Patrick Sandoval

P Boston Red Sox

Age 30 LHP 6'3" / 190 lbs MLB peak 0.7 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	26	MLBLAA	28	28	145	4.11	3.90	19.6%	11.0%	2.6
2024	27	MLBLAA	16	16	80	5.08	3.61	22.9%	9.3%	1.7
2026	29	MIX3 levels	23	21	94	4.69	3.76	22.5%	10.6%	1.8	K 20.8% · BB 10.6% · FIP 3.83
2027PROJ	30	MLBBOS	9	9	53	4.43	4.33	21.4%	10.2%	0.7
2028PROJ	31	MLBBOS	8	8	49	4.51	4.56	20.7%	10.6%	0.5
2029PROJ	32	MLBBOS	6	6	38	4.70	4.82	19.9%	11.0%	0.3

Last 3 seasons WAR 6.2 · Next 3 projected WAR 1.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%53

FB Velo34

xwOBA vs29

Form6.75 ERA (proj 4.43) · K 21.9% (proj 21.4%) · BB 8.8% (proj 10.2%) · last 60d · 25 IP through Oct 27

CompsJorge Castillo (age 33) · Marlon Arias (age 30) · Brandon Mann (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 101110vR 118

ERA4.43

FIP4.33

K%21.4%

BB%10.2%

K-BB%11.2%

Proj IP53

Value2027 · WAR

Runs saved vs league avg · 53 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p3493.4

Whiff%

p5025.3%

p5322.7%

xwOBA vs

p290.336

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	18	ROK MLE	GCL Astros	6	6	13	67	11	4	1	100%	2.2	11.2%	7.5%	3.7%	2.1%	6.07	4.25	—
2015	18	ROK RAW	GCL Astros	6	6	13	67	11	4	1	100%	2.2	16.4%	6.0%	10.4%	1.5%	6.07	3.33	—
2016	19	ROK MLE	Greeneville Astros	13	8	53	229	51	25	4	62%	4.1	15.1%	13.6%	1.5%	2.4%	5.30	4.89	—
2016	19	ROK RAW	Greeneville Astros	13	8	53	229	51	25	4	62%	4.1	22.3%	10.9%	11.4%	1.7%	5.30	3.57	—
2017	20	MIX MLE	2 levels	10	7	43	190	50	17	1	70%	4.3	20.1%	10.7%	9.4%	0.6%	4.22	3.11	—
2017	20	MIX RAW	2 levels	10	7	43	190	50	17	1	70%	4.3	26.3%	8.9%	17.4%	0.5%	4.22	2.26	—
2018	21	MIX MLE	3 levels	26	20	122	482	145	29	5	77%	4.7	22.9%	6.9%	16.0%	1.4%	2.06	2.79	—
2018	21	MIX RAW	3 levels	26	20	122	482	145	29	5	77%	4.7	30.1%	6.0%	24.1%	1.0%	2.06	1.97	—
2019	22	MIX MLE	3 levels	30	28	120	554	140	61	14	93%	4.0	22.7%	11.5%	11.3%	2.6%	5.49	4.05	+1.9
2019	22	MIX RAW	3 levels	30	28	120	554	140	61	14	93%	4.0	25.3%	11.0%	14.3%	2.5%	5.49	3.81	+2.3
2020	23	MLB MLE	Los Angeles Angels	9	6	37	159	33	12	10	67%	4.1	20.8%	7.5%	13.2%	6.2%	5.65	5.69	-0.1
2020	23	MLB RAW	Los Angeles Angels	9	6	37	159	33	12	10	67%	4.1	20.8%	7.5%	13.2%	6.3%	5.65	5.83	-0.2
2021	24	MLB MLE	Los Angeles Angels	17	14	87	363	94	36	11	82%	5.1	25.9%	9.9%	16.0%	3.0%	3.62	3.80	+1.7
2021	24	MLB RAW	Los Angeles Angels	17	14	87	363	94	36	11	82%	5.1	25.9%	9.9%	16.0%	3.0%	3.62	3.82	+1.6
2022	25	MLB MLE	Los Angeles Angels	27	27	149	638	151	60	8	100%	5.5	23.7%	9.4%	14.3%	1.2%	2.91	2.97	+4.5
2022	25	MLB RAW	Los Angeles Angels	27	27	149	638	151	60	8	100%	5.5	23.7%	9.4%	14.3%	1.3%	2.91	2.98	+4.5
2023	26	MLB MLE	Los Angeles Angels	28	28	145	652	128	72	12	100%	5.2	19.6%	11.0%	8.6%	1.8%	4.11	3.83	+2.7
2023	26	MLB RAW	Los Angeles Angels	28	28	145	652	128	72	12	100%	5.2	19.6%	11.0%	8.6%	1.8%	4.11	3.90	+2.6
2024	27	MLB MLE	Los Angeles Angels	16	16	80	353	81	33	8	100%	5.0	22.9%	9.3%	13.6%	2.2%	5.08	3.57	+1.8
2024	27	MLB MLE	Los Angeles Angels	16	16	80	353	81	33	8	100%	5.0	22.9%	9.3%	13.6%	2.2%	5.08	3.57	+1.8
2024	27	MLB RAW	Los Angeles Angels	16	16	80	353	81	33	8	100%	5.0	22.9%	9.3%	13.6%	2.3%	5.08	3.61	+1.7
2026	29	MIX MLE	3 levels	23	21	94	423	95	45	9	91%	4.1	20.8%	10.6%	10.2%	2.1%	4.69	3.83	+1.8
2026	29	MIX MLE	3 levels	23	21	94	423	95	45	9	91%	4.1	20.8%	10.6%	10.2%	2.1%	4.69	3.83	+1.8
2026	29	MIX RAW	3 levels	23	21	94	423	95	45	9	91%	4.1	22.5%	10.6%	11.8%	2.1%	4.69	3.76	+1.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			122	113	606	2655	602	264	62	93%	5.0	22.7%	9.9%	12.7%	2.3%	4.14	3.75	+12.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	BOS	9	9	53	224	48	23	6	100%	5.9	21.4%	10.2%	11.2%	2.5%	4.43	4.33	+0.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	BOS	8	8	49	209	43	22	6	100%	6.2	20.7%	10.6%	10.1%	2.7%	4.51	4.56	+0.5
2029	32	MLB MLE	BOS	6	6	38	163	32	18	5	100%	6.4	19.9%	11.0%	8.9%	3.0%	4.70	4.82	+0.3
2028	31	MLB CTX	BOS	8	8	49	207	43	22	6	100%	6.1	20.7%	10.6%	10.1%	2.7%	4.51	4.56	+0.5
2029	32	MLB CTX	BOS	6	6	38	160	32	18	5	100%	6.3	19.9%	11.0%	8.9%	3.0%	4.70	4.82	+0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		137	128	694	3026	677	304	72	93%	5.1	22.4%	10.0%	12.3%	2.4%	4.20	3.80	+13.4
/ 200 IP	—	RAW		39	37	200	872	195	88	21	93%	5.1	22.4%	10.0%	12.3%	2.4%	4.20	3.80	+3.9
Career	—	MLE		205	181	941	4110	892	411	86	88%	4.6	21.7%	10.0%	11.7%	2.1%	4.08	3.68	+12.7
/ 200 IP	—	MLE		44	38	200	873	190	87	18	88%	4.6	21.7%	10.0%	11.7%	2.1%	4.08	3.68	+3.7

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 AAAWorcester Red Sox · 16 IPFIP 4.45 → 4.51 (+0.06)K% 14.7% → 12.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.67%	—	×0.860	12.61%
BB% (unintentional) /BF	14.67%	—	×0.900	13.20%
HR / BF /BF	1.33%	÷1.000	×1.100	1.47%

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 4.45 → 4.51, 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).

2026 AAPortland Sea Dogs · 8 IPFIP 2.63 → 3.78 (+1.15)K% 40.7% → 33.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.74%	—	×0.810	33.00%
BB% (unintentional) /BF	7.41%	—	×1.100	8.15%
HR / BF /BF	3.70%	÷1.000	×1.200	4.44%

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 100).

Result: FIP 2.63 → 3.78, 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 AAASalt Lake Bees · 60 IPFIP 3.99 → 4.34 (+0.34)K% 21.9% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.85%	—	×0.880	19.23%
BB% (unintentional) /BF	11.59%	—	×1.050	12.17%
HR / BF /BF	2.32%	÷1.059	×1.100	2.41%

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 112).

Result: FIP 3.99 → 4.34, 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 AAMobile BayBears · 20 IPFIP 1.57 → 2.39 (+0.82)K% 38.6% → 31.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.55%	—	×0.810	31.23%
BB% (unintentional) /BF	8.43%	—	×1.100	9.28%
HR / BF /BF	1.20%	÷1.050	×1.200	1.38%

Result: FIP 1.57 → 2.39, 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 AQuad Cities River Bandits · 65 IPFIP 2.18 → 3.15 (+0.97)K% 27.0% → 20.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.00%	—	×0.740	19.98%
BB% (unintentional) /BF	4.18%	—	×1.180	4.94%
HR / BF /BF	1.52%	÷0.973	×1.300	2.03%

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 95).

Result: FIP 2.18 → 3.15, 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 A+Inland Empire 66ers · 38 IPFIP 1.58 → 2.47 (+0.88)K% 33.1% → 25.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.10%	—	×0.770	25.49%
BB% (unintentional) /BF	7.04%	—	×1.150	8.10%
HR / BF /BF	0.70%	÷0.978	×1.250	0.90%

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 96).

Result: FIP 1.58 → 2.47, 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 AAMobile BayBears · 20 IPFIP 1.45 → 2.15 (+0.69)K% 35.1% → 28.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.06%	—	×0.810	28.40%
BB% (unintentional) /BF	10.39%	—	×1.100	11.43%
HR / BF /BF	0.00%	÷0.959	×1.200	0.00%

Result: FIP 1.45 → 2.15, 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 AQuad Cities River Bandits · 40 IPFIP 2.27 → 3.11 (+0.84)K% 27.1% → 20.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.12%	—	×0.740	20.07%
BB% (unintentional) /BF	9.04%	—	×1.180	10.67%
HR / BF /BF	0.56%	÷1.154	×1.300	0.64%

Result: FIP 2.27 → 3.11, 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
Patrick Sandoval (OGILVIE)	—	—	—	—	—	2.5%	—	4.67	20.3%	9.6%	88	+0.34	—	—	—
Jorge Castillo L 2015 · 4.50 FIP	33	SP	4.50	16.8%	9.2%	3.0%	.292	4.95	12.4%	7.2%	186	+0.45	4.50	408	70
Marlon Arias L 2015 · 4.32 FIP	30	SP	4.32	16.8%	12.2%	2.0%	.330	3.73	17.2%	9.5%	22	-0.59	3.73	154	70
Brandon Mann L 2016 · 4.72 FIP	32	SP	4.72	19.4%	7.9%	4.1%	.320	3.97	19.0%	7.6%	128	-0.75	3.59	190	68
Orlando Lara L 2015 · 4.29 FIP	30	SP	4.29	15.2%	10.3%	2.1%	.348	4.08	14.2%	7.2%	230	-0.21	3.85	426	67
Alejandro Soto L 2016 · 3.96 FIP	30	SP	3.96	14.9%	6.4%	2.4%	.367	4.15	15.9%	10.3%	97	+0.19	3.96	280	66
Tsuyoshi Wada L 2014 · 3.60 FIP	33	SP	3.60	19.7%	6.2%	2.5%	.290	3.66	16.8%	9.7%	119	+0.06	3.60	188	65
Ryan Bollinger L 2018 · 3.56 FIP	27	SP	3.56	17.1%	7.5%	1.7%	.290	—	—	—	—	—	3.56	112	64
Tomas Solis L 2015 · 4.20 FIP	32	SP	4.20	15.0%	6.2%	2.9%	.264	5.09	12.0%	3.7%	159	+0.89	4.20	327	64
John Bale L 2003 · 4.12 FIP	29	SP	4.12	19.0%	5.1%	3.6%	.305	—	—	—	—	—	2.49	168	63
Aríel Miranda L 2018 · 4.21 FIP	29	SP	4.21	17.9%	12.8%	1.8%	.315	—	—	—	—	—	3.46	431	63
Alejandro Astorga L 2017 · 4.01 FIP	30	SP	4.01	14.4%	8.2%	2.0%	.306	—	—	—	—	—	3.72	311	62
Arturo Lopez L 2015 · 4.80 FIP	32	SP	4.80	13.7%	6.2%	3.8%	.301	4.80	10.9%	4.2%	217	+0.00	4.34	445	62
Jack Snodgrass L 2016 · 5.01 FIP	28	SP-	5.01	15.7%	10.1%	3.6%	.305	—	—	—	—	—	4.66	118	62
Darin Gorski L 2016 · 4.50 FIP	28	SP	4.50	15.0%	9.8%	2.6%	.299	—	—	—	—	—	4.50	206	62
Kwang Hyun Kim L 2020 · 3.76 FIP	32	SP	3.76	15.6%	6.5%	2.1%	.214	4.21	17.7%	9.5%	107	+0.45	3.76	146	61
Misael Siverio L 2017 · 4.32 FIP	28	SP	4.32	17.3%	9.1%	2.8%	.299	—	—	—	—	—	3.89	185	61
Eric Stults L 2009 · 3.95 FIP	29	SP	3.95	14.8%	10.8%	1.4%	.294	4.00	13.3%	6.9%	111	+0.05	3.50	773	61
Danny Wirchansky L 2024 · 4.47 FIP	27	SP-	4.47	16.2%	7.4%	3.3%	.246	5.88	13.0%	0.2%	68	+1.41	4.47	179	61
Carlos Teller L 2017 · 4.63 FIP	30	SP	4.63	12.2%	9.6%	2.5%	.322	4.90	13.1%	-1.1%	60	+0.27	3.75	333	60
Jose De Paula L 2019 · 3.31 FIP	31	SP	3.31	19.0%	7.6%	1.6%	.394	—	—	—	—	—	3.31	118	60

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	3.7	75	22.3%	10.2%	0.93	4.21
Darren Dreifort ('04)	4.4	97	21.2%	10.3%	0.93	4.36
Brandon Morrow ('18)	4.3	78	24.0%	10.1%	0.92	3.96
Don Aase ('77-'90, 12 WAR*)	5.5	92	20.7%*	9.6%*	0.94*	4.14*
Dick Kelley ('64-'71, 3 WAR*)	2.6	87	21.4%*	10.6%*	1.04*	4.26*
Jim Gott ('82-'95, 10 WAR*)	5.6	93	23.6%*	9.7%*	0.91*	4.20*
Byung-Hyun Kim ('07)	4.2	105	21.9%	10.2%	1.01	4.42