 3.37 → 3.62 (+0.25)K% 22.6% → 19.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.58%	—	×0.880	19.87%
BB% (unintentional) /BF	3.23%	—	×1.050	3.39%
HR / BF /BF	3.23%	÷1.099	×1.100	3.23%

Result: FIP 3.37 → 3.62, 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
Rafael Montero (OGILVIE)	—	—	—	—	—	2.9%	—	5.13	21.2%	8.8%	36	+0.52	—	—	—
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	74
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	71
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	69
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	68
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	68
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	68
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	64
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	64
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	63
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	61
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	61
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	61
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	58
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	58
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	55
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	55
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	55
Pete Walker R 2005 · 4.79 FIP	36	SU	4.79	12.0%	9.2%	2.8%	.261	4.45	19.6%	11.6%	30	-0.34	4.45	327	54
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	51
Leo Rosales R 2017 · 4.24 FIP	36	SU	4.24	7.8%	8.2%	1.4%	.203	—	—	—	—	—	3.85	130	48

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	3.1	56	22.6%	11.0%	1.07	4.69
Buck Farmer ('24)	2.6	51	21.6%	10.5%	1.24	4.71
Dan Miceli ('06)	3.5	64	20.6%	10.1%	1.19	4.48
Jorge Julio ('09)	2.3	58	21.7%	11.3%	1.25	4.43
George Frazier ('78-'87, 2 WAR*)	3.4	68	21.6%*	10.5%*	0.92*	4.42*
Jerry Don Gleaton ('79-'92, 2 WAR*)	2.2	64	19.4%*	10.2%*	1.05*	4.44*
Ed Farmer ('71-'83, -2 WAR*)	3.1	69	21.3%*	12.2%*	1.03*	4.71*