Jose Augusto Figueroa

PosCF

B/TL/R

Born1992-12-09

Age (2027)34

No MLB projection yet. Career stats below are historical.

Overview

INTLLAST SEASON 2022

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2020	27	INTLVenados de Ma…	147	.301/.363/.429	2	0	124	—
2021	28	INTLVenados de Ma…	52	.106/.176/.170	0	0	-4	—
2022	29	INTLYaquis de Obr…	29	.185/.241/.222	0	1	33	—

Contract

No contract data on file.

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 these numbers are built

All seasons translated to park-neutral MLB-equivalent (MLE) and combined per year. 2027 = full-season projection (no games played yet). 2028+ via Advanced Marcel + aging curve.

Year	Age	Lvl	Team	MLB%	PA	AB	H	2B	3B	HR	BB	SO	HBP	SB	CS	AVG	OBP	SLG	OPS	ISO	BABIP	K%	BB%	wOBA	wRC+	BAT	RUN	FLD	POS	REP	WAR
▸ Career — actual stats, one row per stint (as recorded, no adjustments)
2011	18	ROK	DSL Yankees 1	—	88	70	14	5	1	1	13	22	2	1	2	200	341	343	684	143	271	25	15	307	20	—	—	—	—	—	—
2012	19	ROK	DSL Yankees 1	—	213	186	71	12	11	3	18	25	8	4	9	382	458	613	1.070	231	430	12	8	463	76	—	—	—	—	—	—
2012	19	INTL	Yaquis de Obregon	—	78	70	18	2	0	1	5	16	0	0	2	257	307	329	635	71	321	21	6	278	75	—	—	—	—	—	—
2013	20	ROK	GCL Yankees2	—	188	159	40	5	2	1	23	31	1	13	3	252	350	327	677	75	300	16	12	306	41	—	—	—	—	—	—
2013	20	INTL	Yaquis de Obregon	—	50	44	6	2	1	0	4	17	0	1	0	136	208	227	436	91	222	34	8	194	17	—	—	—	—	—	—
2014	21	ROK	GCL Yankees2	—	204	186	54	11	2	3	16	46	2	13	5	290	353	419	772	129	372	23	8	346	41	—	—	—	—	—	—
2014	21	INTL	Venados de Mazatlan	—	118	106	24	4	1	1	5	29	0	3	1	226	261	311	573	85	291	25	4	242	50	—	—	—	—	—	—
2015	22	AAA	Diablos Rojos del Mexico	—	75	72	22	4	1	0	2	17	1	3	1	306	333	389	722	83	400	23	3	324	61	—	—	—	—	—	—
2015	22	INTL	Venados de Mazatlan	—	98	88	16	3	0	1	4	23	0	0	2	182	217	250	467	68	221	23	4	200	21	—	—	—	—	—	—
2016	23	AAA	Diablos Rojos del Mexico	—	303	275	79	13	5	4	17	67	1	3	4	287	330	415	744	127	361	22	6	321	68	—	—	—	—	—	—
2016	23	INTL	Venados de Mazatlan	—	62	52	8	1	0	0	6	14	1	1	0	154	254	173	427	19	211	23	10	202	23	—	—	—	—	—	—
2017	24	AAA	Diablos Rojos del Mexico	—	123	117	41	7	3	1	4	26	1	1	2	350	377	487	864	137	444	21	3	379	101	—	—	—	—	—	—
2017	24	INTL	Yaquis de Obregon	—	31	26	6	2	0	0	3	10	1	3	0	231	333	308	641	77	353	32	10	291	83	—	—	—	—	—	—
2019	26	AAA	Guerreros de Oaxaca	—	25	22	4	1	0	0	2	6	0	0	0	182	250	227	477	45	250	24	8	216	2	—	—	—	—	—	—
2019	26	INTL	Venados de Mazatlan	—	36	35	6	0	0	0	0	12	0	0	1	171	171	171	343		261	33	0	151	-12	—	—	—	—	—	—
2020	27	INTL	Venados de Mazatlan	—	147	133	40	7	2	2	12	25	1	0	6	301	363	429	792	128	358	17	8	352	124	—	—	—	—	—	—
2021	28	INTL	Venados de Mazatlan	—	52	47	5	1	1	0	4	19	0	0	1	106	176	170	347	64	179	37	8	162	-4	—	—	—	—	—	—
2022	29	INTL	Yaquis de Obregon	—	29	27	5	1	0	0	2	13	0	1	0	185	241	222	464	37	357	45	7	217	33	—	—	—	—	—	—
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2011	18	ROK MLE	DSL Yankees 1	—	88	74	9	3	1	0	9	27	2	1	2	122	235	189	424	68	165	31	10	198	20	—	—	—	—	—	—
2012	19	ROK MLE	DSL Yankees 1	—	213	192	43	7	6	1	12	30	8	4	9	224	297	339	636	115	260	14	6	280	76	—	—	—	—	—	—
2013	20	ROK MLE	GCL Yankees2	—	188	168	33	4	1	1	14	45	1	13	3	196	262	250	512	54	246	24	7	229	41	—	—	—	—	—	—
2014	21	ROK MLE	GCL Yankees2	—	204	192	38	7	1	2	10	66	2	13	5	198	245	276	521	78	287	32	5	229	41	—	—	—	—	—	—
2015	22	AAA MLE	Diablos Rojos del Mexico	—	75	73	19	3	1	0	1	23	1	3	1	260	280	329	609	68	368	31	1	258	61	—	—	—	—	—	—
2016	23	AAA MLE	Diablos Rojos del Mexico	—	303	281	70	10	4	2	12	90	1	3	4	249	282	335	617	85	338	30	4	268	68	—	—	—	—	—	—
2017	24	AAA MLE	Diablos Rojos del Mexico	—	123	118	37	5	3	1	3	32	1	1	2	314	336	432	768	119	415	26	2	318	101	—	—	—	—	—	—
2019	26	AAA MLE	Guerreros de Oaxaca	—	25	23	4	1	0	0	1	7	0	0	0	174	208	217	426	43	214	28	4	171	2	—	—	—	—	—	—
2011	18	ROK MLE	DSL Yankees 1	—	88	74	9	3	1	0	9	27	2	1	2	122	235	189	424	68	188	31	10	198	20	—	—	—	—	—	—
2011	18	ROK RAW	DSL Yankees 1	—	88	70	14	5	1	1	13	22	2	1	2	200	341	343	684	143	271	25	15	307	20	—	—	—	—	—	—
2012	19	ROK MLE	2 teams	—	291	262	60	9	9	2	17	42	8	4	11	229	296	355	651	126	266	14	6	280	76	—	—	—	—	—	—
2012	19	ROK RAW	2 teams	—	291	256	89	14	11	4	23	41	8	4	11	348	418	535	953	188	403	14	8	413	76	—	—	—	—	—	—
2013	20	ROK MLE	2 teams	—	238	212	42	5	2	1	18	56	1	14	3	198	264	255	519	57	259	24	8	229	41	—	—	—	—	—	—
2013	20	ROK RAW	2 teams	—	238	203	46	7	3	1	27	48	1	14	3	227	320	305	626	79	287	20	11	282	41	—	—	—	—	—	—
2014	21	ROK MLE	2 teams	—	322	298	59	11	2	3	15	105	2	16	6	198	241	279	520	81	290	33	5	229	41	—	—	—	—	—	—
2014	21	ROK RAW	2 teams	—	322	292	78	15	3	4	21	75	2	16	6	267	321	380	701	113	343	23	7	308	41	—	—	—	—	—	—
2015	22	INTL MLE	2 teams	—	173	163	43	7	2	0	3	52	1	3	3	264	281	331	613	67	374	30	2	258	61	—	—	—	—	—	—
2015	22	INTL RAW	2 teams	—	173	160	38	7	1	1	6	40	1	3	3	237	269	312	582	75	301	23	3	253	61	—	—	—	—	—	—
2016	23	AAA MLE	2 teams	—	365	337	86	12	5	3	14	108	2	4	4	255	289	347	636	92	361	30	4	268	68	—	—	—	—	—	—
2016	23	AAA RAW	2 teams	—	365	327	87	14	5	4	23	81	2	4	4	266	317	376	693	110	337	22	6	301	68	—	—	—	—	—	—
2017	24	AAA MLE	2 teams	—	154	146	46	7	3	1	4	40	2	4	2	315	342	425	767	110	425	26	3	318	101	—	—	—	—	—	—
2017	24	AAA RAW	2 teams	—	154	143	47	9	3	1	7	36	2	4	2	329	368	455	823	126	430	23	5	361	101	—	—	—	—	—	—
2019	26	INTL MLE	2 teams	—	61	56	9	2	0	0	3	18	0	0	1	161	203	196	400	36	237	30	5	171	2	—	—	—	—	—	—
2019	26	INTL RAW	2 teams	—	61	57	10	1	0	0	2	18	0	0	1	175	203	193	396	18	256	30	3	177	2	—	—	—	—	—	—
2020	27	INTL MLE	Venados de Mazatlan	—	147	133	40	7	2	2	12	25	1	0	6	301	363	429	792	128	358	17	8	—	—	—	—	—	—	—	—
2020	27	INTL RAW	Venados de Mazatlan	—	147	133	40	7	2	2	12	25	1	0	6	301	363	429	792	128	358	17	8	352	124	—	—	—	—	—	—
2021	28	INTL MLE	Venados de Mazatlan	—	52	47	5	1	1	0	4	19	0	0	1	106	176	170	347	64	179	37	8	—	—	—	—	—	—	—	—
2021	28	INTL RAW	Venados de Mazatlan	—	52	47	5	1	1	0	4	19	0	0	1	106	176	170	347	64	179	37	8	162	-4	—	—	—	—	—	—
2022	29	INTL MLE	Yaquis de Obregon	—	29	27	5	1	0	0	2	13	0	1	0	185	241	222	464	37	357	45	7	—	—	—	—	—	—	—	—
2022	29	INTL RAW	Yaquis de Obregon	—	29	27	5	1	0	0	2	13	0	1	0	185	241	222	464	37	357	45	7	217	33	—	—	—	—	—	—
2011	18	ROK MLE	DSL Yankees 1	—	88	77	9	3	1	0	9	27	2	1	2	117	235	182	417	65	165	31	10	198	20	—	—	—	—	—	—
2012	19	ROK MLE	2 teams	—	291	264	60	9	9	2	17	42	8	4	11	227	296	352	648	125	260	14	6	280	76	—	—	—	—	—	—
2013	20	ROK MLE	2 teams	—	238	217	42	5	2	1	18	56	1	14	3	194	264	249	513	55	246	24	8	229	41	—	—	—	—	—	—
2014	21	ROK MLE	2 teams	—	322	302	59	11	2	3	15	105	2	16	6	195	241	275	516	79	287	33	5	229	41	—	—	—	—	—	—
2015	22	INTL MLE	2 teams	—	173	167	43	7	2	0	3	52	1	3	3	257	281	323	605	66	368	30	2	258	61	—	—	—	—	—	—
2016	23	AAA MLE	2 teams	—	365	346	86	12	5	3	14	108	2	4	4	249	289	338	627	90	338	30	4	268	68	—	—	—	—	—	—
2017	24	AAA MLE	2 teams	—	154	147	46	7	3	1	4	40	2	4	2	313	342	422	764	109	415	26	3	318	101	—	—	—	—	—	—
2019	26	INTL MLE	2 teams	—	61	58	9	2	0	0	3	18	0	0	1	155	203	190	393	34	214	30	5	171	2	—	—	—	—	—	—
▸ Career Totals
Career	—	MLE		—	1920	1755	404	65	27	12	101	505	19	47	39	230	279	319	598	88	313	26	5	223	37	-143	-6	+0	+7	+42	-10.3
/ 162G	—	MLE		—	650	594	137	22	9	4	34	171	6	16	13	231	279	318	597	88	314	26	5	223	37	-48	-2	+0	+2	+14	-3.5

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.

2019 AAAGuerreros de Oaxaca · 25 PAwOBA .211 → .171 (−39)K% 24.0% → 28.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.00%	÷0.998	×1.200	—	—	28.87%
BB% (unintentional) /PA	8.00%	÷1.036	×0.690	—	×1.003	5.34%
HBP% /PA	0.00%	—	×0.940	—	×1.003	0.00%
Singles /BIP	18.75%	÷1.025	×0.930	—	×1.003	17.06%
Doubles /BIP	6.25%	÷1.200	×0.830	—	×1.003	4.34%
Triples /BIP	0.00%	÷1.058	×0.920	—	×1.003	0.00%
Home runs /BIP	0.00%	÷0.972	×0.700	—	×1.003	0.00%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 94, 1B 105, 2B 140, 3B 112, K 100, BB 107 — applied at half strength. Age: age 27 vs a level average of 27.2 → +0.3% on BB/HBP/hit rates (0.2 yr younger than average).

Result: wOBA .211 → .171; ISO .045 → .032; BABIP .250 → .214. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2017 AAADiablos Rojos del Mexico · 123 PAwOBA .368 → .318 (−50)K% 21.1% → 26.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.14%	÷0.967	×1.200	—	—	26.22%
BB% (unintentional) /PA	3.25%	÷1.003	×0.690	—	×1.033	2.31%
HBP% /PA	0.81%	—	×0.940	—	×1.033	0.79%
Singles /BIP	32.97%	÷1.036	×0.930	×1.050	×1.033	32.10%
Doubles /BIP	7.69%	÷1.143	×0.830	×1.050	×1.033	6.06%
Triples /BIP	3.30%	÷1.093	×0.920	×1.050	×1.033	3.01%
Home runs /BIP	1.10%	÷1.080	×0.700	—	×1.033	0.74%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 116, 1B 107, 2B 129, 3B 119, K 93, BB 101 — applied at half strength. Age: age 25 vs a level average of 27.2 → +3.3% on BB/HBP/hit rates (2.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .368 → .318; ISO .137 → .106; BABIP .444 → .415. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2016 AAADiablos Rojos del Mexico · 303 PAwOBA .315 → .268 (−47)K% 22.1% → 29.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.11%	÷0.894	×1.200	—	—	29.68%
BB% (unintentional) /PA	5.28%	÷1.001	×0.690	—	×1.048	3.81%
HBP% /PA	0.33%	—	×0.940	—	×1.048	0.33%
Singles /BIP	27.40%	÷1.027	×0.930	×1.044	×1.048	27.14%
Doubles /BIP	6.25%	÷1.188	×0.830	×1.044	×1.048	4.78%
Triples /BIP	2.40%	÷1.150	×0.920	×1.044	×1.048	2.10%
Home runs /BIP	1.92%	÷1.183	×0.700	—	×1.048	1.19%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 137, 1B 105, 2B 138, 3B 130, K 79, BB 100 — applied at half strength. Age: age 24 vs a level average of 27.2 → +4.8% on BB/HBP/hit rates (3.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.044.

Result: wOBA .315 → .268; ISO .127 → .092; BABIP .361 → .338. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2015 AAADiablos Rojos del Mexico · 75 PAwOBA .315 → .258 (−56)K% 22.7% → 30.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.67%	÷0.900	×1.200	—	—	30.22%
BB% (unintentional) /PA	2.67%	÷1.204	×0.690	—	×1.050	1.60%
HBP% /PA	1.33%	—	×0.940	—	×1.050	1.32%
Singles /BIP	30.91%	÷1.045	×0.930	×1.019	×1.050	29.44%
Doubles /BIP	7.27%	÷1.122	×0.830	×1.019	×1.050	5.76%
Triples /BIP	1.82%	÷1.123	×0.920	×1.019	×1.050	1.59%
Home runs /BIP	0.00%	÷1.043	×0.700	—	×1.050	0.00%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 109, 1B 109, 2B 124, 3B 125, K 80, BB 141 — applied at half strength. Age: age 23 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (4.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.019.

Result: wOBA .315 → .258; ISO .083 → .062; BABIP .400 → .368. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2014 ROKGCL Yankees2 · 204 PAwOBA .337 → .229 (−108)K% 22.5% → 32.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.55%	÷1.000	×1.440	—	—	32.47%
BB% (unintentional) /PA	7.84%	÷1.000	×0.620	—	×0.975	4.74%
HBP% /PA	0.98%	—	×0.720	—	×0.975	0.69%
Singles /BIP	27.14%	÷1.000	×0.820	×1.001	×0.975	21.71%
Doubles /BIP	7.86%	÷1.000	×0.720	×1.001	×0.975	5.52%
Triples /BIP	1.43%	÷1.000	×0.720	×1.001	×0.975	1.00%
Home runs /BIP	2.14%	÷1.000	×0.780	—	×0.975	1.63%

Factors used: level-wide ROK factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 22 vs a level average of 19.5 → −2.5% on BB/HBP/hit rates (2.5 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.001.

Result: wOBA .337 → .229; ISO .129 → .085; BABIP .372 → .287. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2013 ROKGCL Yankees2 · 188 PAwOBA .303 → .229 (−74)K% 16.5% → 23.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.49%	÷1.000	×1.440	—	—	23.74%
BB% (unintentional) /PA	12.23%	÷1.000	×0.620	—	×0.985	7.47%
HBP% /PA	0.53%	—	×0.720	—	×0.985	0.38%
Singles /BIP	25.00%	÷1.000	×0.820	×1.040	×0.985	21.00%
Doubles /BIP	3.91%	÷1.000	×0.720	×1.040	×0.985	2.88%
Triples /BIP	1.56%	÷1.000	×0.720	×1.040	×0.985	1.15%
Home runs /BIP	0.78%	÷1.000	×0.780	—	×0.985	0.60%

Factors used: level-wide ROK factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 21 vs a level average of 19.5 → −1.5% on BB/HBP/hit rates (1.5 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.040.

Result: wOBA .303 → .229; ISO .075 → .057; BABIP .300 → .246. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2012 DSLDSL Yankees 1 · 213 PAwOBA .451 → .280 (−171)K% 11.7% → 14.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.74%	÷1.000	×1.220	—	—	14.32%
BB% (unintentional) /PA	8.45%	÷1.000	×0.700	—	×0.985	5.83%
HBP% /PA	3.76%	—	×0.700	—	×0.985	2.59%
Singles /BIP	27.95%	÷1.000	×0.680	×0.950	×0.985	17.79%
Doubles /BIP	7.45%	÷1.000	×0.600	×0.950	×0.985	4.18%
Triples /BIP	6.83%	÷1.000	×0.600	×0.950	×0.985	3.84%
Home runs /BIP	1.86%	÷1.000	×0.470	—	×0.985	0.86%

Factors used: level-wide DSL factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 20 vs a level average of 18.5 → −1.5% on BB/HBP/hit rates (1.5 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .451 → .280; ISO .231 → .128; BABIP .430 → .260. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2011 DSLDSL Yankees 1 · 88 PAwOBA .303 → .198 (−105)K% 25.0% → 30.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.00%	÷1.000	×1.220	—	—	30.50%
BB% (unintentional) /PA	14.77%	÷1.000	×0.700	—	×0.995	10.29%
HBP% /PA	2.27%	—	×0.700	—	×0.995	1.58%
Singles /BIP	14.58%	÷1.000	×0.680	×0.961	×0.995	9.48%
Doubles /BIP	10.42%	÷1.000	×0.600	×0.961	×0.995	5.97%
Triples /BIP	2.08%	÷1.000	×0.600	×0.961	×0.995	1.19%
Home runs /BIP	2.08%	÷1.000	×0.470	—	×0.995	0.97%

Factors used: level-wide DSL factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 19 vs a level average of 18.5 → −0.5% on BB/HBP/hit rates (0.5 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.961.

Result: wOBA .303 → .198; ISO .143 → .082; BABIP .271 → .165. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

Not traceable per stint: some stints lack the counting stats the component translation needs. Level-wide factors that apply instead — .