Kenyon Yovan

P Rocket City Trash Pandas

Age 29 RHP 6'2" / 221 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	33	0	43	4.15	3.47	19.7%	9.0%	—	K 14.5% · BB 8.5% · FIP 4.33
2025	27	MIX3 levels	44	2	54	4.36	2.91	22.9%	10.4%	—	K 19.2% · BB 9.4% · FIP 3.18
2026	28	AALAA	47	0	55	3.09	2.63	28.6%	9.8%	—	K 23.2% · BB 10.8% · FIP 3.36
2027PROJ	29	MLBLAA	1	0	1	4.30	4.30	21.2%	10.1%	0.0
2028PROJ31% MLB	30	MLBLAA	9	0	9	4.44	4.51	21.1%	10.3%	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) · Neil Wagner (age 29) · Dustin Molleken (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 115107vR 99

ERA4.30

FIP4.30

K%21.2%

BB%10.1%

K-BB%11.1%

Proj IP36

Value2027 · WAR

Runs saved vs league avg · 36 IP

Grades · Contract

Scouting Grades

K†5050

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	2 levels	17	0	20	79	39	5	0	0%	1.2	34.5%	7.9%	26.7%	0.0%	1.77	1.18	—
2022	24	MIX RAW	2 levels	17	0	20	79	39	5	0	0%	1.2	49.4%	6.3%	43.0%	0.0%	1.77	0.00	—
2023	25	MIX MLE	2 levels	51	0	55	240	56	28	10	0%	1.1	18.4%	11.7%	6.7%	5.0%	5.73	5.77	—
2023	25	MIX RAW	2 levels	51	0	55	240	56	28	10	0%	1.1	23.3%	11.7%	11.7%	4.2%	5.73	4.95	—
2024	26	MIX MLE	2 levels	33	0	43	188	37	17	3	0%	1.3	14.5%	8.5%	6.0%	2.5%	4.15	4.33	—
2024	26	MIX MLE	2 levels	33	0	43	188	37	17	3	0%	1.3	14.5%	8.5%	6.0%	2.5%	4.15	4.33	—
2024	26	MIX RAW	2 levels	33	0	43	188	37	17	3	0%	1.3	19.7%	9.0%	10.6%	1.6%	4.15	3.47	—
2025	27	MIX MLE	3 levels	44	2	54	249	57	26	2	5%	1.2	19.2%	9.4%	9.8%	0.9%	4.36	3.18	—
2025	27	MIX MLE	3 levels	44	2	54	249	57	26	2	5%	1.2	19.2%	9.4%	9.8%	0.9%	4.36	3.18	—
2025	27	MIX RAW	3 levels	44	2	54	249	57	26	2	5%	1.2	22.9%	10.4%	12.4%	0.8%	4.36	2.91	—
2026	28	AA MLE	Rocket City Trash Pandas	47	0	55	234	67	23	3	0%	1.2	23.2%	10.8%	12.4%	1.5%	3.09	3.36	—
2026	28	AA MLE	Rocket City Trash Pandas	47	0	55	234	67	23	3	0%	1.2	23.2%	10.8%	12.4%	1.5%	3.09	3.36	—
2026	28	AA RAW	Rocket City Trash Pandas	47	0	55	234	67	23	3	0%	1.2	28.6%	9.8%	18.8%	1.3%	3.09	2.63	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	LAA	1	0	1	4	1	0	0	0%	1.0	21.2%	10.1%	11.1%	2.4%	4.30	4.30	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	LAA	27	0	28	118	25	12	3	0%	1.0	21.1%	10.3%	10.8%	2.7%	4.41	4.48	+0.0
2028	30	MLB CTX	LAA	9	0	9	38	8	4	1	0%	1.0	21.1%	10.3%	10.8%	2.7%	4.44	4.51	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		8	0	9	37	8	4	1	0%	1.1	21.1%	10.3%	10.8%	2.7%	4.44	4.13	+0.0
/ 200 IP	—	RAW		190	0	200	844	183	91	23	0%	1.0	21.1%	10.3%	10.8%	2.7%	4.44	4.13	+1.0
Career	—	MLE		192	2	228	990	201	99	23	1%	1.2	20.3%	10.0%	10.3%	2.3%	4.11	3.91	+0.0
/ 200 IP	—	MLE		169	2	200	870	177	87	20	1%	1.2	20.3%	10.0%	10.3%	2.3%	4.11	3.91	+0.2

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 AARocket City Trash Pandas · 55 IPFIP 2.63 → 3.36 (+0.72)K% 28.6% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.63%	—	×0.810	23.19%
BB% (unintentional) /BF	9.83%	—	×1.100	10.81%
HR / BF /BF	1.28%	÷1.000	×1.200	1.54%

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 2.63 → 3.36, 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).

2025 AAASalt Lake Bees · 39 IPFIP 3.19 → 3.39 (+0.19)K% 22.5% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.53%	—	×0.860	19.37%
BB% (unintentional) /BF	10.99%	—	×0.900	9.89%
HR / BF /BF	1.10%	÷0.989	×1.100	1.22%

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 98).

Result: FIP 3.19 → 3.39, 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 AARocket City Trash Pandas · 12 IPFIP 2.04 → 2.51 (+0.46)K% 23.2% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.21%	—	×0.810	18.80%
BB% (unintentional) /BF	7.14%	—	×1.100	7.86%
HR / BF /BF	0.00%	÷1.028	×1.200	0.00%

Result: FIP 2.04 → 2.51, 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).

2024 AAASalt Lake Bees · 33 IPFIP 3.98 → 4.33 (+0.35)K% 16.9% → 14.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.89%	—	×0.860	14.53%
BB% (unintentional) /BF	9.46%	—	×0.900	8.51%
HR / BF /BF	2.03%	÷0.886	×1.100	2.52%

Result: FIP 3.98 → 4.33, 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 AARocket City Trash Pandas · 11 IPFIP 1.52 → 2.09 (+0.58)K% 30.0% → 24.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.810	24.30%
BB% (unintentional) /BF	7.50%	—	×1.100	8.25%
HR / BF /BF	0.00%	÷0.976	×1.200	0.00%

Result: FIP 1.52 → 2.09, 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 AARocket City Trash Pandas · 46 IPFIP 4.74 → 5.77 (+1.02)K% 22.7% → 18.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.73%	—	×0.810	18.41%
BB% (unintentional) /BF	10.61%	—	×1.100	11.67%
HR / BF /BF	4.04%	÷0.970	×1.200	5.00%

Result: FIP 4.74 → 5.77, 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).

2023 AAASalt Lake Bees · 9 IPFIP 5.61 → 5.96 (+0.35)K% 26.2% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.19%	—	×0.860	22.52%
BB% (unintentional) /BF	16.67%	—	×0.900	15.00%
HR / BF /BF	4.76%	÷1.003	×1.100	5.22%

Result: FIP 5.61 → 5.96, 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 AInland Empire 66ers · 15 IPFIP 0.00 → 1.18 (+1.18)K% 46.7% → 34.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	46.67%	—	×0.740	34.53%
BB% (unintentional) /BF	6.67%	—	×1.180	7.87%
HR / BF /BF	0.00%	÷0.889	×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 78).

Result: FIP 0.00 → 1.18, 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).

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
Kenyon Yovan (OGILVIE)	—	—	4.30	21.2%	10.1%	2.4%	—	4.48	21.1%	10.8%	28	+0.18	—	—	—
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	91
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	88
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	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	87
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	86
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	85
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	84
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	83
Tyler Walker R 2005 · 4.19 FIP	29	SU	4.19	19.4%	7.5%	3.2%	.303	3.61	19.7%	11.3%	93	-0.58	2.38	289	83
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	83
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	82
Kevin Gryboski R 2003 · 3.67 FIP	29	SU	3.67	16.8%	8.9%	1.6%	.295	3.92	9.4%	0.2%	82	+0.25	3.67	178	82
Grant Sides R 2019 · 3.31 FIP	30	SU	3.31	21.7%	9.8%	1.5%	.267	—	—	—	—	—	3.31	159	81
Mark Serrano R 2016 · 3.16 FIP	30	SU	3.16	17.7%	6.5%	1.3%	.290	4.36	21.1%	14.0%	88	+1.20	3.16	120	81
Chuck King R 2024 · 3.17 FIP	26	SU	3.17	19.4%	9.9%	0.8%	.308	4.25	15.8%	8.7%	256	+1.08	3.17	294	81
Francisco Mendoza R 2016 · 3.81 FIP	28	SU	3.81	17.9%	7.7%	2.3%	.344	—	—	—	—	—	3.13	120	81
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	81
Justin Miller R 2016 · 3.74 FIP	29	SU	3.74	20.8%	9.7%	2.1%	.353	4.72	22.5%	16.5%	127	+0.98	2.63	292	80
Joe Colón J R 2017 · 4.25 FIP	27	SU	4.25	20.4%	12.1%	2.4%	.315	—	—	—	—	—	3.27	113	80
Marc Magliaro R 2018 · 3.94 FIP	28	SU	3.94	19.6%	7.3%	2.9%	.307	—	—	—	—	—	3.94	125	80

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	32	21.2%	10.2%	0.98	4.35
Ed Vosberg ('86-'02, 1 WAR*)	1.2	47	21.0%*	9.6%*	0.86*	4.06*
Manny Acosta ('12)	1.2	41	20.5%	10.9%	1.09	3.99
Yency Almonte ('24)	1.1	45	22.5%	9.9%	1.13	4.44
José Cisnero ('24)	1.4	46	24.0%	11.3%	1.01	4.16
Kevin Hickey ('81-'91, 0 WAR*)	1.2	47	17.7%*	10.2%*	1.10*	4.27*
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*