Jason Adam

P San Diego Padres

Age 35 RHP 6'3" / 229 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	32	MLBSD	74	74	1.95	2.56	28.7%	6.7%	1.8
2025	33	MLBSD	65	65	1.93	2.76	25.9%	8.1%	1.4
2026	34	MIX2 levels	45	41	4.20	4.82	14.5%	8.1%	-0.1	K 15.5% · BB 7.0% · FIP 4.57
2027PROJ	35	MLBSD	36	38	3.60	3.93	23.2%	7.5%	0.3
2028PROJ	36	MLBSD	24	25	4.02	4.29	22.2%	8.1%	0.1

Last 3 seasons WAR 3.0 · Next 2 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs62

FB Velo36

K%10

CompsBrendan Donnelly (age 35) · Jumbo Diaz (age 35) · Jean Machi (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 108100vR 92

ERA3.60

FIP3.93

K%23.2%

BB%7.5%

K-BB%15.8%

Proj IP38

Value2027 · WAR

Runs saved vs league avg · 38 IP

Grades · Statcast · Contract

Scouting Grades

STUF7070

CTRL5050

CMD6060

K†5555

Statcast Percentiles

FB Velo

p3693.5

Whiff%

p7028.5%

p1015.5%

xwOBA vs

p620.300

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)
2017	25	MIX MLE	2 levels	13	0	15	63	25	6	0	0%	1.2	39.7%	9.5%	30.2%	0.0%	4.80	0.96	—
2017	25	MIX RAW	2 levels	13	0	15	63	25	6	0	0%	1.2	39.7%	9.5%	30.2%	0.0%	4.80	0.97	—
2018	26	MIX MLE	3 levels	48	0	56	238	69	25	9	0%	1.2	25.9%	11.0%	14.9%	5.0%	4.15	5.06	-0.3
2018	26	MIX RAW	3 levels	48	0	56	238	69	25	9	0%	1.2	29.0%	10.5%	18.5%	3.8%	4.15	4.06	+0.3
2019	27	MIX MLE	4 levels	37	1	39	161	42	16	3	3%	1.1	24.2%	9.0%	15.2%	2.2%	2.54	3.39	+0.5
2019	27	MIX RAW	4 levels	37	1	39	161	42	16	3	3%	1.1	26.1%	9.9%	16.1%	1.9%	2.54	3.18	+0.6
2020	28	MLB MLE	Chicago Cubs	13	0	14	58	21	8	2	0%	1.1	36.2%	13.8%	22.4%	3.6%	3.29	3.76	+0.1
2020	28	MLB RAW	Chicago Cubs	13	0	14	58	21	8	2	0%	1.1	36.2%	13.8%	22.4%	3.4%	3.29	3.69	+0.1
2021	29	MIX MLE	3 levels	20	1	20	88	28	8	1	5%	1.0	38.0%	12.0%	26.0%	2.1%	3.60	2.55	+0.5
2021	29	MIX RAW	3 levels	20	1	20	88	28	8	1	5%	1.0	31.8%	9.1%	22.7%	1.1%	3.60	2.15	+0.6
2022	30	MLB MLE	Tampa Bay Rays	67	0	63	237	75	15	5	0%	0.9	31.6%	6.3%	25.3%	2.2%	1.56	2.42	+1.6
2022	30	MLB RAW	Tampa Bay Rays	67	0	63	237	75	15	5	0%	0.9	31.6%	6.3%	25.3%	2.1%	1.56	2.47	+1.6
2023	31	MLB MLE	Tampa Bay Rays	56	0	54	222	69	20	7	0%	1.0	31.1%	9.0%	22.1%	3.2%	2.98	3.40	+0.7
2023	31	MLB RAW	Tampa Bay Rays	56	0	54	222	69	20	7	0%	1.0	31.1%	9.0%	22.1%	3.2%	2.98	3.34	+0.8
2024	32	MLB MLE	San Diego Padres	74	0	74	282	81	19	5	0%	1.0	28.7%	6.7%	22.0%	1.7%	1.95	2.48	+1.8
2024	32	MLB MLE	San Diego Padres	74	0	74	282	81	19	5	0%	1.0	28.7%	6.7%	22.0%	1.7%	1.95	2.48	+1.8
2024	32	MLB RAW	San Diego Padres	74	0	74	282	81	19	5	0%	1.0	28.7%	6.7%	22.0%	1.8%	1.95	2.56	+1.8
2025	33	MLB MLE	San Diego Padres	65	0	65	270	70	22	4	0%	1.0	25.9%	8.1%	17.8%	1.4%	1.93	2.74	+1.4
2025	33	MLB MLE	San Diego Padres	65	0	65	270	70	22	4	0%	1.0	25.9%	8.1%	17.8%	1.4%	1.93	2.74	+1.4
2025	33	MLB RAW	San Diego Padres	65	0	65	270	70	22	4	0%	1.0	25.9%	8.1%	17.8%	1.5%	1.93	2.76	+1.4
2026	34	MIX MLE	2 levels	45	0	41	173	25	14	6	0%	0.9	15.5%	7.0%	8.5%	3.4%	4.20	4.57	-0.0
2026	34	MIX MLE	2 levels	45	0	41	173	25	14	6	0%	0.9	15.5%	7.0%	8.5%	3.4%	4.20	4.57	-0.0
2026	34	MIX RAW	2 levels	45	0	41	173	25	14	6	0%	0.9	14.5%	8.1%	6.4%	3.5%	4.20	4.82	-0.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			379	0	369	1494	412	123	39	0%	1.0	27.6%	8.2%	19.3%	2.6%	2.68	3.24	+5.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	SD	36	0	38	160	37	12	4	0%	1.1	23.2%	7.5%	15.8%	2.7%	3.60	3.93	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	SD	24	0	25	105	23	8	3	0%	1.0	22.2%	8.1%	14.2%	3.0%	4.02	4.29	+0.1
2028	36	MLB CTX	SD	24	0	25	106	23	9	3	0%	1.0	22.2%	8.1%	14.2%	3.0%	4.02	4.29	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		403	0	394	1599	435	131	42	0%	1.0	27.2%	8.2%	19.0%	2.6%	2.76	3.29	+5.9
/ 200 IP	—	RAW		204	0	200	811	221	66	21	0%	1.0	27.2%	8.2%	19.0%	2.6%	2.76	3.29	+3.0
Career	—	MLE		438	2	441	1792	502	153	46	0%	1.0	28.0%	8.6%	19.4%	2.6%	2.77	3.24	+5.8
/ 200 IP	—	MLE		198	1	200	812	227	70	21	0%	1.0	28.0%	8.6%	19.4%	2.6%	2.77	3.24	+3.0

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 AAAEl Paso Chihuahuas · 6 IPFIP 5.69 → 5.82 (+0.13)K% 9.7% → 8.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	9.68%	—	×0.860	8.32%
BB% (unintentional) /BF	12.90%	—	×0.900	11.61%
HR / BF /BF	3.23%	÷1.000	×1.100	3.55%

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 5.69 → 5.82, 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 AAAIowa Cubs · 6 IPFIP 1.52 → 1.80 (+0.28)K% 25.0% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.880	22.00%
BB% (unintentional) /BF	4.17%	—	×1.050	4.38%
HR / BF /BF	0.00%	÷1.081	×1.100	0.00%

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 116).

Result: FIP 1.52 → 1.80, 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).

2019 AAABuffalo Bisons · 14 IPFIP 2.95 → 3.55 (+0.60)K% 35.7% → 31.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.71%	—	×0.880	31.43%
BB% (unintentional) /BF	7.14%	—	×1.050	7.50%
HR / BF /BF	3.57%	÷1.000	×1.100	3.93%

Result: FIP 2.95 → 3.55, 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 · 13 IPFIP 2.37 → 2.75 (+0.38)K% 28.8% → 25.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.85%	—	×0.880	25.38%
BB% (unintentional) /BF	13.46%	—	×1.050	14.13%
HR / BF /BF	0.00%	÷1.101	×1.100	0.00%

Result: FIP 2.37 → 2.75, 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 AANorthwest Arkansas Naturals · 11 IPFIP 0.99 → 1.72 (+0.74)K% 38.6% → 31.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.64%	—	×0.810	31.30%
BB% (unintentional) /BF	9.09%	—	×1.100	10.00%
HR / BF /BF	0.00%	÷1.008	×1.200	0.00%

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 0.99 → 1.72, 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 AANorthwest Arkansas Naturals · 8 IPFIP 1.36 → 2.12 (+0.76)K% 38.2% → 31.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.24%	—	×0.810	30.97%
BB% (unintentional) /BF	11.76%	—	×1.100	12.94%
HR / BF /BF	0.00%	÷1.114	×1.200	0.00%

Result: FIP 1.36 → 2.12, 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 ROKAZL Padres · 7 IPFIP 0.48 → 1.82 (+1.34)K% 41.4% → 28.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	41.38%	—	×0.680	28.14%
BB% (unintentional) /BF	6.90%	—	×1.250	8.62%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. 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 0.48 → 1.82, 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
Jason Adam (OGILVIE)	—	—	3.93	23.2%	7.5%	2.7%	—	4.29	22.2%	14.2%	25	+0.36	—	—	—
Brendan Donnelly R 2006 · 4.21 FIP	35	SU	4.21	19.1%	9.0%	2.9%	.260	3.19	18.6%	10.1%	60	-1.02	2.23	385	79
Jumbo Diaz R 2019 · 2.98 FIP	35	SU	2.98	25.0%	7.7%	1.8%	.290	—	—	—	—	—	2.80	319	78
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	76
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	72
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	71
Toby Borland R 2004 · 4.28 FIP	35	SU	4.28	20.9%	8.1%	3.5%	.259	—	—	—	—	—	2.92	248	69
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	69
Tony Pena Jr. R 2016 · 2.87 FIP	35	SU	2.87	21.5%	4.3%	1.9%	.249	3.43	10.9%	6.6%	22	+0.56	2.80	128	69
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	68
Hector Navarro R 2015 · 4.35 FIP	35	SU	4.35	15.0%	5.4%	3.3%	.281	3.00	15.2%	9.0%	81	-1.35	2.30	126	68
Juan Sandoval R 2016 · 3.24 FIP	35	SU	3.24	18.2%	10.2%	0.7%	.288	4.40	16.3%	9.1%	116	+1.16	3.24	225	66
Leo Rosales R 2015 · 3.99 FIP	34	SU	3.99	18.1%	10.8%	1.9%	.261	4.24	7.8%	-0.4%	22	+0.25	3.85	130	65
Spencer Patton R 2023 · 4.33 FIP	35	SU	4.33	18.9%	9.7%	2.9%	.295	—	—	—	—	—	2.79	239	64
Chris Nichting R 2001 · 4.11 FIP	35	SU	4.11	21.2%	3.7%	4.2%	.351	4.67	16.6%	13.3%	36	+0.56	4.11	103	63
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	63
Seunghwan Oh R 2017 · 3.90 FIP	35	SU	3.90	20.5%	2.3%	4.1%	.299	3.45	26.6%	20.5%	87	-0.45	1.87	226	63
Rodolfo Aguirre R 2019 · 4.50 FIP	34	SU	4.50	21.3%	5.1%	4.7%	.244	—	—	—	—	—	3.39	212	63
Roy Corcoran R 2015 · 2.87 FIP	35	SU	2.87	17.7%	8.2%	0.4%	.359	—	—	—	—	—	2.87	129	63
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	62
Brian Schlitter R 2021 · 4.70 FIP	35	SU	4.70	14.9%	9.1%	3.1%	.318	—	—	—	—	—	3.27	300	62

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	2.2	48	26.9%	8.8%	1.02	2.84
Pedro Báez ('22)	1.8	60	25.3%	8.3%	0.99	3.08
J.J. Putz ('14)	2.8	57	25.7%	7.9%	0.79	3.08
Andrew Bailey ('17)	1.4	55	24.7%	8.2%	0.95	3.12
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Jeff Montgomery ('87-'99, 19 WAR*)	4.3	72	26.2%*	7.6%*	1.01*	3.16*