Teoscar Hernández

LF Los Angeles Dodgers

Age 34 R/R 6'0" / 215 lbs MLB HR +8%BB -8% Svc 9.564 · Ctrl thru 2027

Overview

MLB40-MANExtFA 2028

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2024	31	MLBLAD	652	.272/.339/.501	33	12	136	2.5
2025	32	MLBLAD	546	.247/.287/.454	25	5	106	-0.0
2026	33	MLBLAD	491	.267/.336/.425	16	5	117	1.5
2027PROJ	34	MLBLAD	408	.257/.316/.462	19	6	118	1.3
2028PROJ	35	MLBLAD	326	.245/.303/.435	14	2	106	-0.1

Last 3 seasons WAR 4.0 · Next 2 projected WAR 1.2Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service9.564First FA2028Options2

2027Ext$22M

Skills · percentileStatcast 2024-2026

Barrel%79

xwOBA73

Speed73

Avg EV69

Bat Spd57

Form.266/.333/.476 ▲ rolling true talent (60d window) · proj .260/.317/.462

CompsScott Hairston (age 33) · Olmedo Saenz (age 34) · Kit Pellow (age 34)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 120118vR 110

AVG/OBP/SLG.257 / .316 / .462

PA408

HR19

SB6

K%26

BB%7

BABIP309

ISO204

MLB%100%

Value2027 · WAR

trajectory · projected WAR by year

Tools · Recent Form · Splits · Contract

ToolsStatcast %ile · 2024-2026

CONTACT

xAVG

480.250

POWER

Barrel%

7912.6

Avg EV

6990.4

Bat Spd

5772.1

SPEED

Sprint

7328.4

Recent Formrolling true talent ⓘ

K%26%·26% over 108 PA (60d) · proj 27%

BB%9%▲10% over 108 PA (60d) · proj 7%

ISO.210·.221 over 95 AB (60d) · proj .203

HR/BB7.1%·7.5% over 67 BBE (60d) · proj 6.8%

BABIP.324▲.435 over 62 BIP (60d) · proj .315

SLASH.266/.333/.476▲proj .260/.317/.462

Platoon Splits2024-2026 · 1291 PA

vs LHP

vs RHP

Edge

322

969

—

wRC+

131

112

◀L

wOBA

362

334

◀L

ISO

243

208

◀L

BABIP

308

317

—

23.0%

28.6%

◀◀L

BB%

6.5%

6.3%

◀L

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status FA

MLB Debut 2016

Service 9.564 yrs.days

Team Control Through 2027

First FA Year 2028

OPTDEF 20%

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$22M	+1.3	$10M	-$12M
Totals		$22M		$10M	-$12M

Options · projected decision
2028	CLUB OPT	$22.0M / $0.0M buyout	DECLINE
2029	CLUB OPT	$15.0M / $6.5M buyout	DECLINE

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 Astros	—	261	226	62	13	7	7	28	42	4	16	4	274	364	487	851	212	306	16	11	371	38	—	—	—	—	—	—
2012	19	A	Lexington Legends	—	30	25	6	2	0	1	3	12	0	1	0	240	321	440	761	200	385	40	10	315	59	—	—	—	—	—	—
2012	19	ROK	GCL Astros	—	200	177	43	11	2	4	19	54	3	10	1	243	327	395	722	153	325	27	10	323	25	—	—	—	—	—	—
2013	20	A	Quad Cities River Bandits	—	565	499	135	25	9	13	41	135	4	24	11	271	331	435	766	164	344	24	7	327	65	—	—	—	—	—	—
2013	20	INTL	Toros del Este	—	37	33	7	2	0	0	2	12	0	3	1	212	257	273	530	61	318	32	5	229	41	—	—	—	—	—	—
2014	21	AA	Corpus Christi Hooks	—	98	95	27	4	1	4	2	36	0	2	3	284	299	474	773	189	418	37	2	334	61	—	—	—	—	—	—
2014	21	A+	Lancaster JetHawks	—	455	391	115	33	8	17	49	117	5	31	6	294	380	550	930	256	374	26	11	396	87	—	—	—	—	—	—
2014	21	INTL	Toros del Este	—	87	79	18	4	0	2	5	28	1	4	3	228	282	354	637	127	320	32	6	280	76	—	—	—	—	—	—
2015	22	AA	Corpus Christi Hooks	—	514	470	103	12	2	17	33	126	4	33	7	219	276	362	638	143	261	25	6	282	36	—	—	—	—	—	—
2015	22	INTL	Toros del Este	—	44	40	11	1	0	1	2	9	0	1	2	275	310	375	685	100	312	20	5	293	85	—	—	—	—	—	—
2016	23	MLB	Houston Astros	—	112	100	23	7	0	4	11	28	0	0	2	230	306	420	726	190	275	25	10	320	97	-0	-1	-2	-7	+2	-0.8
2016	23	AAA	Fresno Grizzlies	—	160	144	45	9	3	4	13	25	0	5	4	312	369	500	869	188	350	16	8	375	88	—	—	—	—	—	—
2016	23	AA	Corpus Christi Hooks	—	322	279	85	19	0	6	32	55	5	29	11	305	385	437	822	133	359	17	10	364	91	—	—	—	—	—	—
2016	23	INTL	Toros del Este	—	77	68	11	2	1	0	5	15	1	5	0	162	230	221	450	59	208	19	6	203	24	—	—	—	—	—	—
2017	24	MLB	Toronto Blue Jays	—	95	88	23	6	0	8	6	36	0	0	1	261	309	602	911	341	333	38	6	386	147	+5	-0	-4	-7	+2	-0.4
2017	24	AAA	Buffalo Bisons	—	456	400	106	26	5	18	47	102	7	16	8	265	352	490	842	225	312	22	10	369	95	—	—	—	—	—	—
2017	24	INTL	Toros del Este	—	96	87	17	3	1	1	9	22	0	1	3	195	271	287	558	92	250	23	9	257	60	—	—	—	—	—	—
2018	25	MLB	Toronto Blue Jays	—	523	476	114	29	7	22	41	163	3	5	5	239	304	468	772	229	313	31	8	336	113	+8	-1	-9	-6	+11	0.3
2018	25	AAA	Buffalo Bisons	—	18	18	5	1	0	2	0	7	0	2	0	278	278	667	944	389	333	39	0	403	80	—	—	—	—	—	—
2018	25	INTL	Toros del Este	—	54	45	12	2	1	1	7	12	0	1	1	267	358	422	781	156	333	22	13	341	117	—	—	—	—	—	—
2019	26	MLB	Toronto Blue Jays	—	464	417	96	19	2	26	45	153	1	6	3	230	307	472	779	242	293	33	10	341	115	+9	+0	-4	+2	+10	1.6
2019	26	AAA	Buffalo Bisons	—	83	75	19	0	1	5	6	21	1	3	0	253	317	480	797	227	280	25	7	346	80	—	—	—	—	—	—
2020	27	MLB	Toronto Blue Jays	—	207	190	55	7	0	16	14	63	1	6	1	289	340	579	919	289	348	30	7	395	153	+13	+1	-2	-2	+4	1.5
2021	28	MLB	Toronto Blue Jays	—	595	550	163	29	0	32	36	148	7	12	4	296	347	524	871	227	352	25	6	380	142	+30	+1	-2	-6	+13	3.6
2022	29	MLB	Toronto Blue Jays	—	535	499	133	35	1	25	34	152	2	6	3	267	316	491	807	224	335	28	6	353	125	+16	+0	-4	-6	+12	1.8
2023	30	MLB	Seattle Mariners	—	678	625	161	29	2	26	38	211	8	7	2	258	308	435	744	178	342	31	6	326	108	+6	+1	-1	-7	+15	1.3
2024	31	MLB	Los Angeles Dodgers	—	652	589	160	32	2	33	53	188	8	12	3	272	339	501	840	229	344	29	8	368	136	+28	+1	-12	-7	+14	2.5
2025	32	MLB	Los Angeles Dodgers	—	546	511	126	29	1	25	26	134	3	5	2	247	287	454	741	207	283	25	5	321	106	+4	+0	-10	-6	+12	-0.0
2026	33	MLB	Los Angeles Dodgers	—	491	442	118	18	2	16	42	135	4	5	1	267	336	425	761	158	347	27	9	338	117	+10	+1	-0	-5	+11	1.5
2026	33	AAA	Oklahoma City Comets	—	16	14	3	0	0	3	2	4	0	0	0	214	312	857	1.170	643		25	12	477	90	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	4898	4487	1172	240	17	233	346	1411	37	64	27	261	319	478	797	217	327	29	7	348	122	+128	+2	-51	-58	+106	12.8
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2011	18	ROK MLE	DSL Astros	—	261	235	38	8	4	3	19	51	4	16	4	162	236	268	505	106	184	20	7	225	38	—	—	—	—	—	—
2012	19	A MLE	Lexington Legends	—	30	26	6	2	0	1	2	15	0	1	0	231	286	423	709	192	344	50	7	256	59	—	—	—	—	—	—
2012	19	ROK MLE	GCL Astros	—	200	184	29	7	1	3	12	78	3	10	1	158	221	255	477	98	240	39	6	205	25	—	—	—	—	—	—
2013	20	A MLE	Quad Cities River Bandits	—	565	511	116	22	8	10	29	178	4	24	11	227	274	360	634	133	303	32	5	265	65	—	—	—	—	—	—
2014	21	AA MLE	Corpus Christi Hooks	—	98	96	21	3	1	3	1	44	0	2	3	219	227	365	591	146	377	45	1	258	61	—	—	—	—	—	—
2014	21	A+ MLE	Lancaster JetHawks	—	455	405	90	25	6	12	35	166	5	31	6	222	292	402	695	180	323	36	8	297	87	—	—	—	—	—	—
2015	22	AA MLE	Corpus Christi Hooks	—	514	480	87	10	2	13	23	159	4	33	7	181	225	292	517	110	232	31	4	222	36	—	—	—	—	—	—
2016	23	MLB MLE	Houston Astros	—	112	101	23	7	0	4	10	28	0	0	2	228	297	416	713	188	275	25	9	311	97	-0	-1	-2	-7	+2	-0.8
2016	23	AAA MLE	Fresno Grizzlies	—	160	147	38	7	2	3	10	32	0	5	4	259	306	395	700	136	296	20	6	299	88	—	—	—	—	—	—
2016	23	AA MLE	Corpus Christi Hooks	—	322	288	75	15	0	4	24	63	5	29	11	260	328	354	682	94	315	20	7	303	91	—	—	—	—	—	—
2017	24	MLB MLE	Toronto Blue Jays	—	95	88	23	6	0	8	6	36	0	0	1	261	309	602	911	341	333	38	6	386	147	+5	-0	-4	-7	+2	-0.4
2017	24	AAA MLE	Buffalo Bisons	—	456	416	94	22	5	17	31	129	7	16	8	226	291	425	716	200	282	28	7	309	95	—	—	—	—	—	—
2018	25	MLB MLE	Toronto Blue Jays	—	523	476	114	29	7	22	41	163	3	5	5	239	304	468	772	229	313	31	8	335	113	+8	-1	-9	-6	+11	0.3
2018	25	AAA MLE	Buffalo Bisons	—	18	18	4	1	0	1	0	8	0	2	0	222	222	444	667	222	298	44	0	287	80	—	—	—	—	—	—
2019	26	MLB MLE	Toronto Blue Jays	—	464	418	96	19	2	26	44	153	1	6	3	230	305	471	776	242	293	33	9	339	115	+9	+0	-4	+2	+10	1.6
2019	26	AAA MLE	Buffalo Bisons	—	83	77	17	0	1	4	4	26	1	3	0	221	268	403	671	182	257	31	5	287	80	—	—	—	—	—	—
2020	27	MLB MLE	Toronto Blue Jays	—	207	191	55	7	0	16	14	63	1	6	1	288	340	576	916	288	348	30	7	394	153	+13	+1	-2	-2	+4	1.5
2021	28	MLB MLE	Toronto Blue Jays	—	595	551	162	29	0	32	35	148	7	12	4	294	344	521	865	227	352	25	6	379	142	+30	+1	-2	-6	+13	3.6
2022	29	MLB MLE	Toronto Blue Jays	—	535	499	133	35	1	25	34	152	2	6	3	267	316	491	807	224	335	28	6	353	125	+16	+0	-4	-6	+12	1.8
2023	30	MLB MLE	Seattle Mariners	—	678	627	163	29	2	27	36	211	8	7	2	260	308	442	750	182	342	31	5	328	108	+6	+1	-1	-7	+15	1.3
2024	31	MLB MLE	Los Angeles Dodgers	—	652	593	162	34	2	32	50	188	8	12	3	273	338	499	837	226	344	29	8	369	136	+28	+1	-12	-7	+14	2.5
2025	32	MLB MLE	Los Angeles Dodgers	—	546	512	128	31	1	24	26	134	3	5	2	250	290	455	745	205	283	25	5	325	106	+4	+0	-10	-6	+12	-0.0
2026	33	MLB MLE	Los Angeles Dodgers	—	491	444	119	19	2	15	40	135	4	5	1	268	334	421	755	153	347	27	8	341	117	+10	+1	-0	-5	+11	1.5
2026	33	AAA MLE	Oklahoma City Comets	—	16	15	2	0	0	2	1	5	0	0	0	133	188	533	721	400		31	6	301	90	—	—	—	—	—	—
2011	18	ROK MLE	DSL Astros	—	261	235	38	8	4	3	19	51	4	16	4	162	236	268	505	106	190	20	7	225	38	—	—	—	—	—	—
2011	18	ROK RAW	DSL Astros	—	261	226	62	13	7	7	28	42	4	16	4	274	364	487	851	212	306	16	11	371	38	—	—	—	—	—	—
2012	19	ROK MLE	2 teams	—	230	210	34	8	1	4	14	93	3	11	1	162	225	267	491	105	261	40	6	212	29	—	—	—	—	—	—
2012	19	ROK RAW	2 teams	—	230	202	49	13	2	5	22	66	3	11	1	243	326	401	727	158	331	29	10	322	29	—	—	—	—	—	—
2013	20	A MLE	2 teams	—	602	544	124	24	8	11	31	190	4	27	12	228	275	362	637	134	325	32	5	265	65	—	—	—	—	—	—
2013	20	A RAW	2 teams	—	602	532	142	27	9	13	43	147	4	27	12	267	326	425	751	158	342	24	7	321	65	—	—	—	—	—	—
2014	21	A+ MLE	3 teams	—	640	578	129	33	8	17	43	243	6	37	12	223	284	396	680	173	346	38	7	290	82	—	—	—	—	—	—
2014	21	A+ RAW	3 teams	—	640	565	160	41	9	23	56	181	6	37	12	283	354	510	864	227	373	28	9	371	82	—	—	—	—	—	—
2015	22	AA MLE	2 teams	—	558	520	94	11	2	14	25	173	4	34	9	181	224	290	514	110	237	31	4	222	36	—	—	—	—	—	—
2015	22	AA RAW	2 teams	—	558	510	114	13	2	18	35	135	4	34	9	224	279	363	641	139	266	24	6	283	36	—	—	—	—	—	—
2016	23	AA MLE	4 teams	—	671	603	154	33	2	13	50	140	6	39	17	255	319	381	700	126	310	21	7	303	92	—	—	—	—	—	—
2016	23	AA RAW	4 teams	—	671	591	164	37	4	14	61	123	6	39	17	277	351	425	775	147	327	18	9	341	92	—	—	—	—	—	—
2017	24	AAA MLE	3 teams	—	647	594	138	33	6	29	43	193	7	17	12	232	292	455	746	222	291	30	7	322	104	—	—	—	—	—	—
2017	24	AAA RAW	3 teams	—	647	575	146	35	6	27	62	160	7	17	12	254	334	477	810	223	304	25	10	355	104	—	—	—	—	—	—
2018	25	MLB MLE	3 teams	—	595	543	129	33	7	26	45	189	3	8	6	238	299	468	767	230	310	32	8	334	112	+8	-1	-9	-6	+13	0.5
2018	25	MLB RAW	3 teams	—	595	539	131	32	8	25	48	182	3	8	6	243	308	471	779	228	315	31	8	339	112	+8	-1	-9	-6	+13	0.5
2019	26	MLB MLE	2 teams	—	547	495	112	19	3	30	48	179	2	9	3	226	297	459	756	232	285	33	9	331	110	+7	+1	-4	+2	+12	1.7
2019	26	MLB RAW	2 teams	—	547	492	115	19	3	31	51	174	2	9	3	234	308	474	782	240	291	32	9	342	110	+7	+1	-4	+2	+12	1.7
2020	27	MLB MLE	Toronto Blue Jays	—	207	191	55	7	0	16	14	63	1	6	1	288	340	576	916	288	345	30	7	394	153	+13	+1	-2	-2	+4	1.5
2020	27	MLB RAW	Toronto Blue Jays	—	207	190	55	7	0	16	14	63	1	6	1	289	340	579	919	289	348	30	7	395	153	+13	+1	-2	-2	+4	1.5
2021	28	MLB MLE	Toronto Blue Jays	—	595	551	162	29	0	32	35	148	7	12	4	294	344	521	865	227	349	25	6	379	142	+30	+1	-2	-6	+13	3.6
2021	28	MLB RAW	Toronto Blue Jays	—	595	550	163	29	0	32	36	148	7	12	4	296	347	524	871	227	352	25	6	380	142	+30	+1	-2	-6	+13	3.6
2022	29	MLB MLE	Toronto Blue Jays	—	535	499	133	35	1	25	34	152	2	6	3	267	316	491	807	224	335	28	6	353	125	+16	+0	-4	-6	+12	1.8
2022	29	MLB RAW	Toronto Blue Jays	—	535	499	133	35	1	25	34	152	2	6	3	267	316	491	807	224	335	28	6	353	125	+16	+0	-4	-6	+12	1.8
2023	30	MLB MLE	Seattle Mariners	—	678	627	163	29	2	27	36	211	8	7	2	260	308	442	750	182	343	31	5	328	108	+6	+1	-1	-7	+15	1.3
2023	30	MLB RAW	Seattle Mariners	—	678	625	161	29	2	26	38	211	8	7	2	258	308	435	744	178	342	31	6	326	108	+6	+1	-1	-7	+15	1.3
2024	31	MLB MLE	Los Angeles Dodgers	—	652	593	162	34	2	32	50	188	8	12	3	273	338	499	837	226	348	29	8	369	136	+28	+1	-12	-7	+14	2.5
2024	31	MLB RAW	Los Angeles Dodgers	—	652	589	160	32	2	33	53	188	8	12	3	272	339	501	840	229	344	29	8	368	136	+28	+1	-12	-7	+14	2.5
2025	32	MLB MLE	Los Angeles Dodgers	—	546	512	128	31	1	24	26	134	3	5	2	250	290	455	745	205	290	25	5	325	106	+4	+0	-10	-6	+12	-0.0
2025	32	MLB RAW	Los Angeles Dodgers	—	546	511	126	29	1	25	26	134	3	5	2	247	287	454	741	207	283	25	5	321	106	+4	+0	-10	-6	+12	-0.0
2026	33	MLB MLE	2 teams	—	507	459	121	19	2	17	41	140	4	5	1	264	329	425	754	161	341	28	8	339	116	+10	+1	-0	-5	+11	1.6
2026	33	MLB RAW	2 teams	—	507	456	121	18	2	19	44	139	4	5	1	265	335	439	774	173	339	27	9	343	116	+10	+1	-0	-5	+11	1.6
2011	18	ROK MLE	DSL Astros	—	261	236	38	8	4	3	19	51	4	16	4	161	236	267	503	106	184	20	7	225	38	—	—	—	—	—	—
2012	19	ROK MLE	2 teams	—	230	211	34	8	1	4	14	93	3	11	1	161	225	265	490	104	254	40	6	212	29	—	—	—	—	—	—
2013	20	A MLE	2 teams	—	602	562	124	24	8	11	31	190	4	27	12	221	275	351	625	130	303	32	5	265	65	—	—	—	—	—	—
2014	21	A+ MLE	3 teams	—	640	586	129	33	8	17	43	243	6	37	12	220	284	391	675	171	333	38	7	290	82	—	—	—	—	—	—
2015	22	AA MLE	2 teams	—	558	524	94	11	2	14	25	173	4	34	9	179	224	288	512	109	232	31	4	222	36	—	—	—	—	—	—
2016	23	AA MLE	4 teams	—	671	610	154	33	2	13	50	140	6	39	17	252	319	377	696	125	302	21	7	303	92	—	—	—	—	—	—
2017	24	AAA MLE	3 teams	—	647	592	138	33	6	29	43	193	7	17	12	233	292	456	748	223	291	30	7	322	104	—	—	—	—	—	—
2018	25	MLB MLE	3 teams	—	595	542	129	33	7	26	45	189	3	8	6	238	299	469	768	231	312	32	8	334	112	—	—	—	—	—	—
2019	26	MLB MLE	2 teams	—	547	492	112	19	3	30	48	179	2	9	3	228	297	461	759	234	287	33	9	331	110	—	—	—	—	—	—
2020	27	MLB MLE	Toronto Blue Jays	—	207	190	55	7	0	16	14	63	1	6	1	289	340	579	919	289	348	30	7	394	153	—	—	—	—	—	—
2021	28	MLB MLE	Toronto Blue Jays	—	595	548	162	29	0	32	35	148	7	12	4	296	344	524	868	228	352	25	6	379	142	—	—	—	—	—	—
2022	29	MLB MLE	Toronto Blue Jays	—	535	494	133	35	1	25	34	152	2	6	3	269	316	496	812	227	335	28	6	353	125	—	—	—	—	—	—
2023	30	MLB MLE	Seattle Mariners	—	678	629	163	29	2	27	36	211	8	7	2	259	308	440	749	181	342	31	5	328	108	—	—	—	—	—	—
2024	31	MLB MLE	Los Angeles Dodgers	—	652	589	162	34	2	32	50	188	8	12	3	275	338	503	840	228	344	29	8	369	136	—	—	—	—	—	—
2025	32	MLB MLE	Los Angeles Dodgers	—	546	512	128	31	1	24	26	134	3	5	2	250	290	455	745	205	283	25	5	325	106	—	—	—	—	—	—
2026	33	MLB MLE	2 teams	—	507	457	121	19	2	17	41	140	4	5	1	265	329	427	756	162	336	28	8	339	116	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	31	MLB MLE	Los Angeles Dodgers	—	652	589	162	34	2	32	50	188	8	12	3	275	338	503	840	228	344	29	8	369	136	—	—	—	—	—	—
2025	32	MLB MLE	Los Angeles Dodgers	—	546	512	128	31	1	24	26	134	3	5	2	250	290	455	745	205	283	25	5	325	106	—	—	—	—	—	—
2026	33	MLB MLE	2 teams	—	507	457	121	19	2	17	41	140	4	5	1	265	329	427	756	162	336	28	8	339	116	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	34	MLB MLE	LAD	100%	490	448	116	21	2	22	35	130	4	7	2	260	319	462	782	203	315	27	7	342	118	+10	+1	-2	-5	+11	1.3
▸ 2027 — real world (MLB)
2027	34	MLB CTX	LAD	100%	408	373	96	17	1	19	29	108	3	6	1	257	316	462	778	205	309	26	7	340	118	+9	+1	-1	-4	+9	1.3
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	35	MLB MLE	LAD	100%	326	299	74	13	1	14	22	89	2	2	1	248	305	436	741	188	304	27	7	325	106	+2	+0	-7	-3	+7	-0.1
2028	35	MLB CTX	LAD	100%	326	299	73	12	1	14	22	89	2	2	1	245	303	435	738	190	298	27	7	324	106	+2	+0	-7	-3	+7	-0.1
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	5224	4786	1245	252	18	247	368	1500	39	66	28	260	318	475	793	215	326	29	7	347	101	+130	+2	-58	-62	+113	12.8
/ 162G	—	A+C		—	650	596	155	31	2	31	46	187	5	8	3	260	318	475	793	215	325	29	7	347	101	+16	+0	-7	-8	+14	1.6
Career	—	MLE		—	9205	8426	2047	417	52	352	605	2684	77	259	94	243	300	430	730	187	311	29	7	317	101	+139	+3	-60	-66	+122	14.0
/ 162G	—	MLE		—	650	595	145	29	4	25	43	190	5	18	7	244	300	432	732	188	312	29	7	317	101	+16	+0	-7	-8	+14	1.6

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.

2026 AAAOklahoma City Comets · 16 PAwOBA .462 → .301 (−161)K% 25.0% → 30.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.00%	÷1.000	×1.200	—	—	30.00%
BB% (unintentional) /PA	12.50%	÷1.000	×0.690	—	×0.940	8.11%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	0.00%	÷1.000	×0.930	—	×0.940	0.00%
Doubles /BIP	0.00%	÷1.000	×0.830	—	×0.940	0.00%
Triples /BIP	0.00%	÷1.000	×0.970	—	×0.940	0.00%
Home runs /BIP	30.00%	÷1.000	×0.700	—	×0.940	19.74%

Factors used: league row International League. Park (home-season share, regressed): no park row on file (treated as neutral) — 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).

Result: wOBA .462 → .301; ISO .643 → .419; BABIP .000 → .000. 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 AAABuffalo Bisons · 83 PAwOBA .336 → .287 (−49)K% 25.3% → 31.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.30%	÷0.968	×1.200	—	—	31.35%
BB% (unintentional) /PA	6.02%	÷0.953	×0.690	—	×1.003	4.38%
HBP% /PA	1.20%	—	×0.940	—	×1.003	1.14%
Singles /BIP	24.07%	÷1.011	×0.930	—	×1.003	22.20%
Doubles /BIP	0.00%	÷0.954	×0.830	—	×1.003	0.00%
Triples /BIP	1.85%	÷0.909	×0.970	—	×1.003	1.98%
Home runs /BIP	9.26%	÷1.000	×0.840	—	×1.003	7.80%

Factors used: league row IL. Park (home-season share, regressed): HR 100, 1B 102, 2B 91, 3B 82, K 94, BB 91 — 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 .336 → .287; ISO .227 → .191; BABIP .280 → .257. 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 AAABuffalo Bisons · 18 PAwOBA .390 → .287 (−103)K% 38.9% → 47.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	38.89%	÷0.993	×1.200	—	—	47.00%
BB% (unintentional) /PA	0.00%	÷0.954	×0.690	—	×1.018	0.00%
HBP% /PA	0.00%	—	×0.940	—	×1.018	0.00%
Singles /BIP	18.18%	÷0.970	×0.930	—	×1.018	17.75%
Doubles /BIP	9.09%	÷0.987	×0.830	—	×1.018	7.78%
Triples /BIP	0.00%	÷1.150	×0.970	—	×1.018	0.00%
Home runs /BIP	18.18%	÷1.080	×0.840	—	×1.018	14.40%

Factors used: league row IL. Park (home-season share, regressed): HR 116, 1B 94, 2B 97, 3B 130, K 99, BB 91 — applied at half strength. Age: age 26 vs a level average of 27.2 → +1.8% on BB/HBP/hit rates (1.2 yr younger than average).

Result: wOBA .390 → .287; ISO .389 → .270; BABIP .333 → .298. 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 AAABuffalo Bisons · 456 PAwOBA .360 → .309 (−50)K% 22.4% → 28.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.37%	÷0.950	×1.200	—	—	28.25%
BB% (unintentional) /PA	9.87%	÷1.027	×0.690	—	×1.033	6.85%
HBP% /PA	1.54%	—	×0.940	—	×1.033	1.49%
Singles /BIP	19.13%	÷1.021	×0.930	×0.967	×1.033	17.41%
Doubles /BIP	8.72%	÷0.940	×0.830	×0.967	×1.033	7.70%
Triples /BIP	1.68%	÷0.961	×0.970	×0.967	×1.033	1.69%
Home runs /BIP	6.04%	÷0.903	×0.840	—	×1.033	5.81%

Factors used: league row IL. Park (home-season share, regressed): HR 80, 1B 104, 2B 88, 3B 92, K 90, BB 106 — 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 ×0.967.

Result: wOBA .360 → .309; ISO .225 → .206; BABIP .312 → .282. 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 AACorpus Christi Hooks · 322 PAwOBA .357 → .303 (−53)K% 17.1% → 19.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.08%	÷1.055	×1.220	—	—	19.74%
BB% (unintentional) /PA	9.94%	÷0.932	×0.700	—	×1.012	7.55%
HBP% /PA	1.55%	—	×0.870	—	×1.012	1.37%
Singles /BIP	26.79%	÷0.967	×0.920	×0.950	×1.012	24.51%
Doubles /BIP	8.48%	÷1.017	×0.840	×0.950	×1.012	6.74%
Triples /BIP	0.00%	÷0.977	×0.940	×0.950	×1.012	0.00%
Home runs /BIP	2.68%	÷1.196	×0.800	—	×1.012	1.81%

Factors used: league row TL. Park (home-season share, regressed): HR 139, 1B 93, 2B 103, 3B 95, K 111, BB 86 — applied at half strength. Age: age 24 vs a level average of 24.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .357 → .303; ISO .133 → .100; BABIP .359 → .315. 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 AAAFresno Grizzlies · 160 PAwOBA .370 → .299 (−71)K% 15.6% → 20.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.62%	÷0.996	×1.290	—	—	20.23%
BB% (unintentional) /PA	8.12%	÷1.008	×0.710	—	×1.048	6.00%
HBP% /PA	0.00%	—	×0.980	—	×1.048	0.00%
Singles /BIP	24.37%	÷1.000	×0.900	×0.950	×1.048	21.84%
Doubles /BIP	7.56%	÷1.061	×0.810	×0.950	×1.048	5.75%
Triples /BIP	2.52%	÷1.150	×0.810	×0.950	×1.048	1.77%
Home runs /BIP	3.36%	÷1.040	×0.780	—	×1.048	2.64%

Factors used: league row PCL. Park (home-season share, regressed): HR 108, 1B 100, 2B 112, 3B 130, K 99, BB 102 — 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 ×0.950.

Result: wOBA .370 → .299; ISO .188 → .141; BABIP .350 → .296. 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 AACorpus Christi Hooks · 514 PAwOBA .275 → .222 (−53)K% 24.5% → 31.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.51%	÷0.964	×1.220	—	—	31.03%
BB% (unintentional) /PA	6.23%	÷1.003	×0.700	—	×1.027	4.46%
HBP% /PA	0.78%	—	×0.870	—	×1.027	0.70%
Singles /BIP	20.93%	÷1.022	×0.920	×0.972	×1.027	18.80%
Doubles /BIP	3.49%	÷0.963	×0.840	×0.972	×1.027	3.04%
Triples /BIP	0.58%	÷0.900	×0.940	×0.972	×1.027	0.61%
Home runs /BIP	4.94%	÷1.059	×0.800	—	×1.027	3.83%

Factors used: league row TL. Park (home-season share, regressed): HR 112, 1B 104, 2B 93, 3B 80, K 93, BB 101 — applied at half strength. Age: age 23 vs a level average of 24.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.972.

Result: wOBA .275 → .222; ISO .143 → .110; BABIP .261 → .232. 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 A+Lancaster JetHawks · 455 PAwOBA .390 → .297 (−93)K% 25.7% → 36.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.71%	÷0.948	×1.340	—	—	36.37%
BB% (unintentional) /PA	10.77%	÷0.987	×0.710	—	×1.012	7.84%
HBP% /PA	1.10%	—	×0.790	—	×1.012	0.88%
Singles /BIP	20.80%	÷0.999	×0.910	×0.982	×1.012	18.82%
Doubles /BIP	12.04%	÷0.963	×0.820	×0.982	×1.012	10.19%
Triples /BIP	2.92%	÷0.915	×0.760	×0.982	×1.012	2.41%
Home runs /BIP	6.20%	÷1.234	×0.920	—	×1.012	4.68%

Factors used: league row CAL. Park (home-season share, regressed): HR 147, 1B 100, 2B 93, 3B 83, K 90, BB 97 — applied at half strength. Age: age 22 vs a level average of 22.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.982.

Result: wOBA .390 → .297; ISO .256 → .186; BABIP .374 → .323. 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 — .

PLAYER · career totals + /162 games	162s	PA	HR	SB	AVG	OBP	SLG	POS	FLD	WAR	JAWS
#1Jermaine Dye'96-'09 * career totals — 14 seasons, 7,214 PA	10.9	7,214	325	46	263*	328*	476*	-80	-29	24.8	24.1
/162	664	30	4	-7.3	-2.7	2.3	pk7: 23
#2Hunter Pencelast yr '20 career totals — 14 seasons, 7,006 PA	10.6	7,006	244	120	279	334	461	-68	-67	24.3	22.9
/162	664	23	11	-6.5	-6.4	2.3	pk7: 22
#3Jeromy Burnitz'93-'06 * career totals — 13 seasons, 6,579 PA	9.9	6,579	315	74	243*	330*	466*	-62	-31	22.9	21.7
/162	664	32	7	-6.3	-3.1	2.3	pk7: 20
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 13 projected seasons	8.5	5,632	265	72	262	317	478	-60	-76	23.1	20.3
/162	664	31	8	-7.1	-9.0	2.7	pk7: 18
#4Jay Brucelast yr '20 career totals — 13 seasons, 6,603 PA	9.9	6,603	318	65	245	314	469	-71	-54	19.6	19.5
/162	664	32	7	-7.1	-5.4	2.0	pk7: 19
#5Corey Hartlast yr '15 career totals — 10 seasons, 4,113 PA	6.2	4,113	162	85	271	329	478	-48	-61	12.6	13.1
/162	664	26	14	-7.8	-9.9	2.0	pk7: 14
#6Mike Marshall'81-'91 * career totals — 10 seasons, 3,908 PA	5.9	3,908	148	26	268*	321*	481*	-48	-39	9.3	9.4
/162	664	25	4	-8.2	-6.6	1.6	pk7: 9

Career Path Comps

What happened to players with a similar stat profile at this age · Y-axis: wRC+ (100 = league avg, 150 = elite, 80 = below) · Bands: P10–P90 outer, P25–P75 inner · Gold: OGILVIE projection

		Scouting				At Match Age									Next 3 Yrs									Peak 3 Yrs
Comp	Age	HIT	PWR	SPD	EYE	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR
Teoscar Hernández (OGILVIE)	34	—	—	—	—	258/317/458	201	315	18	6	27%	7%	118	+1.8	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Scott Hairston 2013 · MLB	33	48→51	59→60	50	45	255/304/472	217	284	27	11	21%	6%	116	+2.5	233/284/424	191	273	25	9	25%	6%	97	+1.1	184/235/398	214	183	34	7	25%	5%	74	-0.2
✕Olmedo Saenz 2005 · MLB	34	50	59→60	46→30	49	265/338/467	202	305	28	6	23%	8%	126	+3.2	260/328/472	212	291	26	3	20%	8%	124	+3.1	254/327/470	216	281	26	0	20%	8%	124	+1.2
●Kit Pellow 2008 · AAA	34	45→55	56→60	49→40	43	239/311/425	185	271	25	5	22%	5%	107	+1.9	255/308/425	170	281	22	5	18%	5%	106	+1.8	262/310/432	170	279	23	2	15%	4%	109	+1.4
●Darin Ruf 2021 · MLB	35	51→53	58→60	50→40	55	267/349/479	212	311	26	9	23%	11%	132	+3.7	246/345/439	193	313	24	5	27%	12%	122	+3.0	229/335/403	174	296	21	3	28%	13%	111	+1.0
✕Jorge Cantu 2016 · AAA	34	51→58	55	48→40	47	264/320/449	185	285	23	6	18%	6%	116	+2.5	226/282/349	123	273	13	6	22%	6%	78	-0.3	201/259/306	105	251	12	3	24%	6%	59	-0.9
✕Garrett Jones 2014 · MLB	33	47→52	58→60	49→50	47	250/305/460	210	280	26	5	22%	7%	112	+2.2	245/299/418	173	290	19	3	22%	7%	101	+1.4	243/299/410	166	289	18	0	22%	7%	99	+0.7
✕Jonny Gomes 2014 · MLB	33	47→53	57→60	46→50	54	243/342/438	195	300	25	6	27%	11%	121	+2.9	234/325/377	143	309	17	4	28%	11%	101	+1.4	222/318/338	116	303	15	1	29%	11%	91	+0.3
✕Mario Valenzuela 2012 · AAA	35	51→57	54→55	48→40	47	265/318/442	178	302	23	6	21%	7%	114	+2.3	252/300/391	139	310	17	4	23%	6%	94	+0.9	250/294/370	119	314	14	2	24%	5%	86	+0.1
✕Eduardo Escobar 2022 · MLB	33	47→57	57→60	51→50	48	249/304/451	202	272	25	3	20%	7%	110	+2.1	241/294/413	172	289	20	3	23%	7%	97	+1.1	236/286/400	163	288	18	1	24%	6%	92	+0.5
✕Rickie Weeks Jr. 2017 · MLB	34	47→57	53→55	50→55	50	240/324/415	175	297	21	10	26%	9%	109	+2.0	235/321/397	162	339	18	10	33%	10%	104	+1.6	205/311/338	133	377	13	5	43%	11%	88	+0.1
✕Kole Calhoun 2022 · MLB	34	43→53	60	46→50	52	235/317/448	213	262	29	5	24%	10%	115	+2.4	217/282/369	152	272	18	4	26%	7%	83	+0.1	207/270/350	143	261	16	2	27%	7%	74	-0.4
●Robinson Chirinos 2019 · MLB	35	44→46	58→60	48→50	50	233/339/438	205	298	26	4	30%	10%	121	+2.9	229/329/416	187	300	22	4	30%	10%	112	+2.2	212/314/387	175	281	21	1	30%	10%	101	+0.6
✕Alex Dickerson 2022 · MLB	32	49→56	58→60	49→45	48	257/324/462	206	292	26	4	22%	8%	120	+2.8	217/275/369	152	264	18	5	25%	7%	80	-0.1	183/235/311	127	226	14	1	25%	6%	49	-1.6
✕Yadiel Hernandez 2022 · MLB	34	49→54	53→55	46→45	46	254/308/422	168	300	23	7	24%	7%	105	+1.7	249/297/389	140	311	16	7	24%	6%	93	+0.8	266/311/405	139	325	16	4	23%	6%	103	+0.8
✕Eduardo Perez 2004 · MLB	34	49	57→60	46→50	54	258/337/459	202	283	25	9	19%	10%	124	+3.1	256/339/456	200	277	25	6	18%	10%	124	+3.1	250/340/466	216	253	30	0	16%	10%	128	+1.1
✕Jeff Baker 2014 · MLB	33	50→53	54→60	49→45	46	255/312/436	181	308	22	8	25%	7%	109	+2.0	252/310/414	162	318	17	6	25%	7%	103	+1.6	269/311/405	137	341	8	3	23%	5%	101	+0.5
✕Mendy López 2008 · AAA	34	49→54	56→55	45→30	52	253/331/444	190	298	25	3	24%	10%	118	+2.7	247/314/401	154	306	18	3	24%	9%	102	+1.5	246/318/398	152	315	20	1	27%	9%	102	+0.7
✕Randy Ruiz 2012 · MLB	34	47→54	55→60	48→40	42	245/299/433	188	296	23	7	25%	6%	105	+1.7	247/292/414	167	319	23	7	29%	5%	97	+1.1	246/288/398	152	316	22	4	29%	5%	90	+0.3
●Josh Willingham 2012 · MLB	33	48→53	61→65	49→45	55	254/349/478	223	292	29	7	23%	11%	133	+3.7	239/345/453	214	284	27	4	25%	12%	126	+3.2	227/344/433	206	272	26	2	26%	13%	122	+2.4
✕Mitch Haniger 2024 · MLB	33	43→53	56→65	48→50	46	234/300/423	189	273	25	3	25%	8%	103	+1.5	223/295/369	146	291	18	4	28%	9%	88	+0.5	209/288/338	129	278	17	0	30%	9%	79	-0.1

Teoscar Hernández

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps