Luis Castillo

P Chicago White Sox

Age 34 RHP 6'2" / 200 lbs MLB peak 1.0 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2024	31	MLBSEA	30	30	175	3.64	3.73	24.3%	6.3%	3.5
2025	32	MLBSEA	32	32	181	3.54	3.73	21.7%	6.2%	3.7
2026	33	MLBCWS	29	23	131	5.85	4.84	18.9%	7.1%	0.8
2027PROJ	34	MLBCWS	17	17	104	4.48	4.57	21.0%	6.9%	1.0
2028PROJ	35	MLBCWS	14	14	81	4.78	4.95	20.0%	7.5%	0.5
2029PROJ	36	MLBCWS	9	9	55	5.10	5.32	19.0%	8.1%	0.1

Last 3 seasons WAR 8.0 · Next 3 projected WAR 1.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo60

K%27

xwOBA vs25

Form8.42 ERA (proj 4.48) · K 17.4% (proj 21.0%) · BB 8.1% (proj 6.9%) ▼ last 90d · 31 IP through Oct 27

CompsJose Contreras (age 34) · Merrill Kelly (age 34) · Masato Yoshii (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 122114vR 106

ERA4.48

FIP4.57

K%21.0%

BB%6.9%

K-BB%14.1%

Proj IP104

Value2027 · WAR

Runs saved vs league avg · 104 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6095.2

Whiff%

p3923.7%

p2718.9%

xwOBA vs

p250.341

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)
2015	22	MIX MLE	2 levels	35	16	107	446	94	33	4	46%	3.1	15.8%	8.6%	7.2%	1.2%	3.20	3.54	—
2015	22	MIX RAW	2 levels	35	16	107	446	94	33	4	46%	3.1	21.1%	7.4%	13.7%	0.9%	3.20	2.75	—
2016	23	MIX MLE	2 levels	26	24	132	520	103	25	3	92%	5.1	15.3%	5.5%	9.9%	0.7%	2.26	2.87	—
2016	23	MIX RAW	2 levels	26	24	132	520	103	25	3	92%	5.1	19.8%	4.8%	15.0%	0.6%	2.26	2.40	—
2017	24	MIX MLE	2 levels	29	29	170	676	179	44	16	100%	5.9	24.2%	6.7%	17.5%	2.1%	2.86	3.08	+4.9
2017	24	MIX RAW	2 levels	29	29	170	676	179	44	16	100%	5.9	26.5%	6.5%	20.0%	2.4%	2.86	2.99	+5.1
2018	25	MLB MLE	Cincinnati Reds	31	31	170	708	165	48	28	100%	5.5	23.3%	6.8%	16.5%	3.7%	4.30	4.00	+2.8
2018	25	MLB RAW	Cincinnati Reds	31	31	170	708	165	48	28	100%	5.5	23.3%	6.8%	16.5%	4.0%	4.30	4.15	+2.5
2019	26	MLB MLE	Cincinnati Reds	32	32	191	781	226	79	22	100%	6.0	28.9%	10.1%	18.8%	2.6%	3.40	3.37	+4.8
2019	26	MLB RAW	Cincinnati Reds	32	32	191	781	226	79	22	100%	6.0	28.9%	10.1%	18.8%	2.8%	3.40	3.47	+4.5
2020	27	MLB MLE	Cincinnati Reds	12	12	70	292	89	24	5	100%	5.8	30.5%	8.2%	22.3%	1.6%	3.21	2.44	+2.7
2020	27	MLB RAW	Cincinnati Reds	12	12	70	292	89	24	5	100%	5.8	30.5%	8.2%	22.3%	1.7%	3.21	2.51	+2.6
2021	28	MLB MLE	Cincinnati Reds	33	33	188	803	192	71	19	100%	5.7	23.9%	8.8%	15.1%	2.2%	3.98	3.40	+4.6
2021	28	MLB RAW	Cincinnati Reds	33	33	188	803	192	71	19	100%	5.7	23.9%	8.8%	15.1%	2.4%	3.98	3.50	+4.3
2022	29	MIX MLE	3 levels	28	28	161	660	183	53	13	100%	5.7	27.2%	7.3%	19.8%	2.2%	2.86	2.92	+5.0
2022	29	MIX RAW	3 levels	28	28	161	660	183	53	13	100%	5.7	27.7%	8.0%	19.7%	2.0%	2.86	2.86	+5.2
2023	30	MLB MLE	Seattle Mariners	33	33	197	802	219	56	28	100%	6.0	27.3%	7.0%	20.3%	3.6%	3.34	3.64	+4.2
2023	30	MLB RAW	Seattle Mariners	33	33	197	802	219	56	28	100%	6.0	27.3%	7.0%	20.3%	3.5%	3.34	3.58	+4.4
2024	31	MLB MLE	Seattle Mariners	30	30	175	719	175	45	25	100%	5.8	24.3%	6.3%	18.1%	3.6%	3.64	3.79	+3.4
2024	31	MLB MLE	Seattle Mariners	30	30	175	719	175	45	25	100%	5.8	24.3%	6.3%	18.1%	3.6%	3.64	3.79	+3.4
2024	31	MLB RAW	Seattle Mariners	30	30	175	719	175	45	25	100%	5.8	24.3%	6.3%	18.1%	3.5%	3.64	3.73	+3.5
2025	32	MLB MLE	Seattle Mariners	32	32	181	745	162	46	23	100%	5.6	21.7%	6.2%	15.6%	3.2%	3.54	3.78	+3.5
2025	32	MLB MLE	Seattle Mariners	32	32	181	745	162	46	23	100%	5.6	21.7%	6.2%	15.6%	3.2%	3.54	3.78	+3.5
2025	32	MLB RAW	Seattle Mariners	32	32	181	745	162	46	23	100%	5.6	21.7%	6.2%	15.6%	3.1%	3.54	3.73	+3.7
2026	33	MLB MLE	Chicago White Sox	29	23	131	592	112	42	25	79%	4.5	18.9%	7.1%	11.8%	4.2%	5.85	4.68	+1.1
2026	33	MLB MLE	Chicago White Sox	29	23	131	592	112	42	25	79%	4.5	18.9%	7.1%	11.8%	4.2%	5.85	4.68	+1.1
2026	33	MLB RAW	Chicago White Sox	29	23	131	592	112	42	25	79%	4.5	18.9%	7.1%	11.8%	4.2%	5.85	4.84	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			272	266	1541	6416	1605	487	199	98%	5.7	25.0%	7.6%	17.4%	3.1%	3.75	3.64	+32.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	CWS	17	17	104	439	92	30	16	100%	6.1	21.0%	6.9%	14.1%	3.6%	4.48	4.57	+1.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	CWS	14	14	81	342	69	26	14	100%	5.8	20.0%	7.5%	12.6%	4.0%	4.74	4.91	+0.5
2029	36	MLB MLE	CWS	9	9	55	232	44	19	10	100%	6.1	19.0%	8.1%	10.9%	4.3%	5.06	5.28	+0.1
2028	35	MLB CTX	CWS	14	14	81	342	69	26	14	100%	5.8	20.0%	7.5%	12.6%	4.0%	4.78	4.95	+0.5
2029	36	MLB CTX	CWS	9	9	55	232	44	19	10	100%	6.1	19.0%	8.1%	10.9%	4.4%	5.10	5.32	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		295	289	1677	6990	1718	531	223	98%	5.7	24.6%	7.6%	17.0%	3.2%	3.84	3.74	+33.6
/ 200 IP	—	RAW		35	34	200	833	205	63	27	98%	5.7	24.6%	7.6%	17.0%	3.2%	3.84	3.74	+4.0
Career	—	MLE		350	323	1871	7744	1833	572	209	92%	5.3	23.7%	7.4%	16.3%	2.7%	3.55	3.52	+33.2
/ 200 IP	—	MLE		37	35	200	828	196	61	22	92%	5.3	23.7%	7.4%	16.3%	2.7%	3.55	3.52	+4.0

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.

2022 AAALouisville Bats · 8 IPFIP 1.98 → 2.43 (+0.45)K% 35.3% → 31.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.29%	—	×0.880	31.06%
BB% (unintentional) /BF	14.71%	—	×1.050	15.44%
HR / BF /BF	0.00%	÷1.282	×1.100	0.00%

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 156).

Result: FIP 1.98 → 2.43, 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 AAPensacola Blue Wahoos · 80 IPFIP 2.33 → 2.67 (+0.34)K% 25.6% → 20.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.55%	—	×0.810	20.70%
BB% (unintentional) /BF	4.10%	—	×1.100	4.51%
HR / BF /BF	1.58%	÷1.399	×1.200	1.35%

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 180).

Result: FIP 2.33 → 2.67, 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 A+Jupiter Hammerheads · 118 IPFIP 2.16 → 2.71 (+0.55)K% 19.9% → 15.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.91%	—	×0.770	15.33%
BB% (unintentional) /BF	3.94%	—	×1.150	4.53%
HR / BF /BF	0.44%	÷0.918	×1.250	0.60%

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 84).

Result: FIP 2.16 → 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).

2016 AAJacksonville Suns · 14 IPFIP 3.77 → 4.08 (+0.31)K% 19.0% → 15.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.05%	—	×0.810	15.43%
BB% (unintentional) /BF	11.11%	—	×1.100	12.22%
HR / BF /BF	1.59%	÷1.423	×1.200	1.34%

Result: FIP 3.77 → 4.08, 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 AGreensboro Grasshoppers · 63 IPFIP 2.20 → 2.88 (+0.67)K% 23.9% → 17.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.86%	—	×0.740	17.66%
BB% (unintentional) /BF	7.20%	—	×1.180	8.49%
HR / BF /BF	0.38%	÷1.391	×1.300	0.35%

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 178).

Result: FIP 2.20 → 2.88, 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 A+Jupiter Hammerheads · 44 IPFIP 3.54 → 4.49 (+0.95)K% 17.0% → 13.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.03%	—	×0.770	13.12%
BB% (unintentional) /BF	7.69%	—	×1.150	8.85%
HR / BF /BF	1.65%	÷0.819	×1.250	2.52%

Result: FIP 3.54 → 4.49, 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
Luis Castillo (OGILVIE)	—	—	—	—	—	3.6%	—	5.06	19.6%	11.9%	136	+0.49	—	—	—
Jose Contreras R 2006 · 3.83 FIP	34	SP	3.83	16.1%	6.1%	2.4%	.277	4.08	14.7%	7.3%	442	+0.25	2.43	1,279	65
Merrill Kelly R 2023 · 3.73 FIP	34	SP	3.73	25.9%	9.6%	2.9%	.276	4.36	19.7%	12.0%	420	+0.63	3.60	1,170	63
Masato Yoshii R 2000 · 5.32 FIP	35	SP	5.32	12.1%	6.5%	4.4%	.302	4.38	13.1%	8.0%	244	-0.94	4.15	757	63
Orlando Hernandez R 2004 · 3.77 FIP	34	SP	3.77	23.4%	10.0%	2.5%	.278	4.34	20.5%	11.7%	438	+0.57	3.35	1,315	58
Hiroki Kuroda R 2009 · 3.54 FIP	34	SP	3.54	17.9%	4.7%	2.5%	.270	3.43	19.1%	14.2%	618	-0.11	2.96	1,319	58
Hisashi Iwakuma R 2015 · 3.74 FIP	34	SP	3.74	21.5%	3.9%	3.6%	.270	4.44	16.9%	11.2%	230	+0.70	3.19	884	56
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	45
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	44
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	43
Jonathan Castellanos R 2016 · 3.40 FIP	34	SP	3.40	15.2%	7.4%	1.2%	.314	5.04	9.4%	0.3%	174	+1.64	3.40	394	41
Jorge Martinez R 2019 · 3.71 FIP	34	SP	3.71	16.3%	5.7%	2.3%	.348	—	—	—	—	—	3.53	214	41
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	39
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	37
Guillermo Moscoso R 2018 · 5.07 FIP	34	SP-	5.07	18.2%	8.4%	4.5%	.318	4.35	12.6%	5.9%	104	-0.72	3.56	486	37
Josh Lowey R 2019 · 3.45 FIP	34	SP	3.45	22.4%	6.8%	2.5%	.303	—	—	—	—	—	2.22	426	37
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	36
Logan Duran R 2016 · 3.77 FIP	34	SP	3.77	15.3%	8.9%	1.5%	.299	3.66	15.5%	6.6%	29	-0.11	3.42	187	35
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	35
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	35
Angel Castro R 2017 · 3.51 FIP	34	SP	3.51	14.5%	7.4%	1.3%	.332	—	—	—	—	—	3.51	292	35

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	8.9	137	24.3%	7.7%	1.24	3.88
Don Gullett ('70-'78, 21 WAR*)	7.0	154	22.8%*	8.5%*	1.16*	3.77*
Jake Odorizzi ('22)	6.3	139	22.2%	8.1%	1.23	3.99
John Montefusco ('74-'86, 20 WAR*)	8.3	150	22.5%*	7.3%*	1.20*	4.16*
Lance Lynn ('24)	10.0	154	23.7%	8.3%	1.04	3.74
Sonny Siebert ('64-'75, 28 WAR*)	10.8	179	22.1%*	7.5%*	1.17*	3.86*
Mike Minor ('22)	7.0	128	21.6%	7.0%	1.30	4.25