Kervin Castro

P Scranton/Wilkes-Barre RailRiders

Age 28 RHP 5'10" / 234 lbs AAA peak 0.3 · 2027 ILInjured 60-Day (D60)

Overview

AAARPIL-6040-MANROOKIE

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	24	AAADET	10	16	2.30	2.40	29.2%	13.8%	—	K 25.1% · BB 12.5% · FIP 2.56
2025	26	AAAScranton/Wilk…	35	47	1.53	2.44	27.4%	10.5%	—	K 23.5% · BB 9.5% · FIP 2.61
2026	27	MIX2 levels	28	36	1.73	2.27	29.6%	5.9%	1.0	K 24.6% · BB 4.8% · FIP 2.71
2027PROJ	28	MLBNYY	25	26	3.78	3.89	24.3%	7.5%	0.2
2028PROJ80% MLB	29	MLBNYY	38	40	3.99	4.09	23.6%	7.9%	0.2
2029PROJ85% MLB	30	MLBNYY	34	36	4.19	4.32	22.9%	8.3%	0.1

Last 3 seasons WAR 1.0 · Next 3 projected WAR 0.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2022

K%30

FB Velo25

xwOBA vs10

Career WAR0.0 WAR median (P10 -0.2 / P90 0.7, comp-based) · star 0% · didn't stick 93%

CompsBlake Parker (age 28) · Chris Martin (age 28) · Matt Ramsey (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10799vR 91

ERA3.78

FIP3.89

K%24.3%

BB%7.5%

K-BB%16.7%

Proj IP36

Value2027 · WAR

Runs saved vs league avg · 36 IP

Grades · Statcast · Contract

Scouting Grades

STUF5555

CTRL4545

CMD5050

K†6060

Statcast Percentiles

FB Velo

p2592.7

Whiff%

p3823.6%

p3019.3%

xwOBA vs

p100.363

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)
2016	17	ROK MLE	DSL Giants	13	0	21	93	25	12	0	0%	1.6	18.3%	16.1%	2.2%	0.0%	4.71	3.60	—
2016	17	ROK RAW	DSL Giants	13	0	21	93	25	12	0	0%	1.6	26.9%	12.9%	14.0%	0.0%	4.71	2.43	—
2018	19	ROK MLE	DSL Giants	1	0	1	4	2	1	0	0%	1.0	50.0%	25.0%	25.0%	0.0%	0.00	2.04	—
2018	19	ROK RAW	DSL Giants	1	0	1	4	2	1	0	0%	1.0	50.0%	25.0%	25.0%	0.0%	0.00	2.10	—
2021	22	MIX MLE	2 levels	40	0	57	239	73	25	3	0%	1.4	27.5%	10.9%	16.6%	1.4%	2.20	2.92	+1.1
2021	22	MIX RAW	2 levels	40	0	57	239	73	25	3	0%	1.4	30.5%	10.5%	20.1%	1.3%	2.20	2.54	+1.4
2022	23	MIX MLE	2 levels	47	0	58	259	61	37	7	0%	1.2	21.2%	14.9%	6.4%	2.9%	5.55	4.79	-0.2
2022	23	MIX RAW	2 levels	47	0	58	259	61	37	7	0%	1.2	23.6%	14.3%	9.3%	2.7%	5.55	4.47	+0.1
2023	24	AAA MLE	Toledo Mud Hens	10	0	16	65	19	9	0	0%	1.6	25.1%	12.5%	12.7%	0.0%	2.30	2.56	—
2023	24	AAA RAW	Toledo Mud Hens	10	0	16	65	19	9	0	0%	1.6	29.2%	13.8%	15.4%	0.0%	2.30	2.40	—
2025	26	AAA MLE	Scranton/Wilkes-Barre RailRiders	35	0	47	190	52	20	1	0%	1.3	23.5%	9.5%	14.1%	0.5%	1.53	2.61	—
2025	26	AAA MLE	Scranton/Wilkes-Barre RailRiders	35	0	47	190	52	20	1	0%	1.3	23.5%	9.5%	14.1%	0.5%	1.53	2.61	—
2025	26	AAA RAW	Scranton/Wilkes-Barre RailRiders	35	0	47	190	52	20	1	0%	1.3	27.4%	10.5%	16.8%	0.5%	1.53	2.44	—
2026	27	MIX MLE	2 levels	28	0	36	135	40	8	2	0%	1.3	24.6%	4.8%	19.7%	2.0%	1.73	2.71	+0.8
2026	27	MIX MLE	2 levels	28	0	36	135	40	8	2	0%	1.3	24.6%	4.8%	19.7%	2.0%	1.73	2.71	+0.8
2026	27	MIX RAW	2 levels	28	0	36	135	40	8	2	0%	1.3	29.6%	5.9%	23.7%	1.5%	1.73	2.27	+1.0
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			25	0	32	136	32	12	3	0%	1.3	23.5%	8.8%	14.7%	2.2%	4.22	3.44	+0.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	NYY	25	0	26	110	27	8	3	0%	1.0	24.3%	7.5%	16.7%	2.7%	3.78	3.89	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	NYY	48	0	50	211	50	17	6	0%	1.0	23.6%	7.9%	15.7%	2.9%	3.99	4.09	+0.3
2029	30	MLB MLE	NYY	41	0	43	182	42	15	6	0%	1.0	22.9%	8.3%	14.6%	3.1%	4.19	4.32	+0.1
2028	29	MLB CTX	NYY	38	0	40	169	40	13	5	0%	1.1	23.6%	7.9%	15.7%	2.9%	3.99	4.09	+0.2
2029	30	MLB CTX	NYY	34	0	36	152	35	13	5	0%	1.1	22.9%	8.3%	14.6%	3.1%	4.19	4.32	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		98	0	108	459	107	38	13	0%	1.1	23.3%	8.3%	15.0%	2.8%	4.12	3.71	+1.1
/ 200 IP	—	RAW		180	0	200	846	197	70	24	0%	1.1	23.3%	8.3%	15.0%	2.8%	4.12	3.71	+2.0
Career	—	MLE		174	0	237	985	234	113	15	0%	1.4	23.8%	11.5%	12.3%	1.5%	3.04	3.36	+0.8
/ 200 IP	—	MLE		147	0	200	832	198	96	12	0%	1.4	23.8%	11.5%	12.3%	1.5%	3.04	3.36	+1.3

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 · 30 IPFIP 2.35 → 2.71 (+0.37)K% 28.6% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.860	24.57%
BB% (unintentional) /BF	5.36%	—	×0.900	4.82%
HR / BF /BF	1.79%	÷1.000	×1.100	1.96%

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 2.35 → 2.71, 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 AAAScranton/Wilkes-Barre RailRiders · 47 IPFIP 2.41 → 2.61 (+0.20)K% 27.4% → 23.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.37%	—	×0.860	23.54%
BB% (unintentional) /BF	10.53%	—	×0.900	9.47%
HR / BF /BF	0.53%	÷1.070	×1.100	0.54%

Result: FIP 2.41 → 2.61, 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).

2023 AAAToledo Mud Hens · 16 IPFIP 2.39 → 2.56 (+0.17)K% 29.2% → 25.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.23%	—	×0.860	25.14%
BB% (unintentional) /BF	13.85%	—	×0.900	12.46%
HR / BF /BF	0.00%	÷0.901	×1.100	0.00%

Result: FIP 2.39 → 2.56, 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 AAAIowa Cubs · 46 IPFIP 3.98 → 4.44 (+0.46)K% 24.8% → 21.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.75%	—	×0.880	21.78%
BB% (unintentional) /BF	14.85%	—	×1.050	15.59%
HR / BF /BF	1.98%	÷0.996	×1.100	2.19%

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 99).

Result: FIP 3.98 → 4.44, 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 AAASacramento River Cats · 44 IPFIP 2.75 → 3.26 (+0.50)K% 32.8% → 28.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.79%	—	×0.880	28.85%
BB% (unintentional) /BF	12.02%	—	×1.050	12.62%
HR / BF /BF	1.64%	÷0.995	×1.100	1.81%

Result: FIP 2.75 → 3.26, 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).

2016 ROKDSL Giants · 21 IPFIP 2.46 → 3.60 (+1.14)K% 26.9% → 18.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.88%	—	×0.680	18.28%
BB% (unintentional) /BF	12.90%	—	×1.250	16.13%
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 2.46 → 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).

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
Kervin Castro (OGILVIE)	—	—	3.89	24.3%	7.5%	2.7%	—	4.20	23.3%	15.2%	93	+0.31	—	—	—
Blake Parker R 2013 · 2.80 FIP	28	SU	2.80	28.2%	7.2%	2.1%	.287	3.24	27.6%	20.2%	78	+0.44	2.52	406	75
Chris Martin R 2014 · 3.53 FIP	28	SU	3.53	20.3%	5.8%	2.6%	.408	3.24	18.1%	11.1%	49	-0.29	1.76	431	74
Matt Ramsey R 2018 · 3.78 FIP	28	SU	3.78	24.9%	8.8%	3.0%	.276	2.42	21.7%	12.6%	34	-1.36	2.42	125	74
JT Chargois R 2019 · 3.63 FIP	28	SU	3.63	27.2%	8.9%	3.1%	.311	3.57	23.1%	15.8%	76	-0.06	2.58	362	73
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	73
Michael Mariot R 2017 · 3.51 FIP	28	SU	3.51	22.6%	9.0%	2.1%	.327	4.79	20.9%	11.8%	74	+1.28	2.40	701	73
Luke Barker R 2019 · 2.74 FIP	27	SU	2.74	25.8%	7.5%	1.6%	.159	3.64	27.4%	22.0%	86	+0.90	2.74	261	73
Scott Atchison R 2004 · 3.61 FIP	28	SU	3.61	27.1%	9.0%	3.0%	.309	4.55	19.1%	11.5%	31	+0.94	2.52	340	73
Matt Pushard R 2024 · 3.87 FIP	26	SU	3.87	24.1%	8.9%	3.0%	.242	3.29	22.2%	14.0%	115	-0.58	2.84	220	72
Rafael Betancourt R 2003 · 3.81 FIP	28	SU	3.81	23.4%	7.1%	3.2%	.216	2.84	25.0%	20.8%	191	-0.97	1.79	686	72
Heath Hembree R 2017 · 3.84 FIP	28	SU	3.84	25.8%	6.6%	3.8%	.358	4.69	27.2%	17.7%	119	+0.85	2.52	382	72
Dugan Darnell R 2024 · 3.37 FIP	27	SU	3.37	22.9%	8.1%	2.1%	.368	3.03	24.7%	17.3%	54	-0.34	2.50	238	72
Gerardo Sanchez R 2017 · 3.47 FIP	28	SU	3.47	21.6%	8.4%	2.1%	.355	3.58	20.5%	15.1%	56	+0.11	2.65	137	72
Tyler Sturdevant R 2015 · 4.37 FIP	29	SU	4.37	21.3%	8.5%	3.6%	.241	3.97	18.5%	11.9%	118	-0.40	3.44	149	71
Cristhian Martinez R 2010 · 3.61 FIP	28	SU	3.61	20.0%	4.5%	2.9%	.312	3.43	19.8%	14.6%	151	-0.18	3.09	204	71
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	71
Jason Motte R 2010 · 3.24 FIP	28	SU	3.24	26.0%	7.2%	2.6%	.269	2.69	27.3%	21.8%	140	-0.55	2.22	387	71
Ryan Brasier R 2016 · 3.68 FIP	28	SU	3.68	25.0%	7.7%	3.1%	.289	3.09	24.2%	17.9%	130	-0.59	2.31	426	71
Simon Castro R 2016 · 3.01 FIP	28	SU	3.01	22.7%	5.6%	2.0%	.313	3.99	28.3%	18.0%	75	+0.98	3.01	185	71
Ty Adcock R 2024 · 3.76 FIP	27	SU	3.76	20.8%	8.9%	2.4%	.405	4.85	20.4%	11.6%	66	+1.09	3.76	122	71

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	0.8	55	23.6%	8.3%	1.08	4.04
Dave Baldwin ('66-'73, 2 WAR*)	1.1	56	22.2%*	9.2%*	0.96*	3.97*
Charlie Furbush ('15)	1.3	52	24.5%	8.1%	1.04	3.97
Shawn Tolleson ('16)	1.1	54	23.1%	8.3%	1.28	3.92
Ryan Tepera ('23)	1.8	52	24.3%	8.8%	1.13	3.62
Gary Wayne ('89-'94, 2 WAR*)	1.2	62	19.9%*	9.4%*	1.05*	3.88*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*