Luis Victorino

P Dunedin Blue Jays

Age 24 RHP 6'0" / 167 lbs A

Overview

APROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2025	22	ROKDSL Blue Jays…	17	2	24	2.62	2.85	27.8%	13.0%	—	K 18.9% · BB 16.3% · FIP 4.27
2026	23	ATOR	20	0	31	6.39	5.07	15.1%	11.9%	—	K 11.2% · BB 14.1% · FIP 5.74
2027PROJ	24	MLBTOR	—	—	0	5.54	5.54	16.7%	14.4%	—
2028PROJ43% MLB	25	MLBTOR	9	0	9	5.28	5.18	18.4%	12.8%	-0.1

Next 2 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 0.0 / P90 0.0, comp-based) · star 0% · didn't stick 100%

CompsJulio Mateo (age 24) · Hayden Snelsire (age 23) · Brett Lockwood (age 24)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 138130vR 122

ERA5.54

FIP5.54

K%16.7%

BB%14.4%

K-BB%2.2%

Proj IP18

Value2027 · WAR

Runs saved vs league avg · 18 IP

Grades · Contract

Scouting Grades

K†4045

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)
2022	19	ROK MLE	DSL HOU Blue	12	6	34	137	38	14	2	50%	2.9	18.9%	12.8%	6.1%	2.1%	2.88	4.26	—
2022	19	ROK RAW	DSL HOU Blue	12	6	34	137	38	14	2	50%	2.9	27.7%	10.2%	17.5%	1.5%	2.88	2.87	—
2023	20	ROK MLE	FCL Astros	12	0	19	89	18	19	0	0%	1.6	13.8%	26.7%	-12.9%	0.0%	4.26	5.32	—
2023	20	ROK RAW	FCL Astros	12	0	19	89	18	19	0	0%	1.6	20.2%	21.3%	-1.1%	0.0%	4.26	4.21	—
2024	21	ROK MLE	FCL Astros	14	5	41	185	41	31	2	36%	2.9	15.1%	20.9%	-5.9%	1.4%	3.32	5.23	—
2024	21	ROK MLE	FCL Astros	14	5	41	185	41	31	2	36%	2.9	15.1%	20.9%	-5.9%	1.4%	3.32	5.23	—
2024	21	ROK RAW	FCL Astros	14	5	41	185	41	31	2	36%	2.9	22.2%	16.8%	5.4%	1.1%	3.32	4.01	—
2025	22	ROK MLE	DSL Blue Jays Red	17	2	24	115	32	15	1	12%	1.4	18.9%	16.3%	2.6%	1.3%	2.62	4.27	—
2025	22	ROK MLE	DSL Blue Jays Red	17	2	24	115	32	15	1	12%	1.4	18.9%	16.3%	2.6%	1.3%	2.62	4.27	—
2025	22	ROK RAW	DSL Blue Jays Red	17	2	24	115	32	15	1	12%	1.4	27.8%	13.0%	14.8%	0.9%	2.62	2.85	—
2026	23	A MLE	Dunedin Blue Jays	20	0	31	159	24	19	4	0%	1.6	11.2%	14.1%	-2.9%	3.3%	6.39	5.74	—
2026	23	A MLE	Dunedin Blue Jays	20	0	31	159	24	19	4	0%	1.6	11.2%	14.1%	-2.9%	3.3%	6.39	5.74	—
2026	23	A RAW	Dunedin Blue Jays	20	0	31	159	24	19	4	0%	1.6	15.1%	11.9%	3.1%	2.5%	6.39	5.07	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	24	MLB	TOR	—	—	0	—	—	—	—	—	—	16.7%	14.4%	2.2%	3.0%	5.54	5.54	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	25	MLB MLE	TOR	20	0	20	87	16	11	3	0%	1.0	18.4%	12.8%	5.6%	3.0%	5.28	5.18	-0.1
2028	25	MLB CTX	TOR	9	0	9	38	7	5	1	0%	1.0	18.4%	12.8%	5.6%	3.0%	5.28	5.18	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		8	0	9	37	7	5	1	0%	1.1	18.4%	12.8%	5.6%	3.0%	5.28	4.80	-0.0
/ 200 IP	—	RAW		190	0	200	844	160	114	23	0%	1.1	18.4%	12.8%	5.6%	3.0%	5.28	4.80	-0.6
Career	—	MLE		75	13	149	685	105	121	12	17%	2.0	15.4%	17.7%	-2.3%	1.8%	3.87	5.00	-0.1
/ 200 IP	—	MLE		101	17	200	919	142	163	16	17%	2.0	15.4%	17.7%	-2.3%	1.8%	3.87	5.00	-1.5

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.

2026 ADunedin Blue Jays · 31 IPFIP 4.72 → 5.74 (+1.02)K% 15.1% → 11.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.09%	—	×0.740	11.17%
BB% (unintentional) /BF	11.95%	—	×1.180	14.10%
HR / BF /BF	2.52%	÷1.000	×1.300	3.27%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 100).

Result: FIP 4.72 → 5.74, 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).

2025 ROKDSL Blue Jays Red · 24 IPFIP 2.88 → 4.27 (+1.39)K% 27.8% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.83%	—	×0.680	18.92%
BB% (unintentional) /BF	13.04%	—	×1.250	16.30%
HR / BF /BF	0.87%	÷0.957	×1.400	1.27%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 91).

Result: FIP 2.88 → 4.27, 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).

2024 ROKFCL Astros · 41 IPFIP 3.94 → 5.23 (+1.28)K% 22.2% → 15.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.16%	—	×0.680	15.07%
BB% (unintentional) /BF	16.76%	—	×1.250	20.95%
HR / BF /BF	1.08%	÷1.111	×1.400	1.36%

Result: FIP 3.94 → 5.23, 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 ROKFCL Astros · 19 IPFIP 4.10 → 5.32 (+1.22)K% 20.2% → 13.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.22%	—	×0.680	13.75%
BB% (unintentional) /BF	21.35%	—	×1.250	26.69%
HR / BF /BF	0.00%	÷1.379	×1.400	0.00%

Result: FIP 4.10 → 5.32, 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).

2022 ROKDSL HOU Blue · 34 IPFIP 2.85 → 4.26 (+1.41)K% 27.7% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.74%	—	×0.680	18.86%
BB% (unintentional) /BF	10.22%	—	×1.250	12.77%
HR / BF /BF	1.46%	÷0.989	×1.400	2.07%

Result: FIP 2.85 → 4.26, 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
Luis Victorino (OGILVIE)	—	—	5.54	16.7%	14.4%	3.0%	—	5.18	18.4%	5.6%	20	-0.36	—	—	—
Julio Mateo R 2002 · 4.54 FIP	24	SU	4.54	16.0%	12.8%	2.1%	.277	4.23	17.5%	13.8%	232	-0.31	3.42	318	87
Hayden Snelsire R 2024 · 5.10 FIP	23	SU	5.10	18.9%	13.8%	3.4%	.272	4.28	20.0%	13.7%	135	-0.82	3.83	178	86
Brett Lockwood R 2021 · 5.46 FIP	24	SU	5.46	16.2%	15.5%	3.2%	.312	4.58	11.0%	-6.3%	79	-0.88	4.24	132	84
Francisco Del Rosario R 2006 · 5.13 FIP	25	SU	5.13	19.4%	13.0%	3.7%	.290	3.93	19.7%	10.3%	26	-1.20	3.93	194	84
Tanner Duncan R 2019 · 5.37 FIP	24	SU	5.37	17.5%	14.8%	3.4%	.301	—	—	—	—	—	3.28	123	84
Marc Brakeman R 2018 · 4.67 FIP	24	SU	4.67	18.3%	13.7%	2.5%	.294	—	—	—	—	—	3.85	156	83
Jake Johansen R 2015 · 5.34 FIP	24	SU	5.34	15.8%	12.8%	3.6%	.351	4.14	13.6%	3.0%	114	-1.20	3.65	162	83
Cody Scroggins R 2021 · 5.35 FIP	24	SU	5.35	17.4%	15.1%	3.3%	.263	4.28	18.6%	4.4%	59	-1.07	3.78	108	83
Grayson Thurman R 2023 · 4.63 FIP	24	SU	4.63	19.5%	14.1%	2.5%	.319	4.85	19.9%	10.5%	87	+0.22	4.58	132	83
Lendy Castillo R 2012 · 4.88 FIP	23	SU	4.88	14.8%	13.6%	2.4%	.361	4.81	13.9%	1.0%	49	-0.07	4.41	116	82
Cole Hillier R 2024 · 4.64 FIP	23	SU	4.64	16.9%	14.7%	2.0%	.297	4.45	18.1%	4.5%	111	-0.19	3.80	169	82
Yordy Cabrera R 2016 · 4.99 FIP	25	SU	4.99	15.8%	17.2%	1.9%	.321	4.17	13.7%	2.7%	140	-0.82	3.43	159	82
Ryan Vogelsong R 2001 · 5.34 FIP	24	SU	5.34	14.6%	11.6%	3.7%	.287	4.78	14.9%	5.7%	155	-0.56	3.14	1,184	82
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	81
Reece Karalus R 2018 · 4.73 FIP	24	SU	4.73	15.4%	11.9%	2.6%	.299	—	—	—	—	—	4.73	149	81
Jeremy Guthrie R 2006 · 4.92 FIP	27	SU	4.92	15.1%	15.1%	2.1%	.349	4.57	14.8%	8.1%	566	-0.35	4.08	1,834	81
Ian Villers R 2024 · 4.55 FIP	23	SU	4.55	13.2%	11.9%	1.9%	.311	4.23	15.6%	5.2%	59	-0.32	4.23	107	81
Miguel Nunez R 2017 · 5.12 FIP	24	SU	5.12	13.2%	13.7%	2.6%	.257	—	—	—	—	—	3.72	154	81
Josh Harlow R 2024 · 5.17 FIP	23	SU	5.17	18.5%	13.5%	3.5%	.266	5.73	17.1%	-4.2%	51	+0.56	5.17	107	81
Conner O'Neil R 2019 · 5.78 FIP	24	SU	5.78	12.8%	13.6%	3.7%	.340	6.92	16.4%	4.1%	26	+1.14	2.89	115	81