Donal Montas

P DSL Tigers 2

Age 25 RHP 6'3" / 190 lbs ROK

Overview

ROKPROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2025	23	ROKDSL Tigers 2	15	0	23	7.15	3.36	15.3%	11.9%	—	K 10.4% · BB 14.8% · FIP 4.10
2026	24	ROKDSL Tigers 2	14	0	25	6.12	4.66	11.0%	11.0%	—	K 7.5% · BB 13.8% · FIP 5.51
2027PROJ	25	MLBDET	—	—	0	5.32	5.32	17.0%	13.2%	—
2028PROJ43% MLB	26	MLBDET	7	0	7	5.18	5.15	18.4%	12.7%	-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 WAR0.0 WAR median (P10 0.0 / P90 0.0, comp-based) · star 0% · didn't stick 100%

CompsBrad Deppermann (age 25) · Ryan Vogelsong (age 24) · Francisco Del Rosario (age 25)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 134126vR 118

ERA5.32

FIP5.32

K%17.0%

BB%13.2%

K-BB%3.8%

Proj IP15

Value2027 · WAR

Runs saved vs league avg · 15 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	20	ROK MLE	DSL Tigers 2	12	9	39	165	42	12	5	75%	3.3	17.3%	9.1%	8.2%	4.5%	2.97	5.29	—
2022	20	ROK RAW	DSL Tigers 2	12	9	39	165	42	12	5	75%	3.3	25.5%	7.3%	18.2%	3.0%	2.97	3.53	—
2023	21	ROK MLE	DSL Tigers 1	11	11	39	169	21	18	3	100%	3.5	8.4%	13.3%	-4.9%	2.9%	5.35	5.66	—
2023	21	ROK RAW	DSL Tigers 1	11	11	39	169	21	18	3	100%	3.5	12.4%	10.7%	1.8%	1.8%	5.35	4.42	—
2024	22	ROK MLE	DSL Tigers 1	21	0	30	132	24	15	2	0%	1.4	12.4%	14.2%	-1.8%	1.9%	5.10	4.91	—
2024	22	ROK MLE	DSL Tigers 1	21	0	30	132	24	15	2	0%	1.4	12.4%	14.2%	-1.8%	1.9%	5.10	4.91	—
2024	22	ROK RAW	DSL Tigers 1	21	0	30	132	24	15	2	0%	1.4	18.2%	11.4%	6.8%	1.5%	5.10	3.87	—
2025	23	ROK MLE	DSL Tigers 2	15	0	23	118	18	14	0	0%	1.5	10.4%	14.8%	-4.5%	0.0%	7.15	4.10	—
2025	23	ROK MLE	DSL Tigers 2	15	0	23	118	18	14	0	0%	1.5	10.4%	14.8%	-4.5%	0.0%	7.15	4.10	—
2025	23	ROK RAW	DSL Tigers 2	15	0	23	118	18	14	0	0%	1.5	15.3%	11.9%	3.4%	0.0%	7.15	3.36	—
2026	24	ROK MLE	DSL Tigers 2	14	0	25	118	13	13	2	0%	1.8	7.5%	13.8%	-6.3%	2.4%	6.12	5.51	—
2026	24	ROK MLE	DSL Tigers 2	14	0	25	118	13	13	2	0%	1.8	7.5%	13.8%	-6.3%	2.4%	6.12	5.51	—
2026	24	ROK RAW	DSL Tigers 2	14	0	25	118	13	13	2	0%	1.8	11.0%	11.0%	0.0%	1.7%	6.12	4.66	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	25	MLB	DET	—	—	0	—	—	—	—	—	—	17.0%	13.2%	3.8%	2.9%	5.32	5.32	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	26	MLB MLE	DET	16	0	17	71	13	9	2	0%	1.1	18.4%	12.7%	5.7%	2.9%	5.18	5.15	-0.1
2028	26	MLB CTX	DET	7	0	7	30	5	4	1	0%	1.0	18.4%	12.7%	5.7%	2.9%	5.18	5.15	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		7	0	7	30	6	4	1	0%	1.0	18.4%	12.7%	5.7%	2.9%	5.18	4.77	-0.0
/ 200 IP	—	RAW		190	0	200	844	167	111	28	0%	1.1	18.4%	12.7%	5.7%	2.9%	5.18	4.77	-0.5
Career	—	MLE		73	20	156	702	80	90	18	27%	2.1	11.4%	12.8%	-1.4%	2.5%	5.09	5.14	-0.1
/ 200 IP	—	MLE		94	26	200	902	103	116	23	27%	2.1	11.4%	12.8%	-1.4%	2.5%	5.09	5.14	-1.4

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 ROKDSL Tigers 2 · 25 IPFIP 4.50 → 5.51 (+1.02)K% 11.0% → 7.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	11.02%	—	×0.680	7.49%
BB% (unintentional) /BF	11.02%	—	×1.250	13.77%
HR / BF /BF	1.69%	÷1.000	×1.400	2.37%

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 100).

Result: FIP 4.50 → 5.51, 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 Tigers 2 · 23 IPFIP 3.31 → 4.10 (+0.79)K% 15.3% → 10.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.25%	—	×0.680	10.37%
BB% (unintentional) /BF	11.86%	—	×1.250	14.83%
HR / BF /BF	0.00%	÷1.048	×1.400	0.00%

Result: FIP 3.31 → 4.10, 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 ROKDSL Tigers 1 · 30 IPFIP 3.84 → 4.91 (+1.07)K% 18.2% → 12.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.18%	—	×0.680	12.36%
BB% (unintentional) /BF	11.36%	—	×1.250	14.20%
HR / BF /BF	1.52%	÷1.105	×1.400	1.92%

Result: FIP 3.84 → 4.91, 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 ROKDSL Tigers 1 · 39 IPFIP 4.37 → 5.66 (+1.28)K% 12.4% → 8.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.43%	—	×0.680	8.45%
BB% (unintentional) /BF	10.65%	—	×1.250	13.31%
HR / BF /BF	1.78%	÷0.862	×1.400	2.88%

Result: FIP 4.37 → 5.66, 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 Tigers 2 · 39 IPFIP 3.54 → 5.29 (+1.75)K% 25.5% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.45%	—	×0.680	17.31%
BB% (unintentional) /BF	7.27%	—	×1.250	9.09%
HR / BF /BF	3.03%	÷0.933	×1.400	4.55%

Result: FIP 3.54 → 5.29, 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
Donal Montas (OGILVIE)	—	—	5.32	17.0%	13.2%	2.9%	—	5.15	18.4%	5.7%	17	-0.17	—	—	—
Brad Deppermann R 2021 · 4.51 FIP	25	SW	4.51	17.1%	9.6%	3.0%	.289	3.52	20.0%	7.8%	45	-0.99	1.94	143	84
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
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	81
Andrew McInvale R 2022 · 5.15 FIP	25	SU	5.15	17.6%	15.9%	2.8%	.324	6.44	24.5%	10.0%	23	+1.29	4.92	103	81
Tyler Yates R 2004 · 4.47 FIP	26	SW	4.47	15.4%	9.6%	2.6%	.333	3.64	22.5%	13.5%	116	-0.83	3.23	248	81
Grant Black R 2021 · 4.46 FIP	27	SW	4.46	19.0%	13.9%	2.2%	.327	5.35	15.0%	3.8%	156	+0.89	4.46	215	81
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	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
Zach Nuding R 2015 · 4.76 FIP	25	SU	4.76	12.5%	10.5%	2.5%	.236	5.03	12.6%	3.3%	106	+0.27	4.76	146	79
Haden Erbe R 2024 · 5.28 FIP	25	SU	5.28	18.4%	16.5%	3.0%	.320	5.19	17.8%	0.5%	40	-0.09	5.19	116	78
Tyler Burch R 2023 · 4.92 FIP	25	SU	4.92	15.0%	12.6%	2.7%	.285	—	—	—	—	—	4.56	119	78
Ryan Conroy R 2022 · 4.75 FIP	25	SU	4.75	19.3%	12.6%	3.1%	.361	—	—	—	—	—	4.01	106	78
Casey Fien R 2009 · 5.17 FIP	25	SU	5.17	17.0%	11.3%	3.8%	.306	2.79	22.7%	19.2%	35	-2.38	2.79	308	77
Reece Karalus R 2018 · 4.73 FIP	24	SU	4.73	15.4%	11.9%	2.6%	.299	—	—	—	—	—	4.73	149	77
Dillon McNamara R 2017 · 5.01 FIP	25	SW	5.01	13.3%	11.1%	3.0%	.229	3.00	21.2%	12.3%	69	-2.01	1.73	244	77
Juan Paniagua R 2015 · 4.69 FIP	25	SU	4.69	15.7%	11.9%	2.6%	.262	4.63	16.2%	1.8%	93	-0.06	4.57	151	77
Stephen Johnson R 2016 · 5.15 FIP	25	SU	5.15	16.7%	12.7%	3.4%	.268	—	—	—	—	—	3.12	141	77
Thad Weber R 2013 · 5.18 FIP	28	SU	5.18	16.4%	13.1%	3.3%	.244	4.52	12.2%	6.0%	308	-0.66	4.40	378	77
Fernando Cruz R 2015 · 5.29 FIP	25	SU	5.29	15.6%	12.2%	3.6%	.284	—	—	—	—	—	2.50	371	77
Jordan Brink R 2016 · 5.96 FIP	23	SW	5.96	18.3%	19.2%	3.6%	.233	—	—	—	—	—	3.99	132	77