Dany Jiménez

P Oakland Athletics

Age 33 RHP 6'1" / 182 lbs MLB

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	28	MIX2 levels	36	0	36	3.22	2.88	25.2%	9.3%	0.7	K 23.4% · BB 9.7% · FIP 3.17
2023	29	MIX2 levels	34	2	32	5.34	4.82	26.1%	14.9%	-0.1	K 22.3% · BB 13.8% · FIP 4.89
2024	30	MIX2 levels	46	0	50	4.53	5.44	25.1%	14.6%	-0.5	K 23.2% · BB 14.0% · FIP 5.67
2025	31	No MLB games played
2026	32	No MLB games played
2027PROJ	33	MLBOAK	41	0	43	4.55	4.74	24.8%	11.9%	-0.1
2028PROJ81% MLB	34	MLBOAK	29	0	30	4.80	5.00	23.7%	12.1%	-0.2
2029PROJ76% MLB	35	MLBOAK	21	0	22	5.07	5.29	22.7%	12.4%	-0.2

Last 3 seasons WAR 0.1 · Next 3 projected WAR -0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

K%45

FB Velo22

xwOBA vs20

CompsHirokazu Sawamura (age 33) · Jim Henderson (age 33) · Jose Manuel Lopez (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 123115vR 107

ERA4.55

FIP4.74

K%24.8%

BB%11.9%

K-BB%12.9%

Proj IP50

Value2027 · WAR

Runs saved vs league avg · 50 IP

Grades · Statcast · Contract

Scouting Grades

STUF6565

CTRL2525

CMD4040

K†6060

Statcast Percentiles

FB Velo

p2292.5

Whiff%

p7629.3%

p4521.4%

xwOBA vs

p200.348

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	ROK MLE	DSL Blue Jays	3	0	9	36	12	4	2	0%	2.9	33.3%	11.1%	22.2%	5.6%	5.19	4.74	—
2015	21	ROK RAW	DSL Blue Jays	3	0	9	36	12	4	2	0%	2.9	33.3%	11.1%	22.2%	5.6%	5.19	4.72	—
2016	22	ROK MLE	GCL Blue Jays	11	8	46	186	48	18	1	73%	4.2	17.5%	12.1%	5.5%	0.8%	2.72	3.56	—
2016	22	ROK RAW	GCL Blue Jays	11	8	46	186	48	18	1	73%	4.2	25.8%	9.7%	16.1%	0.5%	2.72	2.47	—
2017	23	ROK MLE	GCL Blue Jays	1	0	1	6	1	0	0	0%	1.0	16.7%	0.0%	16.7%	0.0%	9.00	1.69	—
2017	23	ROK RAW	GCL Blue Jays	1	0	1	6	1	0	0	0%	1.0	16.7%	0.0%	16.7%	0.0%	9.00	1.10	—
2018	24	A MLE	Lansing Lugnuts	38	0	63	274	80	22	10	0%	1.7	21.6%	9.5%	12.1%	4.6%	3.84	4.99	—
2018	24	A RAW	Lansing Lugnuts	38	0	63	274	80	22	10	0%	1.7	29.2%	8.0%	21.2%	3.6%	3.84	3.67	—
2019	25	MIX MLE	2 levels	45	0	59	241	93	19	6	0%	1.3	30.5%	8.8%	21.6%	3.2%	2.59	3.39	—
2019	25	MIX RAW	2 levels	45	0	59	241	93	19	6	0%	1.3	38.6%	7.9%	30.7%	2.5%	2.59	2.24	—
2020	26	MLB MLE	San Francisco Giants	2	0	1	8	1	3	0	0%	0.7	12.5%	37.5%	-25.0%	0.0%	6.75	6.79	-0.0
2020	26	MLB RAW	San Francisco Giants	2	0	1	8	1	3	0	0%	0.7	12.5%	37.5%	-25.0%	0.0%	6.75	8.35	-0.1
2021	27	AAA MLE	Buffalo Bisons	39	1	45	187	73	25	5	3%	1.1	34.4%	14.0%	20.3%	2.7%	2.22	3.47	—
2021	27	AAA RAW	Buffalo Bisons	39	1	45	187	73	25	5	3%	1.1	39.0%	13.4%	25.7%	2.7%	2.22	2.97	—
2022	28	MIX MLE	2 levels	36	0	36	151	38	14	2	0%	1.0	23.4%	9.7%	13.8%	1.5%	3.22	3.17	+0.6
2022	28	MIX RAW	2 levels	36	0	36	151	38	14	2	0%	1.0	25.2%	9.3%	15.9%	1.3%	3.22	2.88	+0.7
2023	29	MIX MLE	2 levels	34	2	32	134	35	20	5	6%	0.9	22.3%	13.8%	8.5%	3.5%	5.34	4.89	-0.1
2023	29	MIX RAW	2 levels	34	2	32	134	35	20	5	6%	0.9	26.1%	14.9%	11.2%	3.7%	5.34	4.82	-0.1
2024	30	MIX MLE	2 levels	46	0	50	219	55	32	10	0%	1.1	23.2%	14.0%	9.2%	5.0%	4.53	5.67	-0.6
2024	30	MIX MLE	2 levels	46	0	50	219	55	32	10	0%	1.1	23.2%	14.0%	9.2%	5.0%	4.53	5.67	-0.6
2024	30	MIX RAW	2 levels	46	0	50	219	55	32	10	0%	1.1	25.1%	14.6%	10.5%	4.6%	4.53	5.44	-0.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			86	1	85	364	81	49	11	1%	1.0	22.3%	13.5%	8.8%	3.0%	3.93	4.61	-0.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	OAK	41	0	43	182	45	22	6	0%	1.0	24.8%	11.9%	12.9%	3.4%	4.55	4.74	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	OAK	36	0	38	159	38	19	6	0%	1.0	23.7%	12.1%	11.6%	3.6%	4.80	5.00	-0.2
2029	35	MLB MLE	OAK	28	0	29	122	28	15	5	0%	1.0	22.7%	12.4%	10.3%	3.9%	5.07	5.29	-0.2
2028	34	MLB CTX	OAK	29	0	30	127	30	15	5	0%	1.0	23.7%	12.1%	11.6%	3.6%	4.80	5.00	-0.2
2029	35	MLB CTX	OAK	21	0	22	93	21	12	4	0%	1.0	22.7%	12.4%	10.3%	3.9%	5.07	5.29	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		136	1	137	586	133	76	19	1%	1.0	22.7%	13.0%	9.7%	3.3%	4.31	4.64	-0.2
/ 200 IP	—	RAW		198	1	200	854	194	111	28	1%	1.0	22.7%	13.0%	9.7%	3.3%	4.31	4.64	-0.2
Career	—	MLE		255	11	342	1442	360	167	47	4%	1.3	24.9%	11.6%	13.4%	3.2%	3.50	4.24	-0.5
/ 200 IP	—	MLE		149	6	200	842	210	97	27	4%	1.3	24.9%	11.6%	13.4%	3.2%	3.50	4.24	-0.7

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 AAALas Vegas Aviators · 24 IPFIP 4.38 → 4.78 (+0.40)K% 29.4% → 25.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.41%	—	×0.860	25.29%
BB% (unintentional) /BF	12.75%	—	×0.900	11.47%
HR / BF /BF	3.92%	÷1.001	×1.100	4.31%

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 4.38 → 4.78, 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).

2023 AAALas Vegas Aviators · 9 IPFIP 5.11 → 5.59 (+0.48)K% 35.0% → 30.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.00%	—	×0.860	30.10%
BB% (unintentional) /BF	17.50%	—	×0.900	15.75%
HR / BF /BF	5.00%	÷0.996	×1.100	5.52%

Result: FIP 5.11 → 5.59, 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).

2021 AAABuffalo Bisons · 45 IPFIP 2.96 → 3.47 (+0.51)K% 39.0% → 34.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.04%	—	×0.880	34.35%
BB% (unintentional) /BF	13.37%	—	×1.050	14.04%
HR / BF /BF	2.67%	÷1.081	×1.100	2.72%

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 2.96 → 3.47, 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 AANew Hampshire Fisher Cats · 34 IPFIP 2.88 → 4.18 (+1.30)K% 34.3% → 27.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.33%	—	×0.810	27.81%
BB% (unintentional) /BF	8.21%	—	×1.100	9.03%
HR / BF /BF	2.99%	÷0.860	×1.200	4.16%

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 72).

Result: FIP 2.88 → 4.18, 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 A+Dunedin Blue Jays · 25 IPFIP 1.36 → 2.41 (+1.04)K% 43.9% → 33.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	43.93%	—	×0.770	33.82%
BB% (unintentional) /BF	7.48%	—	×1.150	8.60%
HR / BF /BF	1.87%	÷1.194	×1.250	1.96%

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 139).

Result: FIP 1.36 → 2.41, 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 ALansing Lugnuts · 63 IPFIP 3.65 → 4.99 (+1.34)K% 29.2% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.20%	—	×0.740	21.61%
BB% (unintentional) /BF	8.03%	—	×1.180	9.47%
HR / BF /BF	3.65%	÷1.034	×1.300	4.59%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 107).

Result: FIP 3.65 → 4.99, 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 ROKGCL Blue Jays · 46 IPFIP 2.44 → 3.56 (+1.12)K% 25.8% → 17.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.81%	—	×0.680	17.55%
BB% (unintentional) /BF	9.68%	—	×1.250	12.10%
HR / BF /BF	0.54%	÷1.000	×1.400	0.75%

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 2.44 → 3.56, 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 ROKDSL Blue Jays · 9 IPFIP 4.74 → 7.21 (+2.47)K% 33.3% → 22.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.680	22.67%
BB% (unintentional) /BF	11.11%	—	×1.250	13.89%
HR / BF /BF	5.56%	÷1.000	×1.400	7.78%

Result: FIP 4.74 → 7.21, 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).

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
Dany Jiménez (OGILVIE)	—	—	4.74	24.8%	11.9%	3.4%	—	5.13	23.3%	11.0%	67	+0.39	—	—	—
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	84
Jim Henderson R 2016 · 4.55 FIP	33	SU	4.55	25.8%	9.0%	4.5%	.287	—	—	—	—	—	1.86	167	73
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	73
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	72
Tony Barnette R 2017 · 3.56 FIP	33	SU	3.56	22.6%	7.1%	2.7%	.335	2.61	25.0%	20.2%	26	-0.95	2.61	144	72
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	71
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	70
Blake Parker R 2018 · 4.12 FIP	33	SU	4.12	25.4%	6.5%	4.3%	.290	4.12	24.9%	16.5%	121	+0.00	2.52	406	70
Scott McGough R 2023 · 4.57 FIP	33	SU	4.57	28.6%	9.3%	4.9%	.266	4.23	19.4%	9.4%	126	-0.34	3.55	429	67
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	67
Austin Adams R 2019 · 5.80 FIP	32	SU	5.80	20.8%	10.6%	5.7%	.280	—	—	—	—	—	3.40	162	66
Jason Bulger R 2010 · 4.36 FIP	31	SU	4.36	22.5%	13.5%	2.6%	.324	—	—	—	—	—	3.78	106	66
Jake Sanchez R 2023 · 4.70 FIP	33	SU	4.70	20.3%	10.6%	3.6%	.317	—	—	—	—	—	1.86	361	66
Jean Machi R 2015 · 4.56 FIP	33	SU	4.56	16.3%	8.6%	3.2%	.276	4.24	18.5%	11.9%	128	-0.32	2.16	306	65
Fernando Rodriguez Jr. R 2016 · 3.61 FIP	32	SU	3.61	22.7%	10.4%	2.0%	.255	4.41	24.1%	13.8%	97	+0.80	3.34	335	65
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
Adrian Ramirez R 2019 · 4.29 FIP	31	SU	4.29	21.7%	6.3%	4.0%	.327	—	—	—	—	—	3.04	137	65
Chad Fox R 2003 · 3.70 FIP	32	SU	3.70	23.2%	13.6%	1.5%	.262	—	—	—	—	—	2.82	194	65
Fernando Nieve R 2015 · 4.27 FIP	33	SU	4.27	22.0%	10.5%	3.1%	.290	2.43	26.9%	22.6%	89	-1.84	1.96	287	64
Felipe Arredondo R 2019 · 4.65 FIP	32	SU	4.65	16.2%	7.1%	3.7%	.272	—	—	—	—	—	3.92	337	64

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	1.0	34	23.2%	13.2%	1.30	4.41
John Hudek ('99)	1.0	40	23.0%	13.7%	1.30	4.43
David Aardsma ('15)	1.7	48	23.1%	12.4%	1.09	4.27
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*
Armando Almanza ('05)	1.1	54	24.4%	14.3%	1.43	4.82
Fred Lasher ('63-'71, -1 WAR*)	1.0	50	21.5%*	11.9%*	1.05*	4.68*