Yamel Pinto

P DSL Cubs Blue

Age 19 RHP ROK

Overview

ROKPROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2026	18	ROKDSL Cubs Blue	13	16	6.61	6.59	15.3%	22.4%	—	K 10.4% · BB 27.9% · FIP 7.57
2027PROJ	19	MLBCHC	10	10	5.97	5.97	20.1%	20.0%	-0.2
2028PROJ49% MLB	20	MLBCHC	10	10	5.60	5.46	20.9%	16.8%	-0.1

Next 2 projected WAR -0.3Projected 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%

CompsAlejandro Lugo (age 18) · Jonathan Jimenez (age 19) · Daniel Lloyd (age 20)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 147138vR 130

ERA5.97

FIP5.97

K%20.1%

BB%20.0%

K-BB%0.1%

Proj IP10

Value2027 · WAR

Runs saved vs league avg · 10 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	18	ROK MLE	DSL Cubs Blue	13	0	16	85	13	19	2	0%	1.3	10.4%	27.9%	-17.5%	3.3%	6.61	7.57	—
2026	18	ROK MLE	DSL Cubs Blue	13	0	16	85	13	19	2	0%	1.3	10.4%	27.9%	-17.5%	3.3%	6.61	7.57	—
2026	18	ROK RAW	DSL Cubs Blue	13	0	16	85	13	19	2	0%	1.3	15.3%	22.4%	-7.1%	2.4%	6.61	6.59	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	19	MLB	CHC	10	0	10	42	8	8	1	0%	1.0	20.1%	20.0%	0.1%	3.0%	5.97	5.97	-0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	20	MLB MLE	CHC	19	0	20	84	18	14	2	0%	1.1	20.9%	16.8%	4.1%	2.9%	5.60	5.46	-0.2
2028	20	MLB CTX	CHC	10	0	10	42	9	7	1	0%	1.0	20.9%	16.8%	4.1%	2.9%	5.60	5.46	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		9	0	10	42	9	7	1	0%	1.1	20.9%	16.8%	4.1%	2.9%	5.60	5.08	-0.1
/ 200 IP	—	RAW		190	0	200	844	183	142	20	0%	1.1	20.9%	16.8%	4.1%	2.9%	5.60	5.08	-1.2
Career	—	MLE		13	0	16	85	9	24	3	0%	1.3	10.4%	27.9%	-17.5%	3.3%	6.61	7.57	-0.2
/ 200 IP	—	MLE		159	0	200	1041	108	291	34	0%	1.3	10.4%	27.9%	-17.5%	3.3%	6.61	7.57	-2.1

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
Yamel Pinto (OGILVIE)	—	—	5.97	20.1%	20.0%	3.0%	—	5.46	20.9%	4.1%	20	-0.51	—	—	—
Alejandro Lugo R 2021 · 4.91 FIP	18	SU	4.91	20.4%	16.7%	2.6%	.265	4.96	16.7%	3.1%	98	+0.05	3.77	159	89
Jonathan Jimenez R 2023 · 5.41 FIP	19	SU	5.41	16.4%	16.2%	3.0%	.333	5.09	14.1%	2.2%	178	-0.32	3.49	189	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
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
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	86
Carlos De Los Santos R 2019 · 5.68 FIP	18	SU	5.68	16.9%	16.0%	3.6%	.286	3.95	20.9%	3.2%	86	-1.73	3.67	173	86
Enrique Saldana R 2018 · 6.54 FIP	19	SU	6.54	15.4%	25.9%	2.6%	.310	3.85	14.6%	6.3%	80	-2.69	2.96	109	85
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	84
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
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	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
Junior Flores R 2021 · 5.28 FIP	19	SU	5.28	20.5%	24.1%	1.6%	.220	4.93	14.5%	-3.9%	92	-0.35	4.87	110	83
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	83
Jose G. Gonzalez R 2021 · 5.42 FIP	19	SU	5.42	16.2%	22.7%	1.5%	.320	4.81	15.6%	0.6%	94	-0.61	3.63	165	83
Leandro Lopez R 2021 · 6.37 FIP	19	SU	6.37	24.7%	24.6%	4.1%	.222	3.74	25.0%	7.8%	104	-2.63	3.03	273	82
Brendan Hardy R 2018 · 5.18 FIP	18	SU	5.18	17.0%	19.7%	1.8%	.227	6.99	15.9%	-17.2%	25	+1.81	2.54	137	82
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	82
Angel Reyes R 2018 · 4.91 FIP	20	SU	4.91	16.5%	19.4%	1.4%	.328	4.51	17.1%	5.7%	119	-0.40	3.99	308	82
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	82
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	82