Dom Hamel

P Scranton/Wilkes-Barre RailRiders

Age 28 RHP 6'2" / 206 lbs AAA peak 0.1 · 2027

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	25	AAANYM	27	27	125	6.79	5.36	21.3%	13.2%	—	K 18.3% · BB 11.9% · FIP 5.31
2025	26	MIX2 levels	32	11	69	5.24	4.15	24.7%	7.2%	0.3	K 21.6% · BB 6.6% · FIP 4.20
2026	27	MIX2 levels	26	24	112	7.07	5.28	18.5%	11.3%	—	K 15.6% · BB 10.5% · FIP 5.42
2027PROJ	28	MLBNYY	2	2	14	5.22	5.22	17.2%	9.3%	0.0
2028PROJ37% MLB	29	MLBNYY	7	7	41	5.20	5.26	17.4%	9.3%	0.1
2029PROJ55% MLB	30	MLBNYY	9	9	52	5.26	5.36	17.4%	9.5%	0.0

Last 3 seasons WAR 0.3 · Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff50Control55Command55

Career WAR0.0 WAR median (P10 -0.5 / P90 0.0, comp-based) · star 0% · didn't stick 99%

CompsTravis Driskill (age 30) · Luke Irvine (age 27) · Nate Long (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 132124vR 116

ERA5.22

FIP5.22

K%17.2%

BB%9.3%

K-BB%7.9%

Proj IP145

Value2027 · WAR

Runs saved vs league avg · 145 IP

Grades · Contract

Scouting Grades

STUF5050

CTRL5555

CMD5555

K†4040

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)
2021	22	ROK MLE	FCL Mets	2	2	3	9	7	0	0	100%	1.5	77.8%	0.0%	77.8%	0.0%	0.00	-3.47	—
2021	22	ROK RAW	FCL Mets	2	2	3	9	7	0	0	100%	1.5	77.8%	0.0%	77.8%	0.0%	0.00	-1.57	—
2022	23	MIX MLE	2 levels	25	24	119	490	145	54	5	96%	4.8	22.4%	12.9%	9.5%	1.4%	3.25	3.61	—
2022	23	MIX RAW	2 levels	25	24	119	490	145	54	5	96%	4.8	29.6%	11.0%	18.6%	1.0%	3.25	2.57	—
2023	24	AA MLE	Binghamton Rumble Ponies	26	25	124	527	160	49	12	96%	4.8	24.6%	10.2%	14.4%	3.0%	3.85	3.94	—
2023	24	AA RAW	Binghamton Rumble Ponies	26	25	124	527	160	49	12	96%	4.8	30.4%	9.3%	21.1%	2.3%	3.85	2.96	—
2024	25	AAA MLE	Syracuse Mets	27	27	125	582	124	77	23	100%	4.6	18.3%	11.9%	6.4%	4.1%	6.79	5.31	—
2024	25	AAA MLE	Syracuse Mets	27	27	125	582	124	77	23	100%	4.6	18.3%	11.9%	6.4%	4.1%	6.79	5.31	—
2024	25	AAA RAW	Syracuse Mets	27	27	125	582	124	77	23	100%	4.6	21.3%	13.2%	8.1%	4.0%	6.79	5.36	—
2025	26	MIX MLE	2 levels	32	11	69	304	75	22	12	34%	2.1	21.6%	6.6%	15.0%	3.8%	5.24	4.20	+0.3
2025	26	MIX MLE	2 levels	32	11	69	304	75	22	12	34%	2.1	21.6%	6.6%	15.0%	3.8%	5.24	4.20	+0.3
2025	26	MIX RAW	2 levels	32	11	69	304	75	22	12	34%	2.1	24.7%	7.2%	17.4%	3.9%	5.24	4.15	+0.3
2026	27	MIX MLE	2 levels	26	24	112	531	98	60	20	92%	4.3	15.6%	10.5%	5.1%	4.2%	7.07	5.42	—
2026	27	MIX MLE	2 levels	26	24	112	531	98	60	20	92%	4.3	15.6%	10.5%	5.1%	4.2%	7.07	5.42	—
2026	27	MIX RAW	2 levels	26	24	112	531	98	60	20	92%	4.3	18.5%	11.3%	7.2%	3.8%	7.07	5.28	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			1	0	1	6	0	0	0	0%	1.0	0.0%	0.0%	0.0%	0.0%	0.00	3.10	+0.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	NYY	2	2	14	59	10	5	2	100%	7.0	17.2%	9.3%	7.9%	3.7%	5.22	5.22	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	NYY	19	19	112	474	82	44	18	100%	5.9	17.4%	9.3%	8.1%	3.8%	5.20	5.26	+0.2
2029	30	MLB MLE	NYY	16	16	95	400	70	38	16	100%	5.9	17.4%	9.5%	7.9%	3.9%	5.26	5.36	+0.0
2028	29	MLB CTX	NYY	7	7	41	173	30	16	7	100%	5.9	17.4%	9.3%	8.1%	3.8%	5.20	5.26	+0.1
2029	30	MLB CTX	NYY	9	9	52	220	38	21	9	100%	5.8	17.4%	9.5%	7.9%	3.9%	5.26	5.36	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		17	16	95	401	69	37	15	94%	5.6	17.1%	9.3%	7.9%	3.8%	5.18	4.91	+0.5
/ 200 IP	—	RAW		35	33	200	848	146	78	32	94%	5.7	17.1%	9.3%	7.9%	3.8%	5.18	4.91	+1.1
Career	—	MLE		138	113	551	2443	502	262	80	82%	4.0	20.5%	10.7%	9.8%	3.3%	5.19	4.53	+0.2
/ 200 IP	—	MLE		50	41	200	886	182	95	29	82%	4.0	20.5%	10.7%	9.8%	3.3%	5.19	4.53	+0.2

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 AAAScranton/Wilkes-Barre RailRiders · 91 IPFIP 5.28 → 5.56 (+0.27)K% 17.2% → 14.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.17%	—	×0.860	14.77%
BB% (unintentional) /BF	11.60%	—	×0.900	10.44%
HR / BF /BF	3.94%	÷1.000	×1.100	4.34%

Factors used: pitcher AAA level factors (Triple-A 2023+ robo-zone set: walks improve on promotion) — K ×0.86, BB ×0.90, HR ×1.10. 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 5.28 → 5.56, 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).

2026 AASomerset Patriots · 21 IPFIP 3.99 → 4.83 (+0.84)K% 24.0% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.00%	—	×0.810	19.44%
BB% (unintentional) /BF	10.00%	—	×1.100	11.00%
HR / BF /BF	3.00%	÷1.000	×1.200	3.60%

Factors used: pitcher AA level factors — K ×0.81, BB ×1.10, HR ×1.20. 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 3.99 → 4.83, 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 AAASyracuse Mets · 68 IPFIP 4.12 → 4.20 (+0.08)K% 25.2% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.17%	—	×0.860	21.64%
BB% (unintentional) /BF	7.38%	—	×0.900	6.64%
HR / BF /BF	4.03%	÷1.167	×1.100	3.80%

Result: FIP 4.12 → 4.20, 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 AAASyracuse Mets · 125 IPFIP 5.15 → 5.31 (+0.17)K% 21.3% → 18.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.31%	—	×0.860	18.32%
BB% (unintentional) /BF	13.23%	—	×0.900	11.91%
HR / BF /BF	3.95%	÷1.059	×1.100	4.10%

Result: FIP 5.15 → 5.31, 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 AABinghamton Rumble Ponies · 124 IPFIP 2.96 → 3.94 (+0.98)K% 30.4% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.36%	—	×0.810	24.59%
BB% (unintentional) /BF	9.30%	—	×1.100	10.23%
HR / BF /BF	2.28%	÷0.925	×1.200	2.95%

Result: FIP 2.96 → 3.94, 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 ASt. Lucie Mets · 63 IPFIP 3.26 → 4.47 (+1.21)K% 26.6% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.59%	—	×0.740	19.68%
BB% (unintentional) /BF	10.86%	—	×1.180	12.82%
HR / BF /BF	1.87%	÷0.949	×1.300	2.56%

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 90).

Result: FIP 3.26 → 4.47, 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 A+Brooklyn Cyclones · 56 IPFIP 1.72 → 2.58 (+0.86)K% 33.2% → 25.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.18%	—	×0.770	25.55%
BB% (unintentional) /BF	11.21%	—	×1.150	12.89%
HR / BF /BF	0.00%	÷0.925	×1.250	0.00%

Factors used: pitcher A+ level factors — K ×0.77, BB ×1.15, HR ×1.25. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 85).

Result: FIP 1.72 → 2.58, 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
Dom Hamel (OGILVIE)	—	—	5.22	17.2%	9.3%	3.7%	—	5.31	17.4%	8.0%	207	+0.09	—	—	—
Travis Driskill R 2002 · 4.95 FIP	30	SP-	4.95	13.2%	8.0%	3.6%	.291	4.26	15.3%	12.0%	48	-0.69	4.26	181	76
Luke Irvine R 2016 · 4.31 FIP	27	SP-	4.31	13.4%	9.8%	2.0%	.302	—	—	—	—	—	4.31	104	73
Nate Long R 2015 · 4.67 FIP	29	SP	4.67	14.8%	10.6%	2.7%	.285	—	—	—	—	—	4.67	151	73
Frank Batista R 2015 · 4.29 FIP	26	SP-	4.29	12.1%	8.2%	2.1%	.280	—	—	—	—	—	4.29	118	73
Edgar Alberto Garcia R 2016 · 4.37 FIP	28	SP-	4.37	14.6%	9.8%	2.3%	.311	4.01	17.3%	6.4%	44	-0.36	4.01	272	72
Brannon Easterling R 2017 · 4.40 FIP	26	SP-	4.40	12.7%	8.2%	2.4%	.322	—	—	—	—	—	3.50	334	72
Marco Antonio Duarte R 2015 · 4.07 FIP	28	SP	4.07	15.5%	9.1%	2.0%	.306	4.73	9.0%	-1.2%	38	+0.66	4.07	203	72
Omar Bencomo R 2017 · 4.50 FIP	28	SP-	4.50	18.9%	6.3%	4.0%	.333	4.48	14.9%	10.1%	186	-0.02	3.70	381	71
Juan Carlos Sulbaran R 2016 · 4.65 FIP	26	SP	4.65	15.0%	8.9%	3.1%	.289	—	—	—	—	—	4.65	278	71
Hiram Burgos R 2016 · 4.08 FIP	28	SP	4.08	16.3%	9.1%	2.2%	.330	4.94	19.3%	11.8%	62	+0.86	3.40	384	70
Andres Ivan Meza R 2015 · 4.45 FIP	28	SP	4.45	10.9%	5.6%	2.9%	.322	4.14	12.5%	4.0%	85	-0.31	4.14	275	70
Jordan Pries R 2016 · 4.43 FIP	26	SP-	4.43	17.4%	8.2%	3.2%	.333	—	—	—	—	—	4.29	219	69
Octavio Acosta R 2019 · 4.19 FIP	28	SP	4.19	14.9%	8.3%	2.4%	.364	—	—	—	—	—	3.88	333	68
Scott McGregor R 2015 · 4.37 FIP	28	SP-	4.37	11.8%	7.6%	2.4%	.293	—	—	—	—	—	4.37	107	68
Anthony Ranaudo R 2016 · 5.10 FIP	26	SP	5.10	15.5%	6.1%	4.6%	.255	—	—	—	—	—	4.42	314	68
Paul Clemens R 2016 · 4.58 FIP	28	SP	4.58	17.6%	9.1%	3.3%	.271	4.72	14.9%	3.9%	53	+0.14	4.58	345	68
Scott Carroll R 2014 · 4.35 FIP	29	SP-	4.35	11.2%	7.7%	2.2%	.296	4.52	11.9%	3.7%	248	+0.17	3.92	377	68
Caleb Boushley R 2022 · 4.37 FIP	28	SP	4.37	15.4%	8.7%	2.7%	.251	4.40	16.7%	10.3%	347	+0.03	3.05	790	68
Matthew Shepherd R 2015 · 4.98 FIP	25	SP-	4.98	13.7%	9.5%	3.3%	.390	—	—	—	—	—	4.98	116	68
Mike Augliera R 2015 · 4.27 FIP	25	SP-	4.27	12.0%	7.2%	2.3%	.350	—	—	—	—	—	4.27	114	67

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 historical, 1901-2023), also matched on career-to-date at the same age. * Historical (Lahman) careers: rates era-adjusted to 2001-24 league levels; WAR is an OGILVIE proxy.

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	1.8	118	17.2%	9.3%	1.45	5.21
Brandon Claussen ('06)	1.6	105	16.0%	8.5%	1.37	5.04
Kevin Foster ('01)	2.5	127	18.9%	10.0%	1.55	4.86
Armando Galarraga ('12)	2.7	108	14.6%	9.6%	1.56	4.78
Jeff Robinson ('87-'92, 0 WAR*)	3.5	118	18.3%*	10.9%*	1.38*	5.14*
Dave Stenhouse ('62-'64, 2 WAR*)	1.9	124	17.9%*	10.5%*	1.33*	4.51*
Jack Armstrong ('88-'94, 2 WAR*)	3.9	131	19.2%*	9.2%*	1.59*	4.89*