Lucas Erceg

P Kansas City Royals

Age 32 RHP 6'2" / 214 lbs MLB peak 0.1 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MLBKC	61	62	3.36	2.08	28.5%	5.5%	1.8
2025	30	MIX2 levels	62	62	2.60	3.12	19.0%	6.0%	1.1	K 19.3% · BB 6.0% · FIP 3.19
2026	31	MIX2 levels	61	59	4.91	4.29	17.4%	12.2%	0.2	K 14.9% · BB 13.2% · FIP 4.81
2027PROJ	32	MLBKC	34	36	4.26	4.39	20.1%	9.9%	0.1
2028PROJ95% MLB	33	MLBKC	19	20	4.40	4.56	19.3%	10.3%	-0.0

Last 3 seasons WAR 3.1 · Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs34

K%10

CompsJumbo Diaz (age 32) · John Axford (age 32) · Jeff Fulchino (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.26

FIP4.39

K%20.1%

BB%9.9%

K-BB%10.2%

Proj IP36

Value2027 · WAR

Runs saved vs league avg · 36 IP

Grades · Statcast · Contract

Scouting Grades

K†5050

Statcast Percentiles

FB Velo

p9097.3

Whiff%

p1920.9%

p1014.9%

xwOBA vs

p340.330

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)
2021	26	AA MLE	Biloxi Shuckers	22	13	48	213	45	35	5	59%	2.2	17.1%	18.1%	-1.0%	3.0%	5.29	5.61	—
2021	26	AA RAW	Biloxi Shuckers	22	13	48	213	45	35	5	59%	2.2	21.1%	16.4%	4.7%	2.3%	5.29	4.78	—
2022	27	MIX MLE	2 levels	49	4	61	283	69	37	6	8%	1.3	20.8%	14.0%	6.8%	2.5%	4.55	4.48	—
2022	27	MIX RAW	2 levels	49	4	61	283	69	37	6	8%	1.3	24.4%	13.1%	11.3%	2.1%	4.55	3.93	—
2023	28	MIX MLE	2 levels	63	0	70	318	84	45	4	0%	1.1	25.7%	13.8%	11.9%	1.5%	5.12	3.50	+0.9
2023	28	MIX RAW	2 levels	63	0	70	318	84	45	4	0%	1.1	26.4%	14.2%	12.3%	1.3%	5.12	3.37	+1.0
2024	29	MLB MLE	Kansas City Royals	61	0	62	253	72	14	3	0%	1.0	28.5%	5.5%	22.9%	1.3%	3.36	2.10	+1.8
2024	29	MLB MLE	Kansas City Royals	61	0	62	253	72	14	3	0%	1.0	28.5%	5.5%	22.9%	1.3%	3.36	2.10	+1.8
2024	29	MLB RAW	Kansas City Royals	61	0	62	253	72	14	3	0%	1.0	28.5%	5.5%	22.9%	1.2%	3.36	2.08	+1.8
2025	30	MIX MLE	2 levels	62	0	62	252	48	15	4	0%	1.0	19.3%	6.0%	13.3%	1.7%	2.60	3.19	+1.0
2025	30	MIX MLE	2 levels	62	0	62	252	48	15	4	0%	1.0	19.3%	6.0%	13.3%	1.7%	2.60	3.19	+1.0
2025	30	MIX RAW	2 levels	62	0	62	252	48	15	4	0%	1.0	19.0%	6.0%	13.1%	1.6%	2.60	3.12	+1.1
2026	31	MIX MLE	2 levels	61	0	59	270	47	33	5	0%	1.0	14.9%	13.2%	1.8%	2.4%	4.91	4.81	-0.2
2026	31	MIX MLE	2 levels	61	0	59	270	47	33	5	0%	1.0	14.9%	13.2%	1.8%	2.4%	4.91	4.81	-0.2
2026	31	MIX RAW	2 levels	61	0	59	270	47	33	5	0%	1.0	17.4%	12.2%	5.2%	1.9%	4.91	4.29	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			224	0	226	981	222	94	13	0%	1.0	22.6%	9.6%	13.0%	1.3%	3.98	3.13	+3.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	KC	34	0	36	152	31	15	4	0%	1.1	20.1%	9.9%	10.2%	2.5%	4.26	4.39	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	KC	20	0	21	87	17	9	2	0%	1.0	19.3%	10.3%	8.9%	2.8%	4.51	4.67	-0.0
2028	33	MLB CTX	KC	19	0	20	84	16	9	2	0%	1.1	19.3%	10.3%	8.9%	2.5%	4.40	4.56	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		243	0	246	1064	238	103	15	0%	1.0	22.4%	9.6%	12.7%	1.4%	4.01	3.21	+3.9
/ 200 IP	—	RAW		197	0	200	865	193	84	12	0%	1.0	22.4%	9.6%	12.7%	1.4%	4.01	3.21	+3.2
Career	—	MLE		318	17	362	1589	338	187	32	5%	1.1	21.3%	11.8%	9.5%	2.0%	4.28	3.91	+3.8
/ 200 IP	—	MLE		176	9	200	878	187	103	18	5%	1.1	21.3%	11.8%	9.5%	2.0%	4.28	3.91	+3.1

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 AAAOmaha Storm Chasers · 10 IPFIP 1.39 → 1.67 (+0.28)K% 31.0% → 26.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.95%	—	×0.860	26.62%
BB% (unintentional) /BF	7.14%	—	×0.900	6.43%
HR / BF /BF	0.00%	÷1.000	×1.100	0.00%

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 1.39 → 1.67, 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).

2023 AAANashville Sounds · 15 IPFIP 5.43 → 6.05 (+0.62)K% 23.9% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.88%	—	×0.860	20.54%
BB% (unintentional) /BF	14.93%	—	×0.900	13.43%
HR / BF /BF	4.48%	÷0.905	×1.100	5.44%

Result: FIP 5.43 → 6.05, 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).

2022 AAANashville Sounds · 39 IPFIP 3.70 → 4.19 (+0.49)K% 24.7% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.71%	—	×0.880	21.74%
BB% (unintentional) /BF	13.53%	—	×1.050	14.21%
HR / BF /BF	1.76%	÷0.946	×1.100	2.05%

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 89).

Result: FIP 3.70 → 4.19, 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).

2022 AABiloxi Shuckers · 22 IPFIP 4.11 → 4.92 (+0.81)K% 23.9% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.89%	—	×0.810	19.35%
BB% (unintentional) /BF	12.39%	—	×1.100	13.63%
HR / BF /BF	2.65%	÷1.012	×1.200	3.15%

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 102).

Result: FIP 4.11 → 4.92, 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).

2021 AABiloxi Shuckers · 48 IPFIP 4.69 → 5.61 (+0.92)K% 21.1% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.13%	—	×0.810	17.11%
BB% (unintentional) /BF	16.43%	—	×1.100	18.08%
HR / BF /BF	2.35%	÷0.929	×1.200	3.03%

Result: FIP 4.69 → 5.61, 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
Lucas Erceg (OGILVIE)	—	—	4.39	20.1%	9.9%	2.5%	—	4.67	19.3%	8.9%	21	+0.28	—	—	—
Jumbo Diaz R 2016 · 3.85 FIP	32	SU	3.85	22.3%	9.2%	2.7%	.247	3.28	23.0%	13.8%	141	-0.57	2.80	319	79
John Axford R 2015 · 3.20 FIP	32	SU	3.20	24.8%	11.2%	1.4%	.333	3.97	21.5%	11.1%	141	+0.77	1.90	518	76
Jeff Fulchino R 2011 · 4.44 FIP	31	SU	4.44	20.4%	11.1%	3.0%	.302	—	—	—	—	—	3.36	178	74
Mike Broadway R 2018 · 4.65 FIP	31	SU	4.65	23.0%	10.6%	3.9%	.282	—	—	—	—	—	1.74	195	73
Brian Sanches R 2011 · 4.35 FIP	32	SU	4.35	19.6%	11.1%	2.7%	.250	—	—	—	—	—	3.39	229	72
Jared Hughes R 2017 · 3.28 FIP	32	SU	3.28	19.7%	7.8%	1.6%	.260	3.99	19.3%	10.9%	172	+0.71	3.00	530	72
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	72
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	71
Jose Manuel Lopez R 2017 · 3.61 FIP	32	SU	3.61	20.4%	10.0%	1.8%	.331	6.14	17.9%	3.7%	41	+2.53	3.50	170	71
Jose Veras R 2013 · 3.51 FIP	32	SU	3.51	23.6%	8.3%	2.5%	.234	4.28	21.9%	10.2%	65	+0.77	3.37	422	71
James Hoyt R 2019 · 3.45 FIP	32	SU	3.45	22.2%	9.9%	1.8%	.355	5.63	22.1%	10.4%	50	+2.18	1.61	312	70
Neil Wagner R 2016 · 4.05 FIP	32	SU	4.05	19.8%	12.3%	1.9%	.360	3.11	20.7%	12.1%	54	-0.94	3.11	135	70
Kiko Calero R 2007 · 3.88 FIP	32	SU	3.88	16.8%	10.3%	1.6%	.326	2.27	28.9%	18.0%	60	-1.61	2.27	298	70
Josh Fields R 2018 · 3.48 FIP	32	SU	3.48	21.4%	6.5%	2.5%	.224	7.56	18.9%	9.5%	25	+4.08	1.80	330	69
Santiago Casilla R 2012 · 3.98 FIP	32	SU	3.98	20.2%	6.6%	3.2%	.246	3.23	21.6%	13.7%	166	-0.75	2.76	647	69
Dan Winkler R 2022 · 3.87 FIP	32	SU	3.87	24.4%	14.0%	1.9%	.281	—	—	—	—	—	2.40	231	69
Scot Shields R 2007 · 3.58 FIP	32	SU	3.58	24.1%	10.3%	2.2%	.271	4.26	20.3%	7.3%	127	+0.68	2.77	686	69
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	68
Arquimedes Caminero R 2019 · 4.62 FIP	32	SU	4.62	18.6%	11.1%	3.1%	.248	—	—	—	—	—	3.54	186	68
Erik Davis R 2018 · 3.52 FIP	31	SU	3.52	17.4%	8.9%	1.4%	.317	—	—	—	—	—	3.05	227	68

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	1.4	47	22.1%	10.3%	0.61	4.05
Cory Gearrin ('20)	1.5	51	22.0%	10.2%	0.68	3.61
Manny Delcarmen ('10)	1.5	59	19.5%	10.6%	0.74	3.97
Nick Masset ('15)	2.0	58	19.4%	10.0%	0.74	4.06
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*
Mark Dewey ('90-'96, 3 WAR*)	1.2	42	18.7%*	9.6%*	0.80*	3.89*
Ed Vosberg ('86-'02, 1 WAR*)	1.2	47	21.0%*	9.6%*	0.86*	4.06*