Freddy Peralta

P Tampa Bay Rays

Age 31 RHP 6'0" / 198 lbs MLB Svc 8.0 · Ctrl thru 2027 peak 1.8 · 2027

Overview

MLBSP40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2024	28	MLBMIL	32	32	174	3.68	3.92	27.6%	9.4%	3.1
2025	29	MLBMIL	33	33	177	2.70	3.46	28.2%	9.1%	4.2
2026	30	MLBTB	32	32	169	4.42	4.14	21.4%	8.6%	2.5
2027PROJ	31	MLBTB	22	22	129	4.00	4.26	24.2%	8.8%	1.7
2028PROJ	32	MLBTB	17	17	104	4.26	4.47	23.4%	9.2%	1.1
2029PROJ	33	MLBTB	14	14	82	4.55	4.75	22.6%	9.7%	0.6

Last 3 seasons WAR 9.8 · Next 3 projected WAR 3.4Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service8.523First FA2028

2027Ext / FA-deal$14M

Skills · percentileStatcast 2026

xwOBA vs60

FB Velo47

K%45

Form0.96 ERA (proj 4.00) · K 21.1% (proj 24.2%) · BB 8.3% (proj 8.8%) ▼ last 60d · 28 IP through Oct 27

CompsChase Anderson (age 31) · Kenta Maeda (age 31) · Hideo Nomo (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 109101vR 92

ERA4.00

FIP4.26

K%24.2%

BB%8.8%

K-BB%15.4%

Proj IP129

Value2027 · WAR

Runs saved vs league avg · 129 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p4794.3

Whiff%

p5526.1%

p4521.4%

xwOBA vs

p600.302

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status FA

MLB Debut 2018

Service 8.523 yrs.days

Team Control Through 2027

First FA Year 2028

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$14M	+1.7	$14M	-$0.3M
Totals		$14M		$14M	-$0.3M

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	19	ROK MLE	AZL Mariners	11	9	57	233	67	8	1	82%	5.2	19.6%	4.3%	15.3%	0.6%	4.11	2.32	—
2015	19	ROK RAW	AZL Mariners	11	9	57	233	67	8	1	82%	5.2	28.8%	3.4%	25.3%	0.4%	4.11	1.40	—
2016	20	MIX MLE	2 levels	24	10	82	350	97	36	7	42%	3.4	20.7%	12.0%	8.6%	2.3%	3.62	4.12	—
2016	20	MIX RAW	2 levels	24	10	82	350	97	36	7	42%	3.4	27.7%	10.3%	17.4%	2.0%	3.62	3.16	—
2017	21	MIX MLE	2 levels	25	19	120	506	169	62	8	76%	4.8	26.4%	13.8%	12.7%	1.9%	2.62	3.65	—
2017	21	MIX RAW	2 levels	25	19	120	506	169	62	8	76%	4.8	33.4%	12.3%	21.1%	1.6%	2.62	2.70	—
2018	22	MIX MLE	3 levels	30	28	141	584	188	66	9	93%	4.7	29.9%	11.7%	18.2%	1.5%	3.76	2.87	+4.5
2018	22	MIX RAW	3 levels	30	28	141	584	188	66	9	93%	4.7	32.2%	11.3%	20.9%	1.5%	3.76	2.67	+4.9
2019	23	MIX MLE	3 levels	44	9	97	430	139	40	16	20%	2.2	30.1%	9.4%	20.7%	3.7%	5.03	3.80	+0.9
2019	23	MIX RAW	3 levels	44	9	97	430	139	40	16	20%	2.2	32.3%	9.3%	23.0%	3.7%	5.03	3.62	+1.1
2020	24	MLB MLE	Milwaukee Brewers	15	1	29	125	47	12	2	7%	2.0	37.6%	9.6%	28.0%	1.5%	3.99	1.98	+0.9
2020	24	MLB RAW	Milwaukee Brewers	15	1	29	125	47	12	2	7%	2.0	37.6%	9.6%	28.0%	1.6%	3.99	2.01	+0.9
2021	25	MLB MLE	Milwaukee Brewers	28	27	144	580	195	55	14	96%	5.2	33.6%	9.5%	24.1%	2.3%	2.81	2.72	+4.9
2021	25	MLB RAW	Milwaukee Brewers	28	27	144	580	195	55	14	96%	5.2	33.6%	9.5%	24.1%	2.4%	2.81	2.80	+4.7
2022	26	MIX MLE	2 levels	20	19	83	338	94	28	7	95%	4.2	27.1%	8.5%	18.6%	1.8%	3.46	2.88	+2.6
2022	26	MIX RAW	2 levels	20	19	83	338	94	28	7	95%	4.2	27.8%	8.3%	19.5%	2.1%	3.46	2.94	+2.5
2023	27	MLB MLE	Milwaukee Brewers	30	30	166	680	210	54	26	100%	5.5	30.9%	7.9%	22.9%	3.6%	3.86	3.50	+3.9
2023	27	MLB RAW	Milwaukee Brewers	30	30	166	680	210	54	26	100%	5.5	30.9%	7.9%	22.9%	3.8%	3.86	3.58	+3.7
2024	28	MLB MLE	Milwaukee Brewers	32	32	174	725	200	68	26	100%	5.4	27.6%	9.4%	18.2%	3.4%	3.68	3.83	+3.3
2024	28	MLB MLE	Milwaukee Brewers	32	32	174	725	200	68	26	100%	5.4	27.6%	9.4%	18.2%	3.4%	3.68	3.83	+3.3
2024	28	MLB RAW	Milwaukee Brewers	32	32	174	725	200	68	26	100%	5.4	27.6%	9.4%	18.2%	3.6%	3.68	3.92	+3.1
2025	29	MLB MLE	Milwaukee Brewers	33	33	177	723	204	66	21	100%	5.4	28.2%	9.1%	19.1%	2.8%	2.70	3.39	+4.4
2025	29	MLB MLE	Milwaukee Brewers	33	33	177	723	204	66	21	100%	5.4	28.2%	9.1%	19.1%	2.8%	2.70	3.39	+4.4
2025	29	MLB RAW	Milwaukee Brewers	33	33	177	723	204	66	21	100%	5.4	28.2%	9.1%	19.1%	2.9%	2.70	3.46	+4.2
2026	30	MLB MLE	Tampa Bay Rays	32	32	169	730	156	63	23	100%	5.3	21.4%	8.6%	12.7%	3.2%	4.42	4.17	+2.5
2026	30	MLB MLE	Tampa Bay Rays	32	32	169	730	156	63	23	100%	5.3	21.4%	8.6%	12.7%	3.2%	4.42	4.17	+2.5
2026	30	MLB RAW	Tampa Bay Rays	32	32	169	730	156	63	23	100%	5.3	21.4%	8.6%	12.7%	3.2%	4.42	4.14	+2.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			243	194	1100	4583	1309	420	141	80%	4.5	28.6%	9.2%	19.4%	3.1%	3.71	3.53	+24.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	TB	22	22	129	545	132	48	17	100%	5.9	24.2%	8.8%	15.4%	3.1%	4.00	4.26	+1.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	TB	17	17	104	440	103	41	15	100%	6.1	23.4%	9.2%	14.2%	3.4%	4.31	4.52	+1.1
2029	33	MLB MLE	TB	14	14	82	345	78	33	13	100%	5.8	22.6%	9.7%	12.9%	3.6%	4.60	4.80	+0.6
2030	34	MLB MLE	TB	10	10	60	255	55	26	10	100%	6.0	21.7%	10.2%	11.5%	3.9%	4.90	5.10	+0.2
2028	32	MLB CTX	TB	17	17	104	439	103	41	14	100%	6.1	23.4%	9.2%	14.2%	3.3%	4.26	4.47	+1.1
2029	33	MLB CTX	TB	14	14	82	346	78	34	12	100%	5.9	22.6%	9.7%	12.9%	3.5%	4.55	4.75	+0.6
2030	34	MLB CTX	TB	10	10	60	253	55	26	10	100%	6.0	21.7%	10.2%	11.5%	3.8%	4.84	5.04	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		284	235	1346	5623	1545	520	177	83%	4.7	27.5%	9.3%	18.2%	3.2%	3.86	3.68	+27.9
/ 200 IP	—	RAW		42	35	200	835	230	77	26	83%	4.7	27.5%	9.3%	18.2%	3.2%	3.86	3.68	+4.1
Career	—	MLE		324	249	1439	6004	1660	577	158	77%	4.4	27.6%	9.6%	18.0%	2.6%	3.60	3.43	+26.6
/ 200 IP	—	MLE		45	35	200	834	231	80	22	77%	4.4	27.6%	9.6%	18.0%	2.6%	3.60	3.43	+3.9

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.

2022 AAANashville Sounds · 5 IPFIP 3.10 → 3.94 (+0.84)K% 38.1% → 33.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.10%	—	×0.880	33.52%
BB% (unintentional) /BF	4.76%	—	×1.050	5.00%
HR / BF /BF	4.76%	÷0.946	×1.100	5.54%

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 89).

Result: FIP 3.10 → 3.94, 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 AAASan Antonio Missions · 7 IPFIP -0.54 → 0.12 (+0.66)K% 58.6% → 51.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	58.62%	—	×0.880	51.59%
BB% (unintentional) /BF	10.34%	—	×1.050	10.86%
HR / BF /BF	0.00%	÷0.977	×1.100	0.00%

Result: FIP -0.54 → 0.12, 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 AAAColorado Springs Sky Sox · 61 IPFIP 1.78 → 2.19 (+0.41)K% 34.0% → 29.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.98%	—	×0.880	29.91%
BB% (unintentional) /BF	10.55%	—	×1.050	11.07%
HR / BF /BF	0.39%	÷1.124	×1.100	0.38%

Result: FIP 1.78 → 2.19, 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 AABiloxi Shuckers · 64 IPFIP 2.09 → 2.74 (+0.65)K% 34.6% → 28.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.60%	—	×0.810	28.03%
BB% (unintentional) /BF	11.79%	—	×1.100	12.97%
HR / BF /BF	0.76%	÷1.393	×1.200	0.65%

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 179).

Result: FIP 2.09 → 2.74, 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 A+Carolina Mudcats · 56 IPFIP 3.36 → 4.64 (+1.28)K% 32.1% → 24.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.10%	—	×0.770	24.72%
BB% (unintentional) /BF	12.76%	—	×1.150	14.67%
HR / BF /BF	2.47%	÷0.959	×1.250	3.22%

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 92).

Result: FIP 3.36 → 4.64, 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).

2016 AWisconsin Timber Rattlers · 60 IPFIP 2.38 → 3.35 (+0.97)K% 30.7% → 22.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.68%	—	×0.740	22.70%
BB% (unintentional) /BF	9.56%	—	×1.180	11.28%
HR / BF /BF	1.20%	÷1.161	×1.300	1.34%

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 132).

Result: FIP 2.38 → 3.35, 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).

2016 A+Brevard County Manatees · 22 IPFIP 5.15 → 6.09 (+0.94)K% 20.2% → 15.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.20%	—	×0.770	15.56%
BB% (unintentional) /BF	12.12%	—	×1.150	13.94%
HR / BF /BF	4.04%	÷1.093	×1.250	4.62%

Result: FIP 5.15 → 6.09, 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).

2015 ROKAZL Mariners · 57 IPFIP 1.34 → 2.32 (+0.98)K% 28.8% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.76%	—	×0.680	19.55%
BB% (unintentional) /BF	3.43%	—	×1.250	4.29%
HR / BF /BF	0.43%	÷1.000	×1.400	0.60%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. 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 1.34 → 2.32, 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
Freddy Peralta (OGILVIE)	—	—	—	—	—	3.1%	—	4.76	22.7%	13.1%	246	+0.50	—	—	—
Chase Anderson R 2019 · 4.39 FIP	31	SP	4.39	20.9%	8.1%	3.7%	.259	5.39	19.2%	10.0%	218	+1.00	3.30	1,260	72
Kenta Maeda R 2019 · 3.68 FIP	31	SP-	3.68	27.1%	8.0%	3.4%	.240	3.44	27.8%	22.0%	173	-0.24	2.87	1,092	66
Hideo Nomo R 2000 · 4.66 FIP	31	SP	4.66	21.9%	10.6%	3.7%	.303	4.13	22.0%	11.4%	637	-0.53	2.76	1,972	64
Miguel González R 2015 · 4.53 FIP	31	SP	4.53	17.5%	7.9%	3.5%	.289	4.23	15.9%	9.0%	325	-0.30	3.72	905	63
Tanner Roark R 2018 · 3.99 FIP	31	SP	3.99	19.2%	6.2%	3.2%	.289	4.57	21.1%	13.1%	259	+0.58	2.40	1,194	60
Tom Koehler R 2017 · 4.66 FIP	31	SP-	4.66	22.4%	9.0%	4.2%	.298	—	—	—	—	—	3.76	822	57
Rolando Arrojo R 2000 · 4.47 FIP	32	SP	4.47	16.1%	8.0%	3.1%	.291	3.68	16.4%	9.2%	185	-0.79	3.50	700	56
Mike Fiers R 2016 · 4.30 FIP	31	SP	4.30	18.5%	5.8%	3.7%	.308	4.96	19.2%	12.2%	510	+0.66	2.83	1,140	55
Orlando Hernandez R 2001 · 5.30 FIP	31	SP	5.30	18.6%	9.9%	4.6%	.256	3.78	20.4%	13.1%	231	-1.52	3.35	1,315	54
Robert Person R 2001 · 4.60 FIP	31	SP	4.60	21.1%	8.9%	3.9%	.253	5.06	16.0%	2.7%	99	+0.46	3.41	885	52
Miguel Batista R 2002 · 3.81 FIP	31	SP-	3.81	14.2%	8.5%	1.5%	.267	4.13	14.9%	6.3%	467	+0.32	3.39	1,943	51
Marco Estrada R 2014 · 4.70 FIP	31	SW	4.70	20.4%	7.1%	4.4%	.255	4.28	20.9%	12.6%	543	-0.42	3.28	1,244	51
Tim Adleman R 2019 · 4.30 FIP	31	SP	4.30	22.8%	7.8%	3.9%	.266	4.71	18.5%	11.1%	90	+0.41	3.63	605	50
Ramon Ortiz R 2004 · 4.44 FIP	31	SW	4.44	15.1%	6.3%	3.3%	.296	4.95	12.0%	6.2%	466	+0.51	4.32	1,525	49
Cory Lidle R 2003 · 4.40 FIP	31	SP	4.40	13.3%	6.8%	2.9%	.297	4.23	15.4%	9.5%	567	-0.17	3.48	1,318	48
Brad Peacock R 2019 · 4.22 FIP	31	SP-	4.22	25.1%	8.1%	4.0%	.261	4.15	22.8%	12.5%	96	-0.07	2.97	748	47
Jeremy Guthrie R 2010 · 4.08 FIP	31	SP	4.08	13.6%	5.6%	2.6%	.247	4.68	13.2%	6.8%	601	+0.60	4.08	1,834	47
Paul Wilson R 2004 · 4.57 FIP	31	SP	4.57	14.7%	7.3%	3.3%	.278	5.32	13.4%	6.3%	46	+0.75	2.59	942	45
Elmer Dessens R 2002 · 4.53 FIP	31	SP	4.53	12.6%	5.6%	3.3%	.257	4.20	14.6%	8.4%	346	-0.33	3.55	1,167	44
Ryan Franklin R 2004 · 5.05 FIP	31	SP	5.05	12.0%	6.9%	3.8%	.284	4.65	12.1%	6.0%	348	-0.40	3.12	1,201	44

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	7.4	113	27.2%	9.3%	1.23	3.88
Jose DeLeon ('83-'95, 22 WAR*)	9.5	146	25.8%*	10.4%*	1.01*	4.17*
Frankie Montas ('25)	3.9	98	23.6%	8.2%	1.20	4.20
Francisco Liriano ('19)	9.1	130	23.3%	10.5%	0.91	4.15
Kenta Maeda ('25)	4.9	123	25.8%	7.0%	1.30	4.20
Bob Johnson ('69-'77, 6 WAR*)	3.5	115	23.4%*	8.9%*	1.49*	4.12*
Tom Underwood ('74-'84, 14 WAR*)	7.9	159	21.7%*	9.6%*	1.04*	4.25*