Yency Almonte

P Knoxville Smokies

Age 33 RHP 6'5" / 223 lbs AA peak 0.2 · 2027

Overview

AARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	29	MLBLAD	49	0	48	5.06	4.06	23.6%	10.6%	0.3
2024	30	MLBCHC	17	0	16	3.45	2.72	29.9%	10.4%	0.3
2025	31	MIX2 levels	15	0	19	1.86	4.24	23.0%	13.5%	—	K 23.0% · BB 13.5% · FIP 4.36
2026	32	No MLB games played
2027PROJ	33	MLBCHC	3	0	3	4.09	4.16	25.2%	9.4%	0.0
2028PROJMLE	34	MLBCHC	—	—	0	4.31	4.46	24.3%	9.9%	—
2029PROJMLE	35	MLBCHC	—	—	0	4.60	4.80	23.4%	10.4%	—

Last 3 seasons WAR 0.6 · Next 3 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

K%90

xwOBA vs77

FB Velo43

CompsGiovanni Carrara (age 33) · Tony Barnette (age 33) · D.J. Carrasco (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 112104vR 96

ERA4.09

FIP4.16

K%25.2%

BB%9.4%

K-BB%15.8%

Proj IP165

Value2027 · WAR

Runs saved vs league avg · 165 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p4394.0

Whiff%

p8630.9%

p9029.9%

xwOBA vs

p770.286

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	21	MIX MLE	2 levels	24	22	137	568	110	38	9	92%	5.7	14.5%	7.8%	6.7%	2.4%	3.41	4.16	—
2015	21	MIX RAW	2 levels	24	22	137	568	110	38	9	92%	5.7	19.4%	6.7%	12.7%	1.6%	3.41	3.18	—
2016	22	MIX MLE	2 levels	27	27	168	709	156	55	18	100%	6.2	17.1%	8.8%	8.3%	3.4%	3.58	4.66	—
2016	22	MIX RAW	2 levels	27	27	168	709	156	55	18	100%	6.2	22.0%	7.8%	14.2%	2.5%	3.58	3.62	—
2017	23	MIX MLE	2 levels	22	21	111	469	93	52	11	95%	5.1	16.4%	12.0%	4.4%	2.2%	2.91	4.46	—
2017	23	MIX RAW	2 levels	22	21	111	469	93	52	11	95%	5.1	19.8%	11.1%	8.7%	2.3%	2.91	4.11	—
2018	24	MIX MLE	2 levels	32	10	58	249	48	18	9	31%	1.8	17.6%	7.5%	10.1%	3.4%	4.63	4.43	+0.1
2018	24	MIX RAW	2 levels	32	10	58	249	48	18	9	31%	1.8	19.3%	7.2%	12.0%	3.6%	4.63	4.39	+0.1
2019	25	MIX MLE	2 levels	58	0	64	304	61	38	9	0%	1.1	18.8%	12.9%	5.9%	2.7%	4.92	4.62	-0.0
2019	25	MIX RAW	2 levels	58	0	64	304	61	38	9	0%	1.1	20.1%	12.5%	7.6%	3.0%	4.92	4.80	-0.2
2020	26	MLB MLE	Colorado Rockies	24	0	28	113	23	6	2	0%	1.2	20.4%	5.3%	15.0%	1.6%	2.93	2.92	+0.5
2020	26	MLB RAW	Colorado Rockies	24	0	28	113	23	6	2	0%	1.2	20.4%	5.3%	15.0%	1.8%	2.93	3.03	+0.5
2021	27	MIX MLE	2 levels	50	0	50	224	48	29	9	0%	1.0	21.7%	12.9%	8.8%	3.7%	7.25	4.92	-0.2
2021	27	MIX RAW	2 levels	50	0	50	224	48	29	9	0%	1.0	21.4%	12.9%	8.5%	4.0%	7.25	5.27	-0.4
2022	28	MIX MLE	2 levels	47	0	53	204	61	10	5	0%	1.1	28.3%	4.9%	23.3%	2.6%	2.02	2.75	+1.2
2022	28	MIX RAW	2 levels	47	0	53	204	61	10	5	0%	1.1	29.9%	4.9%	25.0%	2.5%	2.02	2.59	+1.3
2023	29	MLB MLE	Los Angeles Dodgers	49	0	48	208	49	22	6	0%	1.0	23.6%	10.6%	13.0%	2.8%	5.06	3.97	+0.3
2023	29	MLB RAW	Los Angeles Dodgers	49	0	48	208	49	22	6	0%	1.0	23.6%	10.6%	13.0%	2.9%	5.06	4.06	+0.3
2024	30	MLB MLE	Chicago Cubs	17	0	16	67	20	7	1	0%	0.9	29.9%	10.4%	19.4%	1.6%	3.45	2.75	+0.3
2024	30	MLB MLE	Chicago Cubs	17	0	16	67	20	7	1	0%	0.9	29.9%	10.4%	19.4%	1.6%	3.45	2.75	+0.3
2024	30	MLB RAW	Chicago Cubs	17	0	16	67	20	7	1	0%	0.9	29.9%	10.4%	19.4%	1.5%	3.45	2.72	+0.3
2025	31	MIX MLE	2 levels	15	0	19	74	17	10	2	0%	1.3	23.0%	13.5%	9.5%	2.7%	1.86	4.36	—
2025	31	MIX MLE	2 levels	15	0	19	74	17	10	2	0%	1.3	23.0%	13.5%	9.5%	2.7%	1.86	4.36	—
2025	31	MIX RAW	2 levels	15	0	19	74	17	10	2	0%	1.3	23.0%	13.5%	9.5%	2.7%	1.86	4.24	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			213	0	223	957	215	90	28	0%	1.0	22.5%	9.4%	13.1%	2.9%	4.44	4.01	+1.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	CHC	3	0	3	13	3	1	0	0%	1.0	25.2%	9.4%	15.8%	2.9%	4.09	4.16	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	CHC	121	0	127	535	130	53	17	0%	1.0	24.3%	9.9%	14.4%	3.2%	4.31	4.46	+0.1
2029	35	MLB MLE	CHC	95	0	100	422	99	44	15	0%	1.1	23.4%	10.4%	12.9%	3.6%	4.60	4.80	-0.3
2028	34	MLB CTX	CHC	—	—	0	—	—	—	—	—	—	24.3%	9.9%	14.4%	3.2%	4.31	4.46	—
2029	35	MLB CTX	CHC	—	—	0	—	—	—	—	—	—	23.4%	10.4%	12.9%	3.6%	4.60	4.80	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		213	0	223	957	215	90	28	0%	1.0	22.5%	9.4%	13.1%	2.9%	4.44	4.00	+1.5
/ 200 IP	—	RAW		191	0	200	858	193	81	25	0%	1.0	22.5%	9.4%	13.1%	2.9%	4.44	4.00	+1.3
Career	—	MLE		365	80	753	3189	597	305	89	22%	2.1	18.7%	9.6%	9.1%	2.8%	3.80	4.26	+1.2
/ 200 IP	—	MLE		97	21	200	847	158	81	24	22%	2.1	18.7%	9.6%	9.1%	2.8%	3.80	4.26	+0.6

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.

2025 AAKnoxville Smokies · 11 IPFIP 4.83 → 5.72 (+0.89)K% 18.2% → 14.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.18%	—	×0.810	14.73%
BB% (unintentional) /BF	15.91%	—	×1.100	17.50%
HR / BF /BF	2.27%	÷0.911	×1.200	2.99%

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 82).

Result: FIP 4.83 → 5.72, 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).

2025 A+South Bend Cubs · 9 IPFIP 3.66 → 4.67 (+1.01)K% 30.0% → 23.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.770	23.10%
BB% (unintentional) /BF	10.00%	—	×1.150	11.50%
HR / BF /BF	3.33%	÷1.106	×1.250	3.77%

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 121).

Result: FIP 3.66 → 4.67, 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 AAAOklahoma City Dodgers · 18 IPFIP 2.24 → 2.91 (+0.67)K% 40.6% → 35.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.58%	—	×0.880	35.71%
BB% (unintentional) /BF	1.45%	—	×1.050	1.52%
HR / BF /BF	4.35%	÷0.997	×1.100	4.80%

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 99).

Result: FIP 2.24 → 2.91, 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 AAAAlbuquerque Isotopes · 30 IPFIP 4.08 → 4.38 (+0.30)K% 21.8% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.77%	—	×0.880	19.16%
BB% (unintentional) /BF	16.33%	—	×1.050	17.14%
HR / BF /BF	1.36%	÷1.136	×1.100	1.32%

Result: FIP 4.08 → 4.38, 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 AAAAlbuquerque Isotopes · 44 IPFIP 4.84 → 4.95 (+0.11)K% 18.0% → 15.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.99%	—	×0.880	15.83%
BB% (unintentional) /BF	7.41%	—	×1.050	7.78%
HR / BF /BF	4.23%	÷1.162	×1.100	4.01%

Result: FIP 4.84 → 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).

2017 AAHartford Yard Goats · 76 IPFIP 3.14 → 3.64 (+0.50)K% 22.8% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.83%	—	×0.810	18.49%
BB% (unintentional) /BF	9.97%	—	×1.100	10.96%
HR / BF /BF	1.29%	÷1.183	×1.200	1.30%

Result: FIP 3.14 → 3.64, 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 AAAAlbuquerque Isotopes · 35 IPFIP 6.04 → 6.06 (+0.02)K% 13.9% → 12.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.92%	—	×0.880	12.25%
BB% (unintentional) /BF	13.29%	—	×1.050	13.96%
HR / BF /BF	4.43%	÷1.202	×1.100	4.06%

Result: FIP 6.04 → 6.06, 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+Modesto Nuts · 138 IPFIP 3.32 → 4.46 (+1.14)K% 23.0% → 17.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.02%	—	×0.770	17.73%
BB% (unintentional) /BF	6.70%	—	×1.150	7.71%
HR / BF /BF	2.41%	÷0.877	×1.250	3.43%

Result: FIP 3.32 → 4.46, 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
Yency Almonte (OGILVIE)	—	—	—	—	—	2.9%	—	4.61	23.9%	13.8%	227	+0.45	—	—	—
Giovanni Carrara R 2001 · 4.06 FIP	33	SU	4.06	20.1%	6.0%	3.5%	.249	4.28	15.7%	7.9%	173	+0.22	2.50	462	54
Tony Barnette R 2017 · 3.56 FIP	33	SU	3.56	22.6%	7.1%	2.7%	.335	2.61	25.0%	20.2%	26	-0.95	2.61	144	52
D.J. Carrasco R 2010 · 3.45 FIP	33	SU	3.45	19.7%	9.4%	1.5%	.275	4.47	12.0%	6.7%	49	+1.02	3.23	490	50
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	50
Akinori Otsuka R 2005 · 3.05 FIP	33	SU	3.05	21.7%	9.4%	1.1%	.278	2.59	19.4%	14.2%	92	-0.46	2.39	232	50
Joe Nelson R 2008 · 3.08 FIP	33	SU	3.08	26.1%	7.8%	2.2%	.252	5.35	19.8%	5.5%	40	+2.27	3.08	139	50
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	50
Aquilino López R 2008 · 3.70 FIP	33	SU	3.70	17.7%	5.2%	2.6%	.301	—	—	—	—	—	3.10	207	49
Alan Busenitz R 2024 · 3.57 FIP	33	SU	3.57	18.6%	6.8%	2.1%	.304	7.93	16.1%	9.7%	25	+4.36	2.92	436	49
Huascar Brazobán R 2023 · 3.52 FIP	33	SU	3.52	25.3%	11.3%	2.1%	.304	3.54	21.9%	11.8%	195	+0.02	3.08	370	49
Felipe Arredondo R 2019 · 4.65 FIP	32	SU	4.65	16.2%	7.1%	3.7%	.272	—	—	—	—	—	3.92	337	49
Jim Miller R 2015 · 3.95 FIP	33	SU	3.95	18.8%	4.1%	3.5%	.275	5.43	20.4%	6.3%	17	+1.48	3.95	140	49
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	48
Tim Dillard R 2016 · 3.13 FIP	33	SU	3.13	22.6%	8.2%	1.7%	.358	4.14	13.7%	6.9%	250	+1.01	2.92	432	48
Tommy Nance R 2024 · 3.51 FIP	33	SU	3.51	20.2%	5.8%	2.5%	.327	2.84	26.3%	19.5%	123	-0.67	2.08	360	47
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	47
Rafael Betancourt R 2008 · 4.12 FIP	33	SU	4.12	20.7%	6.5%	3.6%	.304	2.37	31.4%	27.3%	181	-1.75	1.79	686	47
Shawn Camp R 2009 · 3.73 FIP	33	SU	3.73	17.4%	7.5%	2.1%	.272	3.57	14.4%	9.7%	216	-0.16	2.79	589	47
Scott McGough R 2023 · 4.57 FIP	33	SU	4.57	28.6%	9.3%	4.9%	.266	4.23	19.4%	9.4%	126	-0.34	3.55	429	47
Dale Thayer R 2014 · 3.56 FIP	33	SU	3.56	23.4%	4.9%	3.3%	.243	4.34	15.8%	8.8%	38	+0.78	2.75	239	47

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	3.1	77	23.7%	9.9%	1.20	4.35
John Henry Johnson ('78-'87, 6 WAR*)	3.0	75	24.6%*	9.6%*	1.15*	4.15*
Junior Guerra ('21)	2.5	85	21.2%	10.9%	1.17	4.06
Dave Hamilton ('72-'80, 2 WAR*)	3.5	78	21.3%*	10.2%*	1.13*	4.58*
Mike Hartley ('89-'95, 2 WAR*)	1.6	80	24.3%*	9.9%*	1.09*	4.09*
Brad Peacock ('21)	2.8	93	24.7%	10.1%	1.27	4.11
David Hernandez ('19)	3.2	65	23.9%	9.1%	1.22	4.12