Jorge López

P Washington Nationals

Age 34 RHP 6'3" / 200 lbs MLB peak 0.7 · 2027

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	30	MLBBAL	61	0	59	5.95	5.05	18.4%	7.1%	-0.3
2024	31	MIX3 levels	58	0	58	4.01	3.75	22.3%	7.7%	0.6	K 23.0% · BB 8.3% · FIP 3.78
2025	32	MLBWSH	26	0	25	6.57	3.10	16.0%	6.6%	0.4
2026	33	No MLB games played
2027PROJ	34	MLBWSH	147	0	154	4.24	4.18	22.1%	8.4%	0.7
2028PROJ	35	MLBWSH	106	0	111	4.40	4.51	21.2%	9.0%	0.1
2029PROJ	36	MLBWSH	80	0	84	4.69	4.88	20.2%	9.6%	-0.3

Last 3 seasons WAR 0.6 · Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

FB Velo52

xwOBA vs15

K%10

CompsChris Jakubauskas (age 32) · Tiago Da Silva (age 34) · Fernando Rodriguez Jr. (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 113104vR 96

ERA4.24

FIP4.18

K%22.1%

BB%8.4%

K-BB%13.7%

Proj IP154

Value2027 · WAR

Runs saved vs league avg · 154 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p5294.7

Whiff%

p1017.1%

p1016.2%

xwOBA vs

p150.354

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	26	26	153	618	147	57	9	100%	5.9	19.4%	10.0%	9.4%	1.7%	2.47	3.68	+3.2
2015	22	MIX RAW	2 levels	26	26	153	618	147	57	9	100%	5.9	23.8%	9.2%	14.6%	1.5%	2.47	3.06	+4.5
2016	23	MIX MLE	2 levels	25	24	125	582	113	70	17	96%	5.0	16.5%	12.8%	3.8%	3.2%	5.78	5.06	—
2016	23	MIX RAW	2 levels	25	24	125	582	113	70	17	96%	5.0	19.4%	12.0%	7.4%	2.9%	5.78	4.74	—
2017	24	MIX MLE	2 levels	40	13	106	447	105	37	7	32%	2.6	19.5%	9.1%	10.4%	1.4%	4.26	3.36	+1.5
2017	24	MIX RAW	2 levels	40	13	106	447	105	37	7	32%	2.6	23.5%	8.3%	15.2%	1.6%	4.26	3.02	+2.0
2018	25	MIX MLE	2 levels	43	9	91	397	72	31	11	21%	2.1	17.1%	7.9%	9.2%	2.9%	5.12	4.25	+0.3
2018	25	MIX RAW	2 levels	43	9	91	397	72	31	11	21%	2.1	18.1%	7.8%	10.3%	2.8%	5.12	4.11	+0.5
2019	26	MLB MLE	Kansas City Royals	39	18	124	548	109	42	27	46%	3.2	19.9%	7.7%	12.2%	5.3%	6.33	5.31	-1.1
2019	26	MLB RAW	Kansas City Royals	39	18	124	548	109	42	27	46%	3.2	19.9%	7.7%	12.2%	4.9%	6.33	5.19	-0.9
2020	27	MLB MLE	Baltimore Orioles	10	6	39	174	28	11	7	60%	3.9	16.1%	6.3%	9.8%	3.6%	6.69	4.54	+0.4
2020	27	MLB RAW	Baltimore Orioles	10	6	39	174	28	11	7	60%	3.9	16.1%	6.3%	9.8%	4.0%	6.69	4.84	+0.3
2021	28	MLB MLE	Baltimore Orioles	33	25	122	555	112	54	21	76%	3.7	20.2%	9.7%	10.5%	3.4%	6.07	4.50	+1.3
2021	28	MLB RAW	Baltimore Orioles	33	25	122	555	112	54	21	76%	3.7	20.2%	9.7%	10.5%	3.8%	6.07	4.83	+0.8
2022	29	MLB MLE	Minnesota Twins	67	0	71	298	72	26	4	0%	1.1	24.2%	8.7%	15.4%	1.3%	2.54	2.90	+1.4
2022	29	MLB RAW	Minnesota Twins	67	0	71	298	72	26	4	0%	1.1	24.2%	8.7%	15.4%	1.3%	2.54	2.90	+1.4
2023	30	MLB MLE	Baltimore Orioles	61	0	59	266	49	19	12	0%	1.0	18.4%	7.1%	11.3%	4.5%	5.95	4.94	-0.3
2023	30	MLB RAW	Baltimore Orioles	61	0	59	266	49	19	12	0%	1.0	18.4%	7.1%	11.3%	4.5%	5.95	5.05	-0.3
2024	31	MIX MLE	3 levels	58	0	58	247	55	19	7	0%	1.0	23.0%	8.3%	14.7%	2.9%	4.01	3.78	+0.5
2024	31	MIX MLE	3 levels	58	0	58	247	55	19	7	0%	1.0	23.0%	8.3%	14.7%	2.9%	4.01	3.78	+0.5
2024	31	MIX RAW	3 levels	58	0	58	247	55	19	7	0%	1.0	22.3%	7.7%	14.6%	2.8%	4.01	3.75	+0.6
2025	32	MLB MLE	Washington Nationals	26	0	25	106	17	7	1	0%	0.9	16.0%	6.6%	9.4%	0.9%	6.57	3.10	+0.4
2025	32	MLB MLE	Washington Nationals	26	0	25	106	17	7	1	0%	0.9	16.0%	6.6%	9.4%	0.9%	6.57	3.10	+0.4
2025	32	MLB RAW	Washington Nationals	26	0	25	106	17	7	1	0%	0.9	16.0%	6.6%	9.4%	0.9%	6.57	3.10	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			308	58	558	2454	485	204	84	19%	1.8	19.8%	8.3%	11.5%	3.4%	5.31	4.42	+0.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	WSH	147	0	154	650	144	55	18	0%	1.0	22.1%	8.4%	13.7%	2.7%	4.24	4.18	+0.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	WSH	106	0	111	470	100	42	14	0%	1.1	21.2%	9.0%	12.2%	3.1%	4.40	4.51	+0.1
2029	36	MLB MLE	WSH	80	0	84	355	72	34	12	0%	1.1	20.2%	9.6%	10.6%	3.4%	4.69	4.88	-0.3
2028	35	MLB CTX	WSH	106	0	111	469	99	42	14	0%	1.0	21.2%	9.0%	12.2%	3.1%	4.40	4.51	+0.1
2029	36	MLB CTX	WSH	80	0	84	355	72	34	12	0%	1.1	20.2%	9.6%	10.6%	3.4%	4.69	4.88	-0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		494	58	753	3279	656	280	111	12%	1.5	20.0%	8.5%	11.5%	3.4%	5.11	4.35	+1.8
/ 200 IP	—	RAW		131	15	200	871	174	74	29	12%	1.5	20.0%	8.5%	11.5%	3.4%	5.11	4.35	+0.5
Career	—	MLE		428	121	972	4238	815	388	125	28%	2.3	19.2%	9.1%	10.1%	3.0%	4.86	4.26	+0.6
/ 200 IP	—	MLE		88	25	200	872	168	80	26	28%	2.3	19.2%	9.1%	10.1%	3.0%	4.86	4.26	+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.

2024 AAAIowa Cubs · 4 IPFIP 4.94 → 5.08 (+0.15)K% 13.0% → 11.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.04%	—	×0.860	11.22%
BB% (unintentional) /BF	4.35%	—	×0.900	3.91%
HR / BF /BF	4.35%	÷1.079	×1.100	4.43%

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 4.94 → 5.08, 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 · 38 IPFIP 3.80 → 4.05 (+0.25)K% 20.9% → 18.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.86%	—	×0.880	18.36%
BB% (unintentional) /BF	6.13%	—	×1.050	6.44%
HR / BF /BF	3.07%	÷1.101	×1.100	3.06%

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 120).

Result: FIP 3.80 → 4.05, 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 · 104 IPFIP 2.99 → 3.36 (+0.37)K% 24.0% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.03%	—	×0.810	19.46%
BB% (unintentional) /BF	8.24%	—	×1.100	9.06%
HR / BF /BF	1.60%	÷1.393	×1.200	1.38%

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 2.99 → 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).

2016 AAAColorado Springs Sky Sox · 79 IPFIP 5.12 → 5.60 (+0.48)K% 17.0% → 14.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.97%	—	×0.880	14.93%
BB% (unintentional) /BF	13.88%	—	×1.050	14.58%
HR / BF /BF	3.08%	÷0.973	×1.100	3.49%

Result: FIP 5.12 → 5.60, 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 AABiloxi Shuckers · 45 IPFIP 3.52 → 3.96 (+0.45)K% 24.4% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.35%	—	×0.810	19.73%
BB% (unintentional) /BF	8.29%	—	×1.100	9.12%
HR / BF /BF	2.59%	÷1.243	×1.200	2.50%

Result: FIP 3.52 → 3.96, 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).

2015 AABiloxi Shuckers · 143 IPFIP 3.09 → 3.68 (+0.59)K% 24.0% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.95%	—	×0.810	19.40%
BB% (unintentional) /BF	9.09%	—	×1.100	10.00%
HR / BF /BF	1.57%	÷1.084	×1.200	1.74%

Result: FIP 3.09 → 3.68, 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).

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
Jorge López (OGILVIE)	—	—	—	—	—	2.7%	—	4.67	20.7%	11.5%	196	+0.49	—	—	—
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	57
Tiago Da Silva R 2019 · 3.95 FIP	34	SW	3.95	15.0%	7.2%	2.2%	.365	—	—	—	—	—	2.21	287	56
Fernando Rodriguez Jr. R 2018 · 4.07 FIP	34	SW	4.07	23.7%	9.9%	3.1%	.253	5.11	25.0%	13.8%	32	+1.04	3.34	335	56
Giovanni Carrara R 2002 · 4.78 FIP	34	SU	4.78	14.5%	7.2%	3.6%	.239	3.83	17.1%	7.9%	158	-0.95	2.50	462	54
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	52
Jean Machi R 2016 · 4.36 FIP	34	SU	4.36	18.3%	6.6%	3.6%	.282	4.12	18.6%	12.0%	67	-0.24	2.16	306	51
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	51
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	51
Brendan Donnelly R 2005 · 4.02 FIP	34	SU	4.02	19.6%	5.9%	3.3%	.264	4.02	17.6%	8.5%	98	-0.00	2.23	385	49
Huascar Brazobán R 2024 · 3.08 FIP	34	SU	3.08	23.5%	11.5%	0.9%	.259	3.71	21.3%	11.7%	144	+0.63	3.08	370	46
Tim Dillard R 2017 · 3.62 FIP	34	SU	3.62	15.1%	8.8%	1.2%	.310	4.32	13.2%	7.1%	188	+0.70	2.92	432	46
Greg Burke R 2015 · 3.61 FIP	32	SU	3.61	22.1%	7.3%	2.7%	.241	—	—	—	—	—	3.61	139	46
Juan Sandoval R 2015 · 3.82 FIP	34	SU	3.82	15.4%	6.0%	2.3%	.294	3.33	17.4%	8.8%	116	-0.49	3.24	225	46
Paolo Espino R 2021 · 4.32 FIP	34	SP-	4.32	20.2%	5.1%	4.2%	.277	4.81	16.6%	10.9%	260	+0.49	3.42	933	46
Anthony Telford R 2000 · 3.87 FIP	34	SU	3.87	20.6%	6.7%	3.0%	.287	4.54	16.2%	5.1%	24	+0.67	3.16	441	46
Scott McGough R 2024 · 4.56 FIP	34	SU	4.56	18.6%	12.2%	2.7%	.289	3.82	20.3%	13.1%	57	-0.74	3.55	429	45
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	45
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	45
Rafael Martín R 2017 · 3.40 FIP	33	SU	3.40	20.4%	5.1%	2.5%	.305	4.71	22.5%	16.5%	47	+1.31	2.94	227	45
Josh Lueke R 2019 · 4.11 FIP	34	SU	4.11	20.4%	6.4%	3.5%	.311	—	—	—	—	—	2.29	191	45

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	4.5	82	20.4%	8.9%	1.26	4.96
Brian Matusz ('16)	2.6	76	19.8%	8.7%	1.34	4.92
Brad Havens ('81-'89, 2 WAR*)	3.0	84	21.0%*	9.5%*	1.43*	4.96*
Ross Ohlendorf ('16)	2.9	84	17.2%	8.6%	1.38	4.82
Daniel Norris ('23)	2.9	73	21.5%	8.9%	1.39	4.73
Mark Grant ('84-'93, 0 WAR*)	3.2	91	18.0%*	8.5%*	1.41*	4.84*
Hank Fischer ('62-'67, -1 WAR*)	2.7	91	19.8%*	8.2%*	1.33*	5.07*