Adam Cimber

P Los Angeles Angels

Age 36 RHP 6'3" / 195 lbs MLB

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	31	MLBTOR	77	0	71	2.80	3.07	19.8%	4.1%	1.2
2023	32	MIX3 levels	28	0	25	6.75	6.30	14.5%	6.8%	-0.5	K 12.6% · BB 7.4% · FIP 6.43
2024	33	MIX2 levels	39	0	40	7.03	4.06	20.5%	8.9%	0.2	K 19.1% · BB 8.7% · FIP 4.31
2025	34	No MLB games played
2026	35	No MLB games played
2027PROJ	36	MLBLAA	46	0	48	4.46	4.70	18.7%	9.2%	-0.1
2028PROJ91% MLB	37	MLBLAA	27	0	28	4.84	5.10	17.6%	9.9%	-0.2

Last 3 seasons WAR 1.0 · Next 2 projected WAR -0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

K%20

FB Velo10

xwOBA vs10

CompsRafael Martín (age 35) · Keiichi Yabu (age 36) · Rudy Gonzalez (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 122114vR 106

ERA4.46

FIP4.70

K%18.7%

BB%9.2%

K-BB%9.5%

Proj IP48

Value2027 · WAR

Runs saved vs league avg · 48 IP

Grades · Statcast · Contract

Scouting Grades

STUF4040

CTRL5555

CMD4545

K†4545

Statcast Percentiles

FB Velo

p1085.0

Whiff%

p1016.3%

p2017.8%

xwOBA vs

p100.364

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	24	MIX MLE	2 levels	46	0	59	247	46	14	5	0%	1.3	15.7%	6.6%	9.1%	2.3%	3.05	3.90	—
2015	24	MIX RAW	2 levels	46	0	59	247	46	14	5	0%	1.3	18.6%	5.7%	13.0%	2.0%	3.05	3.35	—
2016	25	MIX MLE	2 levels	46	0	57	239	31	12	4	0%	1.2	10.4%	5.7%	4.6%	0.6%	3.77	3.29	—
2016	25	MIX RAW	2 levels	46	0	57	239	31	12	4	0%	1.2	13.0%	5.0%	7.9%	1.7%	3.77	3.55	—
2017	26	MIX MLE	2 levels	49	2	81	313	65	10	11	4%	1.6	18.0%	3.4%	14.6%	3.6%	2.90	4.01	—
2017	26	MIX RAW	2 levels	49	2	81	313	65	10	11	4%	1.6	20.8%	3.2%	17.6%	3.5%	2.90	3.63	—
2018	27	MLB MLE	Cleveland Indians	70	0	68	284	58	8	5	0%	1.0	20.4%	2.8%	17.6%	1.8%	3.42	2.70	+1.5
2018	27	MLB RAW	Cleveland Indians	70	0	68	284	58	8	5	0%	1.0	20.4%	2.8%	17.6%	1.8%	3.42	2.70	+1.5
2019	28	MLB MLE	Cleveland Indians	68	0	57	244	41	17	6	0%	0.8	16.8%	7.0%	9.8%	2.5%	4.45	3.92	+0.4
2019	28	MLB RAW	Cleveland Indians	68	0	57	244	41	17	6	0%	0.8	16.8%	7.0%	9.8%	2.5%	4.45	3.93	+0.4
2020	29	MLB MLE	Cleveland Indians	14	0	11	49	5	2	1	0%	0.8	10.2%	4.1%	6.1%	2.0%	3.97	3.88	+0.1
2020	29	MLB RAW	Cleveland Indians	14	0	11	49	5	2	1	0%	0.8	10.2%	4.1%	6.1%	2.0%	3.97	3.89	+0.1
2021	30	MLB MLE	Toronto Blue Jays	72	0	72	286	51	12	2	0%	1.0	17.8%	4.2%	13.6%	0.7%	2.26	2.51	+1.8
2021	30	MLB RAW	Toronto Blue Jays	72	0	72	286	51	12	2	0%	1.0	17.8%	4.2%	13.6%	0.7%	2.26	2.54	+1.7
2022	31	MLB MLE	Toronto Blue Jays	77	0	71	293	58	12	6	0%	0.9	19.8%	4.1%	15.7%	2.0%	2.80	3.07	+1.2
2022	31	MLB RAW	Toronto Blue Jays	77	0	71	293	58	12	6	0%	0.9	19.8%	4.1%	15.7%	2.0%	2.80	3.07	+1.2
2023	32	MIX MLE	3 levels	28	0	25	117	17	8	7	0%	0.9	12.6%	7.4%	5.3%	6.3%	6.75	6.43	-0.6
2023	32	MIX RAW	3 levels	28	0	25	117	17	8	7	0%	0.9	14.5%	6.8%	7.7%	6.0%	6.75	6.30	-0.5
2024	33	MIX MLE	2 levels	39	0	40	190	39	17	5	0%	1.0	19.1%	8.7%	10.4%	3.1%	7.03	4.31	+0.1
2024	33	MIX MLE	2 levels	39	0	40	190	39	17	5	0%	1.0	19.1%	8.7%	10.4%	3.1%	7.03	4.31	+0.1
2024	33	MIX RAW	2 levels	39	0	40	190	39	17	5	0%	1.0	20.5%	8.9%	11.6%	2.6%	7.03	4.06	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			351	0	324	1358	244	70	27	0%	0.9	18.0%	5.2%	12.8%	2.0%	3.75	3.33	+4.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	LAA	46	0	48	203	38	19	6	0%	1.0	18.7%	9.2%	9.5%	3.0%	4.46	4.70	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	LAA	29	0	30	129	23	13	4	0%	1.1	17.6%	9.9%	7.7%	3.4%	4.84	5.10	-0.2
2028	37	MLB CTX	LAA	27	0	28	118	21	12	4	0%	1.0	17.6%	9.9%	7.7%	3.4%	4.84	5.10	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		377	0	351	1475	265	82	31	0%	0.9	17.9%	5.5%	12.4%	2.1%	3.84	3.44	+4.6
/ 200 IP	—	RAW		215	0	200	840	151	47	18	0%	0.9	17.9%	5.5%	12.4%	2.1%	3.84	3.44	+2.6
Career	—	MLE		509	2	541	2262	384	117	52	0%	1.1	17.0%	5.2%	11.8%	2.3%	3.65	3.58	+4.5
/ 200 IP	—	MLE		188	1	200	837	142	43	19	0%	1.1	17.0%	5.2%	11.8%	2.3%	3.65	3.58	+2.5

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.

2024 AAASalt Lake Bees · 15 IPFIP 4.47 → 5.32 (+0.85)K% 24.1% → 20.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.10%	—	×0.860	20.72%
BB% (unintentional) /BF	6.02%	—	×0.900	5.42%
HR / BF /BF	4.82%	÷0.886	×1.100	5.98%

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 77).

Result: FIP 4.47 → 5.32, 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 AAAEl Paso Chihuahuas · 65 IPFIP 3.95 → 4.21 (+0.26)K% 21.0% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.97%	—	×0.880	18.45%
BB% (unintentional) /BF	3.23%	—	×1.050	3.39%
HR / BF /BF	4.03%	÷1.087	×1.100	4.08%

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 117).

Result: FIP 3.95 → 4.21, 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 AASan Antonio Missions · 16 IPFIP 2.65 → 3.24 (+0.59)K% 20.0% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.810	16.20%
BB% (unintentional) /BF	3.08%	—	×1.100	3.38%
HR / BF /BF	1.54%	÷0.940	×1.200	1.96%

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 88).

Result: FIP 2.65 → 3.24, 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 AASan Antonio Missions · 53 IPFIP 2.94 → 3.29 (+0.35)K% 12.8% → 10.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.80%	—	×0.810	10.36%
BB% (unintentional) /BF	5.21%	—	×1.100	5.73%
HR / BF /BF	0.47%	÷0.933	×1.200	0.61%

Result: FIP 2.94 → 3.29, 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 AAAEl Paso Chihuahuas · 5 IPFIP 8.23 → 8.09 (−0.14)K% 14.3% → 12.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.29%	—	×0.880	12.57%
BB% (unintentional) /BF	3.57%	—	×1.050	3.75%
HR / BF /BF	10.71%	÷1.160	×1.100	10.16%

Result: FIP 8.23 → 8.09, 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 AASan Antonio Missions · 56 IPFIP 3.17 → 3.90 (+0.72)K% 19.4% → 15.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.40%	—	×0.810	15.71%
BB% (unintentional) /BF	6.03%	—	×1.100	6.64%
HR / BF /BF	1.72%	÷0.885	×1.200	2.34%

Result: FIP 3.17 → 3.90, 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
Adam Cimber (OGILVIE)	—	—	4.70	18.7%	9.2%	3.0%	—	5.10	17.6%	7.7%	30	+0.40	—	—	—
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	64
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	62
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	58
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	56
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	56
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	55
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	55
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	52
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	51
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	51
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	51
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	50
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	49
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	49
Leo Rosales R 2017 · 4.24 FIP	36	SU	4.24	7.8%	8.2%	1.4%	.203	—	—	—	—	—	3.85	130	48
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	47
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	46
Pete Walker R 2005 · 4.79 FIP	36	SU	4.79	12.0%	9.2%	2.8%	.261	4.45	19.6%	11.6%	30	-0.34	4.45	327	44
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	39
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	31

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	2.0	50	18.0%	7.1%	0.86	3.92
Manny Corpas ('13)	1.9	53	16.6%	7.0%	0.91	4.14
Mike Perez ('90-'97, 3 WAR*)	1.7	58	18.5%*	8.2%*	0.88*	3.86*
Brandon Kintzler ('21)	2.4	60	16.8%	6.5%	0.88	3.50
Scott Atchison ('15)	1.7	50	17.7%	6.2%	0.91	3.63
Tony Castillo ('88-'98, 4 WAR*)	2.6	59	18.5%*	7.3%*	0.98*	3.76*
Ramon Hernandez ('67-'77, 6 WAR*)	2.2	61	19.7%*	7.3%*	0.77*	3.65*