Ty Adcock

P El Paso Chihuahuas

Age 30 RHP 6'0" / 213 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MIX3 levels	31	0	33	6.27	5.19	21.9%	10.0%	-0.2	K 20.8% · BB 8.9% · FIP 3.76
2025	28	MIX2 levels	34	1	40	4.54	4.76	24.8%	8.7%	-0.1	K 20.5% · BB 7.3% · FIP 4.77
2026	29	MIX3 levels	34	1	39	6.28	4.99	24.7%	10.7%	—	K 20.2% · BB 10.6% · FIP 4.95
2027PROJ	30	MLBSD	2	0	2	4.87	4.87	19.2%	9.4%	-0.0
2028PROJ37% MLB	31	MLBSD	6	0	6	4.95	5.04	18.9%	9.7%	-0.0

Last 3 seasons WAR -0.3 · 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 99%

CompsNeil Wagner (age 29) · Wirfin Obispo (age 30) · Esteban Haro (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 126118vR 109

ERA4.87

FIP4.87

K%19.2%

BB%9.4%

K-BB%9.9%

Proj IP18

Value2027 · WAR

Runs saved vs league avg · 18 IP

Grades · Contract

Scouting Grades

K†4545

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	25	MIX MLE	2 levels	8	2	8	36	11	4	0	25%	1.0	30.6%	11.1%	19.4%	0.0%	7.88	1.93	—
2022	25	MIX RAW	2 levels	8	2	8	36	11	4	0	25%	1.0	30.6%	11.1%	19.4%	0.0%	7.88	1.85	—
2023	26	MIX MLE	3 levels	31	0	36	133	33	5	5	0%	1.2	19.8%	3.0%	16.7%	5.1%	2.48	4.59	-0.0
2023	26	MIX RAW	3 levels	31	0	36	133	33	5	5	0%	1.2	24.8%	3.8%	21.1%	3.8%	2.48	3.49	+0.5
2024	27	MIX MLE	3 levels	31	0	33	160	35	16	7	0%	1.1	20.8%	8.9%	12.0%	2.4%	6.27	3.76	+0.3
2024	27	MIX MLE	3 levels	31	0	33	160	35	16	7	0%	1.1	20.8%	8.9%	12.0%	2.4%	6.27	3.76	+0.3
2024	27	MIX RAW	3 levels	31	0	33	160	35	16	7	0%	1.1	21.9%	10.0%	11.9%	4.4%	6.27	5.19	-0.2
2025	28	MIX MLE	2 levels	34	1	40	161	40	14	8	3%	1.2	20.5%	7.3%	13.1%	4.5%	4.54	4.77	-0.1
2025	28	MIX MLE	2 levels	34	1	40	161	40	14	8	3%	1.2	20.5%	7.3%	13.1%	4.5%	4.54	4.77	-0.1
2025	28	MIX RAW	2 levels	34	1	40	161	40	14	8	3%	1.2	24.8%	8.7%	16.1%	5.0%	4.54	4.76	-0.1
2026	29	MIX MLE	3 levels	34	1	39	178	44	19	8	3%	1.1	20.2%	10.6%	9.6%	4.0%	6.28	4.95	—
2026	29	MIX MLE	3 levels	34	1	39	178	44	19	8	3%	1.1	20.2%	10.6%	9.6%	4.0%	6.28	4.95	—
2026	29	MIX RAW	3 levels	34	1	39	178	44	19	8	3%	1.1	24.7%	10.7%	14.0%	4.5%	6.28	4.99	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			18	0	23	93	19	4	9	0%	1.3	20.4%	4.3%	16.1%	9.7%	5.48	7.06	-0.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	SD	2	0	2	8	2	1	0	0%	1.0	19.2%	9.4%	9.9%	3.3%	4.87	4.87	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	SD	16	0	17	71	13	7	2	0%	1.0	18.9%	9.7%	9.2%	3.5%	4.95	5.04	-0.1
2028	31	MLB CTX	SD	6	0	6	25	5	2	1	0%	1.0	18.9%	9.7%	9.2%	3.5%	4.95	5.04	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		24	0	29	119	24	7	10	0%	1.2	20.1%	5.5%	14.6%	8.3%	5.37	6.67	-0.7
/ 200 IP	—	RAW		164	0	200	816	165	48	69	0%	1.2	20.1%	5.5%	14.6%	8.3%	5.37	6.67	-4.9
Career	—	MLE		138	4	156	668	140	53	25	3%	1.1	20.9%	7.9%	13.0%	3.7%	5.03	4.39	-0.8
/ 200 IP	—	MLE		177	5	200	858	179	68	32	3%	1.1	20.9%	7.9%	13.0%	3.7%	5.03	4.39	-3.8

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 AAAEl Paso Chihuahuas · 30 IPFIP 4.62 → 4.95 (+0.33)K% 23.5% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.53%	—	×0.860	20.24%
BB% (unintentional) /BF	11.76%	—	×0.900	10.59%
HR / BF /BF	3.68%	÷1.000	×1.100	4.04%

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 4.62 → 4.95, 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 A+Fort Wayne TinCaps · 6 IPFIP 7.19 → 9.26 (+2.08)K% 29.0% → 22.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.03%	—	×0.770	22.35%
BB% (unintentional) /BF	9.68%	—	×1.150	11.13%
HR / BF /BF	9.68%	÷1.000	×1.250	12.10%

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 100).

Result: FIP 7.19 → 9.26, 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).

2025 AAASyracuse Mets · 37 IPFIP 4.74 → 4.77 (+0.03)K% 23.8% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.81%	—	×0.860	20.48%
BB% (unintentional) /BF	8.16%	—	×0.900	7.35%
HR / BF /BF	4.76%	÷1.167	×1.100	4.49%

Result: FIP 4.74 → 4.77, 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 · 27 IPFIP 3.55 → 3.76 (+0.21)K% 24.2% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.24%	—	×0.860	20.85%
BB% (unintentional) /BF	9.85%	—	×0.900	8.86%
HR / BF /BF	2.27%	÷1.059	×1.100	2.36%

Result: FIP 3.55 → 3.76, 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 AAArkansas Travelers · 14 IPFIP 2.77 → 3.69 (+0.92)K% 25.5% → 20.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.49%	—	×0.810	20.65%
BB% (unintentional) /BF	5.88%	—	×1.100	6.47%
HR / BF /BF	1.96%	÷0.853	×1.200	2.76%

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 71).

Result: FIP 2.77 → 3.69, 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+Everett AquaSox · 7 IPFIP 0.99 → 1.88 (+0.89)K% 37.5% → 28.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.50%	—	×0.770	28.88%
BB% (unintentional) /BF	8.33%	—	×1.150	9.58%
HR / BF /BF	0.00%	÷1.012	×1.250	0.00%

Result: FIP 0.99 → 1.88, 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 AModesto Nuts · 6 IPFIP 2.16 → 2.82 (+0.66)K% 22.2% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.22%	—	×0.740	16.44%
BB% (unintentional) /BF	7.41%	—	×1.180	8.74%
HR / BF /BF	0.00%	÷0.855	×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 71).

Result: FIP 2.16 → 2.82, 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
Ty Adcock (OGILVIE)	—	—	4.87	19.2%	9.4%	3.3%	—	5.04	18.9%	9.2%	17	+0.17	—	—	—
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
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	81
Esteban Haro R 2016 · 4.55 FIP	30	SU	4.55	17.1%	10.2%	2.9%	.338	3.83	11.6%	6.3%	75	-0.72	3.76	164	80
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	79
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	79
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	77
Jose Manuel Lopez R 2015 · 5.21 FIP	30	SU	5.21	18.6%	7.6%	4.9%	.373	3.56	18.5%	9.3%	93	-1.65	3.50	170	76
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	76
Mike Broadway R 2017 · 4.73 FIP	30	SU	4.73	18.3%	10.2%	3.4%	.245	4.65	23.0%	12.4%	49	-0.08	1.74	195	75
Adrian Garza R 2017 · 4.47 FIP	29	SU	4.47	16.0%	7.0%	3.4%	.351	5.18	9.7%	4.1%	113	+0.71	3.94	229	75
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	75
Erik Davis R 2017 · 4.25 FIP	30	SU	4.25	22.2%	11.1%	3.0%	.349	3.52	17.4%	8.5%	65	-0.73	3.05	227	74
Ivan Zavala R 2017 · 5.02 FIP	30	SU	5.02	12.8%	8.0%	3.6%	.286	5.15	12.7%	5.3%	61	+0.13	4.27	209	73
Carlos Fisher R 2011 · 4.33 FIP	28	SU	4.33	15.5%	9.1%	2.5%	.275	—	—	—	—	—	2.48	245	73
Bo Schultz R 2016 · 3.90 FIP	30	SU	3.90	14.4%	5.2%	2.5%	.276	2.73	16.4%	8.4%	27	-1.17	2.73	141	73
Francisco Mendoza R 2016 · 3.81 FIP	28	SU	3.81	17.9%	7.7%	2.3%	.344	—	—	—	—	—	3.13	120	73
Jeremy Fikac R 2004 · 4.91 FIP	29	SU	4.91	19.6%	8.0%	4.5%	.276	—	—	—	—	—	3.10	136	72
Marc Magliaro R 2018 · 3.94 FIP	28	SU	3.94	19.6%	7.3%	2.9%	.307	—	—	—	—	—	3.94	125	72
Jhan Mariñez R 2018 · 4.09 FIP	29	SU	4.09	21.2%	7.9%	3.2%	.331	—	—	—	—	—	3.16	253	72
Greg Burke R 2013 · 3.70 FIP	30	SU	3.70	17.9%	8.3%	1.9%	.357	3.61	22.1%	14.8%	62	-0.09	3.61	139	72

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	29	19.6%	7.5%	2.25	5.13
Josh Osich ('21)	1.1	55	20.7%	9.0%	1.84	5.02
Oscar Zamora ('74-'78, -1 WAR*)	1.1	75	15.2%*	5.7%*	2.30*	5.10*
Francisco Oliveras ('89-'92, 0 WAR*)	1.2	59	17.4%*	7.1%*	2.13*	4.28*
Mike Mayers ('23)	1.3	53	23.0%	8.3%	1.61	5.21
A.J. Cole ('21)	1.0	41	23.0%	9.1%	1.71	4.51
Julio Navarro ('62-'70, 1 WAR*)	1.1	53	21.7%*	7.8%*	1.55*	4.13*