Yadiel Hernandez

PosLF

B/TL/R

Born1987-10-09

Age (2027)39

No MLB projection yet. Career stats below are historical.

Overview

INTL

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2023	35	AAA/INTL2 orgs	263	.278/.366/.317	0	1	95	—	.154/.175/.179
2024	36	INTLAguilas de Me…	184	.191/.309/.289	3	0	71	—
2025	37	INTLTigres de Ara…	137	.257/.378/.381	3	1	119	—

Contract

No contract data on file.

Skills · percentileStatcast 2020-2022

xwOBA51

Avg EV51

Barrel%43

Speed12

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)
2014	26	INTL	Cuba	—	15	12	0	0	0	0	2	2	0	0	0		143		143			13	13	93	-51	—	—	—	—	—	—
2017	29	AA	Harrisburg Senators	—	460	397	116	21	1	12	56	66	1	5	2	292	381	441	822	149	323	14	12	364	96	—	—	—	—	—	—
2017	29	INTL	Cangrejeros de Santurce	—	67	59	17	3	0	0	6	8	0	2	2	288	354	339	693	51	327	12	9	309	95	—	—	—	—	—	—
2018	30	AAA	Syracuse Chiefs	—	363	325	90	15	1	11	36	88	0	2	1	277	349	431	780	154	348	24	10	347	74	—	—	—	—	—	—
2018	30	AA	Harrisburg Senators	—	129	108	34	3	0	7	20	18	1	2	1	315	426	537	963	222	325	14	16	425	122	—	—	—	—	—	—
2018	30	INTL	Estrellas Orientales	—	46	42	9	3	0	0	3	14	1	0	1	214	283	286	568	71	321	30	7	263	64	—	—	—	—	—	—
2019	31	AAA	Fresno Grizzlies	—	508	439	142	22	1	33	63	106	1	7	5	323	410	604	1.013	280	357	21	12	435	109	—	—	—	—	—	—
2019	31	INTL	Naranjeros de Hermosillo	—	266	211	71	7	0	5	50	50	2	1	5	336	468	441	908	104	415	19	19	410	164	—	—	—	—	—	—
2020	32	MLB	Washington Nationals	—	28	26	5	3	0	1	1	12	0	0	0	192	222	423	645	231	286	43	4	269	67	-1	+0	-0	-3	+1	-0.3
2020	32	INTL	Naranjeros de Hermosillo	—	212	177	60	10	1	4	34	47	1	5	2	339	448	475	923	136	444	22	16	415	167	—	—	—	—	—	—
2021	33	MLB	Washington Nationals	—	289	264	72	8	1	9	22	59	1	3	0	273	331	413	744	140	318	20	8	331	109	+3	+1	-6	-4	+6	0.0
2021	33	AAA	Rochester Red Wings	—	64	59	17	2	0	5	5	11	0	0	1	288	344	576	920	288	279	17	8	399	82	—	—	—	—	—	—
2022	34	MLB	Washington Nationals	—	327	305	82	16	0	9	19	74	1	2	1	269	314	410	724	141	326	23	6	321	102	+1	+0	-5	-4	+7	-0.0
2023	35	AAA	Rochester Red Wings	—	40	39	8	2	0	0	1	15	0	0	0	205	225	256	481	51	333	38	2	217	-12	—	—	—	—	—	—
2023	35	INTL	Dominican Republic	—	223	191	56	7	0	0	29	25	2	1	3	293	392	330	722	37	335	11	13	336	114	—	—	—	—	—	—
2024	36	INTL	Aguilas de Mexicali	—	184	152	29	6	0	3	24	35	2	0	0	191	309	289	598	99	220	19	13	273	71	—	—	—	—	—	—
2025	37	INTL	Tigres de Aragua	—	137	113	29	3	1	3	20	31	2	1	0	257	378	381	758	124	321	23	15	343	119	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	644	595	159	27	1	19	42	145	2	5	1	267	318	412	729	145	321	23	7	323	105	+4	+1	-11	-10	+14	-0.2
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2017	29	AA MLE	Harrisburg Senators	—	460	417	111	19	1	10	36	79	1	5	2	266	326	388	714	122	299	17	8	310	96	—	—	—	—	—	—
2018	30	AAA MLE	Syracuse Chiefs	—	363	338	79	14	1	7	23	101	0	2	1	234	283	343	626	109	309	28	6	278	74	—	—	—	—	—	—
2018	30	AA MLE	Harrisburg Senators	—	129	117	33	3	0	6	11	22	1	2	1	282	349	462	810	179	297	17	9	349	122	—	—	—	—	—	—
2019	31	AAA MLE	Fresno Grizzlies	—	508	462	117	17	1	25	40	143	1	7	5	253	314	457	771	203	300	28	8	330	109	—	—	—	—	—	—
2020	32	MLB MLE	Washington Nationals	—	28	26	5	3	0	1	1	12	0	0	0	192	222	423	645	231	286	43	4	267	67	-1	+0	-0	-3	+1	-0.3
2021	33	MLB MLE	Washington Nationals	—	289	265	72	8	1	9	21	59	1	3	0	272	328	411	739	140	318	20	7	329	109	+3	+1	-6	-4	+6	0.0
2021	33	AAA MLE	Rochester Red Wings	—	64	61	14	2	0	3	3	14	0	0	1	230	266	410	675	180	233	22	5	289	82	—	—	—	—	—	—
2022	34	MLB MLE	Washington Nationals	—	327	305	82	16	0	9	19	74	1	2	1	269	314	410	724	141	326	23	6	320	102	+1	+0	-5	-4	+7	-0.0
2023	35	AAA MLE	Rochester Red Wings	—	40	39	6	1	0	0	1	18	0	0	0	154	175	179	354	26	277	45	2	150	-12	—	—	—	—	—	—
2014	26	INTL MLE	Cuba	—	15	12	0	0	0	0	2	2	0	0	0		143		143			13	13	—	—	—	—	—	—	—	—
2014	26	INTL RAW	Cuba	—	15	12	0	0	0	0	2	2	0	0	0		143		143			13	13	93	-51	—	—	—	—	—	—
2017	29	AA MLE	2 teams	—	527	477	126	21	1	11	41	90	1	7	4	264	324	382	705	117	303	17	8	310	96	—	—	—	—	—	—
2017	29	AA RAW	2 teams	—	527	456	133	24	1	12	62	74	1	7	4	292	378	428	805	136	324	14	12	357	96	—	—	—	—	—	—
2018	30	AAA MLE	3 teams	—	538	496	122	18	1	15	38	135	2	4	3	246	302	377	679	131	308	25	7	297	87	—	—	—	—	—	—
2018	30	AAA RAW	3 teams	—	538	475	133	21	1	18	59	120	2	4	3	280	362	442	804	162	340	22	11	358	87	—	—	—	—	—	—
2019	31	AAA MLE	2 teams	—	774	703	176	25	1	38	60	218	3	8	10	250	312	451	763	201	303	28	8	330	109	—	—	—	—	—	—
2019	31	AAA RAW	2 teams	—	774	650	213	29	1	38	113	156	3	8	10	328	430	551	980	223	377	20	15	427	109	—	—	—	—	—	—
2020	32	INTL MLE	2 teams	—	240	229	43	25	0	9	9	103	1	5	2	188	222	415	637	227	288	43	4	267	67	—	—	—	—	—	—
2020	32	INTL RAW	2 teams	—	240	203	65	13	1	5	35	59	1	5	2	320	423	468	891	148	429	25	15	398	67	—	—	—	—	—	—
2021	33	MLB MLE	2 teams	—	353	326	85	9	1	12	24	73	1	3	1	261	313	405	718	144	300	21	7	322	104	+2	+0	-6	-4	+8	0.0
2021	33	MLB RAW	2 teams	—	353	323	89	10	1	14	27	70	1	3	1	276	333	443	776	167	311	20	8	343	104	+2	+0	-6	-4	+8	0.0
2022	34	MLB MLE	Washington Nationals	—	327	305	82	16	0	9	19	74	1	2	1	269	314	410	724	141	326	23	6	320	102	+1	+0	-5	-4	+7	-0.0
2022	34	MLB RAW	Washington Nationals	—	327	305	82	16	0	9	19	74	1	2	1	269	314	410	724	141	326	23	6	321	102	+1	+0	-5	-4	+7	-0.0
2023	35	INTL MLE	2 teams	—	263	256	38	8	0	0	4	121	2	1	3	148	168	180	348	31	279	46	2	150	-12	—	—	—	—	—	—
2023	35	INTL RAW	2 teams	—	263	230	64	9	0	0	30	40	2	1	3	278	366	317	684	39	335	15	11	318	-12	—	—	—	—	—	—
2024	36	INTL MLE	Aguilas de Mexicali	—	184	152	29	6	0	3	24	35	2	0	0	191	309	289	598	99	220	19	13	—	—	—	—	—	—	—	—
2024	36	INTL RAW	Aguilas de Mexicali	—	184	152	29	6	0	3	24	35	2	0	0	191	309	289	598	99	220	19	13	273	71	—	—	—	—	—	—
2025	37	INTL MLE	Tigres de Aragua	—	137	113	29	3	1	3	20	31	2	1	0	257	378	381	758	124	321	23	15	—	—	—	—	—	—	—	—
2025	37	INTL RAW	Tigres de Aragua	—	137	113	29	3	1	3	20	31	2	1	0	257	378	381	758	124	321	23	15	343	119	—	—	—	—	—	—
2017	29	AA MLE	2 teams	—	527	480	126	21	1	11	41	90	1	7	4	263	324	379	703	117	299	17	8	310	96	—	—	—	—	—	—
2018	30	AAA MLE	3 teams	—	538	493	122	18	1	15	38	135	2	4	3	247	302	379	682	132	306	25	7	297	87	—	—	—	—	—	—
2019	31	AAA MLE	2 teams	—	774	705	176	25	1	38	60	218	3	8	10	250	312	450	762	200	300	28	8	330	109	—	—	—	—	—	—
2020	32	INTL MLE	2 teams	—	240	228	43	25	0	9	9	103	1	5	2	189	222	417	638	228	286	43	4	267	67	—	—	—	—	—	—
2021	33	MLB MLE	2 teams	—	353	325	85	9	1	12	24	73	1	3	1	262	313	406	720	145	303	21	7	322	104	—	—	—	—	—	—
2022	34	MLB MLE	Washington Nationals	—	327	304	82	16	0	9	19	74	1	2	1	270	314	411	725	141	326	23	6	320	102	—	—	—	—	—	—
2023	35	INTL MLE	2 teams	—	263	255	38	8	0	0	4	121	2	1	3	149	168	180	348	31	277	46	2	150	-12	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	644	595	159	27	1	19	42	145	2	5	1	267	318	412	729	145	321	23	7	323	68	+4	+1	-11	-10	+14	-0.2
/ 162G	—	A+C		—	650	601	160	27	1	19	42	146	2	5	1	266	316	409	726	143	320	22	6	323	68	+4	+1	-11	-10	+14	-0.2
Career	—	MLE		—	3358	3069	730	131	5	100	241	882	15	31	24	238	297	382	678	144	298	26	7	268	68	+4	+1	-11	-10	+14	-0.2
/ 162G	—	MLE		—	650	594	141	25	1	19	47	171	3	6	5	237	297	379	676	141	298	26	7	268	68	+4	+1	-11	-10	+14	-0.2

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.

2023 AAARochester Red Wings · 40 PAwOBA .211 → .150 (−61)K% 37.5% → 45.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	37.50%	÷0.980	×1.200	—	—	45.91%
BB% (unintentional) /PA	2.50%	÷0.956	×0.690	—	×0.940	1.70%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	25.00%	÷0.996	×0.930	—	×0.940	21.95%
Doubles /BIP	8.33%	÷1.137	×0.830	—	×0.940	5.72%
Triples /BIP	0.00%	÷1.150	×0.970	—	×0.940	0.00%
Home runs /BIP	0.00%	÷1.058	×0.700	—	×0.940	0.00%

Factors used: league row International League. Park (home-season share, regressed): HR 112, 1B 99, 2B 127, 3B 130, K 96, BB 91 — applied at half strength. Age: age 36 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (8.8 yr older than average).

Result: wOBA .211 → .150; ISO .051 → .031; BABIP .333 → .277. 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).

2021 AAARochester Red Wings · 64 PAwOBA .387 → .289 (−98)K% 17.2% → 21.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.19%	÷0.979	×1.200	—	—	21.06%
BB% (unintentional) /PA	7.81%	÷1.014	×0.690	—	×0.940	5.00%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	20.83%	÷0.994	×0.930	×1.006	×0.940	18.43%
Doubles /BIP	4.17%	÷1.005	×0.830	×1.006	×0.940	3.25%
Triples /BIP	0.00%	÷1.150	×0.920	×1.006	×0.940	0.00%
Home runs /BIP	10.42%	÷0.968	×0.700	—	×0.940	7.08%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 94, 1B 99, 2B 101, 3B 130, K 96, BB 103 — applied at half strength. Age: age 34 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (6.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.006.

Result: wOBA .387 → .289; ISO .288 → .196; BABIP .279 → .233. 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).

2019 AAAFresno Grizzlies · 508 PAwOBA .425 → .330 (−95)K% 20.9% → 28.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.87%	÷0.953	×1.290	—	—	28.25%
BB% (unintentional) /PA	11.42%	÷0.990	×0.710	—	×0.952	7.79%
HBP% /PA	0.20%	—	×0.980	—	×0.952	0.18%
Singles /BIP	25.83%	÷0.959	×0.900	×0.988	×0.952	22.78%
Doubles /BIP	6.61%	÷0.976	×0.810	×0.988	×0.952	5.16%
Triples /BIP	0.30%	÷1.140	×0.810	×0.988	×0.952	0.20%
Home runs /BIP	9.91%	÷0.962	×0.780	—	×0.952	7.65%

Factors used: league row PCL. Park (home-season share, regressed): HR 92, 1B 92, 2B 95, 3B 128, K 91, BB 98 — applied at half strength. Age: age 32 vs a level average of 27.2 → −4.8% on BB/HBP/hit rates (4.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.988.

Result: wOBA .425 → .330; ISO .280 → .210; BABIP .357 → .300. 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).

2018 AAASyracuse Chiefs · 363 PAwOBA .336 → .278 (−58)K% 24.2% → 27.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.24%	÷1.044	×1.200	—	—	27.86%
BB% (unintentional) /PA	9.09%	÷0.956	×0.690	—	×0.962	6.31%
HBP% /PA	0.00%	—	×0.940	—	×0.962	0.00%
Singles /BIP	26.58%	÷1.044	×0.930	×1.050	×0.962	23.92%
Doubles /BIP	6.33%	÷0.933	×0.830	×1.050	×0.962	5.69%
Triples /BIP	0.42%	÷0.900	×0.970	×1.050	×0.962	0.46%
Home runs /BIP	4.64%	÷1.205	×0.840	—	×0.962	3.11%

Factors used: league row IL. Park (home-season share, regressed): HR 141, 1B 109, 2B 87, 3B 80, K 109, BB 91 — applied at half strength. Age: age 31 vs a level average of 27.2 → −3.8% on BB/HBP/hit rates (3.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .336 → .278; ISO .154 → .117; BABIP .348 → .309. 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).

2018 AAHarrisburg Senators · 129 PAwOBA .410 → .349 (−61)K% 14.0% → 17.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.95%	÷0.987	×1.220	—	—	17.25%
BB% (unintentional) /PA	13.95%	÷1.037	×0.700	—	×0.940	8.85%
HBP% /PA	0.78%	—	×0.850	—	×0.940	0.62%
Singles /BIP	26.67%	÷1.017	×0.970	×1.050	×0.940	25.11%
Doubles /BIP	3.33%	÷1.012	×0.860	×1.050	×0.940	2.79%
Triples /BIP	0.00%	÷1.070	×0.950	×1.050	×0.940	0.00%
Home runs /BIP	7.78%	÷0.925	×0.780	—	×0.940	6.16%

Factors used: league row EL. Park (home-season share, regressed): HR 85, 1B 103, 2B 102, 3B 114, K 97, BB 107 — applied at half strength. Age: age 31 vs a level average of 24.8 → −6.0% on BB/HBP/hit rates (6.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .410 → .349; ISO .222 → .186; BABIP .325 → .297. 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 AAHarrisburg Senators · 460 PAwOBA .355 → .310 (−45)K% 14.3% → 17.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.35%	÷1.022	×1.220	—	—	17.13%
BB% (unintentional) /PA	11.52%	÷0.974	×0.700	—	×0.948	7.85%
HBP% /PA	0.22%	—	×0.850	—	×0.948	0.18%
Singles /BIP	24.77%	÷1.016	×0.970	×1.050	×0.948	23.53%
Doubles /BIP	6.34%	÷0.998	×0.860	×1.050	×0.948	5.44%
Triples /BIP	0.30%	÷0.906	×0.950	×1.050	×0.948	0.32%
Home runs /BIP	3.63%	÷0.970	×0.780	—	×0.948	2.76%

Factors used: league row EL. Park (home-season share, regressed): HR 94, 1B 103, 2B 100, 3B 81, K 104, BB 95 — applied at half strength. Age: age 30 vs a level average of 24.8 → −5.2% on BB/HBP/hit rates (5.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .355 → .310; ISO .149 → .125; BABIP .323 → .299. 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 — .