Angel Sánchez

P Durham Bulls

Age 37 RHP 6'1" / 190 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2017	27	MIX2 levels	47	0	68	4.66	3.23	26.5%	4.9%	1.1	K 23.7% · BB 5.2% · FIP 3.80
2023	33	MIX2 levels	26	7	55	6.42	4.82	21.6%	11.4%	—	K 19.0% · BB 10.5% · FIP 4.90
2024	34	AAATB	6	5	27	2.36	3.62	13.8%	5.5%	—	K 11.8% · BB 5.0% · FIP 3.77
2025	35	No MLB games played
2026	36	No MLB games played
2027PROJ	37	MLBTB	1	0	1	4.48	4.48	19.5%	8.0%	0.0
2028PROJMLE	38	MLBTB	—	—	0	4.72	4.89	18.7%	8.8%	—

Last 3 seasons WAR 1.1 · Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2017

FB Velo71

K%25

xwOBA vs14

CompsSalvador Valdez (age 36) · Keiichi Yabu (age 36) · Logan Duran (age 37)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 118110vR 102

ERA4.48

FIP4.48

K%19.5%

BB%8.0%

K-BB%11.5%

Proj IP13

Value2027 · WAR

Runs saved vs league avg · 13 IP

Grades · Statcast · Contract

Scouting Grades

K†4545

Statcast Percentiles

FB Velo

p7196.0

Whiff%

p5626.2%

p2518.5%

xwOBA vs

p140.355

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	25	MIX MLE	2 levels	23	23	137	564	99	34	10	100%	6.0	14.8%	6.5%	8.3%	2.3%	2.69	3.94	—
2015	25	MIX RAW	2 levels	23	23	137	564	99	34	10	100%	6.0	17.6%	6.0%	11.5%	1.8%	2.69	3.35	—
2017	27	MIX MLE	2 levels	47	0	68	283	75	14	9	0%	1.4	23.7%	5.2%	18.6%	3.7%	4.66	3.80	+0.6
2017	27	MIX RAW	2 levels	47	0	68	283	75	14	9	0%	1.4	26.5%	4.9%	21.6%	3.2%	4.66	3.23	+1.1
2023	33	MIX MLE	2 levels	26	7	55	255	55	29	9	27%	2.1	19.0%	10.5%	8.5%	3.8%	6.42	4.90	—
2023	33	MIX RAW	2 levels	26	7	55	255	55	29	9	27%	2.1	21.6%	11.4%	10.2%	3.5%	6.42	4.82	—
2024	34	AAA MLE	Durham Bulls	6	5	27	109	15	6	2	83%	4.4	11.8%	5.0%	6.9%	1.9%	2.36	3.77	—
2024	34	AAA MLE	Durham Bulls	6	5	27	109	15	6	2	83%	4.4	11.8%	5.0%	6.9%	1.9%	2.36	3.77	—
2024	34	AAA RAW	Durham Bulls	6	5	27	109	15	6	2	83%	4.4	13.8%	5.5%	8.3%	1.8%	2.36	3.62	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			8	0	12	54	10	1	5	0%	1.5	18.5%	1.9%	16.7%	9.3%	8.76	6.99	-0.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	37	MLB	TB	1	0	1	4	1	0	0	0%	1.0	19.5%	8.0%	11.5%	3.0%	4.48	4.48	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	38	MLB MLE	TB	14	0	15	64	12	6	2	0%	1.1	18.7%	8.8%	9.9%	3.4%	4.72	4.89	-0.1
2028	38	MLB CTX	TB	—	—	0	—	—	—	—	—	—	18.7%	8.8%	9.9%	3.4%	4.72	4.89	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		8	0	12	54	10	1	5	0%	1.5	18.5%	1.9%	16.7%	9.3%	8.76	6.85	-0.3
/ 200 IP	—	RAW		130	0	200	876	162	16	81	0%	1.5	18.5%	1.9%	16.7%	9.3%	8.76	6.85	-5.3
Career	—	MLE		102	35	286	1211	212	83	35	34%	2.8	17.5%	6.9%	10.6%	2.9%	3.83	4.10	-0.4
/ 200 IP	—	MLE		71	24	200	846	148	58	25	34%	2.8	17.5%	6.9%	10.6%	2.9%	3.83	4.10	-2.9

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.

2024 AAADurham Bulls · 27 IPFIP 3.64 → 3.77 (+0.13)K% 13.8% → 11.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.76%	—	×0.860	11.83%
BB% (unintentional) /BF	5.50%	—	×0.900	4.95%
HR / BF /BF	1.83%	÷1.061	×1.100	1.90%

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 112).

Result: FIP 3.64 → 3.77, 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 AAAEl Paso Chihuahuas · 53 IPFIP 4.69 → 4.90 (+0.20)K% 22.1% → 19.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.09%	—	×0.860	19.00%
BB% (unintentional) /BF	11.65%	—	×0.900	10.48%
HR / BF /BF	3.61%	÷1.052	×1.100	3.78%

Result: FIP 4.69 → 4.90, 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 AAAIndianapolis Indians · 55 IPFIP 2.38 → 2.99 (+0.61)K% 28.4% → 25.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.38%	—	×0.880	24.98%
BB% (unintentional) /BF	5.68%	—	×1.050	5.96%
HR / BF /BF	1.75%	÷0.850	×1.100	2.26%

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 70).

Result: FIP 2.38 → 2.99, 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).

2015 AAAltoona Curve · 77 IPFIP 3.25 → 3.79 (+0.54)K% 15.4% → 12.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.41%	—	×0.810	12.48%
BB% (unintentional) /BF	5.97%	—	×1.100	6.57%
HR / BF /BF	1.26%	÷0.912	×1.200	1.66%

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 82).

Result: FIP 3.25 → 3.79, 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).

2015 AAAIndianapolis Indians · 60 IPFIP 3.49 → 4.14 (+0.65)K% 20.3% → 17.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.33%	—	×0.880	17.89%
BB% (unintentional) /BF	6.10%	—	×1.050	6.40%
HR / BF /BF	2.44%	÷0.847	×1.100	3.17%

Result: FIP 3.49 → 4.14, 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
Angel Sánchez (OGILVIE)	—	—	4.48	19.5%	8.0%	3.0%	—	4.89	18.7%	9.9%	15	+0.41	—	—	—
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	64
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	63
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	62
Jonathan Castellanos R 2019 · 5.24 FIP	37	SW	5.24	11.0%	9.0%	3.5%	.348	—	—	—	—	—	3.40	394	58
Matt Bush R 2023 · 3.79 FIP	37	SU	3.79	26.2%	8.7%	3.3%	.225	—	—	—	—	—	2.87	249	57
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	57
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	56
Hector Navarro R 2017 · 3.53 FIP	37	SU	3.53	11.5%	5.6%	1.3%	.283	—	—	—	—	—	2.30	126	54
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	54
Pete Walker R 2006 · 4.45 FIP	37	SU	4.45	19.6%	8.0%	3.6%	.337	—	—	—	—	—	4.45	327	53
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	52
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	51
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	51
Scott Atchison R 2013 · 3.80 FIP	37	SU	3.80	14.4%	6.2%	2.1%	.273	3.60	16.7%	12.9%	92	-0.20	2.52	340	49
Tony Pena Jr. R 2017 · 3.43 FIP	36	SU	3.43	10.9%	4.3%	1.3%	.312	—	—	—	—	—	2.80	128	48
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	40
Kenshin Kawakami R 2010 · 4.06 FIP	35	SP	4.06	15.1%	5.6%	2.8%	.293	—	—	—	—	—	3.97	244	32
Heriberto Ruelas L 2017 · 3.42 FIP	35	SU	3.42	17.2%	6.7%	1.7%	.294	—	—	—	—	—	2.95	117	30
Jon Leicester R 2015 · 3.76 FIP	36	SP	3.76	12.3%	5.9%	1.7%	.333	—	—	—	—	—	3.76	113	28
Masato Yoshii R 2002 · 4.15 FIP	37	SP-	4.15	13.4%	5.4%	2.7%	.295	—	—	—	—	—	4.15	757	25