Seth Lugo

P Kansas City Royals

Age 37 RHP 6'4" / 225 lbs MLB

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2024	34	MLBKC	33	33	207	3.00	3.04	21.7%	5.6%	6.2
2025	35	MLBKC	26	26	145	4.15	4.91	20.5%	8.8%	0.8
2026	36	MLBKC	32	32	172	5.17	4.22	17.8%	7.2%	2.4
2027PROJ	37	MLBKC	115	0	121	4.51	4.78	18.2%	7.4%	-0.3
2028PROJ	38	MLBKC	50	0	52	4.93	5.21	17.1%	8.2%	-0.4
2029PROJ	39	MLBKC	20	0	21	5.36	5.65	15.9%	8.8%	-0.3

Last 3 seasons WAR 9.4 · Next 3 projected WAR -0.9Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs24

K%20

FB Velo11

Form9.00 ERA (proj 4.51) · K 16.2% (proj 18.2%) · BB 6.3% (proj 7.4%) · last 60d · 22 IP through Oct 27

CompsMasato Yoshii (age 37) · Kenshin Kawakami (age 35) · Hiroki Kuroda (age 37)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 125117vR 109

ERA4.51

FIP4.78

K%18.2%

BB%7.4%

K-BB%10.8%

Proj IP121

Value2027 · WAR

Runs saved vs league avg · 121 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1191.7

Whiff%

p1018.7%

p2017.8%

xwOBA vs

p240.343

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	25	MIX MLE	2 levels	24	24	136	580	127	34	11	100%	5.7	18.1%	6.4%	11.7%	2.0%	3.84	3.50	—
2015	25	MIX RAW	2 levels	24	24	136	580	127	34	11	100%	5.7	21.9%	5.9%	16.0%	1.9%	3.84	3.03	—
2016	26	MIX MLE	2 levels	38	22	137	601	107	38	17	58%	3.6	16.6%	6.5%	10.1%	2.8%	4.72	4.09	+2.1
2016	26	MIX RAW	2 levels	38	22	137	601	107	38	17	58%	3.6	17.8%	6.3%	11.5%	2.8%	4.72	3.98	+2.3
2017	27	MIX MLE	3 levels	23	21	121	519	104	27	16	91%	5.3	19.9%	5.4%	14.5%	2.9%	4.69	3.66	+2.5
2017	27	MIX RAW	3 levels	23	21	121	519	104	27	16	91%	5.3	20.0%	5.2%	14.8%	3.1%	4.69	3.77	+2.4
2018	28	MLB MLE	New York Mets	54	5	101	410	103	24	9	9%	1.9	25.1%	5.9%	19.3%	2.2%	2.66	2.93	+2.0
2018	28	MLB RAW	New York Mets	54	5	101	410	103	24	9	9%	1.9	25.1%	5.9%	19.3%	2.2%	2.66	2.93	+2.0
2019	29	MLB MLE	New York Mets	61	0	80	314	104	12	8	0%	1.3	33.1%	3.8%	29.3%	2.6%	2.70	2.19	+2.3
2019	29	MLB RAW	New York Mets	61	0	80	314	104	12	8	0%	1.3	33.1%	3.8%	29.3%	2.5%	2.70	2.25	+2.2
2020	30	MLB MLE	New York Mets	16	7	37	160	47	9	8	44%	2.3	29.4%	5.6%	23.8%	5.0%	5.15	4.08	+0.2
2020	30	MLB RAW	New York Mets	16	7	37	160	47	9	8	44%	2.3	29.4%	5.6%	23.8%	5.0%	5.15	4.11	+0.2
2021	31	MIX MLE	3 levels	50	1	51	217	60	21	6	2%	1.0	28.2%	8.7%	19.5%	3.1%	3.37	3.52	+0.6
2021	31	MIX RAW	3 levels	50	1	51	217	60	21	6	2%	1.0	27.6%	9.7%	18.0%	2.8%	3.37	3.51	+0.6
2022	32	MLB MLE	New York Mets	62	0	65	272	69	17	9	0%	1.0	25.4%	6.2%	19.1%	3.3%	3.60	3.57	+0.8
2022	32	MLB RAW	New York Mets	62	0	65	272	69	17	9	0%	1.0	25.4%	6.2%	19.1%	3.3%	3.60	3.56	+0.8
2023	33	MLB MLE	San Diego Padres	26	26	146	604	140	36	19	100%	5.6	23.2%	6.0%	17.2%	3.1%	3.57	3.58	+3.2
2023	33	MLB RAW	San Diego Padres	26	26	146	604	140	36	19	100%	5.6	23.2%	6.0%	17.2%	3.1%	3.57	3.61	+3.2
2024	34	MLB MLE	Kansas City Royals	33	33	207	836	181	47	16	100%	6.3	21.7%	5.6%	16.0%	2.1%	3.00	3.12	+5.9
2024	34	MLB MLE	Kansas City Royals	33	33	207	836	181	47	16	100%	6.3	21.7%	5.6%	16.0%	2.1%	3.00	3.12	+5.9
2024	34	MLB RAW	Kansas City Royals	33	33	207	836	181	47	16	100%	6.3	21.7%	5.6%	16.0%	1.9%	3.00	3.04	+6.2
2025	35	MLB MLE	Kansas City Royals	26	26	145	611	125	54	27	100%	5.6	20.5%	8.8%	11.6%	4.8%	4.15	5.11	+0.5
2025	35	MLB MLE	Kansas City Royals	26	26	145	611	125	54	27	100%	5.6	20.5%	8.8%	11.6%	4.8%	4.15	5.11	+0.5
2025	35	MLB RAW	Kansas City Royals	26	26	145	611	125	54	27	100%	5.6	20.5%	8.8%	11.6%	4.4%	4.15	4.91	+0.8
2026	36	MLB MLE	Kansas City Royals	32	32	172	751	134	54	23	100%	5.4	17.8%	7.2%	10.7%	3.3%	5.17	4.32	+2.2
2026	36	MLB MLE	Kansas City Royals	32	32	172	751	134	54	23	100%	5.4	17.8%	7.2%	10.7%	3.3%	5.17	4.32	+2.2
2026	36	MLB RAW	Kansas City Royals	32	32	172	751	134	54	23	100%	5.4	17.8%	7.2%	10.7%	3.1%	5.17	4.22	+2.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			392	155	1165	4849	1088	312	145	40%	3.0	22.4%	6.4%	16.0%	3.0%	3.74	3.65	+12.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	37	MLB	KC	115	0	121	511	93	38	18	0%	1.1	18.2%	7.4%	10.8%	3.5%	4.51	4.78	-0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	38	MLB MLE	KC	49	0	52	218	37	18	9	0%	1.1	17.1%	8.2%	8.9%	3.9%	4.93	5.21	-0.4
2029	39	MLB MLE	KC	20	0	21	87	14	8	4	0%	1.0	15.9%	8.8%	7.1%	4.4%	5.36	5.65	-0.2
2028	38	MLB CTX	KC	50	0	52	220	38	18	9	0%	1.0	17.1%	8.2%	8.9%	3.9%	4.93	5.21	-0.4
2029	39	MLB CTX	KC	20	0	21	89	14	8	4	0%	1.1	15.9%	8.8%	7.1%	4.4%	5.36	5.65	-0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		461	155	1238	5155	1139	338	157	34%	2.7	22.1%	6.5%	15.6%	3.1%	3.81	3.74	+12.0
/ 200 IP	—	RAW		74	25	200	833	184	55	25	34%	2.7	22.1%	6.5%	15.6%	3.1%	3.81	3.74	+1.9
Career	—	MLE		445	197	1399	5875	1272	376	174	44%	3.1	21.7%	6.4%	15.3%	3.0%	3.90	3.71	+11.8
/ 200 IP	—	MLE		64	28	200	840	182	54	25	44%	3.1	21.7%	6.4%	15.3%	3.0%	3.90	3.71	+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.

2017 AABinghamton Rumble Ponies · 13 IPFIP 2.21 → 2.59 (+0.38)K% 28.8% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.85%	—	×0.810	23.37%
BB% (unintentional) /BF	3.85%	—	×1.100	4.23%
HR / BF /BF	1.92%	÷1.365	×1.200	1.69%

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 173).

Result: FIP 2.21 → 2.59, 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+St. Lucie Mets · 7 IPFIP 5.96 → 7.59 (+1.63)K% 12.9% → 9.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.90%	—	×0.770	9.94%
BB% (unintentional) /BF	3.23%	—	×1.150	3.71%
HR / BF /BF	6.45%	÷0.911	×1.250	8.85%

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 82).

Result: FIP 5.96 → 7.59, 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 AAALas Vegas 51s · 73 IPFIP 3.92 → 4.16 (+0.24)K% 18.2% → 16.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.18%	—	×0.880	16.00%
BB% (unintentional) /BF	5.87%	—	×1.050	6.16%
HR / BF /BF	2.93%	÷1.088	×1.100	2.96%

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 3.92 → 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 AABinghamton Mets · 109 IPFIP 3.07 → 3.58 (+0.51)K% 20.8% → 16.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.82%	—	×0.810	16.86%
BB% (unintentional) /BF	6.22%	—	×1.100	6.85%
HR / BF /BF	1.72%	÷1.088	×1.200	1.89%

Result: FIP 3.07 → 3.58, 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 AAALas Vegas 51s · 27 IPFIP 2.88 → 3.17 (+0.30)K% 26.3% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.32%	—	×0.880	23.16%
BB% (unintentional) /BF	4.39%	—	×1.050	4.61%
HR / BF /BF	2.63%	÷1.099	×1.100	2.63%

Result: FIP 2.88 → 3.17, 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
Seth Lugo (OGILVIE)	—	—	—	—	—	3.5%	—	5.34	16.8%	8.4%	72	+0.56	—	—	—
Masato Yoshii R 2002 · 4.15 FIP	37	SP-	4.15	13.4%	5.4%	2.7%	.295	—	—	—	—	—	4.15	757	63
Kenshin Kawakami R 2010 · 4.06 FIP	35	SP	4.06	15.1%	5.6%	2.8%	.293	—	—	—	—	—	3.97	244	30
Hiroki Kuroda R 2012 · 3.66 FIP	37	SP	3.66	18.7%	5.5%	2.6%	.277	3.42	18.0%	13.3%	400	-0.24	2.96	1,319	25
Jonathan Castellanos R 2019 · 5.24 FIP	37	SW	5.24	11.0%	9.0%	3.5%	.348	—	—	—	—	—	3.40	394	23
Jarrett Grube R 2017 · 4.41 FIP	35	SP	4.41	15.8%	8.1%	3.0%	.296	—	—	—	—	—	3.75	393	19
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	17
Jose Oyervides R 2019 · 5.66 FIP	37	SP	5.66	16.3%	11.2%	4.6%	.320	—	—	—	—	—	3.01	407	17
Scott Atchison R 2013 · 3.80 FIP	37	SU	3.80	14.4%	6.2%	2.1%	.273	3.60	16.7%	12.9%	92	-0.20	2.52	340	16
Walter Silva R 2015 · 4.15 FIP	38	SP	4.15	14.8%	7.4%	2.5%	.311	3.91	10.4%	3.7%	209	-0.24	3.67	453	16
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	15
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	14
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	13
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	13
Pablo Ortega R 2015 · 4.52 FIP	38	SP	4.52	8.6%	7.3%	2.2%	.291	4.24	11.5%	6.0%	236	-0.28	4.01	375	13
Pete Walker R 2006 · 4.45 FIP	37	SU	4.45	19.6%	8.0%	3.6%	.337	—	—	—	—	—	4.45	327	12
Matt Bush R 2023 · 3.79 FIP	37	SU	3.79	26.2%	8.7%	3.3%	.225	—	—	—	—	—	2.87	249	9
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	9
Ivan Maldonado R 2016 · 5.41 FIP	36	SP	5.41	6.4%	9.7%	3.0%	.325	—	—	—	—	—	4.60	172	9
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	9
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	8

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	6.8	97	21.7%	6.9%	1.20	3.88
Collin McHugh ('23)	5.0	90	23.3%	6.7%	0.96	3.72
Gene Garber ('69-'88, 18 WAR*)	7.5	89	20.9%*	6.9%*	1.14*	3.65*
Rick Aguilera ('85-'00, 22 WAR*)	6.5	81	24.3%*	6.1%*	1.18*	3.65*
Joe Hesketh ('84-'94, 10 WAR*)	4.8	87	23.2%*	8.8%*	1.07*	3.95*
Daniel Hudson ('24)	4.3	71	22.8%	7.7%	1.01	3.74
Carlos Villanueva ('16)	5.0	91	20.4%	7.6%	1.25	4.31