Trevin Michael

P Erie SeaWolves

Age 29 RHP 6'2" / 200 lbs AA peak 0.1 · 2027

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	26	MIX2 levels	39	1	58	3.12	3.15	29.7%	7.0%	—	K 22.9% · BB 7.6% · FIP 4.40
2025	27	MIX3 levels	39	0	66	2.18	2.54	29.7%	8.6%	—	K 23.0% · BB 9.1% · FIP 3.77
2026	28	AADET	38	1	49	1.46	2.59	30.2%	13.9%	—	K 24.5% · BB 15.2% · FIP 3.29
2027PROJ	29	MLBDET	1	0	1	4.40	4.40	23.7%	12.1%	0.0
2028PROJ31% MLB	30	MLBDET	8	0	8	4.50	4.57	23.3%	12.1%	-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 0.1, comp-based) · star 0% · didn't stick 99%

CompsJoe Nelson (age 31) · Jim Miller (age 30) · Dustin Molleken (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.40

FIP4.40

K%23.7%

BB%12.1%

K-BB%11.6%

Proj IP30

Value2027 · WAR

Runs saved vs league avg · 30 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 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	24	MIX MLE	3 levels	10	0	12	49	17	4	3	0%	1.2	34.7%	8.2%	26.5%	6.1%	5.84	4.56	—
2022	24	MIX RAW	3 levels	10	0	12	49	17	4	3	0%	1.2	34.7%	8.2%	26.5%	6.1%	5.84	4.48	—
2023	25	MIX MLE	3 levels	42	1	50	212	67	24	1	2%	1.2	23.2%	8.3%	14.9%	0.9%	3.44	2.66	—
2023	25	MIX RAW	3 levels	42	1	50	212	67	24	1	2%	1.2	31.6%	11.3%	20.3%	0.5%	3.44	2.11	—
2024	26	MIX MLE	2 levels	39	1	58	229	68	16	7	3%	1.5	22.9%	7.6%	15.2%	4.1%	3.12	4.40	—
2024	26	MIX MLE	2 levels	39	1	58	229	68	16	7	3%	1.5	22.9%	7.6%	15.2%	4.1%	3.12	4.40	—
2024	26	MIX RAW	2 levels	39	1	58	229	68	16	7	3%	1.5	29.7%	7.0%	22.7%	3.1%	3.12	3.15	—
2025	27	MIX MLE	3 levels	39	0	66	266	79	23	4	0%	1.7	23.0%	9.1%	13.9%	2.7%	2.18	3.77	—
2025	27	MIX MLE	3 levels	39	0	66	266	79	23	4	0%	1.7	23.0%	9.1%	13.9%	2.7%	2.18	3.77	—
2025	27	MIX RAW	3 levels	39	0	66	266	79	23	4	0%	1.7	29.7%	8.6%	21.1%	1.5%	2.18	2.54	—
2026	28	AA MLE	Erie SeaWolves	38	1	49	202	61	28	1	3%	1.3	24.5%	15.2%	9.2%	0.6%	1.46	3.29	—
2026	28	AA MLE	Erie SeaWolves	38	1	49	202	61	28	1	3%	1.3	24.5%	15.2%	9.2%	0.6%	1.46	3.29	—
2026	28	AA RAW	Erie SeaWolves	38	1	49	202	61	28	1	3%	1.3	30.2%	13.9%	16.3%	0.5%	1.46	2.59	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	DET	1	0	1	4	1	1	0	0%	1.0	23.7%	12.1%	11.6%	2.5%	4.40	4.40	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	DET	23	0	24	102	24	12	3	0%	1.1	23.3%	12.1%	11.2%	2.8%	4.50	4.57	-0.0
2028	30	MLB CTX	DET	8	0	8	34	8	4	1	0%	1.0	23.3%	12.1%	11.2%	2.8%	4.50	4.57	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		7	0	8	32	7	4	1	0%	1.1	23.3%	12.1%	11.2%	2.8%	4.50	4.19	+0.0
/ 200 IP	—	RAW		190	0	200	844	185	106	26	0%	1.1	23.3%	12.1%	11.2%	2.8%	4.50	4.19	+0.9
Career	—	MLE		168	3	235	958	229	94	23	2%	1.4	23.9%	9.8%	14.1%	2.4%	2.72	3.62	-0.0
/ 200 IP	—	MLE		143	3	200	815	195	80	19	2%	1.4	23.9%	9.8%	14.1%	2.4%	2.72	3.62	-0.0

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 AAErie SeaWolves · 49 IPFIP 2.58 → 3.29 (+0.71)K% 30.2% → 24.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.20%	—	×0.810	24.46%
BB% (unintentional) /BF	13.86%	—	×1.100	15.25%
HR / BF /BF	0.50%	÷1.000	×1.200	0.59%

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 2.58 → 3.29, 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).

2025 A+West Michigan Whitecaps · 29 IPFIP 3.32 → 5.01 (+1.69)K% 32.8% → 25.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.76%	—	×0.770	25.22%
BB% (unintentional) /BF	8.62%	—	×1.150	9.91%
HR / BF /BF	3.45%	÷0.850	×1.250	5.07%

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 70).

Result: FIP 3.32 → 5.01, 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 AAErie SeaWolves · 27 IPFIP 1.91 → 2.41 (+0.50)K% 25.5% → 20.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.47%	—	×0.810	20.63%
BB% (unintentional) /BF	7.55%	—	×1.100	8.30%
HR / BF /BF	0.00%	÷1.056	×1.200	0.00%

Result: FIP 1.91 → 2.41, 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 AAAToledo Mud Hens · 10 IPFIP 1.85 → 2.08 (+0.23)K% 31.8% → 27.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.82%	—	×0.860	27.36%
BB% (unintentional) /BF	11.36%	—	×0.900	10.23%
HR / BF /BF	0.00%	÷0.872	×1.100	0.00%

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 74).

Result: FIP 1.85 → 2.08, 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 AAErie SeaWolves · 54 IPFIP 3.37 → 4.40 (+1.03)K% 28.2% → 22.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.24%	—	×0.810	22.88%
BB% (unintentional) /BF	6.94%	—	×1.100	7.64%
HR / BF /BF	3.24%	÷0.941	×1.200	4.13%

Result: FIP 3.37 → 4.40, 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 A+West Michigan Whitecaps · 39 IPFIP 1.80 → 2.66 (+0.86)K% 30.1% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.12%	—	×0.770	23.19%
BB% (unintentional) /BF	7.23%	—	×1.150	8.31%
HR / BF /BF	0.60%	÷0.882	×1.250	0.85%

Result: FIP 1.80 → 2.66, 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).

2023 ALakeland Flying Tigers · 9 IPFIP 1.56 → 2.98 (+1.41)K% 45.5% → 33.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	45.45%	—	×0.740	33.64%
BB% (unintentional) /BF	18.18%	—	×1.180	21.45%
HR / BF /BF	0.00%	÷1.216	×1.300	0.00%

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 143).

Result: FIP 1.56 → 2.98, 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+West Michigan Whitecaps · 9 IPFIP 6.24 → 8.99 (+2.75)K% 28.2% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.21%	—	×0.770	21.72%
BB% (unintentional) /BF	10.26%	—	×1.150	11.79%
HR / BF /BF	7.69%	÷0.847	×1.250	11.35%

Result: FIP 6.24 → 8.99, 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
Trevin Michael (OGILVIE)	—	—	4.40	23.7%	12.1%	2.5%	—	4.57	23.3%	11.2%	24	+0.17	—	—	—
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	88
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	86
Dustin Molleken R 2015 · 3.74 FIP	30	SU	3.74	20.4%	11.7%	1.6%	.287	3.90	20.6%	8.4%	77	+0.16	3.38	129	86
Coleman Huntley III R 2022 · 4.05 FIP	29	SU	4.05	24.4%	10.9%	3.0%	.311	2.38	20.9%	12.6%	25	-1.67	2.38	160	84
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	83
Jason Bulger R 2008 · 4.60 FIP	29	SU	4.60	27.4%	12.3%	4.1%	.293	3.94	25.1%	13.3%	90	-0.66	3.78	106	83
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	81
Cody Hall R 2017 · 4.03 FIP	29	SU	4.03	26.8%	10.0%	3.5%	.299	3.92	25.9%	8.4%	19	-0.11	3.51	134	80
Gerardo Sanchez R 2017 · 3.47 FIP	28	SU	3.47	21.6%	8.4%	2.1%	.355	3.58	20.5%	15.1%	56	+0.11	2.65	137	80
Tyler Sturdevant R 2015 · 4.37 FIP	29	SU	4.37	21.3%	8.5%	3.6%	.241	3.97	18.5%	11.9%	118	-0.40	3.44	149	80
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	80
Joe Colón J R 2017 · 4.25 FIP	27	SU	4.25	20.4%	12.1%	2.4%	.315	—	—	—	—	—	3.27	113	80
Diego Moreno R 2016 · 3.75 FIP	29	SU	3.75	21.0%	8.2%	2.5%	.347	3.13	17.8%	11.9%	54	-0.62	2.29	187	79
Michael Dimock R 2018 · 3.37 FIP	28	SU	3.37	23.5%	10.7%	1.6%	.346	—	—	—	—	—	3.08	166	79
Brandon Cunniff R 2017 · 4.79 FIP	28	SU	4.79	20.1%	11.6%	3.5%	.275	6.80	13.9%	-1.9%	30	+2.01	3.89	180	79
Jose Veras R 2010 · 4.10 FIP	29	SU	4.10	26.9%	14.4%	2.6%	.239	3.42	25.3%	14.8%	201	-0.68	3.37	422	79
Dalier Hinojosa R 2015 · 3.41 FIP	29	SU	3.41	20.4%	9.8%	1.4%	.283	4.30	18.7%	6.1%	24	+0.89	3.41	104	79
Grant Sides R 2019 · 3.31 FIP	30	SU	3.31	21.7%	9.8%	1.5%	.267	—	—	—	—	—	3.31	159	78
Manny Acosta R 2010 · 3.61 FIP	29	SU	3.61	26.8%	10.8%	2.5%	.277	4.12	21.9%	12.6%	94	+0.51	3.08	350	78
Cole McDonald R 2024 · 5.01 FIP	27	SU	5.01	20.9%	12.7%	3.8%	.281	—	—	—	—	—	2.81	153	78

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 1,234 reliever careers (754 from 1990-2025 + 480 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	0.3	27	23.5%	12.1%	1.02	4.44
José Cisnero ('24)	1.4	46	24.0%	11.3%	1.01	4.16
Yorkis Perez ('02)	1.4	40	20.8%	11.8%	0.93	4.44
Fred Lasher ('63-'71, -1 WAR*)	1.0	50	21.5%*	11.9%*	1.05*	4.68*
Fernando Rodriguez Jr. ('16)	1.2	58	24.6%	11.1%	0.93	4.35
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*