Germán Márquez

P San Diego Padres

Age 32 RHP 6'1" / 230 lbs MLB

Overview

MLBSP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MIX6 levels	8	8	23	7.04	4.75	24.5%	8.2%	0.2	K 24.5% · BB 8.2% · FIP 4.56
2025	30	MIX2 levels	28	28	134	6.53	5.12	15.0%	7.7%	0.4	K 14.0% · BB 8.0% · FIP 4.82
2026	31	MIX2 levels	25	20	99	5.02	6.07	15.1%	10.6%	-0.7	K 14.1% · BB 10.0% · FIP 5.95
2027PROJ	32	MLBSD	6	6	39	5.60	5.61	14.8%	9.2%	-0.1
2028PROJ	33	MLBSD	5	5	28	5.84	5.95	14.0%	9.6%	-0.1
2029PROJ	34	MLBSD	3	3	17	6.09	6.26	13.1%	10.1%	-0.1

Last 3 seasons WAR -0.1 · Next 3 projected WAR -0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo51

K%10

xwOBA vs10

CompsZack Segovia (age 32) · Stan Spencer (age 30) · Juan Delgadillo (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 140131vR 123

ERA5.60

FIP5.61

K%14.8%

BB%9.2%

K-BB%5.7%

Proj IP39

Value2027 · WAR

Runs saved vs league avg · 39 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p5194.6

Whiff%

p1019.0%

p1015.7%

xwOBA vs

p100.362

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	20	A+ MLE	Charlotte Stone Crabs	26	23	139	596	104	29	6	88%	5.3	13.4%	5.6%	7.8%	1.5%	3.56	3.51	—
2015	20	A+ RAW	Charlotte Stone Crabs	26	23	139	596	104	29	6	88%	5.3	17.4%	4.9%	12.6%	1.0%	3.56	2.79	—
2016	21	MIX MLE	3 levels	32	29	187	775	170	44	16	91%	5.9	18.4%	6.1%	12.3%	2.1%	3.36	3.47	+4.4
2016	21	MIX RAW	3 levels	32	29	187	775	170	44	16	91%	5.9	21.9%	5.7%	16.3%	2.1%	3.36	3.10	+5.4
2017	22	MIX MLE	2 levels	32	31	172	738	165	46	27	97%	5.4	21.0%	6.6%	14.4%	3.2%	4.29	3.90	+3.1
2017	22	MIX RAW	2 levels	32	31	172	738	165	46	27	97%	5.4	22.4%	6.2%	16.1%	3.7%	4.29	4.02	+2.8
2018	23	MLB MLE	Colorado Rockies	33	33	196	817	230	52	24	100%	5.9	28.2%	6.4%	21.8%	2.6%	3.77	2.96	+6.0
2018	23	MLB RAW	Colorado Rockies	33	33	196	817	230	52	24	100%	5.9	28.2%	6.4%	21.8%	2.9%	3.77	3.14	+5.5
2019	24	MLB MLE	Colorado Rockies	28	28	174	721	175	35	29	100%	6.2	24.3%	4.9%	19.4%	3.6%	4.76	3.62	+3.8
2019	24	MLB RAW	Colorado Rockies	28	28	174	721	175	35	29	100%	6.2	24.3%	4.9%	19.4%	4.0%	4.76	3.86	+3.2
2020	25	MLB MLE	Colorado Rockies	13	13	82	344	73	25	6	100%	6.3	21.2%	7.3%	14.0%	1.5%	3.75	3.08	+2.4
2020	25	MLB RAW	Colorado Rockies	13	13	82	344	73	25	6	100%	6.3	21.2%	7.3%	14.0%	1.7%	3.75	3.19	+2.3
2021	26	MLB MLE	Colorado Rockies	32	32	180	756	176	59	21	100%	5.6	23.3%	7.8%	15.5%	2.5%	4.40	3.47	+4.2
2021	26	MLB RAW	Colorado Rockies	32	32	180	756	176	59	21	100%	5.6	23.3%	7.8%	15.5%	2.8%	4.40	3.64	+3.8
2022	27	MLB MLE	Colorado Rockies	31	31	182	779	150	63	30	100%	5.9	19.3%	8.1%	11.2%	3.4%	4.95	4.37	+2.2
2022	27	MLB RAW	Colorado Rockies	31	31	182	779	150	63	30	100%	5.9	19.3%	8.1%	11.2%	3.9%	4.95	4.64	+1.6
2023	28	MLB MLE	Colorado Rockies	4	4	20	80	17	3	4	100%	5.0	21.2%	3.8%	17.5%	4.4%	4.95	4.21	+0.3
2023	28	MLB RAW	Colorado Rockies	4	4	20	80	17	3	4	100%	5.0	21.2%	3.8%	17.5%	5.0%	4.95	4.45	+0.2
2024	29	MIX MLE	6 levels	8	8	23	110	27	9	5	100%	2.9	24.5%	8.2%	16.4%	4.5%	7.04	4.56	+0.2
2024	29	MIX MLE	6 levels	8	8	23	110	27	9	5	100%	2.9	24.5%	8.2%	16.4%	4.5%	7.04	4.56	+0.2
2024	29	MIX RAW	6 levels	8	8	23	110	27	9	5	100%	2.9	24.5%	8.2%	16.4%	4.5%	7.04	4.75	+0.2
2025	30	MIX MLE	2 levels	28	28	134	621	93	48	24	100%	4.8	14.0%	8.0%	6.1%	3.5%	6.53	4.82	+0.9
2025	30	MIX MLE	2 levels	28	28	134	621	93	48	24	100%	4.8	14.0%	8.0%	6.1%	3.5%	6.53	4.82	+0.9
2025	30	MIX RAW	2 levels	28	28	134	621	93	48	24	100%	4.8	15.0%	7.7%	7.2%	3.9%	6.53	5.12	+0.4
2026	31	MIX MLE	2 levels	25	20	99	445	67	47	22	80%	3.9	14.1%	10.0%	4.1%	5.0%	5.02	5.95	-0.6
2026	31	MIX MLE	2 levels	25	20	99	445	67	47	22	80%	3.9	14.1%	10.0%	4.1%	5.0%	5.02	5.95	-0.6
2026	31	MIX RAW	2 levels	25	20	99	445	67	47	22	80%	3.9	15.1%	10.6%	4.5%	4.9%	5.02	6.07	-0.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			216	210	1199	5143	1106	364	179	97%	5.6	21.5%	7.1%	14.4%	3.5%	4.70	4.11	+18.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	SD	6	6	39	165	24	15	7	100%	6.5	14.8%	9.2%	5.7%	4.1%	5.60	5.61	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	SD	5	5	28	118	17	11	5	100%	5.6	14.0%	9.6%	4.4%	4.3%	5.78	5.89	-0.1
2029	34	MLB MLE	SD	3	3	17	72	9	7	3	100%	5.7	13.1%	10.1%	3.0%	4.6%	6.03	6.20	-0.1
2028	33	MLB CTX	SD	5	5	28	118	17	11	5	100%	5.6	14.0%	9.6%	4.4%	4.4%	5.84	5.95	-0.1
2029	34	MLB CTX	SD	3	3	17	72	9	7	3	100%	5.7	13.1%	10.1%	3.0%	4.7%	6.09	6.26	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		224	218	1244	5333	1132	383	188	97%	5.6	21.2%	7.2%	14.1%	3.5%	4.74	4.15	+18.4
/ 200 IP	—	RAW		36	35	200	857	182	62	30	97%	5.6	21.2%	7.2%	14.1%	3.5%	4.74	4.15	+3.0
Career	—	MLE		292	280	1587	6782	1375	469	199	96%	5.4	20.3%	6.9%	13.4%	2.9%	4.45	3.87	+18.0
/ 200 IP	—	MLE		37	35	200	855	173	59	25	96%	5.4	20.3%	6.9%	13.4%	2.9%	4.45	3.87	+2.9

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.

2026 AAASt. Paul Saints · 46 IPFIP 5.38 → 5.62 (+0.24)K% 14.4% → 12.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.35%	—	×0.860	12.34%
BB% (unintentional) /BF	11.00%	—	×0.900	9.90%
HR / BF /BF	3.83%	÷1.000	×1.100	4.21%

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 100).

Result: FIP 5.38 → 5.62, 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 AAAAlbuquerque Isotopes · 7 IPFIP 2.54 → 3.04 (+0.50)K% 33.3% → 28.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.860	28.67%
BB% (unintentional) /BF	3.33%	—	×0.900	3.00%
HR / BF /BF	3.33%	÷1.017	×1.100	3.61%

Result: FIP 2.54 → 3.04, 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).

2024 AAHartford Yard Goats · 9 IPFIP 2.64 → 3.57 (+0.93)K% 27.0% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.03%	—	×0.810	21.89%
BB% (unintentional) /BF	2.70%	—	×1.100	2.97%
HR / BF /BF	2.70%	÷0.916	×1.200	3.54%

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 83).

Result: FIP 2.64 → 3.57, 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 AAAAlbuquerque Isotopes · 10 IPFIP 1.96 → 2.20 (+0.24)K% 48.6% → 42.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	48.65%	—	×0.880	42.81%
BB% (unintentional) /BF	0.00%	—	×1.050	0.00%
HR / BF /BF	5.41%	÷1.202	×1.100	4.95%

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 140).

Result: FIP 1.96 → 2.20, 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 AAHartford Yard Goats · 136 IPFIP 2.80 → 3.33 (+0.53)K% 22.8% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.78%	—	×0.810	18.46%
BB% (unintentional) /BF	5.79%	—	×1.100	6.37%
HR / BF /BF	1.63%	÷1.094	×1.200	1.78%

Result: FIP 2.80 → 3.33, 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).

2016 AAAAlbuquerque Isotopes · 31 IPFIP 3.95 → 4.03 (+0.08)K% 23.4% → 20.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.39%	—	×0.880	20.58%
BB% (unintentional) /BF	4.84%	—	×1.050	5.08%
HR / BF /BF	4.03%	÷1.202	×1.100	3.69%

Result: FIP 3.95 → 4.03, 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 A+Charlotte Stone Crabs · 139 IPFIP 2.80 → 3.51 (+0.72)K% 17.4% → 13.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.45%	—	×0.770	13.44%
BB% (unintentional) /BF	4.87%	—	×1.150	5.60%
HR / BF /BF	1.01%	÷0.822	×1.250	1.53%

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 64).

Result: FIP 2.80 → 3.51, 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
Germán Márquez (OGILVIE)	—	—	—	—	—	4.1%	—	6.01	13.6%	3.8%	45	+0.40	—	—	—
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	71
Stan Spencer R 2000 · 4.42 FIP	30	SP	4.42	19.2%	8.7%	3.4%	.259	—	—	—	—	—	3.61	119	70
Juan Delgadillo R 2015 · 4.54 FIP	32	SP	4.54	15.5%	7.2%	3.3%	.316	4.47	10.9%	4.7%	39	-0.07	4.47	143	68
Scott Carroll R 2017 · 5.71 FIP	32	SP	5.71	14.4%	8.6%	5.0%	.300	—	—	—	—	—	3.92	377	66
Miguel Ruiz R 2017 · 5.60 FIP	31	SP	5.60	10.6%	11.0%	3.6%	.279	—	—	—	—	—	5.60	117	65
Jeremy Hefner R 2016 · 4.64 FIP	30	SP	4.64	16.8%	9.1%	3.3%	.305	—	—	—	—	—	3.56	322	64
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	63
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	63
David Fletcher R 2024 · 5.67 FIP	30	SP-	5.67	7.5%	10.3%	3.5%	.294	—	—	—	—	—	5.67	100	63
Walter Silva R 2009 · 5.72 FIP	32	SP	5.72	9.1%	12.4%	3.3%	.330	—	—	—	—	—	3.67	453	62
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	61
Justin Lehr R 2009 · 6.01 FIP	31	SP	6.01	11.5%	9.4%	4.9%	.274	—	—	—	—	—	4.23	148	61
Cody Mincey R 2022 · 5.56 FIP	30	SP	5.56	13.9%	8.4%	4.7%	.297	—	—	—	—	—	2.49	147	60
Osvaldo Fernandez R 2001 · 4.53 FIP	32	SP-	4.53	9.6%	8.2%	2.2%	.324	—	—	—	—	—	4.25	387	59
Mitch Atkins R 2017 · 4.77 FIP	31	SP	4.77	14.3%	8.1%	3.4%	.303	—	—	—	—	—	3.65	215	59
Mario Gonzalez R 2017 · 4.53 FIP	30	SP	4.53	14.1%	5.0%	3.6%	.345	—	—	—	—	—	3.94	368	59
Reinier Roibal R 2019 · 4.91 FIP	30	SP	4.91	17.5%	11.8%	3.3%	.426	—	—	—	—	—	3.11	133	59
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	59
Elih Villanueva R 2017 · 5.48 FIP	31	SP	5.48	14.1%	6.0%	5.1%	.365	—	—	—	—	—	3.83	225	59
Marco Carrillo R 2019 · 6.08 FIP	32	SP	6.08	9.9%	9.1%	4.9%	.334	—	—	—	—	—	5.33	194	58

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	6.4	99	21.0%	7.5%	1.37	4.77
Derek Holland ('21)	7.3	113	19.7%	8.4%	1.35	4.62
Chase Anderson ('24)	5.5	101	19.7%	8.1%	1.48	4.41
Sterling Hitchcock ('04)	6.4	107	17.7%	8.4%	1.27	4.80
Larry McWilliams ('78-'90, 10 WAR*)	7.8	130	19.0%*	8.1%*	1.24*	4.56*
Dennis Bennett ('62-'68, 7 WAR*)	4.3	123	19.8%*	8.1%*	1.41*	4.40*
Frank Pastore ('79-'86, 6 WAR*)	4.9	123	18.1%*	7.2%*	1.17*	4.88*