Johan Figuera

P DSL Blue Jays Blue

Age 20 RHP ROK

Overview

ROKPROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2026	19	ROKDSL Blue Jays…	8	1	11	6.75	5.26	29.3%	32.8%	—	K 19.9% · BB 40.9% · FIP 6.60
2027PROJ	20	MLBTOR	6	0	6	5.71	5.71	22.8%	20.0%	-0.1
2028PROJ49% MLB	21	MLBTOR	10	0	10	5.37	5.24	23.2%	16.8%	-0.1

Next 2 projected WAR -0.2Projected 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 99%

CompsJordy Luciano (age 21) · Will Varmette (age 21) · Michael Perez (age 21)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 142133vR 125

ERA5.71

FIP5.71

K%22.8%

BB%20.0%

K-BB%2.7%

Proj IP6

Value2027 · WAR

Runs saved vs league avg · 6 IP

Grades · Contract

Scouting Grades

K†5555

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	19	ROK MLE	DSL Blue Jays Blue	8	1	11	58	17	19	0	12%	1.3	19.9%	40.9%	-21.0%	0.0%	6.75	6.60	—
2026	19	ROK MLE	DSL Blue Jays Blue	8	1	11	58	17	19	0	12%	1.3	19.9%	40.9%	-21.0%	0.0%	6.75	6.60	—
2026	19	ROK RAW	DSL Blue Jays Blue	8	1	11	58	17	19	0	12%	1.3	29.3%	32.8%	-3.4%	0.0%	6.75	5.26	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	20	MLB	TOR	6	0	6	25	6	5	1	0%	1.0	22.8%	20.0%	2.7%	3.0%	5.71	5.71	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	21	MLB MLE	TOR	19	0	20	84	20	14	2	0%	1.1	23.2%	16.8%	6.4%	2.9%	5.37	5.24	-0.2
2028	21	MLB CTX	TOR	10	0	10	42	10	7	1	0%	1.0	23.2%	16.8%	6.4%	2.9%	5.37	5.24	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		9	0	10	42	10	7	1	0%	1.1	23.2%	16.8%	6.4%	2.9%	5.37	4.86	-0.0
/ 200 IP	—	RAW		190	0	200	844	203	142	20	0%	1.1	23.2%	16.8%	6.4%	2.9%	5.37	4.86	-0.7
Career	—	MLE		8	1	11	58	12	24	0	12%	1.3	19.9%	40.9%	-21.0%	0.0%	6.75	6.60	-0.2
/ 200 IP	—	MLE		150	19	200	1088	217	445	0	12%	1.3	19.9%	40.9%	-21.0%	0.0%	6.75	6.60	-1.6

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
Johan Figuera (OGILVIE)	—	—	5.71	22.8%	20.0%	3.0%	—	5.24	23.2%	6.4%	20	-0.47	—	—	—
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	86
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	85
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	85
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	84
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	83
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	83
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	83
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	83
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
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	81
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	81
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	81
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	81
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	80
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	80
James Acuna R 2021 · 5.12 FIP	23	SU	5.12	21.3%	17.0%	3.0%	.302	4.33	19.4%	4.6%	60	-0.79	4.33	107	80
Andrew Marrero R 2021 · 5.09 FIP	21	SU	5.09	21.7%	22.0%	1.9%	.395	4.61	24.3%	9.1%	148	-0.48	3.45	221	80
Will Kobos R 2018 · 5.58 FIP	20	SW	5.58	19.3%	16.0%	3.8%	.313	3.46	26.6%	11.6%	76	-2.12	2.44	139	80
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	80
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	80