Wily Peralta

P Indianapolis Indians

Age 38 RHP 6'1" / 261 lbs AAA peak 0.6 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	33	MIX3 levels	34	2	46	2.56	3.60	21.0%	13.5%	0.5	K 19.0% · BB 14.3% · FIP 3.98
2023	34	AAAWSH	24	24	103	6.31	4.41	21.7%	12.0%	—	K 18.7% · BB 10.8% · FIP 4.50
2024	35	AAAPIT	17	4	33	7.44	5.40	20.0%	9.7%	—	K 17.2% · BB 8.7% · FIP 5.96
2025	36	No MLB games played
2026	37	No MLB games played
2027PROJ	38	MLBPIT	1	1	2	4.49	4.44	22.7%	9.1%	0.0
2028PROJ	39	MLBPIT	3	3	20	4.57	4.73	21.5%	9.8%	0.1

Last 3 seasons WAR 0.5 · Next 2 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2022

FB Velo62

xwOBA vs39

K%28

CompsYasutomo Kubo (age 38) · Jonathan Castellanos (age 37) · Walter Silva (age 38)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 116108vR 100

ERA4.49

FIP4.44

K%22.7%

BB%9.1%

K-BB%13.5%

Proj IP16

Value2027 · WAR

Runs saved vs league avg · 16 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6295.4

Whiff%

p2121.2%

p2819.0%

xwOBA vs

p390.324

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)
2012	23	MLB MLE	Milwaukee Brewers	6	5	29	113	23	11	0	83%	4.8	20.4%	9.7%	10.6%	0.0%	2.48	2.61	+1.0
2012	23	MLB RAW	Milwaukee Brewers	6	5	29	113	23	11	0	83%	4.8	20.4%	9.7%	10.6%	0.0%	2.48	2.65	+1.0
2013	24	MLB MLE	Milwaukee Brewers	32	32	183	802	129	70	19	100%	5.7	16.1%	8.7%	7.4%	2.3%	4.37	4.09	+2.9
2013	24	MLB RAW	Milwaukee Brewers	32	32	183	802	129	70	19	100%	5.7	16.1%	8.7%	7.4%	2.4%	4.37	4.19	+2.6
2014	25	MLB MLE	Milwaukee Brewers	32	32	199	838	154	61	23	100%	6.2	18.4%	7.3%	11.1%	2.6%	3.53	3.90	+3.6
2014	25	MLB RAW	Milwaukee Brewers	32	32	199	838	154	61	23	100%	6.2	18.4%	7.3%	11.1%	2.7%	3.53	3.98	+3.4
2015	26	MIX MLE	3 levels	23	23	120	522	72	37	15	100%	5.2	12.6%	7.3%	5.2%	2.8%	4.42	4.50	+1.3
2015	26	MIX RAW	3 levels	23	23	120	522	72	37	15	100%	5.2	13.8%	7.1%	6.7%	2.9%	4.42	4.45	+1.3
2016	27	MIX MLE	2 levels	33	33	169	743	132	58	24	100%	5.1	17.1%	7.9%	9.2%	3.2%	5.22	4.41	+2.0
2016	27	MIX RAW	2 levels	33	33	169	743	132	58	24	100%	5.1	17.8%	7.8%	10.0%	3.2%	5.22	4.41	+1.9
2017	28	MIX MLE	2 levels	32	8	73	340	62	40	10	25%	2.3	17.9%	11.9%	6.0%	2.8%	6.87	4.64	-0.1
2017	28	MIX RAW	2 levels	32	8	73	340	62	40	10	25%	2.3	18.2%	11.8%	6.5%	2.9%	6.87	4.82	-0.2
2018	29	MIX MLE	2 levels	55	2	69	306	74	41	7	4%	1.3	22.7%	13.7%	8.9%	2.4%	4.02	4.24	+0.3
2018	29	MIX RAW	2 levels	55	2	69	306	74	41	7	4%	1.3	24.2%	13.4%	10.8%	2.3%	4.02	4.05	+0.4
2019	30	MLB MLE	Kansas City Royals	42	0	40	176	24	18	7	0%	1.0	13.6%	10.2%	3.4%	4.3%	5.80	5.60	-0.5
2019	30	MLB RAW	Kansas City Royals	42	0	40	176	24	18	7	0%	1.0	13.6%	10.2%	3.4%	4.0%	5.80	5.50	-0.4
2021	32	MIX MLE	2 levels	25	24	113	482	79	46	14	96%	4.5	15.9%	9.6%	6.2%	3.0%	3.02	4.64	+1.0
2021	32	MIX RAW	2 levels	25	24	113	482	79	46	14	96%	4.5	16.4%	9.5%	6.8%	2.9%	3.02	4.53	+1.1
2022	33	MIX MLE	3 levels	34	2	46	200	42	27	2	6%	1.3	19.0%	14.3%	4.8%	1.2%	2.56	3.98	+0.3
2022	33	MIX RAW	3 levels	34	2	46	200	42	27	2	6%	1.3	21.0%	13.5%	7.5%	1.0%	2.56	3.60	+0.5
2023	34	AAA MLE	Rochester Red Wings	24	24	103	460	100	55	13	100%	4.3	18.7%	10.8%	7.9%	2.9%	6.31	4.50	—
2023	34	AAA RAW	Rochester Red Wings	24	24	103	460	100	55	13	100%	4.3	21.7%	12.0%	9.8%	2.8%	6.31	4.41	—
2024	35	AAA MLE	Indianapolis Indians	17	4	33	145	29	14	7	24%	1.9	17.2%	8.7%	8.5%	5.8%	7.44	5.96	—
2024	35	AAA MLE	Indianapolis Indians	17	4	33	145	29	14	7	24%	1.9	17.2%	8.7%	8.5%	5.8%	7.44	5.96	—
2024	35	AAA RAW	Indianapolis Indians	17	4	33	145	29	14	7	24%	1.9	20.0%	9.7%	10.3%	4.8%	7.44	5.40	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			258	139	911	3949	660	351	110	54%	3.5	16.7%	8.9%	7.8%	2.8%	4.29	4.38	+11.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	38	MLB	PIT	1	1	2	8	2	1	0	100%	2.0	22.7%	9.1%	13.5%	3.1%	4.49	4.44	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	39	MLB MLE	PIT	3	3	20	84	18	8	3	100%	6.6	21.5%	9.8%	11.7%	3.6%	4.72	4.88	+0.1
2028	39	MLB CTX	PIT	3	3	20	84	18	8	3	100%	6.7	21.5%	9.8%	11.7%	3.3%	4.57	4.73	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		261	142	931	4033	678	359	113	54%	3.6	16.8%	8.9%	7.9%	2.8%	4.29	4.34	+11.6
/ 200 IP	—	RAW		56	31	200	866	146	77	24	54%	3.6	16.8%	8.9%	7.9%	2.8%	4.29	4.34	+2.5
Career	—	MLE		355	189	1177	5127	878	477	142	53%	3.3	17.1%	9.3%	7.8%	2.8%	4.55	4.35	+11.1
/ 200 IP	—	MLE		60	32	200	871	149	81	24	53%	3.3	17.1%	9.3%	7.8%	2.8%	4.55	4.35	+2.4

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 AAAIndianapolis Indians · 33 IPFIP 5.28 → 5.96 (+0.67)K% 20.0% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.860	17.20%
BB% (unintentional) /BF	9.66%	—	×0.900	8.69%
HR / BF /BF	4.83%	÷0.909	×1.100	5.84%

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 82).

Result: FIP 5.28 → 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).

2023 AAARochester Red Wings · 103 IPFIP 4.33 → 4.50 (+0.17)K% 21.7% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.860	18.70%
BB% (unintentional) /BF	11.96%	—	×0.900	10.76%
HR / BF /BF	2.83%	÷1.058	×1.100	2.94%

Result: FIP 4.33 → 4.50, 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 AAAToledo Mud Hens · 5 IPFIP 1.96 → 2.35 (+0.38)K% 30.8% → 27.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.77%	—	×0.880	27.08%
BB% (unintentional) /BF	11.54%	—	×1.050	12.12%
HR / BF /BF	0.00%	÷1.026	×1.100	0.00%

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 105).

Result: FIP 1.96 → 2.35, 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 AAAToledo Mud Hens · 20 IPFIP 3.52 → 3.91 (+0.39)K% 26.2% → 23.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.25%	—	×0.880	23.10%
BB% (unintentional) /BF	10.00%	—	×1.050	10.50%
HR / BF /BF	2.50%	÷1.052	×1.100	2.61%

Result: FIP 3.52 → 3.91, 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).

2018 AAAOmaha Storm Chasers · 35 IPFIP 3.58 → 3.91 (+0.33)K% 24.8% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.84%	—	×0.880	21.86%
BB% (unintentional) /BF	12.10%	—	×1.050	12.71%
HR / BF /BF	1.91%	÷1.101	×1.100	1.91%

Result: FIP 3.58 → 3.91, 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).

2017 AAAColorado Springs Sky Sox · 16 IPFIP 3.69 → 3.93 (+0.23)K% 14.1% → 12.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.08%	—	×0.880	12.39%
BB% (unintentional) /BF	14.08%	—	×1.050	14.79%
HR / BF /BF	0.00%	÷0.933	×1.100	0.00%

Result: FIP 3.69 → 3.93, 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 AAAColorado Springs Sky Sox · 41 IPFIP 3.88 → 4.33 (+0.45)K% 20.6% → 18.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.63%	—	×0.880	18.16%
BB% (unintentional) /BF	8.47%	—	×1.050	8.89%
HR / BF /BF	2.65%	÷0.973	×1.100	2.99%

Result: FIP 3.88 → 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).

2015 AABiloxi Shuckers · 9 IPFIP 3.47 → 4.10 (+0.63)K% 23.5% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.53%	—	×0.810	19.06%
BB% (unintentional) /BF	5.88%	—	×1.100	6.47%
HR / BF /BF	2.94%	÷1.084	×1.200	3.26%

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 117).

Result: FIP 3.47 → 4.10, 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
Wily Peralta (OGILVIE)	—	—	—	—	—	3.1%	—	4.88	21.5%	11.7%	20	+0.44	—	—	—
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	45
Jonathan Castellanos R 2019 · 5.24 FIP	37	SW	5.24	11.0%	9.0%	3.5%	.348	—	—	—	—	—	3.40	394	44
Walter Silva R 2015 · 4.15 FIP	38	SP	4.15	14.8%	7.4%	2.5%	.311	3.91	10.4%	3.7%	209	-0.24	3.67	453	41
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	38
Pablo Ortega R 2015 · 4.52 FIP	38	SP	4.52	8.6%	7.3%	2.2%	.291	4.24	11.5%	6.0%	236	-0.28	4.01	375	33
Keiichi Yabu R 2008 · 3.48 FIP	39	SU	3.48	15.9%	9.3%	1.0%	.269	—	—	—	—	—	3.48	126	31
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	30
Juan Sandoval R 2019 · 5.41 FIP	38	SU	5.41	15.9%	7.2%	5.0%	.273	—	—	—	—	—	3.24	225	29
Ivan Maldonado R 2016 · 5.41 FIP	36	SP	5.41	6.4%	9.7%	3.0%	.325	—	—	—	—	—	4.60	172	28
Jorge Castillo L 2019 · 4.91 FIP	37	SP	4.91	19.6%	8.1%	4.5%	.316	—	—	—	—	—	4.50	408	27
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	27
Jon Leicester R 2015 · 3.76 FIP	36	SP	3.76	12.3%	5.9%	1.7%	.333	—	—	—	—	—	3.76	113	25
Hector Navarro R 2017 · 3.53 FIP	37	SU	3.53	11.5%	5.6%	1.3%	.283	—	—	—	—	—	2.30	126	23
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	22
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	21
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	18
Erubiel Gonzalez R 2017 · 3.51 FIP	38	SU	3.51	13.3%	2.5%	2.2%	.388	—	—	—	—	—	2.99	119	16
Mauricio Lara L 2017 · 3.70 FIP	38	SP	3.70	12.0%	6.3%	1.5%	.301	4.96	12.3%	5.5%	111	+1.26	3.62	444	14
Billy Taylor R 2000 · 4.53 FIP	38	SU	4.53	21.0%	11.3%	3.2%	.275	—	—	—	—	—	2.94	323	13
Lorenzo Barcelo R 2016 · 3.48 FIP	38	SP	3.48	12.8%	1.1%	2.4%	.329	—	—	—	—	—	3.14	259	13

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	4.7	79	16.9%	9.2%	1.09	4.30
Cal Koonce ('62-'71, 5 WAR*)	4.9	97	15.6%*	9.4%*	1.09*	4.34*
Mike Bielecki ('84-'97, 9 WAR*)	6.2	95	18.4%*	9.1%*	1.15*	4.47*
Tom Gorzelanny ('16)	4.4	88	18.4%	10.0%	1.00	4.40
Lerrin LaGrow ('70-'80, 2 WAR*)	3.9	87	16.8%*	9.1%*	1.23*	4.50*
Pete Schourek ('01)	5.7	104	16.3%	8.4%	1.10	4.59
Ross Detwiler ('22)	3.8	68	15.3%	8.2%	1.11	4.56