Alaska Abney

P Akron RubberDucks

Age 27 RHP 6'1" / 205 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	MIX2 levels	40	0	55	1.95	2.83	23.2%	9.4%	—	K 18.5% · BB 10.4% · FIP 3.38
2025	25	MIX2 levels	18	0	25	1.44	2.46	25.5%	2.1%	—	K 18.9% · BB 2.9% · FIP 3.71
2026	26	MIX2 levels	28	0	34	5.24	5.05	16.0%	11.1%	—	K 14.2% · BB 12.6% · FIP 5.00
2027PROJ	27	MLBCLE	—	—	0	4.89	4.89	17.4%	10.2%	—
2028PROJ38% MLB	28	MLBCLE	6	0	6	4.88	4.92	17.9%	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.

Career WAR0.0 WAR median (P10 0.0 / P90 0.0, comp-based) · star 0% · didn't stick 97%

CompsFrancisco Rodriguez (age 27) · Scott Proctor (age 27) · Justin Lehr (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 126118vR 110

ERA4.89

FIP4.89

K%17.4%

BB%10.2%

K-BB%7.2%

Proj IP17

Value2027 · WAR

Runs saved vs league avg · 17 IP

Grades · Contract

Scouting Grades

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	21	MIX MLE	2 levels	12	0	16	63	22	7	0	0%	1.3	34.9%	11.1%	23.8%	0.0%	0.56	1.56	—
2021	21	MIX RAW	2 levels	12	0	16	63	22	7	0	0%	1.3	34.9%	11.1%	23.8%	0.0%	0.56	1.66	—
2022	22	A+ MLE	Lake County Captains	42	0	50	196	58	26	5	0%	1.2	22.8%	15.3%	7.5%	2.8%	2.54	4.65	—
2022	22	A+ RAW	Lake County Captains	42	0	50	196	58	26	5	0%	1.2	29.6%	13.3%	16.3%	2.6%	2.54	3.64	—
2023	23	A+ MLE	Lake County Captains	36	0	53	234	57	28	6	0%	1.5	18.8%	13.8%	5.0%	3.0%	5.77	4.92	—
2023	23	A+ RAW	Lake County Captains	36	0	53	234	57	28	6	0%	1.5	24.4%	12.0%	12.4%	2.6%	5.77	4.01	—
2024	24	MIX MLE	2 levels	40	0	55	224	52	21	2	0%	1.4	18.5%	10.4%	8.0%	0.9%	1.95	3.38	—
2024	24	MIX MLE	2 levels	40	0	55	224	52	21	2	0%	1.4	18.5%	10.4%	8.0%	0.9%	1.95	3.38	—
2024	24	MIX RAW	2 levels	40	0	55	224	52	21	2	0%	1.4	23.2%	9.4%	13.8%	0.9%	1.95	2.83	—
2025	25	MIX MLE	2 levels	18	0	25	94	24	2	2	0%	1.4	18.9%	2.9%	16.1%	3.4%	1.44	3.71	—
2025	25	MIX MLE	2 levels	18	0	25	94	24	2	2	0%	1.4	18.9%	2.9%	16.1%	3.4%	1.44	3.71	—
2025	25	MIX RAW	2 levels	18	0	25	94	24	2	2	0%	1.4	25.5%	2.1%	23.4%	2.1%	1.44	2.46	—
2026	26	MIX MLE	2 levels	28	0	34	162	26	18	5	0%	1.2	14.2%	12.6%	1.6%	2.7%	5.24	5.00	—
2026	26	MIX MLE	2 levels	28	0	34	162	26	18	5	0%	1.2	14.2%	12.6%	1.6%	2.7%	5.24	5.00	—
2026	26	MIX RAW	2 levels	28	0	34	162	26	18	5	0%	1.2	16.0%	11.1%	4.9%	3.1%	5.24	5.05	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	CLE	—	—	0	—	—	—	—	—	—	17.4%	10.2%	7.2%	2.9%	4.89	4.89	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	CLE	15	0	16	67	12	7	2	0%	1.1	17.9%	10.2%	7.7%	3.0%	4.88	4.92	-0.1
2028	28	MLB CTX	CLE	6	0	6	25	5	3	1	0%	1.0	17.9%	10.2%	7.7%	3.0%	4.88	4.92	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		6	0	6	25	5	3	1	0%	1.0	17.9%	10.2%	7.7%	3.0%	4.88	4.54	+0.0
/ 200 IP	—	RAW		190	0	200	844	167	100	33	0%	1.1	17.9%	10.2%	7.7%	3.0%	4.88	4.54	+0.0
Career	—	MLE		176	0	233	973	193	116	22	0%	1.3	19.8%	11.9%	7.9%	2.3%	3.28	4.19	-0.1
/ 200 IP	—	MLE		151	0	200	834	165	99	19	0%	1.3	19.8%	11.9%	7.9%	2.3%	3.28	4.19	-0.9

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 AAAkron RubberDucks · 29 IPFIP 4.32 → 5.00 (+0.68)K% 17.6% → 14.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.56%	—	×0.810	14.22%
BB% (unintentional) /BF	11.45%	—	×1.100	12.60%
HR / BF /BF	2.29%	÷1.000	×1.200	2.75%

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 4.32 → 5.00, 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 AAAColumbus Clippers · 6 IPFIP 7.05 → 7.40 (+0.35)K% 9.7% → 8.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	9.68%	—	×0.860	8.32%
BB% (unintentional) /BF	9.68%	—	×0.900	8.71%
HR / BF /BF	6.45%	÷1.000	×1.100	7.10%

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 7.05 → 7.40, 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).

2025 AAAkron RubberDucks · 20 IPFIP 2.88 → 3.71 (+0.83)K% 23.4% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.38%	—	×0.810	18.94%
BB% (unintentional) /BF	2.60%	—	×1.100	2.86%
HR / BF /BF	2.60%	÷0.926	×1.200	3.37%

Result: FIP 2.88 → 3.71, 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 AAAkron RubberDucks · 39 IPFIP 2.48 → 2.95 (+0.47)K% 21.8% → 17.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.79%	—	×0.810	17.65%
BB% (unintentional) /BF	9.62%	—	×1.100	10.58%
HR / BF /BF	0.00%	÷0.966	×1.200	0.00%

Result: FIP 2.48 → 2.95, 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).

2024 A+Lake County Captains · 17 IPFIP 3.60 → 4.36 (+0.76)K% 26.5% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.47%	—	×0.770	20.38%
BB% (unintentional) /BF	8.82%	—	×1.150	10.15%
HR / BF /BF	2.94%	÷1.190	×1.250	3.09%

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 138).

Result: FIP 3.60 → 4.36, 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+Lake County Captains · 53 IPFIP 3.97 → 4.92 (+0.95)K% 24.4% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.36%	—	×0.770	18.76%
BB% (unintentional) /BF	11.97%	—	×1.150	13.76%
HR / BF /BF	2.56%	÷1.060	×1.250	3.02%

Result: FIP 3.97 → 4.92, 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+Lake County Captains · 50 IPFIP 3.68 → 4.65 (+0.97)K% 29.6% → 22.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.59%	—	×0.770	22.79%
BB% (unintentional) /BF	13.27%	—	×1.150	15.26%
HR / BF /BF	2.55%	÷1.134	×1.250	2.81%

Result: FIP 3.68 → 4.65, 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).

2021 ALynchburg Hillcats · 11 IPFIP 1.14 → 2.17 (+1.03)K% 37.2% → 27.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.21%	—	×0.740	27.53%
BB% (unintentional) /BF	9.30%	—	×1.180	10.98%
HR / BF /BF	0.00%	÷0.950	×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 90).

Result: FIP 1.14 → 2.17, 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
Alaska Abney (OGILVIE)	—	—	4.89	17.4%	10.2%	2.9%	—	4.92	17.9%	7.7%	16	+0.03	—	—	—
Francisco Rodriguez R 2010 · 4.33 FIP	27	SU	4.33	17.2%	11.0%	2.4%	.283	5.00	11.9%	3.4%	14	+0.67	3.16	334	89
Scott Proctor R 2004 · 5.43 FIP	27	SU	5.43	17.8%	11.9%	4.2%	.308	4.36	18.8%	11.0%	233	-1.07	3.68	337	87
Justin Lehr R 2005 · 4.58 FIP	27	SU	4.58	14.9%	10.4%	2.6%	.252	4.23	16.0%	8.0%	16	-0.35	4.23	148	87
Jeff Fulchino R 2008 · 4.62 FIP	28	SU	4.62	16.7%	11.1%	2.8%	.380	3.86	21.0%	12.4%	164	-0.76	3.36	178	86
Greg Burke R 2009 · 3.93 FIP	26	SU	3.93	16.2%	8.8%	2.0%	.295	—	—	—	—	—	3.61	139	86
David Holman R 2017 · 5.53 FIP	27	SU	5.53	15.6%	12.7%	3.9%	.324	5.45	9.6%	0.5%	107	-0.08	5.28	129	85
C.C. Lee R 2014 · 3.77 FIP	27	SU	3.77	20.5%	8.7%	2.4%	.310	2.80	25.7%	17.6%	96	-0.97	2.59	149	85
Joe Colón J R 2017 · 4.25 FIP	27	SU	4.25	20.4%	12.1%	2.4%	.315	—	—	—	—	—	3.27	113	85
Ross Wolf R 2010 · 3.95 FIP	27	SU	3.95	16.1%	8.9%	2.0%	.268	4.37	10.1%	3.3%	48	+0.42	3.43	179	84
Francisco Del Rosario R 2007 · 3.93 FIP	26	SU	3.93	19.7%	9.4%	2.4%	.356	—	—	—	—	—	3.93	194	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	84
Chris Hatcher R 2012 · 5.60 FIP	27	SU	5.60	15.2%	9.1%	4.8%	.298	2.68	26.4%	20.9%	95	-2.92	2.51	246	84
Adrian Garza R 2015 · 5.47 FIP	27	SU	5.47	14.9%	10.5%	4.2%	.360	4.17	13.1%	6.5%	84	-1.30	3.94	229	84
Luis Mateo R 2017 · 4.60 FIP	27	SU	4.60	17.6%	11.2%	2.9%	.309	5.55	8.6%	-4.8%	43	+0.95	3.48	160	84
Michael Dimock R 2017 · 4.12 FIP	27	SU	4.12	18.9%	7.8%	2.9%	.339	3.37	23.5%	12.8%	29	-0.75	3.08	166	83
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	83
Leo Rosales R 2008 · 3.91 FIP	27	SU	3.91	13.2%	8.8%	1.5%	.288	4.05	16.2%	9.3%	62	+0.14	3.85	130	83
Derek Eitel R 2015 · 4.35 FIP	27	SU	4.35	18.1%	13.9%	1.8%	.338	4.26	19.7%	5.0%	113	-0.09	3.96	138	83
Rob Scahill R 2014 · 5.07 FIP	27	SU	5.07	15.3%	9.7%	3.7%	.275	3.59	15.6%	8.6%	141	-1.48	3.06	250	83
Tyler Walker R 2004 · 4.28 FIP	28	SU	4.28	17.5%	8.4%	2.9%	.311	3.77	18.5%	10.4%	101	-0.51	2.38	289	83