Trevor Clifton

P New Hampshire Fisher Cats

Age 32 RHP 6'4" / 170 lbs AA peak 0.2 · 2028

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	28	AATOR	8	0	14	4.50	4.31	24.6%	11.5%	—	K 19.9% · BB 12.6% · FIP 5.18
2024	29	AATOR	15	0	21	5.91	4.04	25.0%	10.6%	—	K 20.2% · BB 11.6% · FIP 4.52
2027PROJ	32	MLBTOR	—	—	0	4.46	4.46	22.7%	9.8%	—
2028PROJ31% MLB	33	MLBTOR	1	1	6	4.60	4.70	22.2%	10.2%	0.0

Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsZack Segovia (age 32) · Elian Leyva (age 30) · Reinier Roibal (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 118110vR 102

ERA4.46

FIP4.46

K%22.7%

BB%9.8%

K-BB%12.8%

Proj IP12

Value2027 · WAR

Runs saved vs league avg · 12 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)
2015	20	A MLE	South Bend Cubs	23	22	109	461	103	47	7	96%	4.7	16.5%	12.0%	4.5%	1.9%	3.98	4.25	—
2015	20	A RAW	South Bend Cubs	23	22	109	461	103	47	7	96%	4.7	22.3%	10.2%	12.1%	1.5%	3.98	3.34	—
2016	21	A+ MLE	Myrtle Beach Pelicans	23	23	119	492	129	41	4	100%	5.2	20.2%	9.6%	10.6%	0.8%	2.72	3.03	—
2016	21	A+ RAW	Myrtle Beach Pelicans	23	23	119	492	129	41	4	100%	5.2	26.2%	8.3%	17.9%	0.8%	2.72	2.40	—
2017	22	AA MLE	Tennessee Smokies	21	21	100	451	86	45	8	100%	4.8	15.4%	11.0%	4.5%	2.4%	5.20	4.51	—
2017	22	AA RAW	Tennessee Smokies	21	21	100	451	86	45	8	100%	4.8	19.1%	10.0%	9.1%	1.8%	5.20	3.77	—
2018	23	MIX MLE	2 levels	26	24	126	534	101	51	8	92%	4.8	16.1%	10.2%	5.8%	1.8%	3.43	4.03	—
2018	23	MIX RAW	2 levels	26	24	126	534	101	51	8	92%	4.8	18.9%	9.6%	9.4%	1.5%	3.43	3.54	—
2019	24	AAA MLE	Iowa Cubs	24	20	99	454	84	45	20	83%	4.1	16.3%	10.4%	5.9%	4.6%	5.18	5.58	—
2019	24	AAA RAW	Iowa Cubs	24	20	99	454	84	45	20	83%	4.1	18.5%	9.9%	8.6%	4.4%	5.18	5.39	—
2023	28	AA MLE	New Hampshire Fisher Cats	8	0	14	61	15	7	2	0%	1.8	19.9%	12.6%	7.3%	3.9%	4.50	5.18	—
2023	28	AA RAW	New Hampshire Fisher Cats	8	0	14	61	15	7	2	0%	1.8	24.6%	11.5%	13.1%	3.3%	4.50	4.31	—
2024	29	AA MLE	New Hampshire Fisher Cats	15	0	21	104	26	11	3	0%	1.4	20.2%	11.6%	8.6%	3.0%	5.91	4.52	—
2024	29	AA MLE	New Hampshire Fisher Cats	15	0	21	104	26	11	3	0%	1.4	20.2%	11.6%	8.6%	3.0%	5.91	4.52	—
2024	29	AA RAW	New Hampshire Fisher Cats	15	0	21	104	26	11	3	0%	1.4	25.0%	10.6%	14.4%	2.9%	5.91	4.04	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	TOR	—	—	0	—	—	—	—	—	—	22.7%	9.8%	12.8%	3.0%	4.46	4.46	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	TOR	3	3	20	84	19	9	3	100%	6.7	22.2%	10.2%	12.0%	3.3%	4.60	4.70	+0.2
2028	33	MLB CTX	TOR	1	1	6	25	6	3	1	100%	6.0	22.2%	10.2%	12.0%	3.3%	4.60	4.70	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		1	1	6	26	6	3	1	100%	6.3	22.2%	10.2%	12.0%	3.3%	4.60	4.32	+0.1
/ 200 IP	—	RAW		33	33	200	844	192	96	32	100%	6.0	22.2%	10.2%	12.0%	3.3%	4.60	4.32	+2.5
Career	—	MLE		140	110	588	2557	438	274	59	79%	4.2	17.1%	10.7%	6.4%	2.3%	4.10	4.28	+0.2
/ 200 IP	—	MLE		48	37	200	869	149	93	20	79%	4.2	17.1%	10.7%	6.4%	2.3%	4.10	4.28	+1.6

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.

2024 AANew Hampshire Fisher Cats · 21 IPFIP 3.91 → 4.52 (+0.61)K% 25.0% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.810	20.25%
BB% (unintentional) /BF	10.58%	—	×1.100	11.63%
HR / BF /BF	2.88%	÷1.145	×1.200	3.02%

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 129).

Result: FIP 3.91 → 4.52, 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 AANew Hampshire Fisher Cats · 14 IPFIP 4.28 → 5.18 (+0.90)K% 24.6% → 19.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.59%	—	×0.810	19.92%
BB% (unintentional) /BF	11.48%	—	×1.100	12.62%
HR / BF /BF	3.28%	÷1.000	×1.200	3.93%

Result: FIP 4.28 → 5.18, 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).

2019 AAAIowa Cubs · 99 IPFIP 5.21 → 5.58 (+0.36)K% 18.5% → 16.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.50%	—	×0.880	16.28%
BB% (unintentional) /BF	9.91%	—	×1.050	10.41%
HR / BF /BF	4.41%	÷1.050	×1.100	4.61%

Factors used: pitcher AAA level factors — K ×0.88, BB ×1.05, 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 110).

Result: FIP 5.21 → 5.58, 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).

2018 AAAIowa Cubs · 69 IPFIP 4.18 → 4.72 (+0.54)K% 18.9% → 16.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.86%	—	×0.880	16.59%
BB% (unintentional) /BF	9.43%	—	×1.050	9.90%
HR / BF /BF	2.69%	÷0.919	×1.100	3.22%

Result: FIP 4.18 → 4.72, 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).

2018 AATennessee Smokies · 57 IPFIP 2.73 → 3.15 (+0.43)K% 19.0% → 15.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.99%	—	×0.810	15.38%
BB% (unintentional) /BF	9.70%	—	×1.100	10.68%
HR / BF /BF	0.00%	÷0.947	×1.200	0.00%

Result: FIP 2.73 → 3.15, 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).

2017 AATennessee Smokies · 100 IPFIP 3.73 → 4.51 (+0.78)K% 19.1% → 15.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.07%	—	×0.810	15.45%
BB% (unintentional) /BF	9.98%	—	×1.100	10.98%
HR / BF /BF	1.77%	÷0.882	×1.200	2.41%

Result: FIP 3.73 → 4.51, 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).

2016 A+Myrtle Beach Pelicans · 119 IPFIP 2.39 → 3.03 (+0.65)K% 26.2% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.22%	—	×0.770	20.19%
BB% (unintentional) /BF	8.33%	—	×1.150	9.58%
HR / BF /BF	0.81%	÷1.311	×1.250	0.78%

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 162).

Result: FIP 2.39 → 3.03, 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).

2015 ASouth Bend Cubs · 109 IPFIP 3.34 → 4.25 (+0.91)K% 22.3% → 16.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.34%	—	×0.740	16.53%
BB% (unintentional) /BF	10.20%	—	×1.180	12.03%
HR / BF /BF	1.52%	÷1.064	×1.300	1.86%

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 113).

Result: FIP 3.34 → 4.25, 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
Trevor Clifton (OGILVIE)	—	—	4.46	22.7%	9.8%	3.0%	—	4.70	22.2%	12.0%	20	+0.24	—	—	—
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	73
Elian Leyva R 2019 · 4.30 FIP	30	SP-	4.30	17.0%	8.5%	2.9%	.323	—	—	—	—	—	3.10	158	69
Reinier Roibal R 2019 · 4.91 FIP	30	SP	4.91	17.5%	11.8%	3.3%	.426	—	—	—	—	—	3.11	133	68
Tyler Herron R 2017 · 4.02 FIP	30	SP	4.02	16.4%	8.7%	2.2%	.377	—	—	—	—	—	4.02	117	65
Bryan Evans R 2019 · 3.89 FIP	32	SP	3.89	18.7%	10.4%	1.9%	.450	—	—	—	—	—	3.89	231	64
Brandon Beachy R 2019 · 4.19 FIP	32	SP	4.19	17.4%	9.5%	2.5%	.315	—	—	—	—	—	1.97	364	64
Jesus Adrian Castillo R 2016 · 3.88 FIP	32	SP	3.88	12.8%	6.0%	2.0%	.245	4.27	13.6%	7.2%	58	+0.39	2.23	173	62
Mitch Lively R 2017 · 3.47 FIP	31	SP-	3.47	17.0%	6.4%	1.8%	.304	—	—	—	—	—	2.39	158	61
Stan Spencer R 2000 · 4.42 FIP	30	SP	4.42	19.2%	8.7%	3.4%	.259	—	—	—	—	—	3.61	119	60
Danny Gutierrez R 2017 · 4.11 FIP	30	SP	4.11	15.1%	6.1%	2.8%	.362	—	—	—	—	—	3.34	239	59
Mike Burns R 2009 · 4.90 FIP	31	SP-	4.90	17.2%	6.6%	4.4%	.307	—	—	—	—	—	3.10	104	59
Logan Duran R 2015 · 3.42 FIP	33	SP	3.42	15.8%	10.0%	0.7%	.287	3.77	15.3%	6.4%	95	+0.35	3.42	187	59
Dennis O'Grady R 2019 · 3.62 FIP	30	SP	3.62	17.2%	8.1%	1.7%	.344	—	—	—	—	—	3.62	157	58
Diego Moreno R 2019 · 4.08 FIP	32	SP	4.08	12.4%	6.9%	2.1%	.376	4.75	18.6%	9.2%	30	+0.67	2.29	187	58
Elih Villanueva R 2017 · 5.48 FIP	31	SP	5.48	14.1%	6.0%	5.1%	.365	—	—	—	—	—	3.83	225	57
Marco Quevedo R 2019 · 5.17 FIP	32	SP-	5.17	13.6%	11.4%	3.2%	.344	—	—	—	—	—	4.13	300	57
Miguel Ruiz R 2017 · 5.60 FIP	31	SP	5.60	10.6%	11.0%	3.6%	.279	—	—	—	—	—	5.60	117	56
Juan Delgadillo R 2015 · 4.54 FIP	32	SP	4.54	15.5%	7.2%	3.3%	.316	4.47	10.9%	4.7%	39	-0.07	4.47	143	56
Tyler Cloyd R 2019 · 5.21 FIP	32	SP	5.21	18.9%	9.5%	4.6%	.388	—	—	—	—	—	3.89	343	55
Matt Shoemaker R 2019 · 3.80 FIP	32	SP	3.80	22.2%	8.3%	2.8%	.181	5.06	18.8%	11.1%	159	+1.26	3.12	728	55