Javier Báez

SS Detroit Tigers

Age 34 R/R 5'11" / 190 lbs MLB HR -8% Svc 11.517 · Ctrl thru 2027

Overview

MLB40-MANExtFA 2028

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2024	31	MLBDET	289	.184/.224/.294	6	8	44	-0.8
2025	32	MLBDET	437	.257/.283/.398	12	5	93	1.6
2026	33	MLBDET	196	.241/.272/.364	3	1	80	0.1
2027PROJ	34	MLBDET	180	.240/.273/.370	4	3	82	0.2
2028PROJ	35	MLBDET	142	.230/.261/.348	3	1	72	-0.6

Last 3 seasons WAR 0.9 · Next 2 projected WAR -0.4Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service11.517First FA2028NTC

2027Ext$23M

Skills · percentileStatcast 2024-2026

Bat Spd61

Speed40

Avg EV31

Barrel%30

xwOBA16

Form.235/.271/.376 · rolling true talent (60d window) · proj .244/.275/.379

CompsCharlie Culberson (age 34) · Austin Romine (age 33) · James McCann (age 34)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 8682vR 71

AVG/OBP/SLG.240 / .273 / .370

PA180

HR4

K%23

BB%3

BABIP293

ISO129

MLB%100%

Value2027 · WAR

trajectory · projected WAR by year

Tools · Recent Form · Splits · Contract

ToolsStatcast %ile · 2024-2026

CONTACT

xAVG

230.229

POWER

Barrel%

305.6

Avg EV

3187.7

Bat Spd

6172.5

SPEED

Sprint

4027.0

Recent Formrolling true talent ⓘ

K%26%▼▼29% over 38 PA (60d) · proj 23%

BB%4%▲5% over 38 PA (60d) · proj 3%

ISO.140·.167 over 36 AB (60d) · proj .135

BABIP.296·.208 over 24 BIP (60d) · proj .298

SLASH.235/.271/.376·proj .244/.275/.379

Platoon Splits2024-2026 · 785 PA

vs LHP

vs RHP

Edge

199

586

—

wRC+

◀◀L

wOBA

288

269

◀L

ISO

104

139

▶▶R

BABIP

333

266

◀◀L

23.1%

24.2%

◀L

BB%

2.5%

2.9%

▶R

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

Status FA

MLB Debut 2014

Service 11.517 yrs.days

Team Control Through 2027

First FA Year 2028

NTC

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$23M	+0.1	$0.4M	-$23M
Totals		$23M		$0.4M	-$23M

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)
2012	19	A+	Daytona Cubs	—	86	80	15	3	1	4	5	21	1	4	2	188	244	400	644	213	200	24	6	283	30	—	—	—	—	—	—
2012	19	A	Peoria Chiefs	—	235	213	71	10	5	12	9	48	10	20	3	333	388	596	984	263	378	20	4	423	121	—	—	—	—	—	—
2012	19	INTL	Mesa Solar Sox	—	60	57	12	2	0	4	2	14	1	3	0	211	250	456	706	246	205	23	3	308	94	—	—	—	—	—	—
2013	20	AA	Tennessee Smokies	—	240	218	64	15	0	20	19	69	0	8	2	294	350	638	988	344	333	29	8	418	130	—	—	—	—	—	—
2013	20	A+	Daytona Cubs	—	337	299	82	19	4	17	21	78	11	12	2	274	344	535	880	261	310	23	6	377	91	—	—	—	—	—	—
2014	21	MLB	Chicago Cubs	—	229	213	36	6	0	9	15	95	1	5	1	169	227	324	551	155	248	41	7	247	55	-12	+1	-3	+8	+5	-0.2
2014	21	AAA	Iowa Cubs	—	434	388	101	24	2	23	34	130	5	16	8	260	328	510	838	250	322	30	8	360	74	—	—	—	—	—	—
2014	21	INTL	Cangrejeros de Santurce	—	49	43	10	3	0	2	5	21	0	2	0	233	312	442	754	209	381	43	10	327	108	—	—	—	—	—	—
2015	22	MLB	Chicago Cubs	—	80	76	22	6	0	1	4	24	0	1	2	289	325	408	733	118	412	30	5	327	100	+0	-1	+2	+2	+2	0.4
2015	22	AAA	Iowa Cubs	—	313	281	91	14	2	13	21	76	8	17	3	324	387	527	914	203	402	24	7	399	116	—	—	—	—	—	—
2015	22	ROK	AZL Cubs	—	15	12	5	1	0	0	1	0	2	1	0	417	533	500	1.033	83	417	0	7	471	128	—	—	—	—	—	—
2015	22	INTL	Cangrejeros de Santurce	—	59	52	13	3	0	2	7	12	0	1	2	250	339	423	762	173	289	20	12	341	117	—	—	—	—	—	—
2016	23	MLB	Chicago Cubs	—	450	421	115	19	1	14	15	108	11	12	3	273	315	423	738	150	336	24	3	327	104	+2	+1	+12	+2	+10	2.8
2016	23	AAA	Iowa Cubs	—	15	15	4	0	0	1	0	4	0	1	0	267	267	467	733	200	300	27	0	320	63	—	—	—	—	—	—
2017	24	MLB	Chicago Cubs	—	508	469	128	24	2	23	30	144	1	10	3	273	318	480	798	207	345	28	6	345	106	+4	+1	+11	+5	+11	3.2
2018	25	MLB	Chicago Cubs	—	645	606	176	40	9	34	29	167	5	21	9	290	328	554	883	264	347	26	4	379	136	+28	+1	+7	+2	+14	5.2
2019	26	MLB	Chicago Cubs	—	561	531	149	38	4	29	28	156	0	11	7	281	317	531	848	250	345	28	5	367	132	+21	-1	+27	+6	+12	6.7
2020	27	MLB	Chicago Cubs	—	235	222	45	9	1	8	7	75	4	3	0	203	240	360	601	158	262	32	3	264	65	-10	+1	+8	+3	+5	0.6
2021	28	MLB	New York Mets	—	547	502	133	18	2	31	28	184	13	18	5	265	320	494	814	229	352	34	5	355	129	+19	+2	+5	+6	+12	4.4
2022	29	MLB	Detroit Tigers	—	590	555	132	27	4	17	26	147	6	9	2	238	279	393	672	155	292	25	4	297	90	-7	+1	+4	+6	+13	1.7
2023	30	MLB	Detroit Tigers	—	547	510	113	18	4	9	24	125	9	12	0	222	268	325	594	104	274	23	4	267	70	-20	+2	+10	+6	+12	1.0
2024	31	MLB	Detroit Tigers	—	289	272	50	12	0	6	12	69	2	8	1	184	224	294	518	110	220	24	4	231	44	-19	+1	+1	+3	+6	-0.8
2025	32	MLB	Detroit Tigers	—	437	417	107	17	3	12	10	109	6	5	1	257	283	398	681	141	318	25	2	300	93	-4	+1	+4	+5	+9	1.6
2026	33	MLB	Detroit Tigers	—	196	187	45	12	1	3	6	41	2	1	2	241	272	364	635	123	292	21	3	281	80	-5	-1	+0	+2	+4	0.1
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	5314	4981	1251	246	31	196	234	1444	60	116	36	251	293	431	724	180	313	27	4	317	101	+5	+9	+88	+55	+115	27.7
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2012	19	A+ MLE	Daytona Cubs	—	86	82	12	2	1	3	3	28	1	4	2	146	186	305	491	159	188	33	3	212	30	—	—	—	—	—	—
2012	19	A MLE	Peoria Chiefs	—	235	216	62	8	4	10	6	65	10	20	3	287	336	500	836	213	344	28	3	347	121	—	—	—	—	—	—
2013	20	AA MLE	Tennessee Smokies	—	240	225	58	14	0	17	12	87	0	8	2	258	295	547	842	289	329	36	5	361	130	—	—	—	—	—	—
2013	20	A+ MLE	Daytona Cubs	—	337	304	67	15	3	13	16	104	11	12	2	220	284	418	702	197	274	31	5	302	91	—	—	—	—	—	—
2014	21	MLB MLE	Chicago Cubs	—	229	213	36	6	0	9	15	95	1	5	1	169	227	324	551	155	248	41	7	249	55	-12	+1	-3	+8	+5	-0.2
2014	21	AAA MLE	Iowa Cubs	—	434	403	82	17	1	19	19	168	5	16	8	203	248	392	640	189	276	39	4	278	74	—	—	—	—	—	—
2015	22	MLB MLE	Chicago Cubs	—	80	77	22	6	0	1	3	24	0	1	2	286	312	403	715	117	412	30	4	316	100	+0	-1	+2	+2	+2	0.4
2015	22	AAA MLE	Iowa Cubs	—	313	286	81	12	2	11	16	93	8	17	3	283	339	455	793	171	372	30	5	339	116	—	—	—	—	—	—
2015	22	ROK MLE	AZL Cubs	—	15	12	4	1	0	0	1	0	2	1	0	333	467	417	883	83	322	0	7	358	128	—	—	—	—	—	—
2016	23	MLB MLE	Chicago Cubs	—	450	424	115	18	1	15	12	108	11	12	3	271	309	425	733	153	336	24	3	322	104	+2	+1	+12	+2	+10	2.8
2016	23	AAA MLE	Iowa Cubs	—	15	15	4	0	0	1	0	5	0	1	0	267	267	467	733	200	279	33	0	262	63	—	—	—	—	—	—
2017	24	MLB MLE	Chicago Cubs	—	508	484	128	23	2	24	15	144	1	10	3	264	288	469	757	205	345	28	3	325	106	+4	+1	+11	+5	+11	3.2
2018	25	MLB MLE	Chicago Cubs	—	645	614	175	39	8	35	21	167	5	21	9	285	314	546	860	261	347	26	3	369	136	+28	+1	+7	+2	+14	5.2
2019	26	MLB MLE	Chicago Cubs	—	561	534	148	37	3	30	25	156	0	11	7	277	309	526	836	249	345	28	4	363	132	+21	-1	+27	+6	+12	6.7
2020	27	MLB MLE	Chicago Cubs	—	235	222	45	9	1	8	7	75	4	3	0	203	240	360	601	158	262	32	3	264	65	-10	+1	+8	+3	+5	0.6
2021	28	MLB MLE	New York Mets	—	547	505	136	19	2	31	26	184	13	18	5	269	322	499	821	230	352	34	5	359	129	+19	+2	+5	+6	+12	4.4
2022	29	MLB MLE	Detroit Tigers	—	590	556	135	28	4	18	25	147	6	9	2	243	283	405	687	162	292	25	4	301	90	-7	+1	+4	+6	+13	1.7
2023	30	MLB MLE	Detroit Tigers	—	547	511	114	18	4	9	24	125	9	12	0	223	270	327	597	104	274	23	4	271	70	-20	+2	+10	+6	+12	1.0
2024	31	MLB MLE	Detroit Tigers	—	289	273	50	12	0	6	11	69	2	8	1	183	220	293	513	110	220	24	4	233	44	-19	+1	+1	+3	+6	-0.8
2025	32	MLB MLE	Detroit Tigers	—	437	418	109	17	3	13	10	109	6	5	1	261	288	409	697	148	318	25	2	305	93	-4	+1	+4	+5	+9	1.6
2026	33	MLB MLE	Detroit Tigers	—	196	187	45	12	1	3	6	41	2	1	2	241	272	364	635	123	292	21	3	287	80	-5	-1	+0	+2	+4	0.1
2012	19	A MLE	3 teams	—	381	355	87	12	5	15	11	110	12	27	5	245	291	434	725	189	309	29	3	311	97	—	—	—	—	—	—
2012	19	A RAW	3 teams	—	381	350	98	15	6	20	16	83	12	27	5	280	333	529	862	249	312	22	4	373	97	—	—	—	—	—	—
2013	20	A+ MLE	2 teams	—	577	530	127	30	3	30	27	191	11	20	4	240	290	477	768	238	305	33	5	327	107	—	—	—	—	—	—
2013	20	A+ RAW	2 teams	—	577	517	146	34	4	37	40	147	11	20	4	282	347	578	925	296	319	25	7	394	107	—	—	—	—	—	—
2014	21	AAA MLE	3 teams	—	712	662	127	25	1	30	36	283	6	23	9	192	240	369	609	177	272	40	5	268	68	—	—	—	—	—	—
2014	21	AAA RAW	3 teams	—	712	644	147	33	2	34	54	246	6	23	9	228	294	444	738	216	304	35	8	322	68	—	—	—	—	—	—
2015	22	AAA MLE	4 teams	—	467	432	122	21	2	14	22	134	10	20	7	282	332	438	769	155	378	29	5	335	113	—	—	—	—	—	—
2015	22	AAA RAW	4 teams	—	467	421	131	24	2	16	33	112	10	20	7	311	375	492	867	181	390	24	7	382	113	—	—	—	—	—	—
2016	23	MLB MLE	2 teams	—	465	439	117	18	1	15	12	113	11	13	3	267	303	415	718	148	326	24	3	320	103	+2	+1	+12	+2	+10	2.7
2016	23	MLB RAW	2 teams	—	465	436	119	19	1	15	15	112	11	13	3	273	314	424	738	151	334	24	3	326	103	+2	+1	+12	+2	+10	2.7
2017	24	MLB MLE	Chicago Cubs	—	508	484	128	23	2	24	15	144	1	10	3	264	288	469	757	205	327	28	3	325	106	+4	+1	+11	+5	+11	3.2
2017	24	MLB RAW	Chicago Cubs	—	508	469	128	24	2	23	30	144	1	10	3	273	318	480	798	207	345	28	6	345	106	+4	+1	+11	+5	+11	3.2
2018	25	MLB MLE	Chicago Cubs	—	645	614	175	39	8	35	21	167	5	21	9	285	314	546	860	261	337	26	3	369	136	+28	+1	+7	+2	+14	5.2
2018	25	MLB RAW	Chicago Cubs	—	645	606	176	40	9	34	29	167	5	21	9	290	328	554	883	264	347	26	4	379	136	+28	+1	+7	+2	+14	5.2
2019	26	MLB MLE	Chicago Cubs	—	561	534	148	37	3	30	25	156	0	11	7	277	309	526	836	249	337	28	4	363	132	+21	-1	+27	+6	+12	6.7
2019	26	MLB RAW	Chicago Cubs	—	561	531	149	38	4	29	28	156	0	11	7	281	317	531	848	250	345	28	5	367	132	+21	-1	+27	+6	+12	6.7
2020	27	MLB MLE	Chicago Cubs	—	235	222	45	9	1	8	7	75	4	3	0	203	240	360	601	158	262	32	3	264	65	-10	+1	+8	+3	+5	0.6
2020	27	MLB RAW	Chicago Cubs	—	235	222	45	9	1	8	7	75	4	3	0	203	240	360	601	158	262	32	3	264	65	-10	+1	+8	+3	+5	0.6
2021	28	MLB MLE	New York Mets	—	547	505	136	19	2	31	26	184	13	18	5	269	322	499	821	230	358	34	5	359	129	+19	+2	+5	+6	+12	4.4
2021	28	MLB RAW	New York Mets	—	547	502	133	18	2	31	28	184	13	18	5	265	320	494	814	229	352	34	5	355	129	+19	+2	+5	+6	+12	4.4
2022	29	MLB MLE	Detroit Tigers	—	590	556	135	28	4	18	25	147	6	9	2	243	283	405	687	162	297	25	4	301	90	-7	+1	+4	+6	+13	1.7
2022	29	MLB RAW	Detroit Tigers	—	590	555	132	27	4	17	26	147	6	9	2	238	279	393	672	155	292	25	4	297	90	-7	+1	+4	+6	+13	1.7
2023	30	MLB MLE	Detroit Tigers	—	547	511	114	18	4	9	24	125	9	12	0	223	270	327	597	104	276	23	4	271	70	-20	+2	+10	+6	+12	1.0
2023	30	MLB RAW	Detroit Tigers	—	547	510	113	18	4	9	24	125	9	12	0	222	268	325	594	104	274	23	4	267	70	-20	+2	+10	+6	+12	1.0
2024	31	MLB MLE	Detroit Tigers	—	289	273	50	12	0	6	11	69	2	8	1	183	220	293	513	110	219	24	4	233	44	-19	+1	+1	+3	+6	-0.8
2024	31	MLB RAW	Detroit Tigers	—	289	272	50	12	0	6	12	69	2	8	1	184	224	294	518	110	220	24	4	231	44	-19	+1	+1	+3	+6	-0.8
2025	32	MLB MLE	Detroit Tigers	—	437	418	109	17	3	13	10	109	6	5	1	261	288	409	697	148	321	25	2	305	93	-4	+1	+4	+5	+9	1.6
2025	32	MLB RAW	Detroit Tigers	—	437	417	107	17	3	12	10	109	6	5	1	257	283	398	681	141	318	25	2	300	93	-4	+1	+4	+5	+9	1.6
2026	33	MLB MLE	Detroit Tigers	—	196	187	45	12	1	3	6	41	2	1	2	241	272	364	635	123	292	21	3	287	80	-5	-1	+0	+2	+4	0.1
2026	33	MLB RAW	Detroit Tigers	—	196	187	45	12	1	3	6	41	2	1	2	241	272	364	635	123	292	21	3	281	80	-5	-1	+0	+2	+4	0.1
2012	19	A MLE	3 teams	—	381	355	87	12	5	15	11	110	12	27	5	245	291	434	725	189	302	29	3	311	97	—	—	—	—	—	—
2013	20	A+ MLE	2 teams	—	577	534	127	30	3	30	27	191	11	20	4	238	290	474	764	236	297	33	5	327	107	—	—	—	—	—	—
2014	21	AAA MLE	3 teams	—	712	664	127	25	1	30	36	283	6	23	9	191	240	367	608	176	266	40	5	268	68	—	—	—	—	—	—
2015	22	AAA MLE	4 teams	—	467	432	122	21	2	14	22	134	10	20	7	282	332	438	769	155	378	29	5	335	113	—	—	—	—	—	—
2016	23	MLB MLE	2 teams	—	465	439	117	18	1	15	12	113	11	13	3	267	303	415	718	148	334	24	3	320	103	—	—	—	—	—	—
2017	24	MLB MLE	Chicago Cubs	—	508	487	128	23	2	24	15	144	1	10	3	263	288	466	754	203	345	28	3	325	106	—	—	—	—	—	—
2018	25	MLB MLE	Chicago Cubs	—	645	614	175	39	8	35	21	167	5	21	9	285	314	546	860	261	347	26	3	369	136	—	—	—	—	—	—
2019	26	MLB MLE	Chicago Cubs	—	561	531	148	37	3	30	25	156	0	11	7	279	309	529	839	250	345	28	4	363	132	—	—	—	—	—	—
2020	27	MLB MLE	Chicago Cubs	—	235	222	45	9	1	8	7	75	4	3	0	203	240	360	601	158	262	32	3	264	65	—	—	—	—	—	—
2021	28	MLB MLE	New York Mets	—	547	503	136	19	2	31	26	184	13	18	5	270	322	501	823	231	352	34	5	359	129	—	—	—	—	—	—
2022	29	MLB MLE	Detroit Tigers	—	590	554	135	28	4	18	25	147	6	9	2	244	283	406	689	162	292	25	4	301	90	—	—	—	—	—	—
2023	30	MLB MLE	Detroit Tigers	—	547	509	114	18	4	9	24	125	9	12	0	224	270	328	598	104	274	23	4	271	70	—	—	—	—	—	—
2024	31	MLB MLE	Detroit Tigers	—	289	274	50	12	0	6	11	69	2	8	1	182	220	292	512	109	220	24	4	233	44	—	—	—	—	—	—
2025	32	MLB MLE	Detroit Tigers	—	437	418	109	17	3	13	10	109	6	5	1	261	288	409	697	148	318	25	2	305	93	—	—	—	—	—	—
2026	33	MLB MLE	Detroit Tigers	—	196	186	45	12	1	3	6	41	2	1	2	242	272	366	637	124	292	21	3	287	80	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	31	MLB MLE	Detroit Tigers	—	289	274	50	12	0	6	11	69	2	8	1	182	220	292	512	109	220	24	4	233	44	—	—	—	—	—	—
2025	32	MLB MLE	Detroit Tigers	—	437	418	109	17	3	13	10	109	6	5	1	261	288	409	697	148	318	25	2	305	93	—	—	—	—	—	—
2026	33	MLB MLE	Detroit Tigers	—	196	186	45	12	1	3	6	41	2	1	2	242	272	366	637	124	292	21	3	287	80	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	34	MLB MLE	DET	100%	287	273	67	13	2	7	9	67	3	4	1	244	277	379	656	134	298	23	3	289	82	-6	+0	-0	+3	+6	0.1
▸ 2027 — real world (MLB)
2027	34	MLB CTX	DET	100%	180	170	41	8	1	4	6	42	2	3	1	240	273	370	643	130	293	23	3	284	82	-4	+0	-0	+2	+4	0.2
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	35	MLB MLE	DET	100%	142	135	32	6	1	3	4	34	2	1	0	234	265	356	621	122	289	24	3	275	72	-5	+0	-6	+2	+3	-0.6
2028	35	MLB CTX	DET	100%	142	135	32	6	1	3	4	34	2	1	0	230	261	348	609	118	285	24	3	270	72	-5	+0	-6	+2	+3	-0.6
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	5456	5116	1283	252	32	199	238	1478	62	117	36	251	292	429	721	178	313	27	4	316	98	+0	+9	+82	+57	+118	27.1
/ 162G	—	A+C		—	650	609	153	30	4	24	28	176	7	14	4	251	291	432	723	181	313	27	4	316	98	+0	+1	+10	+7	+14	3.2
Career	—	MLE		—	7479	7027	1738	334	42	288	288	2124	102	205	59	247	287	430	717	182	311	28	4	313	98	-4	+9	+82	+59	+122	27.3
/ 162G	—	MLE		—	650	611	151	29	4	25	25	185	9	18	5	247	287	430	718	183	310	28	4	313	98	-0	+1	+9	+7	+14	3.2

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.

2016 AAAIowa Cubs · 15 PAwOBA .310 → .262 (−47)K% 26.7% → 33.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	26.67%	÷1.037	×1.290	—	—	33.17%
BB% (unintentional) /PA	0.00%	÷1.034	×0.710	—	×1.048	0.00%
HBP% /PA	0.00%	—	×0.980	—	×1.048	0.00%
Singles /BIP	27.27%	÷1.006	×0.900	—	×1.048	25.57%
Doubles /BIP	0.00%	÷0.927	×0.810	—	×1.048	0.00%
Triples /BIP	0.00%	÷0.945	×0.810	—	×1.048	0.00%
Home runs /BIP	9.09%	÷0.890	×0.780	—	×1.048	8.35%

Factors used: league row PCL. Park (home-season share, regressed): HR 78, 1B 101, 2B 85, 3B 89, K 107, BB 107 — 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).

Result: wOBA .310 → .262; ISO .200 → .167; BABIP .300 → .279. 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 AAAIowa Cubs · 313 PAwOBA .391 → .339 (−51)K% 24.3% → 29.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.28%	÷1.051	×1.290	—	—	29.80%
BB% (unintentional) /PA	6.71%	÷0.996	×0.710	—	×1.050	5.02%
HBP% /PA	2.56%	—	×0.980	—	×1.050	2.63%
Singles /BIP	30.24%	÷1.008	×0.900	×1.009	×1.050	28.60%
Doubles /BIP	6.83%	÷0.984	×0.810	×1.009	×1.050	5.95%
Triples /BIP	0.98%	÷0.948	×0.810	×1.009	×1.050	0.88%
Home runs /BIP	6.34%	÷0.916	×0.780	—	×1.050	5.67%

Factors used: league row PCL. Park (home-season share, regressed): HR 83, 1B 102, 2B 97, 3B 90, K 110, BB 99 — 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 ×1.009.

Result: wOBA .391 → .339; ISO .203 → .172; BABIP .402 → .372. 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 ROKAZL Cubs · 15 PAwOBA .460 → .358 (−102)K% 0.0% → 0.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	0.00%	÷1.000	×1.440	—	—	0.00%
BB% (unintentional) /PA	6.67%	÷1.000	×0.620	—	×0.965	3.99%
HBP% /PA	13.33%	—	×0.720	—	×0.965	9.26%
Singles /BIP	33.33%	÷1.000	×0.820	—	×0.965	26.38%
Doubles /BIP	8.33%	÷1.000	×0.720	—	×0.965	5.79%
Triples /BIP	0.00%	÷1.000	×0.720	—	×0.965	0.00%
Home runs /BIP	0.00%	÷1.000	×0.780	—	×0.965	0.00%

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 23 vs a level average of 19.5 → −3.5% on BB/HBP/hit rates (3.5 yr older than average).

Result: wOBA .460 → .358; ISO .083 → .063; BABIP .417 → .322. 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 AAAIowa Cubs · 434 PAwOBA .353 → .278 (−76)K% 30.0% → 38.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	29.95%	÷0.996	×1.290	—	—	38.79%
BB% (unintentional) /PA	7.14%	÷1.249	×0.710	—	×1.050	4.26%
HBP% /PA	1.15%	—	×0.980	—	×1.050	1.19%
Singles /BIP	20.16%	÷1.021	×0.900	×0.996	×1.050	18.59%
Doubles /BIP	9.30%	÷1.111	×0.810	×0.996	×1.050	7.09%
Triples /BIP	0.78%	÷1.150	×0.810	×0.996	×1.050	0.57%
Home runs /BIP	8.91%	÷0.929	×0.780	—	×1.050	7.86%

Factors used: league row PCL. Park (home-season share, regressed): HR 86, 1B 104, 2B 122, 3B 130, K 99, BB 150 — applied at half strength. Age: age 22 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (5.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.996.

Result: wOBA .353 → .278; ISO .250 → .198; BABIP .322 → .276. 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 A+Daytona Cubs · 337 PAwOBA .373 → .302 (−71)K% 23.1% → 30.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.15%	÷1.066	×1.420	—	—	30.84%
BB% (unintentional) /PA	6.23%	÷1.032	×0.750	—	×1.027	4.65%
HBP% /PA	3.26%	—	×0.930	—	×1.027	3.12%
Singles /BIP	19.00%	÷1.015	×0.920	×0.988	×1.027	17.49%
Doubles /BIP	8.60%	÷1.132	×0.960	×0.988	×1.027	7.40%
Triples /BIP	1.81%	÷0.900	×0.810	×0.988	×1.027	1.65%
Home runs /BIP	7.69%	÷1.144	×0.880	—	×1.027	6.08%

Factors used: league row FSL. Park (home-season share, regressed): HR 129, 1B 103, 2B 126, 3B 80, K 113, BB 106 — applied at half strength. Age: age 21 vs a level average of 22.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.988.

Result: wOBA .373 → .302; ISO .261 → .200; BABIP .310 → .274. 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 AATennessee Smokies · 240 PAwOBA .409 → .361 (−48)K% 28.7% → 36.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	28.75%	÷0.968	×1.220	—	—	36.22%
BB% (unintentional) /PA	7.08%	÷1.060	×0.680	—	×1.050	4.77%
HBP% /PA	0.00%	—	×0.840	—	×1.050	0.00%
Singles /BIP	19.46%	÷0.990	×0.920	×1.022	×1.050	19.41%
Doubles /BIP	10.07%	÷0.970	×0.910	×1.022	×1.050	10.13%
Triples /BIP	0.00%	÷0.935	×0.880	×1.022	×1.050	0.00%
Home runs /BIP	13.42%	÷0.920	×0.800	—	×1.050	12.26%

Factors used: league row SL. Park (home-season share, regressed): HR 84, 1B 98, 2B 94, 3B 87, K 94, BB 112 — applied at half strength. Age: age 21 vs a level average of 24.8 → +5.0% on BB/HBP/hit rates (3.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.022.

Result: wOBA .409 → .361; ISO .344 → .305; BABIP .333 → .329. 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 APeoria Chiefs · 235 PAwOBA .416 → .347 (−69)K% 20.4% → 27.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.43%	÷1.010	×1.360	—	—	27.50%
BB% (unintentional) /PA	3.83%	÷1.053	×0.690	—	×1.030	2.58%
HBP% /PA	4.26%	—	×0.780	—	×1.030	3.42%
Singles /BIP	26.67%	÷1.013	×0.920	×1.017	×1.030	25.38%
Doubles /BIP	6.06%	÷1.062	×0.830	×1.017	×1.030	4.97%
Triples /BIP	3.03%	÷1.146	×0.910	×1.017	×1.030	2.52%
Home runs /BIP	7.27%	÷0.952	×0.800	—	×1.030	6.30%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 90, 1B 103, 2B 112, 3B 129, K 102, BB 111 — applied at half strength. Age: age 20 vs a level average of 22.0 → +3.0% on BB/HBP/hit rates (2.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.017.

Result: wOBA .416 → .347; ISO .263 → .212; BABIP .378 → .344. 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 A+Daytona Cubs · 86 PAwOBA .270 → .212 (−58)K% 24.4% → 33.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.42%	÷1.051	×1.420	—	—	32.98%
BB% (unintentional) /PA	4.65%	÷0.995	×0.750	—	×1.042	3.65%
HBP% /PA	1.16%	—	×0.930	—	×1.042	1.13%
Singles /BIP	11.86%	÷0.970	×0.920	×1.019	×1.042	11.95%
Doubles /BIP	5.08%	÷1.112	×0.960	×1.019	×1.042	4.66%
Triples /BIP	1.69%	÷1.150	×0.810	×1.019	×1.042	1.27%
Home runs /BIP	6.78%	÷1.240	×0.880	—	×1.042	5.01%

Factors used: league row FSL. Park (home-season share, regressed): HR 148, 1B 94, 2B 122, 3B 130, K 110, BB 99 — applied at half strength. Age: age 20 vs a level average of 22.8 → +4.2% on BB/HBP/hit rates (2.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.019.

Result: wOBA .270 → .212; ISO .212 → .149; BABIP .200 → .188. 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
#1Terry Pendleton'84-'98 * career totals — 15 seasons, 7,637 PA	11.5	7,637	140	127	265*	312*	410*	+22	+125	32.5	30.7
/162	664	12	11	+1.9	+10.9	2.8	pk7: 29
#2Juan Uribelast yr '16 career totals — 16 seasons, 6,715 PA	10.1	6,715	199	48	255	301	418	+33	+116	33.1	29.1
/162	664	20	5	+3.3	+11.4	3.3	pk7: 25
#3Jose Valentin'92-'07 * career totals — 15 seasons, 6,317 PA	9.5	6,317	249	136	233*	309*	433*	+53	+78	31.1	27.3
/162	664	26	14	+5.6	+8.2	3.3	pk7: 24
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 15 projected seasons	8.5	5,636	203	119	252	290	430	+57	+118	21.7	22.1
/162	664	24	14	+6.8	+13.9	2.6	pk7: 22
#4Gene Alley'63-'73 * career totals — 11 seasons, 4,332 PA	6.5	4,332	55	63	254*	314*	386*	+42	+84	22.6	21.9
/162	664	8	10	+6.4	+12.9	3.5	pk7: 21
#5Brandon Ingelast yr '13 career totals — 13 seasons, 5,617 PA	8.5	5,617	152	45	233	301	384	+26	+73	19.8	20.5
/162	664	18	5	+3.1	+8.6	2.3	pk7: 21
#6Freddy Galvislast yr '21 career totals — 10 seasons, 4,238 PA	6.4	4,238	109	57	246	292	387	+42	+68	18.0	17.6
/162	664	17	9	+6.6	+10.6	2.8	pk7: 17

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
Javier Báez (OGILVIE)	34	—	—	—	—	242/275/376	133	298	4	3	23%	3%	82	+0.0	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Charlie Culberson 2023 · MLB	34	49→54	45	47→50	43	240/287/367	127	300	12	13	24%	6%	84	+0.1	222/270/335	113	309	12	12	30%	6%	70	-0.8	164/189/198	34	266	4	14	39%	3%	8	-1.0
✕Austin Romine 2022 · MLB	33	49→54	46→50	46→45	38	243/280/368	125	324	14	5	29%	5%	81	-0.1	214/260/330	116	289	13	6	30%	5%	65	-1.2	173/207/252	78	235	10	0	31%	4%	26	-1.3
✕James McCann 2024 · MLB	34	44→54	46→50	47→45	42	222/278/352	130	283	14	7	27%	6%	77	-0.3	237/285/389	152	294	19	5	26%	5%	88	+0.5	243/286/397	153	300	21	1	26%	5%	91	+0.3
✕Adonis García 2019 · MLB	34	51→54	48→50	49→50	44	254/296/393	138	277	16	9	16%	4%	93	+0.8	214/271/329	115	258	13	14	22%	6%	70	-0.9	181/235/262	81	221	9	16	22%	4%	39	-1.3
✕Jantzen Witte 2024 · AAA	34	42→52	47→45	44→45	42	217/269/353	136	280	15	8	28%	6%	74	-0.5	214/273/354	140	279	16	8	28%	7%	76	-0.4	187/251/323	136	227	18	0	26%	8%	59	-0.5
✕Noe Mata 2008 · AAA	34	52→57	43→45	43→50	41	256/287/366	111	298	10	10	18%	4%	83	+0.1	240/270/340	100	283	10	9	19%	3%	70	-0.8	232/260/318	86	274	9	7	19%	3%	58	-0.8
✕Jordy Mercer 2021 · MLB	34	49→57	47→50	48→45	45	244/295/381	137	285	14	4	20%	6%	90	+0.6	244/303/378	134	309	14	7	24%	8%	93	+0.8	253/306/362	109	340	9	0	27%	7%	90	+0.1
✕Carlos Valencia 2014 · AAA	34	45→55	47→55	38→40	39	229/257/368	139	264	15	4	20%	3%	73	-0.6	206/239/320	114	240	14	4	21%	4%	55	-2.0	195/227/283	87	234	12	3	22%	4%	41	-1.6
✕Nick Green 2013 · MLB	34	42→46	46→50	42→45	41	215/261/349	134	257	14	5	23%	4%	70	-0.8	201/249/317	116	260	13	6	27%	5%	59	-1.7	161/207/246	84	213	10	1	29%	4%	24	-2.9
✕Hernando Arredondo 2011 · AAA	34	49→53	43→50	45→50	43	242/273/350	108	267	12	12	15%	4%	74	-0.5	240/269/364	124	272	14	8	17%	4%	76	-0.4	234/265/362	127	264	15	3	18%	3%	72	-0.4
✕Manny Rodriguez 2016 · AAA	34	51→61	48→50	46→50	42	254/292/391	138	273	16	9	14%	3%	92	+0.7	242/283/365	123	268	13	15	15%	4%	82	-0.0	221/263/319	98	248	10	11	16%	5%	63	-1.0
✕Lorenzo Quintana 2022 · AAA	33	49	47→50	48→45	40	243/288/379	136	300	15	6	24%	4%	87	+0.4	221/272/366	145	275	16	6	25%	5%	78	-0.3	184/233/337	154	215	20	0	26%	4%	55	-0.9
✕Humberto Quintero 2014 · MLB	34	46	44→45	43→40	40	230/266/349	119	276	12	4	22%	4%	72	-0.7	229/259/336	107	282	11	5	22%	3%	65	-1.2	228/248/318	89	282	9	4	22%	2%	54	-1.0
✕Matt Carson 2014 · MLB	33	45→50	47→60	49→50	39	225/271/356	131	290	15	12	28%	5%	76	-0.4	207/262/322	115	275	13	14	28%	6%	65	-1.2	183/243/288	105	242	12	15	29%	5%	49	-1.0
✕Jose Castillo 2015 · AAA	34	52→57	45→50	48→50	43	257/284/378	122	280	13	10	14%	4%	84	+0.2	240/266/327	87	270	9	9	14%	3%	65	-1.2	242/261/303	61	269	7	6	13%	2%	56	-0.9
✕Ramiro Pena 2019 · MLB	34	49→55	44→45	46→50	45	241/290/362	121	284	12	11	20%	6%	84	+0.1	249/285/378	129	272	14	9	14%	4%	85	+0.2	261/288/382	121	281	13	7	12%	3%	86	+0.2
✕Olmo Rosario 2015 · AAA	34	50→60	43→45	45→50	44	243/281/348	105	276	10	11	17%	4%	76	-0.4	267/305/367	100	294	9	12	12%	5%	90	+0.6	280/319/372	92	304	7	12	10%	5%	93	+0.5
✕Caleb Joseph 2019 · MLB	33	46→49	43→50	45→40	40	227/267/336	109	287	10	4	24%	4%	69	-0.9	217/267/342	125	286	13	6	28%	5%	71	-0.8	180/227/297	117	245	12	0	31%	4%	44	-1.0
✕Benji Gil 2007 · AAA	34	52→56	48→50	45→50	40	260/300/394	134	322	17	14	25%	5%	95	+0.9	257/299/377	120	321	10	13	22%	5%	90	+0.6	264/308/382	118	329	7	11	21%	5%	92	+0.5
✕Raul Sanchez 2007 · AAA	34	53→52	45	46→50	45	262/305/382	120	294	13	11	16%	5%	94	+0.9	245/294/334	89	284	7	13	16%	5%	78	-0.3	254/301/341	88	302	6	4	17%	5%	81	-0.0

Javier Báez

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps