Antony De Los Santos

P DSL Blue Jays Red

Age 20 RHP 6'3" / 160 lbs ROK

Overview

ROKPROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	17	ROKTOR	3	3	8.10	2.50	25.0%	12.5%	—	K 25.0% · BB 12.5% · FIP 2.57
2025	18	ROKDSL Blue Jays…	16	25	4.01	6.38	15.3%	22.0%	—	K 10.4% · BB 27.5% · FIP 7.75
2027PROJ	20	MLBTOR	14	15	5.82	5.82	20.9%	19.4%	-0.2
2028PROJ49% MLB	21	MLBTOR	10	11	5.48	5.35	21.6%	16.4%	-0.1

Next 2 projected WAR -0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsMichael Perez (age 21) · Hudson Leach (age 22) · Jordy Luciano (age 21)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 144136vR 127

ERA5.82

FIP5.82

K%20.9%

BB%19.4%

K-BB%1.6%

Proj IP15

Value2027 · WAR

Runs saved vs league avg · 15 IP

Grades · Contract

Scouting Grades

K†5050

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 industry-standard fWAR.

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)
2024	17	ROK MLE	DSL Blue Jays	3	0	3	16	4	2	0	0%	1.1	25.0%	12.5%	12.5%	0.0%	8.10	2.57	—
2024	17	ROK MLE	DSL Blue Jays	3	0	3	16	4	2	0	0%	1.1	25.0%	12.5%	12.5%	0.0%	8.10	2.57	—
2024	17	ROK RAW	DSL Blue Jays	3	0	3	16	4	2	0	0%	1.1	25.0%	12.5%	12.5%	0.0%	8.10	2.50	—
2025	18	ROK MLE	DSL Blue Jays Red	16	0	25	118	18	26	3	0%	1.5	10.4%	27.5%	-17.2%	3.7%	4.01	7.75	—
2025	18	ROK MLE	DSL Blue Jays Red	16	0	25	118	18	26	3	0%	1.5	10.4%	27.5%	-17.2%	3.7%	4.01	7.75	—
2025	18	ROK RAW	DSL Blue Jays Red	16	0	25	118	18	26	3	0%	1.5	15.3%	22.0%	-6.8%	2.5%	4.01	6.38	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	20	MLB	TOR	14	0	15	63	13	12	2	0%	1.1	20.9%	19.4%	1.6%	3.0%	5.82	5.82	-0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	21	MLB MLE	TOR	22	0	23	98	21	16	3	0%	1.1	21.6%	16.4%	5.2%	3.0%	5.48	5.35	-0.2
2028	21	MLB CTX	TOR	10	0	11	46	10	8	1	0%	1.1	21.6%	16.4%	5.2%	3.0%	5.48	5.35	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		11	0	11	48	10	8	1	0%	1.0	21.6%	16.4%	5.2%	3.0%	5.48	4.97	-0.1
/ 200 IP	—	RAW		190	0	200	844	175	140	17	0%	1.1	21.6%	16.4%	5.2%	3.0%	5.48	4.97	-1.0
Career	—	MLE		19	0	28	134	16	34	4	0%	1.5	12.1%	25.7%	-13.6%	3.3%	4.50	7.13	-0.2
/ 200 IP	—	MLE		136	0	200	957	116	246	31	0%	1.5	12.1%	25.7%	-13.6%	3.3%	4.50	7.13	-1.9

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
Antony De Los Santos (OGILVIE)	—	—	5.82	20.9%	19.4%	3.0%	—	5.35	21.6%	5.2%	23	-0.47	—	—	—
Michael Perez R 2024 · 5.44 FIP	21	SU	5.44	18.3%	16.1%	3.4%	.296	4.65	23.4%	7.7%	132	-0.79	4.38	153	89
Hudson Leach R 2024 · 5.37 FIP	22	SU	5.37	20.5%	20.0%	2.7%	.261	4.54	20.9%	3.1%	71	-0.83	2.77	101	88
Jordy Luciano R 2022 · 4.97 FIP	21	SU	4.97	24.9%	22.3%	2.1%	.306	5.33	17.1%	-4.8%	80	+0.36	4.97	104	87
Elison Joseph R 2021 · 5.31 FIP	20	SU	5.31	17.4%	19.2%	2.3%	.254	4.55	23.5%	6.8%	133	-0.76	3.32	217	87
Daniel Lloyd R 2021 · 5.40 FIP	20	SU	5.40	20.4%	22.4%	2.2%	.367	4.15	18.0%	8.5%	226	-1.25	3.59	319	87
Will Varmette R 2024 · 5.06 FIP	21	SU	5.06	22.8%	19.3%	2.6%	.302	5.13	16.1%	2.3%	135	+0.07	4.69	182	87
Andrew Schultz R 2019 · 5.25 FIP	22	SU	5.25	20.9%	17.8%	3.0%	.345	4.59	23.0%	4.7%	45	-0.66	3.56	176	86
Paul Gervase R 2022 · 4.91 FIP	22	SU	4.91	20.3%	16.2%	2.7%	.345	3.76	29.3%	16.0%	152	-1.15	3.38	211	86
Jackson Rose R 2018 · 5.32 FIP	22	SU	5.32	17.3%	17.2%	2.8%	.267	4.77	23.5%	16.5%	56	-0.55	3.33	147	85
Tommy DeJuneas R 2017 · 4.72 FIP	21	SU	4.72	19.1%	15.5%	2.3%	.357	4.59	20.8%	4.9%	100	-0.13	3.93	178	85
Marcus Crescentini R 2015 · 5.80 FIP	22	SU	5.80	21.8%	21.9%	3.2%	.365	3.95	23.3%	11.6%	123	-1.85	2.74	150	85
Brandon Dufault R 2021 · 5.32 FIP	22	SU	5.32	22.1%	15.9%	3.8%	.278	4.83	21.0%	4.2%	66	-0.49	4.38	152	85
Adrian Rodriguez R 2021 · 6.92 FIP	20	SU	6.92	23.1%	34.0%	2.7%	.368	5.46	23.1%	-1.6%	93	-1.46	2.43	148	84
Greg Weissert R 2016 · 5.22 FIP	21	SU	5.22	19.4%	19.3%	2.4%	.382	3.80	23.0%	10.3%	126	-1.42	2.57	487	84
Adonis Villavicencio R 2022 · 4.65 FIP	21	SU	4.65	21.5%	21.9%	1.1%	.316	3.93	18.0%	3.9%	90	-0.72	3.21	115	83
José Mesa Jr. R 2016 · 5.41 FIP	22	SU	5.41	22.4%	17.2%	3.7%	.311	4.00	22.8%	9.6%	113	-1.41	3.33	176	83
Edgar Isea R 2023 · 6.17 FIP	20	SU	6.17	24.2%	27.6%	2.9%	.310	4.90	20.6%	-4.4%	76	-1.27	4.50	162	83
Carlos Contreras R 2014 · 4.77 FIP	23	SU	4.77	20.2%	18.1%	2.0%	.304	4.35	18.4%	5.0%	107	-0.42	4.29	127	83
Johan Quezada R 2015 · 4.65 FIP	20	SU	4.65	18.0%	17.2%	1.6%	.255	4.39	14.1%	-3.0%	53	-0.26	3.78	234	82
Christian Ruebeck R 2024 · 6.03 FIP	23	SU	6.03	20.6%	24.4%	2.9%	.265	4.57	21.6%	1.9%	70	-1.46	4.35	104	82