Luis Severino

P Athletics

Age 33 RHP 6'2" / 218 lbs MLB peak 0.8 · 2028 ILInjured 60-Day (D60)

Overview

MLBSPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2024	30	MLBNYM	31	31	182	3.91	3.96	21.2%	7.9%	3.1
2025	31	MLBOAK	29	29	163	4.54	3.76	17.6%	7.0%	3.2
2026	32	MLBOAK	12	12	63	4.16	4.38	24.0%	11.4%	0.8
2027PROJ	33	MLBOAK	5	5	31	4.34	4.37	21.3%	8.8%	0.4
2028PROJ	34	MLBOAK	11	11	64	4.38	4.40	22.4%	8.5%	0.7
2029PROJ	35	MLBOAK	8	8	50	4.60	4.74	21.4%	9.0%	0.4

Last 3 seasons WAR 7.1 · Next 3 projected WAR 1.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo84

K%60

xwOBA vs44

CompsChuck Smith (age 31) · Kenta Maeda (age 33) · Miguel González (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114106vR 98

ERA4.34

FIP4.37

K%21.3%

BB%8.8%

K-BB%12.5%

Proj IP31

Value2027 · WAR

Runs saved vs league avg · 31 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p8496.9

Whiff%

p3423.0%

p6024.0%

xwOBA vs

p440.318

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	21	MIX MLE	3 levels	30	30	162	651	154	49	11	100%	5.4	21.3%	7.8%	13.5%	1.6%	2.62	3.17	+4.5
2015	21	MIX RAW	3 levels	30	30	162	651	154	49	11	100%	5.4	23.7%	7.5%	16.1%	1.7%	2.62	2.99	+4.9
2016	22	MIX MLE	3 levels	36	24	151	646	146	42	15	67%	4.2	21.2%	6.8%	14.4%	2.3%	4.52	3.44	+3.6
2016	22	MIX RAW	3 levels	36	24	151	646	146	42	15	67%	4.2	22.6%	6.5%	16.1%	2.3%	4.52	3.29	+3.9
2017	23	MLB MLE	New York Yankees	31	31	193	783	230	51	21	100%	6.2	29.4%	6.5%	22.9%	2.5%	2.98	2.82	+6.4
2017	23	MLB RAW	New York Yankees	31	31	193	783	230	51	21	100%	6.2	29.4%	6.5%	22.9%	2.7%	2.98	2.92	+6.1
2018	24	MLB MLE	New York Yankees	32	32	191	780	220	46	19	100%	6.0	28.2%	5.9%	22.3%	2.3%	3.39	2.72	+6.6
2018	24	MLB RAW	New York Yankees	32	32	191	780	220	46	19	100%	6.0	28.2%	5.9%	22.3%	2.4%	3.39	2.81	+6.3
2019	25	MIX MLE	2 levels	4	4	13	54	19	6	1	100%	3.2	35.2%	11.1%	24.1%	1.9%	2.77	2.55	+0.5
2019	25	MIX RAW	2 levels	4	4	13	54	19	6	1	100%	3.2	35.2%	11.1%	24.1%	1.9%	2.77	2.56	+0.5
2021	27	MIX MLE	4 levels	8	4	17	62	23	4	1	50%	2.1	37.1%	6.5%	30.6%	1.6%	2.16	1.67	+0.8
2021	27	MIX RAW	4 levels	8	4	17	62	23	4	1	50%	2.1	37.1%	6.5%	30.6%	1.6%	2.16	1.84	+0.7
2022	28	MIX MLE	4 levels	22	21	112	445	125	31	15	95%	5.1	27.7%	7.4%	20.2%	3.2%	3.20	3.48	+2.6
2022	28	MIX RAW	4 levels	22	21	112	445	125	31	15	95%	5.1	28.1%	7.0%	21.1%	3.4%	3.20	3.44	+2.7
2023	29	MIX MLE	3 levels	21	20	96	446	85	35	24	95%	4.6	18.9%	7.9%	11.0%	5.2%	6.47	5.34	+0.1
2023	29	MIX RAW	3 levels	21	20	96	446	85	35	24	95%	4.6	19.1%	7.8%	11.2%	5.4%	6.47	5.67	-0.3
2024	30	MLB MLE	New York Mets	31	31	182	761	161	60	23	100%	5.9	21.2%	7.9%	13.3%	3.0%	3.91	3.98	+3.1
2024	30	MLB MLE	New York Mets	31	31	182	761	161	60	23	100%	5.9	21.2%	7.9%	13.3%	3.0%	3.91	3.98	+3.1
2024	30	MLB RAW	New York Mets	31	31	182	761	161	60	23	100%	5.9	21.2%	7.9%	13.3%	3.0%	3.91	3.96	+3.1
2025	31	MLB MLE	Athletics	29	29	163	705	124	49	16	100%	5.6	17.6%	7.0%	10.6%	2.3%	4.54	3.77	+3.2
2025	31	MLB MLE	Athletics	29	29	163	705	124	49	16	100%	5.6	17.6%	7.0%	10.6%	2.3%	4.54	3.77	+3.2
2025	31	MLB RAW	Athletics	29	29	163	705	124	49	16	100%	5.6	17.6%	7.0%	10.6%	2.3%	4.54	3.76	+3.2
2026	32	MLB MLE	Athletics	12	12	63	271	65	31	9	100%	5.2	24.0%	11.4%	12.5%	3.4%	4.16	4.39	+0.7
2026	32	MLB MLE	Athletics	12	12	63	271	65	31	9	100%	5.2	24.0%	11.4%	12.5%	3.4%	4.16	4.39	+0.7
2026	32	MLB RAW	Athletics	12	12	63	271	65	31	9	100%	5.2	24.0%	11.4%	12.5%	3.3%	4.16	4.38	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			213	197	1135	4759	1138	353	145	92%	5.3	23.9%	7.4%	16.5%	3.0%	3.93	3.69	+23.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	OAK	5	5	31	131	28	11	4	100%	6.2	21.3%	8.8%	12.5%	2.9%	4.34	4.37	+0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	OAK	11	11	64	270	60	23	9	100%	5.8	22.4%	8.5%	13.9%	3.2%	4.38	4.40	+0.7
2029	35	MLB MLE	OAK	8	8	50	211	45	19	7	100%	6.2	21.4%	9.0%	12.4%	3.5%	4.60	4.74	+0.4
2028	34	MLB CTX	OAK	11	11	64	270	60	23	9	100%	5.8	22.4%	8.5%	13.9%	3.2%	4.38	4.40	+0.7
2029	35	MLB CTX	OAK	8	8	50	211	45	19	7	100%	6.2	21.4%	9.0%	12.4%	3.5%	4.60	4.74	+0.4
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		232	216	1249	5240	1244	395	161	93%	5.4	23.7%	7.5%	16.2%	3.1%	3.98	3.74	+25.1
/ 200 IP	—	RAW		37	35	200	839	199	63	26	93%	5.4	23.7%	7.5%	16.2%	3.1%	3.98	3.74	+4.0
Career	—	MLE		256	238	1343	5604	1325	410	151	93%	5.2	23.7%	7.3%	16.3%	2.7%	3.79	3.51	+24.5
/ 200 IP	—	MLE		38	35	200	835	197	61	23	93%	5.2	23.7%	7.3%	16.3%	2.7%	3.79	3.51	+3.9

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.

2021 AASomerset Patriots · 6 IPFIP 2.72 → 3.78 (+1.06)K% 40.9% → 33.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.91%	—	×0.810	33.14%
BB% (unintentional) /BF	4.55%	—	×1.100	5.00%
HR / BF /BF	4.55%	÷1.053	×1.200	5.18%

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 111).

Result: FIP 2.72 → 3.78, 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 AAAScranton/Wilkes-Barre RailRiders · 77 IPFIP 2.45 → 2.80 (+0.35)K% 24.1% → 21.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.15%	—	×0.880	21.25%
BB% (unintentional) /BF	5.57%	—	×1.050	5.85%
HR / BF /BF	1.24%	÷0.996	×1.100	1.37%

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 99).

Result: FIP 2.45 → 2.80, 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 AAAScranton/Wilkes-Barre RailRiders · 61 IPFIP 2.23 → 2.49 (+0.26)K% 20.9% → 18.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.92%	—	×0.880	18.41%
BB% (unintentional) /BF	7.11%	—	×1.050	7.47%
HR / BF /BF	0.00%	÷0.878	×1.100	0.00%

Result: FIP 2.23 → 2.49, 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 AATrenton Thunder · 38 IPFIP 2.02 → 2.62 (+0.60)K% 30.6% → 24.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.57%	—	×0.810	24.76%
BB% (unintentional) /BF	6.37%	—	×1.100	7.01%
HR / BF /BF	1.27%	÷1.153	×1.200	1.33%

Result: FIP 2.02 → 2.62, 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).

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
Luis Severino (OGILVIE)	—	—	—	—	—	2.9%	—	4.55	21.9%	13.2%	114	+0.18	—	—	—
Chuck Smith R 2001 · 3.99 FIP	31	SP	3.99	18.4%	8.1%	2.6%	.289	—	—	—	—	—	3.08	211	54
Kenta Maeda R 2021 · 3.79 FIP	33	SP	3.79	24.9%	6.8%	3.5%	.307	4.26	23.6%	17.1%	230	+0.47	2.87	1,092	52
Miguel González R 2017 · 4.54 FIP	33	SP	4.54	14.6%	7.7%	3.1%	.285	6.88	7.6%	-1.5%	12	+2.34	3.72	905	51
Steve Parris R 2001 · 5.31 FIP	33	SP	5.31	10.4%	7.9%	3.8%	.294	5.65	11.3%	3.5%	119	+0.34	3.60	753	49
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	47
Masato Yoshii R 2000 · 5.32 FIP	35	SP	5.32	12.1%	6.5%	4.4%	.302	4.38	13.1%	8.0%	244	-0.94	4.15	757	46
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	46
Logan Duran R 2015 · 3.42 FIP	33	SP	3.42	15.8%	10.0%	0.7%	.287	3.77	15.3%	6.4%	95	+0.35	3.42	187	45
Hector Ambriz R 2016 · 3.63 FIP	32	SP	3.63	16.0%	6.4%	2.0%	.273	6.40	10.4%	2.9%	91	+2.77	2.24	259	45
Diego Moreno R 2019 · 4.08 FIP	32	SP	4.08	12.4%	6.9%	2.1%	.376	4.75	18.6%	9.2%	30	+0.67	2.29	187	42
Jonathan Castellanos R 2015 · 3.63 FIP	33	SP	3.63	12.2%	6.5%	1.3%	.313	4.15	11.8%	3.5%	216	+0.52	3.40	394	41
Jarrett Grube R 2015 · 3.75 FIP	33	SP	3.75	18.7%	4.2%	3.1%	.285	4.57	17.3%	9.6%	253	+0.82	3.75	393	41
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	41
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	41
Junior Guerra R 2018 · 3.99 FIP	33	SP	3.99	22.3%	9.0%	3.0%	.309	4.12	21.2%	9.1%	173	+0.13	3.18	646	40
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	40
Merrill Kelly R 2022 · 3.69 FIP	33	SP	3.69	22.0%	7.3%	2.8%	.267	3.76	23.5%	15.9%	435	+0.07	3.60	1,170	39
Osvaldo Fernandez R 2001 · 4.53 FIP	32	SP-	4.53	9.6%	8.2%	2.2%	.324	—	—	—	—	—	4.25	387	39
Guillermo Moscoso R 2017 · 3.77 FIP	33	SP	3.77	14.5%	6.9%	1.8%	.270	4.64	14.9%	7.5%	176	+0.87	3.56	486	39
Shawn Haviland R 2017 · 5.00 FIP	31	SP-	5.00	16.3%	8.1%	4.1%	.261	—	—	—	—	—	3.69	283	39

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	6.4	107	23.7%	7.6%	1.19	3.99
Pete Richert ('62-'74, 13 WAR*)	5.8	90	24.4%*	8.8%*	1.26*	4.04*
Scott Bankhead ('86-'95, 10 WAR*)	4.5	100	21.2%*	7.3%*	1.36*	4.16*
Skip Lockwood ('69-'80, 11 WAR*)	6.2	103	22.5%*	9.1%*	1.06*	4.28*
Mike Minor ('22)	7.0	128	21.6%	7.0%	1.30	4.25
Frankie Montas ('25)	3.9	98	23.6%	8.2%	1.20	4.20
Collin McHugh ('23)	5.0	90	23.3%	6.7%	0.96	3.72