Jose Acevedo

P DSL LAD Mega

Age 21 RHP ROK

Overview

ROKPROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2026	20	ROKDSL LAD Mega	9	14	4.40	4.91	13.4%	9.0%	—	K 9.1% · BB 11.2% · FIP 6.04
2027PROJ	21	MLB—	8	8	4.73	4.73	20.4%	10.4%	-0.0
2028PROJ49% MLB	22	MLB—	10	10	4.57	4.49	21.6%	9.5%	0.0

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0 WAR median (P10 0 / P90 0, comp-based) · star 0% · didn't stick 100%

CompsMike Morrison (age 22) · Sandy Lugo (age 21) · Chris Gau (age 21)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 123115vR 107

ERA4.73

FIP4.73

K%20.4%

BB%10.4%

K-BB%10.0%

Proj IP8

Value2027 · WAR

Runs saved vs league avg · 8 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)
2026	20	ROK MLE	DSL LAD Mega	9	0	14	67	9	6	2	0%	1.6	9.1%	11.2%	-2.1%	4.2%	4.40	6.04	—
2026	20	ROK MLE	DSL LAD Mega	9	0	14	67	9	6	2	0%	1.6	9.1%	11.2%	-2.1%	4.2%	4.40	6.04	—
2026	20	ROK RAW	DSL LAD Mega	9	0	14	67	9	6	2	0%	1.6	13.4%	9.0%	4.5%	3.0%	4.40	4.91	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	21	MLB	—	8	0	8	34	7	4	1	0%	1.0	20.4%	10.4%	10.0%	3.0%	4.73	4.73	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	22	MLB MLE	—	19	0	20	84	18	8	2	0%	1.1	21.6%	9.5%	12.1%	3.0%	4.57	4.49	+0.0
2028	22	MLB CTX	—	10	0	10	42	9	4	1	0%	1.0	21.6%	9.5%	12.1%	3.0%	4.57	4.49	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		9	0	10	42	9	4	1	0%	1.1	21.6%	9.5%	12.1%	3.0%	4.57	4.11	+0.1
/ 200 IP	—	RAW		190	0	200	844	183	81	20	0%	1.1	21.6%	9.5%	12.1%	3.0%	4.57	4.11	+1.1
Career	—	MLE		9	0	14	67	6	7	3	0%	1.6	9.1%	11.2%	-2.1%	4.2%	4.40	6.04	+0.0
/ 200 IP	—	MLE		126	0	200	935	85	105	39	0%	1.6	9.1%	11.2%	-2.1%	4.2%	4.40	6.04	+0.1

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
Jose Acevedo (OGILVIE)	—	—	4.73	20.4%	10.4%	3.0%	—	4.49	21.6%	12.1%	20	-0.24	—	—	—
Mike Morrison R 2016 · 4.86 FIP	22	SU	4.86	17.3%	10.7%	3.4%	.280	4.02	19.0%	7.1%	128	-0.84	3.36	145	89
Sandy Lugo R 2015 · 3.86 FIP	21	SU	3.86	20.7%	9.5%	2.4%	.352	3.98	23.6%	12.5%	119	+0.12	3.48	146	89
Chris Gau R 2018 · 4.76 FIP	21	SU	4.76	20.0%	8.6%	4.1%	.290	3.19	29.5%	23.5%	37	-1.57	2.96	211	88
Max Gieg R 2022 · 5.10 FIP	21	SU	5.10	21.1%	11.8%	4.2%	.317	4.65	20.4%	2.3%	92	-0.45	3.98	149	88
Heath Holder R 2016 · 4.16 FIP	23	SU	4.16	21.1%	10.1%	2.8%	.287	3.30	22.3%	13.1%	247	-0.86	2.94	367	87
Luis Alvarado R 2018 · 5.24 FIP	21	SU	5.24	20.2%	12.4%	4.2%	.276	5.16	18.7%	4.4%	140	-0.08	4.89	162	87
Andrew Bash R 2019 · 3.87 FIP	22	SU	3.87	18.0%	9.2%	2.1%	.273	4.21	14.9%	4.8%	146	+0.34	3.46	483	86
Mason Fox R 2018 · 3.59 FIP	21	SU	3.59	21.9%	11.5%	1.6%	.260	6.72	21.3%	4.8%	30	+3.13	3.59	112	86
Jeremy Beasley R 2017 · 4.71 FIP	21	SU	4.71	19.5%	11.6%	3.3%	.273	4.10	17.7%	8.7%	234	-0.61	3.17	337	86
Logan Lombana R 2015 · 4.63 FIP	21	SU	4.63	16.9%	10.0%	3.1%	.315	4.02	15.7%	8.1%	123	-0.61	3.80	155	86
Jordan Mikel R 2021 · 4.49 FIP	22	SU	4.49	20.5%	12.5%	2.8%	.330	4.16	19.2%	9.0%	109	-0.33	3.04	195	86
Kyle Zurak R 2017 · 3.96 FIP	22	SU	3.96	21.2%	9.4%	2.6%	.302	3.42	16.7%	7.5%	121	-0.54	3.20	181	85
Peyton Williams R 2021 · 3.69 FIP	23	SU	3.69	22.3%	10.3%	2.1%	.311	4.82	22.2%	10.2%	100	+1.13	3.69	123	85
Nick Lockhart R 2022 · 4.44 FIP	21	SU	4.44	22.0%	9.9%	3.5%	.275	4.63	16.1%	5.5%	43	+0.19	4.44	140	85
Cam Bedrosian R 2014 · 3.94 FIP	22	SU	3.94	21.5%	11.8%	2.1%	.350	2.72	26.8%	17.8%	154	-1.22	1.79	363	85
Caleb Freeman R 2019 · 3.98 FIP	21	SU	3.98	24.6%	12.9%	2.4%	.267	5.15	21.7%	8.1%	58	+1.17	3.98	225	85
Cody Palmquist R 2016 · 4.31 FIP	22	SU	4.31	21.9%	9.0%	3.5%	.278	6.02	14.7%	7.1%	62	+1.71	4.31	104	84
Matt Pidich R 2018 · 4.42 FIP	23	SU	4.42	17.1%	8.3%	3.1%	.303	4.08	20.8%	13.3%	115	-0.34	4.05	196	84
Owen Holt R 2022 · 3.72 FIP	23	SU	3.72	20.7%	10.2%	2.0%	.258	4.51	18.7%	6.4%	107	+0.79	3.72	145	84
Sean Isaac R 2016 · 4.27 FIP	23	SU	4.27	19.5%	12.3%	2.3%	.342	3.76	25.0%	10.7%	128	-0.51	3.27	155	84