Frankie Montas

P New York Mets

Age 34 RHP 6'2" / 255 lbs MLB peak 0.2 · 2027

Overview

MLBSP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	30	MIX2 levels	3	2	4	2.08	2.18	26.3%	10.5%	0.2	K 26.3% · BB 10.5% · FIP 2.21
2024	31	MLBMIL	30	30	151	4.84	4.52	22.6%	10.1%	1.5
2025	32	MIX3 levels	15	13	57	8.16	6.45	16.0%	8.7%	-0.7	K 15.3% · BB 7.6% · FIP 6.14
2026	33	No MLB games played
2027PROJ	34	MLBNYM	7	7	43	4.93	5.00	20.2%	9.0%	0.2
2028PROJ	35	MLBNYM	6	6	36	5.17	5.34	19.2%	9.6%	0.0

Last 3 seasons WAR 1.1 · Next 2 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

FB Velo65

K%21

xwOBA vs15

CompsGuillermo Moscoso (age 34) · Jarrett Grube (age 34) · Andres Ivan Meza (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 130122vR 114

ERA4.93

FIP5.00

K%20.2%

BB%9.0%

K-BB%11.2%

Proj IP43

Value2027 · WAR

Runs saved vs league avg · 43 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6595.6

Whiff%

p4023.8%

p2118.0%

xwOBA vs

p150.353

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	22	MIX MLE	2 levels	30	25	127	531	128	56	4	83%	4.2	20.2%	11.5%	8.8%	0.8%	3.19	3.28	+3.3
2015	22	MIX RAW	2 levels	30	25	127	531	128	56	4	83%	4.2	24.1%	10.5%	13.6%	0.8%	3.19	2.82	+4.1
2016	23	MIX MLE	2 levels	7	4	16	64	22	3	1	57%	2.3	34.4%	4.7%	29.7%	1.6%	2.25	1.65	—
2016	23	MIX RAW	2 levels	7	4	16	64	22	3	1	57%	2.3	34.4%	4.7%	29.7%	1.6%	2.25	1.73	—
2017	24	MIX MLE	2 levels	32	8	61	271	73	27	14	25%	1.9	25.3%	10.1%	15.2%	5.9%	6.16	5.49	-0.7
2017	24	MIX RAW	2 levels	32	8	61	271	73	27	14	25%	1.9	26.9%	10.0%	17.0%	5.2%	6.16	5.01	-0.3
2018	25	MIX MLE	2 levels	28	26	137	586	104	47	12	93%	4.9	16.5%	8.2%	8.3%	2.3%	4.28	4.00	+2.3
2018	25	MIX RAW	2 levels	28	26	137	586	104	47	12	93%	4.9	17.7%	8.0%	9.7%	2.0%	4.28	3.75	+2.7
2019	26	MLB MLE	Oakland Athletics	16	16	96	394	103	22	8	100%	6.0	26.1%	5.6%	20.6%	2.2%	2.62	2.82	+3.1
2019	26	MLB RAW	Oakland Athletics	16	16	96	394	103	22	8	100%	6.0	26.1%	5.6%	20.6%	2.0%	2.62	2.73	+3.3
2020	27	MLB MLE	Oakland Athletics	11	11	53	237	60	23	10	100%	4.8	25.3%	9.7%	15.6%	4.6%	5.60	4.74	+0.4
2020	27	MLB RAW	Oakland Athletics	11	11	53	237	60	23	10	100%	4.8	25.3%	9.7%	15.6%	4.2%	5.60	4.59	+0.5
2021	28	MLB MLE	Oakland Athletics	32	32	187	778	207	56	20	100%	5.8	26.6%	7.2%	19.4%	2.8%	3.37	3.32	+4.8
2021	28	MLB RAW	Oakland Athletics	32	32	187	778	207	56	20	100%	5.8	26.6%	7.2%	19.4%	2.6%	3.37	3.17	+5.2
2022	29	MLB MLE	New York Yankees	27	27	144	608	142	40	18	100%	5.3	23.4%	6.6%	16.8%	2.8%	4.05	3.48	+3.4
2022	29	MLB RAW	New York Yankees	27	27	144	608	142	40	18	100%	5.3	23.4%	6.6%	16.8%	3.0%	4.05	3.58	+3.2
2023	30	MIX MLE	2 levels	3	2	4	19	5	2	0	67%	1.4	26.3%	10.5%	15.8%	0.0%	2.08	2.21	+0.2
2023	30	MIX RAW	2 levels	3	2	4	19	5	2	0	67%	1.4	26.3%	10.5%	15.8%	0.0%	2.08	2.18	+0.2
2024	31	MLB MLE	Milwaukee Brewers	30	30	151	655	148	66	24	100%	5.0	22.6%	10.1%	12.5%	3.5%	4.84	4.38	+1.8
2024	31	MLB MLE	Milwaukee Brewers	30	30	151	655	148	66	24	100%	5.0	22.6%	10.1%	12.5%	3.5%	4.84	4.38	+1.8
2024	31	MLB RAW	Milwaukee Brewers	30	30	151	655	148	66	24	100%	5.0	22.6%	10.1%	12.5%	3.7%	4.84	4.52	+1.5
2025	32	MIX MLE	3 levels	15	13	57	275	44	24	16	87%	3.8	15.3%	7.6%	7.7%	6.1%	8.16	6.14	-0.5
2025	32	MIX MLE	3 levels	15	13	57	275	44	24	16	87%	3.8	15.3%	7.6%	7.7%	6.1%	8.16	6.14	-0.5
2025	32	MIX RAW	3 levels	15	13	57	275	44	24	16	87%	3.8	16.0%	8.7%	7.3%	5.8%	8.16	6.45	-0.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			169	136	783	3358	792	271	104	80%	4.6	23.6%	8.1%	15.5%	3.1%	4.20	3.84	+14.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	NYM	7	7	43	182	37	16	7	100%	6.1	20.2%	9.0%	11.2%	3.8%	4.93	5.00	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	NYM	6	6	36	153	30	15	6	100%	6.0	19.2%	9.6%	9.7%	4.1%	5.17	5.34	+0.0
2028	35	MLB CTX	NYM	6	6	36	152	29	15	6	100%	6.0	19.2%	9.6%	9.7%	4.1%	5.17	5.34	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		175	142	819	3511	822	286	110	81%	4.7	23.4%	8.1%	15.3%	3.1%	4.24	3.88	+14.9
/ 200 IP	—	RAW		43	35	200	857	201	70	27	81%	4.7	23.4%	8.1%	15.3%	3.1%	4.24	3.88	+3.6
Career	—	MLE		231	194	1034	4418	1002	369	133	84%	4.5	22.7%	8.4%	14.3%	3.0%	4.23	3.90	+14.6
/ 200 IP	—	MLE		45	38	200	855	194	71	26	84%	4.5	22.7%	8.4%	14.3%	3.0%	4.23	3.90	+3.6

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 AAASyracuse Mets · 14 IPFIP 8.99 → 8.68 (−0.31)K% 10.5% → 9.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.53%	—	×0.860	9.05%
BB% (unintentional) /BF	7.89%	—	×0.900	7.11%
HR / BF /BF	10.53%	÷1.167	×1.100	9.93%

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 133).

Result: FIP 8.99 → 8.68, 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+Brooklyn Cyclones · 4 IPFIP 3.90 → 4.64 (+0.73)K% 19.0% → 14.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.05%	—	×0.770	14.67%
BB% (unintentional) /BF	19.05%	—	×1.150	21.90%
HR / BF /BF	0.00%	÷1.073	×1.250	0.00%

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 115).

Result: FIP 3.90 → 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).

2018 AAANashville Sounds · 72 IPFIP 3.75 → 4.16 (+0.40)K% 20.1% → 17.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.13%	—	×0.880	17.72%
BB% (unintentional) /BF	8.58%	—	×1.050	9.01%
HR / BF /BF	2.31%	÷0.988	×1.100	2.57%

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 98).

Result: FIP 3.75 → 4.16, 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 AAANashville Sounds · 29 IPFIP 3.06 → 3.91 (+0.85)K% 31.1% → 27.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.09%	—	×0.880	27.36%
BB% (unintentional) /BF	5.88%	—	×1.050	6.18%
HR / BF /BF	3.36%	÷0.868	×1.100	4.26%

Result: FIP 3.06 → 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).

2016 AAAOklahoma City Dodgers · 11 IPFIP 0.94 → 1.29 (+0.35)K% 31.9% → 28.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.91%	—	×0.880	28.09%
BB% (unintentional) /BF	4.26%	—	×1.050	4.47%
HR / BF /BF	0.00%	÷0.947	×1.100	0.00%

Result: FIP 0.94 → 1.29, 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 AABirmingham Barons · 112 IPFIP 2.80 → 3.34 (+0.53)K% 23.2% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.23%	—	×0.810	18.81%
BB% (unintentional) /BF	10.32%	—	×1.100	11.35%
HR / BF /BF	0.65%	÷1.102	×1.200	0.70%

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 120).

Result: FIP 2.80 → 3.34, 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
Frankie Montas (OGILVIE)	—	—	—	—	—	3.8%	—	5.34	19.2%	9.7%	36	+0.34	—	—	—
Guillermo Moscoso R 2018 · 5.07 FIP	34	SP-	5.07	18.2%	8.4%	4.5%	.318	4.35	12.6%	5.9%	104	-0.72	3.56	486	64
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	61
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	58
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	58
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	57
Juan Delgadillo R 2017 · 4.47 FIP	34	SP	4.47	10.9%	6.2%	2.7%	.311	—	—	—	—	—	4.47	143	56
Rolando Valdez R 2019 · 5.36 FIP	33	SP-	5.36	11.7%	7.6%	4.2%	.360	—	—	—	—	—	3.06	258	55
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	55
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	53
Walter Silva R 2009 · 5.72 FIP	32	SP	5.72	9.1%	12.4%	3.3%	.330	—	—	—	—	—	3.67	453	53
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	50
Francisco Rodriguez R 2017 · 4.43 FIP	34	SP	4.43	11.4%	8.2%	2.3%	.305	5.18	10.7%	1.4%	83	+0.75	3.16	334	50
Jesse Estrada R 2017 · 5.39 FIP	33	SP	5.39	8.3%	5.6%	4.2%	.285	4.34	14.4%	7.8%	85	-1.05	3.80	257	49
Cesar Carrillo R 2017 · 4.97 FIP	33	SP	4.97	12.0%	11.9%	2.5%	.320	6.22	11.0%	1.8%	66	+1.25	4.14	336	49
Tim Adleman R 2022 · 5.69 FIP	34	SP-	5.69	17.5%	8.6%	5.4%	.275	—	—	—	—	—	3.63	605	48
Michael Nix R 2016 · 4.60 FIP	33	SP	4.60	11.9%	11.6%	1.9%	.322	—	—	—	—	—	3.82	234	48
Francisco Cordova R 2016 · 4.59 FIP	32	SP	4.59	7.1%	7.7%	2.0%	.300	—	—	—	—	—	4.59	115	48
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	47
Salvador Valdez R 2017 · 3.87 FIP	34	SP	3.87	9.4%	6.1%	1.4%	.328	4.30	17.1%	8.9%	38	+0.43	3.60	326	47
J.D. Martin R 2016 · 4.85 FIP	33	SP	4.85	9.4%	11.0%	2.1%	.302	5.42	12.2%	-0.2%	249	+0.57	4.79	414	46

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	4.3	86	23.2%	8.3%	1.23	4.27
Scott Bankhead ('86-'95, 10 WAR*)	4.5	100	21.2%*	7.3%*	1.36*	4.16*
Joe Hesketh ('84-'94, 10 WAR*)	4.8	87	23.2%*	8.8%*	1.07*	3.95*
Pete Richert ('62-'74, 13 WAR*)	5.8	90	24.4%*	8.8%*	1.26*	4.04*
Drew Smyly ('24)	5.7	104	23.1%	7.7%	1.49	4.20
Rich Hill ('25)	7.1	101	23.9%	9.2%	1.12	4.02
Brad Peacock ('21)	2.8	93	24.7%	10.1%	1.27	4.11