Guillermo Heredia

PosLF

B/TR/L

Born1991-01-31

Age (2027)36

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2022

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2020	29	MLBNYM	36	.212/.278/.394	2	1	91	0.2
2021	30	MLBATL	347	.220/.312/.354	5	0	87	0.8
2022	31	MLBATL	82	.158/.220/.342	3	0	55	-0.5
2023	32	No MLB games played
2024	33	No MLB games played
2025	34	No MLB games played
2026	35	No MLB games played

Last 3 seasons WAR 0.5

Contract

No contract data on file.

Skills · percentileStatcast 2020-2022

Speed63

Barrel%30

xwOBA30

Avg EV15

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)
2016	25	MLB	Seattle Mariners	—	107	92	23	3	0	1	12	15	2	1	1	250	349	315	664	65	289	14	11	308	96	-1	-0	+0	-6	+2	-0.4
2016	25	AAA	Tacoma Rainiers	—	157	138	43	6	1	2	12	15	4	3	0	312	383	413	796	101	333	10	8	354	110	—	—	—	—	—	—
2016	25	AA	Jackson Generals	—	260	205	60	7	2	2	36	32	9	2	5	293	420	376	796	83	322	12	14	356	108	—	—	—	—	—	—
2016	25	INTL	Peoria Javelinas	—	62	51	13	3	1	1	9	12	2	4	3	255	387	412	799	157	316	19	15	363	132	—	—	—	—	—	—
2017	26	MLB	Seattle Mariners	—	426	386	96	16	0	6	27	64	11	1	5	249	316	337	653	88	284	15	6	298	87	-7	-2	+8	+2	+9	1.1
2018	27	MLB	Seattle Mariners	—	337	292	69	14	1	5	32	52	4	2	4	236	320	342	663	106	270	15	9	296	88	-5	-1	+4	+2	+7	0.7
2018	27	AAA	Tacoma Rainiers	—	38	29	8	1	0	0	4	3	4	2	1	276	432	310	743	34	296	8	11	351	101	—	—	—	—	—	—
2018	27	INTL	Aguilas Cibaenas	—	70	67	16	3	0	2	2	17	1	0	0	239	271	373	645	134	292	24	3	287	80	—	—	—	—	—	—
2019	28	MLB	Tampa Bay Rays	—	231	204	46	13	0	5	18	60	6	2	2	225	307	363	670	137	293	26	8	300	90	-3	-0	+3	+1	+5	0.6
2019	28	AAA	Durham Bulls	—	30	28	6	1	0	1	1	10	1	0	1	214	267	357	624	143	294	33	3	281	32	—	—	—	—	—	—
2020	29	MLB	New York Mets	—	36	33	7	0	0	2	3	9	0	1	0	212	278	394	672	182	227	25	8	299	91	-0	+0	+1	+0	+1	0.2
2021	30	MLB	Atlanta Braves	—	347	305	67	26	0	5	32	81	9	0	0	220	312	354	666	134	282	23	9	303	87	-5	+0	+4	+1	+8	0.8
2022	31	MLB	Atlanta Braves	—	82	76	12	3	1	3	6	32	0	0	0	158	220	342	562	184	220	39	7	249	55	-4	+0	-1	-1	+2	-0.5
2022	31	INTL	Estrellas Orientales	—	18	17	7	1	0	0	1	3	0	0	2	412	444	471	915	59	500	17	6	412	165	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1566	1388	320	75	2	27	130	313	32	7	12	231	311	346	657	115	278	20	8	297	87	-24	-3	+20	-2	+34	2.5
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2016	25	MLB MLE	Seattle Mariners	—	107	92	23	3	0	1	12	15	2	1	1	250	349	315	664	65	289	14	11	310	96	-1	-0	+0	-6	+2	-0.4
2016	25	AAA MLE	Tacoma Rainiers	—	157	142	44	6	1	2	8	19	4	3	0	310	364	408	772	99	327	12	5	331	110	—	—	—	—	—	—
2016	25	AA MLE	Jackson Generals	—	260	217	62	8	2	2	24	39	9	2	5	286	380	369	749	83	304	15	9	328	108	—	—	—	—	—	—
2017	26	MLB MLE	Seattle Mariners	—	426	388	96	16	0	6	25	64	11	1	5	247	311	335	646	88	284	15	6	297	87	-7	-2	+8	+2	+9	1.1
2018	27	MLB MLE	Seattle Mariners	—	337	292	69	14	1	5	32	52	4	2	4	236	320	342	663	106	270	15	9	298	88	-5	-1	+4	+2	+7	0.7
2018	27	AAA MLE	Tacoma Rainiers	—	38	30	8	1	0	0	3	4	4	2	1	267	405	300	705	33	267	11	8	317	101	—	—	—	—	—	—
2019	28	MLB MLE	Tampa Bay Rays	—	231	204	45	13	0	5	18	60	6	2	2	221	303	358	660	137	293	26	8	301	90	-3	-0	+3	+1	+5	0.6
2019	28	AAA MLE	Durham Bulls	—	30	28	5	1	0	1	1	12	1	0	1	179	233	321	555	143	264	40	3	216	32	—	—	—	—	—	—
2020	29	MLB MLE	New York Mets	—	36	33	7	0	0	2	3	9	0	1	0	212	278	394	672	182	227	25	8	303	91	-0	+0	+1	+0	+1	0.2
2021	30	MLB MLE	Atlanta Braves	—	347	308	67	26	0	5	29	81	9	0	0	218	303	351	654	133	282	23	8	297	87	-5	+0	+4	+1	+8	0.8
2022	31	MLB MLE	Atlanta Braves	—	82	76	12	3	1	3	6	32	0	0	0	158	220	342	562	184	220	39	7	249	55	-4	+0	-1	-1	+2	-0.5
2016	25	AA MLE	4 teams	—	586	506	143	19	3	5	49	81	17	10	9	283	365	362	727	79	320	14	8	325	106	—	—	—	—	—	—
2016	25	AA RAW	4 teams	—	586	486	139	19	4	6	69	74	17	10	9	286	393	379	772	93	319	13	12	347	106	—	—	—	—	—	—
2017	26	MLB MLE	Seattle Mariners	—	426	388	96	16	0	6	25	64	11	1	5	247	311	335	646	88	282	15	6	297	87	-7	-2	+8	+2	+9	1.1
2017	26	MLB RAW	Seattle Mariners	—	426	386	96	16	0	6	27	64	11	1	5	249	316	337	653	88	284	15	6	298	87	-7	-2	+8	+2	+9	1.1
2018	27	MLB MLE	3 teams	—	445	385	91	18	1	6	41	66	9	4	5	236	324	335	659	99	269	15	9	300	89	-6	-1	+4	+2	+10	0.8
2018	27	MLB RAW	3 teams	—	445	388	93	18	1	7	38	72	9	4	5	240	322	345	667	106	276	16	9	299	89	-6	-1	+4	+2	+10	0.8
2019	28	MLB MLE	2 teams	—	261	232	51	14	0	6	19	72	7	2	3	220	298	358	656	138	290	28	7	291	83	-5	-1	+3	+1	+6	0.4
2019	28	MLB RAW	2 teams	—	261	232	52	14	0	6	19	70	7	2	3	224	302	362	664	138	293	27	7	298	83	-5	-1	+3	+1	+6	0.4
2020	29	MLB MLE	New York Mets	—	36	33	7	0	0	2	3	9	0	1	0	212	278	394	672	182	227	25	8	303	91	-0	+0	+1	+0	+1	0.2
2020	29	MLB RAW	New York Mets	—	36	33	7	0	0	2	3	9	0	1	0	212	278	394	672	182	227	25	8	299	91	-0	+0	+1	+0	+1	0.2
2021	30	MLB MLE	Atlanta Braves	—	347	308	67	26	0	5	29	81	9	0	0	218	303	351	654	133	278	23	8	297	87	-5	+0	+4	+1	+8	0.8
2021	30	MLB RAW	Atlanta Braves	—	347	305	67	26	0	5	32	81	9	0	0	220	312	354	666	134	282	23	9	303	87	-5	+0	+4	+1	+8	0.8
2022	31	MLB MLE	2 teams	—	100	93	15	4	1	4	7	39	0	0	2	161	220	355	575	194	220	39	7	249	55	-5	-1	-1	-1	+2	-0.6
2022	31	MLB RAW	2 teams	—	100	93	19	4	1	3	7	35	0	0	2	204	260	366	626	161	291	35	7	278	55	-5	-1	-1	-1	+2	-0.6
2016	25	AA MLE	4 teams	—	586	515	143	19	3	5	49	81	17	10	9	278	365	355	721	78	308	14	8	325	106	—	—	—	—	—	—
2017	26	MLB MLE	Seattle Mariners	—	426	387	96	16	0	6	25	64	11	1	5	248	311	336	647	88	284	15	6	297	87	—	—	—	—	—	—
2018	27	MLB MLE	3 teams	—	445	392	91	18	1	6	41	66	9	4	5	232	324	329	653	97	270	15	9	300	89	—	—	—	—	—	—
2019	28	MLB MLE	2 teams	—	261	233	51	14	0	6	19	72	7	2	3	219	298	356	655	137	290	28	7	291	83	—	—	—	—	—	—
2020	29	MLB MLE	New York Mets	—	36	33	7	0	0	2	3	9	0	1	0	212	278	394	672	182	227	25	8	303	91	—	—	—	—	—	—
2021	30	MLB MLE	Atlanta Braves	—	347	306	67	26	0	5	29	81	9	0	0	219	303	353	656	134	282	23	8	297	87	—	—	—	—	—	—
2022	31	MLB MLE	2 teams	—	100	93	15	4	1	4	7	39	0	0	2	161	220	355	575	194	220	39	7	249	55	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1566	1388	320	75	2	27	130	313	32	7	12	231	311	346	657	115	278	20	8	297	91	-24	-3	+20	-2	+34	2.5
/ 162G	—	A+C		—	650	576	133	31	1	11	54	130	13	3	5	231	311	345	657	115	279	20	8	297	91	-10	-1	+8	-1	+14	1.0
Career	—	MLE		—	2201	1945	470	97	5	34	173	412	53	18	24	242	321	349	670	107	288	19	8	302	91	-24	-3	+20	-2	+34	2.5
/ 162G	—	MLE		—	650	574	139	29	1	10	51	122	16	5	7	242	321	348	670	106	289	19	8	302	91	-10	-1	+8	-1	+14	1.0

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.

2019 AAADurham Bulls · 30 PAwOBA .273 → .216 (−56)K% 33.3% → 39.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	33.33%	÷1.020	×1.200	—	—	39.21%
BB% (unintentional) /PA	3.33%	÷0.998	×0.690	—	×0.992	2.29%
HBP% /PA	3.33%	—	×0.940	—	×0.992	3.11%
Singles /BIP	22.22%	÷0.986	×0.930	—	×0.992	20.78%
Doubles /BIP	5.56%	÷1.006	×0.830	—	×0.992	4.55%
Triples /BIP	0.00%	÷1.025	×0.970	—	×0.992	0.00%
Home runs /BIP	5.56%	÷1.128	×0.840	—	×0.992	4.10%

Factors used: league row IL. Park (home-season share, regressed): HR 126, 1B 97, 2B 101, 3B 105, K 104, BB 100 — applied at half strength. Age: age 28 vs a level average of 27.2 → −0.8% on BB/HBP/hit rates (0.8 yr older than average).

Result: wOBA .273 → .216; ISO .143 → .100; BABIP .294 → .264. 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 AAATacoma Rainiers · 38 PAwOBA .353 → .317 (−36)K% 7.9% → 9.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	7.89%	÷1.031	×1.290	—	—	9.88%
BB% (unintentional) /PA	10.53%	÷1.103	×0.710	—	×1.003	6.79%
HBP% /PA	10.53%	—	×0.980	—	×1.003	10.35%
Singles /BIP	26.92%	÷1.006	×0.900	—	×1.003	24.16%
Doubles /BIP	3.85%	÷0.885	×0.810	—	×1.003	3.53%
Triples /BIP	0.00%	÷0.900	×0.810	—	×1.003	0.00%
Home runs /BIP	0.00%	÷0.918	×0.780	—	×1.003	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 84, 1B 101, 2B 77, 3B 80, K 106, BB 121 — 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 .353 → .317; ISO .034 → .034; BABIP .296 → .267. 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 AAJackson Generals · 260 PAwOBA .360 → .328 (−32)K% 12.3% → 15.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	12.31%	÷1.001	×1.220	—	—	15.00%
BB% (unintentional) /PA	13.85%	÷1.011	×0.680	—	×0.998	9.29%
HBP% /PA	3.46%	—	×0.840	—	×0.998	2.90%
Singles /BIP	28.32%	÷0.989	×0.920	×1.007	×0.998	26.49%
Doubles /BIP	4.05%	÷0.889	×0.910	×1.007	×0.998	4.17%
Triples /BIP	1.16%	÷1.138	×0.880	×1.007	×0.998	0.90%
Home runs /BIP	1.16%	÷0.992	×0.800	—	×0.998	0.93%

Factors used: league row SL. Park (home-season share, regressed): HR 98, 1B 98, 2B 78, 3B 128, K 100, BB 102 — applied at half strength. Age: age 25 vs a level average of 24.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.007.

Result: wOBA .360 → .328; ISO .083 → .081; BABIP .322 → .304. 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 AAATacoma Rainiers · 157 PAwOBA .349 → .331 (−18)K% 9.6% → 11.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.55%	÷1.041	×1.290	—	—	11.84%
BB% (unintentional) /PA	7.64%	÷1.115	×0.710	—	×1.033	5.03%
HBP% /PA	2.55%	—	×0.980	—	×1.033	2.58%
Singles /BIP	27.64%	÷0.987	×0.900	×1.050	×1.033	27.34%
Doubles /BIP	4.88%	÷0.901	×0.810	×1.050	×1.033	4.75%
Triples /BIP	0.81%	÷1.063	×0.810	×1.050	×1.033	0.67%
Home runs /BIP	1.63%	÷0.896	×0.780	—	×1.033	1.46%

Factors used: league row PCL. Park (home-season share, regressed): HR 79, 1B 97, 2B 80, 3B 113, K 108, BB 123 — 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 .349 → .331; ISO .101 → .096; BABIP .333 → .327. 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 — .