Edwin Escobar

P Iowa Cubs

Age 35 LHP 6'2" / 225 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2015	23	MIX2 levels	20	7	51	4.97	5.69	10.7%	11.2%	—	K 9.6% · BB 11.9% · FIP 5.96
2016	24	MIX2 levels	44	19	122	4.59	4.17	15.1%	8.5%	0.5	K 13.7% · BB 8.8% · FIP 4.28
2024	32	AAACHC	31	0	46	4.86	5.11	22.8%	15.5%	—	K 19.6% · BB 14.0% · FIP 5.12
2025	33	No MLB games played
2026	34	No MLB games played
2027PROJ	35	MLBCHC	5	0	5	5.23	5.28	19.1%	13.0%	-0.0
2028PROJMLE	36	MLBCHC	—	—	0	5.38	5.55	18.5%	13.2%	—
2029PROJMLE	37	MLBCHC	—	—	0	5.63	5.86	17.8%	13.6%	—

Last 3 seasons WAR 0.5 · Next 3 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2016

FB Velo21

K%10

xwOBA vs10

CompsLuke Westphal (age 35) · John Bale (age 35) · Tim Byrdak (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117125vR 133

ERA5.23

FIP5.28

K%19.1%

BB%13.0%

K-BB%6.1%

Proj IP52

Value2027 · WAR

Runs saved vs league avg · 52 IP

Grades · Statcast · Contract

Scouting Grades

STUF4040

CTRL4040

CMD3030

K†4545

Statcast Percentiles

FB Velo

p2192.4

Whiff%

p1720.6%

p1014.7%

xwOBA vs

p100.383

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)
2014	22	MLB MLE	Boston Red Sox	2	0	2	8	2	0	0	0%	1.0	25.0%	0.0%	25.0%	0.0%	4.50	0.99	+0.1
2014	22	MLB RAW	Boston Red Sox	2	0	2	8	2	0	0	0%	1.0	25.0%	0.0%	25.0%	0.0%	4.50	1.10	+0.1
2015	23	MIX MLE	2 levels	20	7	51	224	24	25	8	35%	2.5	9.6%	11.9%	-2.3%	3.9%	4.97	5.96	—
2015	23	MIX RAW	2 levels	20	7	51	224	24	25	8	35%	2.5	10.7%	11.2%	-0.4%	3.6%	4.97	5.69	—
2016	24	MIX MLE	2 levels	44	19	122	542	82	46	12	43%	2.8	13.7%	8.8%	4.9%	2.2%	4.59	4.28	+0.4
2016	24	MIX RAW	2 levels	44	19	122	542	82	46	12	43%	2.8	15.1%	8.5%	6.6%	2.2%	4.59	4.17	+0.5
2024	32	AAA MLE	Iowa Cubs	31	0	46	206	47	32	7	0%	1.5	19.6%	14.0%	5.6%	3.5%	4.86	5.12	—
2024	32	AAA MLE	Iowa Cubs	31	0	46	206	47	32	7	0%	1.5	19.6%	14.0%	5.6%	3.5%	4.86	5.12	—
2024	32	AAA RAW	Iowa Cubs	31	0	46	206	47	32	7	0%	1.5	22.8%	15.5%	7.3%	3.4%	4.86	5.11	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			27	2	26	124	19	9	4	7%	1.0	15.3%	7.3%	8.1%	3.2%	7.01	4.70	-0.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	CHC	5	0	5	21	4	3	1	0%	1.0	19.1%	13.0%	6.1%	3.2%	5.23	5.28	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	CHC	32	0	33	140	26	19	5	0%	1.0	18.5%	13.2%	5.3%	3.6%	5.38	5.55	-0.4
2029	37	MLB MLE	CHC	23	0	24	102	18	14	4	0%	1.0	17.8%	13.6%	4.2%	3.9%	5.63	5.86	-0.3
2028	36	MLB CTX	CHC	—	—	0	—	—	—	—	—	—	18.5%	13.2%	5.3%	3.6%	5.38	5.55	—
2029	37	MLB CTX	CHC	—	—	0	—	—	—	—	—	—	17.8%	13.6%	4.2%	3.9%	5.63	5.86	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		27	2	26	125	19	9	4	7%	1.0	15.4%	7.3%	8.0%	3.2%	6.99	4.50	+0.0
/ 200 IP	—	RAW		210	15	200	965	146	69	31	7%	1.0	15.4%	7.3%	8.0%	3.2%	6.99	4.50	+0.1
Career	—	MLE		97	26	221	980	138	103	28	27%	2.3	14.1%	10.5%	3.5%	2.9%	4.73	4.81	-0.8
/ 200 IP	—	MLE		88	24	200	888	125	94	25	27%	2.3	14.1%	10.5%	3.5%	2.9%	4.73	4.81	-1.9

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.

2024 AAAIowa Cubs · 46 IPFIP 5.01 → 5.12 (+0.11)K% 22.8% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.82%	—	×0.860	19.62%
BB% (unintentional) /BF	15.53%	—	×0.900	13.98%
HR / BF /BF	3.40%	÷1.079	×1.100	3.46%

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 116).

Result: FIP 5.01 → 5.12, 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).

2016 AAAReno Aces · 98 IPFIP 3.94 → 4.16 (+0.22)K% 15.3% → 13.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.26%	—	×0.880	13.43%
BB% (unintentional) /BF	8.69%	—	×1.050	9.12%
HR / BF /BF	1.88%	÷1.092	×1.100	1.89%

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 118).

Result: FIP 3.94 → 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).

2015 AAAPawtucket Red Sox · 50 IPFIP 5.60 → 5.96 (+0.35)K% 10.9% → 9.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.86%	—	×0.880	9.56%
BB% (unintentional) /BF	11.31%	—	×1.050	11.88%
HR / BF /BF	3.62%	÷1.012	×1.100	3.93%

Result: FIP 5.60 → 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).

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
Edwin Escobar (OGILVIE)	—	—	5.28	19.1%	13.0%	3.2%	—	5.68	18.2%	4.9%	57	+0.40	—	—	—
Luke Westphal L 2024 · 5.19 FIP	35	SW	5.19	15.7%	16.4%	2.4%	.299	—	—	—	—	—	2.97	294	65
John Bale L 2009 · 4.31 FIP	35	SU	4.31	17.6%	11.8%	2.2%	.333	—	—	—	—	—	2.49	168	64
Tim Byrdak L 2009 · 5.07 FIP	35	SU	5.07	22.2%	13.8%	3.8%	.185	3.48	23.8%	13.5%	107	-1.59	2.85	320	58
Hideki Okajima L 2010 · 4.27 FIP	34	SU	4.27	15.5%	7.0%	2.9%	.333	—	—	—	—	—	3.05	238	56
Stephen Randolph L 2007 · 5.97 FIP	33	SU	5.97	28.2%	19.2%	5.1%	.450	—	—	—	—	—	4.83	155	48
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	45
Kwang Hyun Kim L 2021 · 4.21 FIP	33	SP-	4.21	17.7%	8.2%	2.9%	.266	—	—	—	—	—	3.76	146	41
Ivan Maldonado R 2015 · 4.60 FIP	35	SW	4.60	12.8%	8.7%	2.7%	.269	5.41	6.4%	-3.3%	104	+0.81	4.60	172	38
Chris Michalak L 2006 · 5.71 FIP	35	SP-	5.71	6.2%	8.6%	3.7%	.273	—	—	—	—	—	4.71	186	38
Brian Schlitter R 2021 · 4.70 FIP	35	SU	4.70	14.9%	9.1%	3.1%	.318	—	—	—	—	—	3.27	300	37
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	36
Tomas Solis L 2017 · 5.48 FIP	34	SP	5.48	11.3%	7.9%	4.2%	.322	6.44	8.9%	1.4%	57	+0.96	4.20	327	35
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	35
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	35
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	34
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	34
Jose Manuel Lopez R 2019 · 6.14 FIP	34	SU	6.14	17.9%	14.2%	5.0%	.299	—	—	—	—	—	3.50	170	34
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	34
Brendan Donnelly R 2006 · 4.21 FIP	35	SU	4.21	19.1%	9.0%	2.9%	.260	3.19	18.6%	10.1%	60	-1.02	2.23	385	32
Fernando Rodriguez Jr. R 2019 · 5.11 FIP	35	SW	5.11	25.0%	11.2%	4.9%	.312	—	—	—	—	—	3.34	335	32

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	0.7	34	18.0%	12.5%	1.33	5.68
Matt Anderson ('05)	1.3	51	19.3%	13.5%	1.05	5.19
Daryl Patterson ('68-'74, -1 WAR*)	1.2	46	18.3%*	11.2%*	1.27*	5.03*
Will Cunnane ('04)	1.4	46	18.9%	10.4%	1.25	5.26
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*
Valerio De Los Santos ('08)	1.3	51	18.7%	11.5%	1.30	4.57
Bill Wilson ('69-'73, -0 WAR*)	1.3	52	20.3%*	11.5%*	1.13*	4.84*