Corey Seager

SS Texas Rangers

Age 33 L/R 6'4" / 215 lbs MLB HR +15%BB +5% Svc 11.186 · Ctrl thru 2031

Overview

MLB40-MANExtFA 2032

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2024	30	MLBTEX	533	.278/.354/.512	30	1	127	4.2
2025	31	MLBTEX	445	.271/.375/.487	21	3	128	3.6
2026	32	MLBTEX	436	.227/.314/.424	20	2	99	1.4
2027PROJ	33	MLBTEX	396	.272/.350/.509	22	3	135	3.0
2028PROJ	34	MLBTEX	272	.262/.339/.485	14	1	126	1.2
2029PROJ	35	MLBTEX	215	.250/.326/.459	11	1	116	0.5

Last 3 seasons WAR 9.2 · Next 3 projected WAR 4.7Projected rows: MLB playing time only, park and league context applied.

ContractCONFIRMED

Control thru2031Service11.186First FA2032

2027Ext$32M2028Ext$32M2029Ext$32M2030Ext$32M2031Ext$32M

Skills · percentileStatcast 2024-2026

Barrel%90

xwOBA90

Avg EV90

Bat Spd55

Speed10

Form.272/.337/.500 · rolling true talent (60d window) · proj .269/.345/.495

CompsDan Uggla (age 32) · Charlie Blackmon (age 34) · Ty Wigginton (age 31)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 114135vR 142

AVG/OBP/SLG.272 / .350 / .509

PA396

HR22

K%22

BB%10

BABIP301

ISO239

MLB%100%

Value2027 · WAR

━ projected WAR━ paid WAR ($/8M) surplus deficit-$62M ctrl surplus

Tools · Recent Form · Splits · Contract

ToolsStatcast %ile · 2024-2026

CONTACT

xAVG

770.273

POWER

Barrel%

9015.1

Avg EV

9092.2

Bat Spd

5571.9

SPEED

Sprint

1025.1

Recent Formrolling true talent ⓘ

K%21%▲20% over 118 PA (60d) · proj 22%

BB%8%▼7% over 118 PA (60d) · proj 10%

ISO.228·.231 over 108 AB (60d) · proj .226

HR/BB7.8%·8.2% over 85 BBE (60d) · proj 7.5%

BABIP.296▼.244 over 78 BIP (60d) · proj .301

SLASH.272/.337/.500·proj .269/.345/.495

Platoon Splits2024-2026 · 1086 PA

vs LHP

vs RHP

Edge

329

757

—

wRC+

152

▶▶R

wOBA

307

393

▶▶R

ISO

171

253

▶▶R

BABIP

280

292

▶R

26.4%

16.2%

▶▶R

BB%

6.1%

10.8%

▶▶R

Contract CONFIRMED · hand-curated extension

Status FA

MLB Debut 2015

Service 11.186 yrs.days

Team Control Through 2031

First FA Year 2032

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$32M	+2.8	$24M	-$8M
2028	Ext	$32M	+1.2	$9M	-$20M
2029	Ext	$32M	+0.5	$2M	-$25M
2030	Ext	beyond projection horizon · no WAR estimate
2031	Ext	beyond projection horizon · no WAR estimate
Totals		$98M		$35M	-$53M

Underwater contract
Projected production over the remaining contract is worth -$53M less than the salary owed. Most often this reflects an aging-curve fade that was visible at signing — the team paid a premium for the early years.

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	18	ROK	Ogden Raptors	—	202	175	54	9	2	8	21	33	2	8	2	309	389	520	909	211	336	16	10	392	90	—	—	—	—	—	—
2013	19	A+	Rancho Cucamonga Quakes	—	114	100	16	2	1	4	12	31	0	1	0	160	250	320	570	160	179	27	11	255	30	—	—	—	—	—	—
2013	19	A	Great Lakes Loons	—	312	272	84	18	3	12	34	58	3	9	4	309	392	529	921	221	353	19	11	401	124	—	—	—	—	—	—
2013	19	INTL	Glendale Desert Dogs	—	79	72	13	3	0	2	7	25	0	0	0	181	253	306	559	125	244	32	9	255	59	—	—	—	—	—	—
2014	20	AA	Chattanooga Lookouts	—	161	148	51	16	3	2	10	39	0	1	1	345	386	534	920	189	450	24	6	396	114	—	—	—	—	—	—
2014	20	A+	Rancho Cucamonga Quakes	—	365	327	115	34	2	18	30	76	5	5	1	352	414	633	1.047	281	411	21	8	450	147	—	—	—	—	—	—
2014	20	INTL	Glendale Desert Dogs	—	99	89	25	10	2	1	9	23	1	0	0	281	354	472	825	191	369	23	9	364	132	—	—	—	—	—	—
2015	21	MLB	Los Angeles Dodgers	—	113	98	33	8	1	4	14	19	1	2	0	337	425	561	986	224	387	17	12	432	181	+11	+0	+0	+7	+2	2.1
2015	21	AAA	Oklahoma City Dodgers	—	464	421	117	30	2	13	32	65	5	3	0	278	336	451	788	173	298	14	7	345	84	—	—	—	—	—	—
2015	21	AA	Tulsa Drillers	—	86	80	30	7	1	5	5	11	0	1	1	375	412	675	1.087	300	385	13	6	464	160	—	—	—	—	—	—
2016	22	MLB	Los Angeles Dodgers	—	687	627	193	40	5	26	54	133	4	3	3	308	366	512	878	204	355	19	8	385	148	+39	-1	-7	+7	+15	5.5
2017	23	MLB	Los Angeles Dodgers	—	613	539	159	33	0	22	67	131	4	4	2	295	377	479	856	184	352	21	11	378	142	+31	+0	+5	+7	+13	5.7
2018	24	MLB	Los Angeles Dodgers	—	115	101	27	5	1	2	11	17	2	0	0	267	351	396	747	129	301	15	10	335	115	+2	+0	-1	+1	+2	0.5
2019	25	MLB	Los Angeles Dodgers	—	541	489	133	44	1	19	44	98	4	1	0	272	337	483	820	211	303	18	8	358	131	+20	+0	+6	+6	+12	4.4
2020	26	MLB	Los Angeles Dodgers	—	232	212	65	12	1	15	17	37	1	1	0	307	361	585	946	278	309	16	7	407	164	+18	+0	+1	+2	+5	2.7
2021	27	MLB	Los Angeles Dodgers	—	409	353	108	22	3	16	48	66	5	1	1	306	397	521	918	215	336	16	12	401	161	+30	-0	-2	+4	+9	4.2
2022	28	MLB	Texas Rangers	—	663	593	145	24	1	33	58	103	7	3	0	245	319	455	774	211	242	16	9	340	106	+5	+1	+6	+7	+14	3.4
2023	29	MLB	Texas Rangers	—	536	477	156	42	0	33	49	88	4	2	1	327	394	623	1.017	296	340	16	9	435	168	+43	+0	+1	+6	+12	6.3
2024	30	MLB	Texas Rangers	—	533	475	132	21	0	30	53	96	3	1	1	278	354	512	866	234	291	18	10	379	127	+17	-0	+7	+6	+12	4.2
2025	31	MLB	Texas Rangers	—	445	380	103	19	0	21	58	87	5	3	0	271	375	487	862	216	299	20	13	380	128	+15	+1	+5	+5	+10	3.6
2026	32	MLB	Texas Rangers	—	436	384	87	16	0	20	45	103	4	2	0	227	314	424	739	198	254	24	10	327	99	-1	+0	+0	+5	+9	1.4
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	5323	4728	1341	286	13	241	518	978	44	23	8	284	360	503	862	219	311	18	10	377	141	+263	+1	+21	+63	+115	47.3
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2012	18	ROK MLE	Ogden Raptors	—	202	183	45	7	2	6	13	47	2	8	2	246	303	404	707	158	283	23	6	301	90	—	—	—	—	—	—
2013	19	A+ MLE	Rancho Cucamonga Quakes	—	114	103	15	2	1	4	9	41	0	1	0	146	214	301	515	155	165	36	8	213	30	—	—	—	—	—	—
2013	19	A MLE	Great Lakes Loons	—	312	285	80	16	3	11	21	76	3	9	4	281	337	474	810	193	341	24	7	352	124	—	—	—	—	—	—
2014	20	AA MLE	Chattanooga Lookouts	—	161	151	46	14	2	2	7	49	0	1	1	305	335	464	799	159	418	30	4	336	114	—	—	—	—	—	—
2014	20	A+ MLE	Rancho Cucamonga Quakes	—	365	336	103	28	2	18	21	98	5	5	1	307	356	562	919	256	373	27	6	386	147	—	—	—	—	—	—
2015	21	MLB MLE	Los Angeles Dodgers	—	113	99	33	8	1	4	13	19	1	2	0	333	416	556	971	222	387	17	12	436	181	+11	+0	+0	+7	+2	2.1
2015	21	AAA MLE	Oklahoma City Dodgers	—	464	433	104	26	2	12	20	85	5	3	0	240	282	393	674	152	264	18	4	293	84	—	—	—	—	—	—
2015	21	AA MLE	Tulsa Drillers	—	86	82	28	6	1	4	3	14	0	1	1	341	365	585	950	244	356	16	3	404	160	—	—	—	—	—	—
2016	22	MLB MLE	Los Angeles Dodgers	—	687	632	196	42	6	25	49	133	4	3	3	310	364	514	878	204	355	19	7	387	148	+39	-1	-7	+7	+15	5.5
2017	23	MLB MLE	Los Angeles Dodgers	—	613	544	161	35	0	21	62	131	4	4	2	296	372	476	848	180	352	21	10	378	142	+31	+0	+5	+7	+13	5.7
2018	24	MLB MLE	Los Angeles Dodgers	—	115	102	27	5	1	2	10	17	2	0	0	265	342	392	734	127	301	15	9	338	115	+2	+0	-1	+1	+2	0.5
2019	25	MLB MLE	Los Angeles Dodgers	—	541	492	136	47	1	18	41	98	4	1	0	276	337	486	823	209	303	18	8	361	131	+20	+0	+6	+6	+12	4.4
2020	26	MLB MLE	Los Angeles Dodgers	—	232	212	65	13	1	14	17	37	1	1	0	307	361	575	936	269	309	16	7	410	164	+18	+0	+1	+2	+5	2.7
2021	27	MLB MLE	Los Angeles Dodgers	—	409	355	110	23	4	15	46	66	5	1	1	310	397	524	920	214	336	16	11	406	161	+30	-0	-2	+4	+9	4.2
2022	28	MLB MLE	Texas Rangers	—	663	600	144	25	1	31	51	103	7	3	0	240	307	440	747	200	242	16	8	325	106	+5	+1	+6	+7	+14	3.4
2023	29	MLB MLE	Texas Rangers	—	536	486	156	44	0	31	40	88	4	2	1	321	377	603	980	282	340	16	7	416	168	+43	+0	+1	+6	+12	6.3
2024	30	MLB MLE	Texas Rangers	—	533	486	131	22	0	28	42	96	3	1	1	270	331	488	819	218	291	18	8	356	127	+17	-0	+7	+6	+12	4.2
2025	31	MLB MLE	Texas Rangers	—	445	390	103	20	0	20	48	87	5	3	0	264	352	469	821	205	299	20	11	358	128	+15	+1	+5	+5	+10	3.6
2026	32	MLB MLE	Texas Rangers	—	436	388	87	17	0	19	41	103	4	2	0	224	305	415	720	191	254	24	9	314	99	-1	+0	+0	+5	+9	1.4
2012	18	ROK MLE	Ogden Raptors	—	202	183	45	7	2	6	13	47	2	8	2	246	303	404	707	158	293	23	6	301	90	—	—	—	—	—	—
2012	18	ROK RAW	Ogden Raptors	—	202	175	54	9	2	8	21	33	2	8	2	309	389	520	909	211	336	16	10	392	90	—	—	—	—	—	—
2013	19	A MLE	3 teams	—	505	461	111	21	4	17	36	139	3	10	4	241	300	414	714	174	304	28	7	315	99	—	—	—	—	—	—
2013	19	A RAW	3 teams	—	505	444	113	23	4	18	53	114	3	10	4	255	338	446	784	191	301	23	10	345	99	—	—	—	—	—	—
2014	20	A+ MLE	3 teams	—	625	580	176	50	5	23	33	175	6	6	2	303	347	526	873	222	395	28	5	371	137	—	—	—	—	—	—
2014	20	A+ RAW	3 teams	—	625	564	191	60	7	21	49	138	6	6	2	339	397	582	979	243	415	22	8	422	137	—	—	—	—	—	—
2015	21	AAA MLE	3 teams	—	663	614	165	40	4	20	36	118	6	6	1	269	316	445	760	176	300	18	5	332	111	—	—	—	—	—	—
2015	21	AAA RAW	3 teams	—	663	599	180	45	4	22	51	95	6	6	1	301	361	499	860	199	323	14	8	375	111	—	—	—	—	—	—
2016	22	MLB MLE	Los Angeles Dodgers	—	687	632	196	42	6	25	49	133	4	3	3	310	364	514	878	204	359	19	7	387	148	+39	-1	-7	+7	+15	5.5
2016	22	MLB RAW	Los Angeles Dodgers	—	687	627	193	40	5	26	54	133	4	3	3	308	366	512	878	204	355	19	8	385	148	+39	-1	-7	+7	+15	5.5
2017	23	MLB MLE	Los Angeles Dodgers	—	613	544	161	35	0	21	62	131	4	4	2	296	372	476	848	180	354	21	10	378	142	+31	+0	+5	+7	+13	5.7
2017	23	MLB RAW	Los Angeles Dodgers	—	613	539	159	33	0	22	67	131	4	4	2	295	377	479	856	184	352	21	11	378	142	+31	+0	+5	+7	+13	5.7
2018	24	MLB MLE	Los Angeles Dodgers	—	115	102	27	5	1	2	10	17	2	0	0	265	342	392	734	127	298	15	9	338	115	+2	+0	-1	+1	+2	0.5
2018	24	MLB RAW	Los Angeles Dodgers	—	115	101	27	5	1	2	11	17	2	0	0	267	351	396	747	129	301	15	10	335	115	+2	+0	-1	+1	+2	0.5
2019	25	MLB MLE	Los Angeles Dodgers	—	541	492	136	47	1	18	41	98	4	1	0	276	337	486	823	209	311	18	8	361	131	+20	+0	+6	+6	+12	4.4
2019	25	MLB RAW	Los Angeles Dodgers	—	541	489	133	44	1	19	44	98	4	1	0	272	337	483	820	211	303	18	8	358	131	+20	+0	+6	+6	+12	4.4
2020	26	MLB MLE	Los Angeles Dodgers	—	232	212	65	13	1	14	17	37	1	1	0	307	361	575	936	269	313	16	7	410	164	+18	+0	+1	+2	+5	2.7
2020	26	MLB RAW	Los Angeles Dodgers	—	232	212	65	12	1	15	17	37	1	1	0	307	361	585	946	278	309	16	7	407	164	+18	+0	+1	+2	+5	2.7
2021	27	MLB MLE	Los Angeles Dodgers	—	409	355	110	23	4	15	46	66	5	1	1	310	397	524	920	214	343	16	11	406	161	+30	-0	-2	+4	+9	4.2
2021	27	MLB RAW	Los Angeles Dodgers	—	409	353	108	22	3	16	48	66	5	1	1	306	397	521	918	215	336	16	12	401	161	+30	-0	-2	+4	+9	4.2
2022	28	MLB MLE	Texas Rangers	—	663	600	144	25	1	31	51	103	7	3	0	240	307	440	747	200	240	16	8	325	106	+5	+1	+6	+7	+14	3.4
2022	28	MLB RAW	Texas Rangers	—	663	593	145	24	1	33	58	103	7	3	0	245	319	455	774	211	242	16	9	340	106	+5	+1	+6	+7	+14	3.4
2023	29	MLB MLE	Texas Rangers	—	536	486	156	44	0	31	40	88	4	2	1	321	377	603	980	282	335	16	7	416	168	+43	+0	+1	+6	+12	6.3
2023	29	MLB RAW	Texas Rangers	—	536	477	156	42	0	33	49	88	4	2	1	327	394	623	1.017	296	340	16	9	435	168	+43	+0	+1	+6	+12	6.3
2024	30	MLB MLE	Texas Rangers	—	533	486	131	22	0	28	42	96	3	1	1	270	331	488	819	218	283	18	8	356	127	+17	-0	+7	+6	+12	4.2
2024	30	MLB RAW	Texas Rangers	—	533	475	132	21	0	30	53	96	3	1	1	278	354	512	866	234	291	18	10	379	127	+17	-0	+7	+6	+12	4.2
2025	31	MLB MLE	Texas Rangers	—	445	390	103	20	0	20	48	87	5	3	0	264	352	469	821	205	291	20	11	358	128	+15	+1	+5	+5	+10	3.6
2025	31	MLB RAW	Texas Rangers	—	445	380	103	19	0	21	58	87	5	3	0	271	375	487	862	216	299	20	13	380	128	+15	+1	+5	+5	+10	3.6
2026	32	MLB MLE	Texas Rangers	—	436	388	87	17	0	19	41	103	4	2	0	224	305	415	720	191	253	24	9	314	99	-1	+0	+0	+5	+9	1.4
2026	32	MLB RAW	Texas Rangers	—	436	384	87	16	0	20	45	103	4	2	0	227	314	424	739	198	254	24	10	327	99	-1	+0	+0	+5	+9	1.4
2012	18	ROK MLE	Ogden Raptors	—	202	185	45	7	2	6	13	47	2	8	2	243	303	400	703	157	283	23	6	301	90	—	—	—	—	—	—
2013	19	A MLE	3 teams	—	505	461	111	21	4	17	36	139	3	10	4	241	300	414	714	174	294	28	7	315	99	—	—	—	—	—	—
2014	20	A+ MLE	3 teams	—	625	581	176	50	5	23	33	175	6	6	2	303	347	525	872	222	387	28	5	371	137	—	—	—	—	—	—
2015	21	AAA MLE	3 teams	—	663	616	165	40	4	20	36	118	6	6	1	268	316	443	759	175	297	18	5	332	111	—	—	—	—	—	—
2016	22	MLB MLE	Los Angeles Dodgers	—	687	629	196	42	6	25	49	133	4	3	3	312	364	517	880	205	355	19	7	387	148	—	—	—	—	—	—
2017	23	MLB MLE	Los Angeles Dodgers	—	613	542	161	35	0	21	62	131	4	4	2	297	372	478	850	181	352	21	10	378	142	—	—	—	—	—	—
2018	24	MLB MLE	Los Angeles Dodgers	—	115	102	27	5	1	2	10	17	2	0	0	265	342	392	734	127	301	15	9	338	115	—	—	—	—	—	—
2019	25	MLB MLE	Los Angeles Dodgers	—	541	491	136	47	1	18	41	98	4	1	0	277	337	487	824	210	303	18	8	361	131	—	—	—	—	—	—
2020	26	MLB MLE	Los Angeles Dodgers	—	232	212	65	13	1	14	17	37	1	1	0	307	361	575	936	269	309	16	7	410	164	—	—	—	—	—	—
2021	27	MLB MLE	Los Angeles Dodgers	—	409	355	110	23	4	15	46	66	5	1	1	310	397	524	920	214	336	16	11	406	161	—	—	—	—	—	—
2022	28	MLB MLE	Texas Rangers	—	663	600	144	25	1	31	51	103	7	3	0	240	307	440	747	200	242	16	8	325	106	—	—	—	—	—	—
2023	29	MLB MLE	Texas Rangers	—	536	487	156	44	0	31	40	88	4	2	1	320	377	602	979	281	340	16	7	416	168	—	—	—	—	—	—
2024	30	MLB MLE	Texas Rangers	—	533	483	131	22	0	28	42	96	3	1	1	271	331	491	822	219	291	18	8	356	127	—	—	—	—	—	—
2025	31	MLB MLE	Texas Rangers	—	445	389	103	20	0	20	48	87	5	3	0	265	352	470	823	206	299	20	11	358	128	—	—	—	—	—	—
2026	32	MLB MLE	Texas Rangers	—	436	388	87	17	0	19	41	103	4	2	0	224	305	415	720	191	254	24	9	314	99	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	30	MLB MLE	Texas Rangers	—	533	483	131	22	0	28	42	96	3	1	1	271	331	491	822	219	291	18	8	356	127	—	—	—	—	—	—
2025	31	MLB MLE	Texas Rangers	—	445	389	103	20	0	20	48	87	5	3	0	265	352	470	823	206	299	20	11	358	128	—	—	—	—	—	—
2026	32	MLB MLE	Texas Rangers	—	436	388	87	17	0	19	41	103	4	2	0	224	305	415	720	191	254	24	9	314	99	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	33	MLB MLE	TEX	100%	426	378	102	20	0	22	41	93	4	3	1	269	347	495	843	226	301	22	10	368	135	+18	+0	-1	+5	+9	2.8
▸ 2027 — real world (MLB)
2027	33	MLB CTX	TEX	100%	396	351	96	18	0	22	38	87	4	3	1	272	350	509	859	237	301	22	10	374	135	+17	+0	-1	+4	+9	3.0
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	34	MLB MLE	TEX	100%	272	242	62	12	0	13	26	62	2	1	0	259	336	472	808	213	292	23	10	354	126	+8	+0	-5	+3	+6	1.2
2029	35	MLB MLE	TEX	100%	215	191	48	9	0	10	20	50	2	1	0	247	324	446	770	199	283	23	9	339	116	+4	+0	-6	+2	+5	0.5
2028	34	MLB CTX	TEX	100%	272	242	63	11	0	14	26	62	2	1	0	262	339	485	824	224	292	23	10	360	126	+8	+0	-5	+3	+6	1.2
2029	35	MLB CTX	TEX	100%	215	191	48	8	0	11	20	50	2	1	0	250	326	459	785	209	283	23	9	345	116	+4	+0	-6	+2	+5	0.5
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	5810	5161	1452	305	13	266	564	1090	48	25	8	281	358	500	858	219	309	19	10	375	129	+275	+2	+10	+68	+126	49.1
/ 162G	—	A+C		—	650	577	162	34	1	30	63	122	5	3	1	281	356	499	855	218	308	19	10	375	129	+31	+0	+1	+8	+14	5.5
Career	—	MLE		—	8088	7309	2017	450	29	333	649	1637	68	56	18	276	341	482	823	206	312	20	8	359	129	+292	+2	+9	+72	+134	52.0
/ 162G	—	MLE		—	650	587	162	36	2	27	52	132	5	5	1	276	340	482	822	206	312	20	8	359	129	+31	+0	+1	+8	+14	5.4

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.

2015 AAAOklahoma City Dodgers · 464 PAwOBA .338 → .293 (−45)K% 14.0% → 18.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.01%	÷0.984	×1.290	—	—	18.36%
BB% (unintentional) /PA	6.47%	÷1.095	×0.710	—	×1.050	4.40%
HBP% /PA	1.08%	—	×0.980	—	×1.050	1.11%
Singles /BIP	20.22%	÷1.028	×0.900	×0.978	×1.050	18.19%
Doubles /BIP	8.43%	÷0.965	×0.810	×0.978	×1.050	7.27%
Triples /BIP	0.56%	÷0.941	×0.810	×0.978	×1.050	0.50%
Home runs /BIP	3.65%	÷0.866	×0.780	—	×1.050	3.45%

Factors used: league row PCL. Park (home-season share, regressed): HR 73, 1B 106, 2B 93, 3B 88, K 97, BB 119 — applied at half strength. Age: age 21 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (6.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.978.

Result: wOBA .338 → .293; ISO .173 → .156; BABIP .298 → .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).

2015 AATulsa Drillers · 86 PAwOBA .453 → .404 (−49)K% 12.8% → 15.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	12.79%	÷0.989	×1.220	—	—	15.78%
BB% (unintentional) /PA	4.65%	÷0.976	×0.700	—	×1.050	3.50%
HBP% /PA	0.00%	—	×0.870	—	×1.050	0.00%
Singles /BIP	24.64%	÷0.938	×0.920	×0.950	×1.050	24.09%
Doubles /BIP	10.14%	÷1.006	×0.840	×0.950	×1.050	8.45%
Triples /BIP	1.45%	÷0.900	×0.940	×0.950	×1.050	1.51%
Home runs /BIP	7.25%	÷1.039	×0.800	—	×1.050	5.86%

Factors used: league row TL. Park (home-season share, regressed): HR 108, 1B 88, 2B 101, 3B 80, K 98, BB 95 — 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 ×0.950.

Result: wOBA .453 → .404; ISO .300 → .252; BABIP .385 → .356. 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+Rancho Cucamonga Quakes · 365 PAwOBA .439 → .386 (−53)K% 20.8% → 26.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.82%	÷1.036	×1.340	—	—	26.94%
BB% (unintentional) /PA	7.67%	÷0.979	×0.710	—	×1.042	5.80%
HBP% /PA	1.37%	—	×0.790	—	×1.042	1.13%
Singles /BIP	24.30%	÷1.034	×0.910	×1.016	×1.042	22.63%
Doubles /BIP	13.55%	÷1.000	×0.820	×1.016	×1.042	11.77%
Triples /BIP	0.80%	÷0.998	×0.760	×1.016	×1.042	0.64%
Home runs /BIP	7.17%	÷0.934	×0.920	—	×1.042	7.36%

Factors used: league row CAL. Park (home-season share, regressed): HR 87, 1B 107, 2B 100, 3B 100, K 107, BB 96 — 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 ×1.016.

Result: wOBA .439 → .386; ISO .281 → .259; BABIP .411 → .373. 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 AAChattanooga Lookouts · 161 PAwOBA .390 → .336 (−53)K% 24.2% → 30.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.22%	÷0.972	×1.220	—	—	30.42%
BB% (unintentional) /PA	6.21%	÷1.036	×0.680	—	×1.050	4.28%
HBP% /PA	0.00%	—	×0.840	—	×1.050	0.00%
Singles /BIP	27.52%	÷1.014	×0.920	×1.005	×1.050	26.35%
Doubles /BIP	14.68%	÷1.053	×0.910	×1.005	×1.050	13.39%
Triples /BIP	2.75%	÷1.150	×0.880	×1.005	×1.050	2.22%
Home runs /BIP	1.83%	÷1.026	×0.800	—	×1.050	1.50%

Factors used: league row SL. Park (home-season share, regressed): HR 105, 1B 103, 2B 111, 3B 130, K 94, BB 107 — applied at half strength. Age: age 20 vs a level average of 24.8 → +5.0% on BB/HBP/hit rates (4.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.005.

Result: wOBA .390 → .336; ISO .189 → .159; BABIP .450 → .418. 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 AGreat Lakes Loons · 312 PAwOBA .389 → .352 (−37)K% 18.6% → 24.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.59%	÷1.035	×1.360	—	—	24.43%
BB% (unintentional) /PA	9.62%	÷1.023	×0.690	—	×1.045	6.78%
HBP% /PA	0.96%	—	×0.780	—	×1.045	0.78%
Singles /BIP	23.83%	÷1.006	×0.920	×1.034	×1.045	23.56%
Doubles /BIP	8.41%	÷0.983	×0.830	×1.034	×1.045	7.68%
Triples /BIP	1.40%	÷0.960	×0.910	×1.034	×1.045	1.44%
Home runs /BIP	5.61%	÷0.898	×0.800	—	×1.045	5.22%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 80, 1B 101, 2B 97, 3B 92, K 107, BB 105 — applied at half strength. Age: age 19 vs a level average of 22.0 → +4.5% on BB/HBP/hit rates (3.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.034.

Result: wOBA .389 → .352; ISO .221 → .204; BABIP .353 → .341. 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+Rancho Cucamonga Quakes · 114 PAwOBA .252 → .213 (−40)K% 27.2% → 35.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.19%	÷1.018	×1.340	—	—	35.79%
BB% (unintentional) /PA	10.53%	÷0.971	×0.710	—	×1.050	8.08%
HBP% /PA	0.00%	—	×0.790	—	×1.050	0.00%
Singles /BIP	13.04%	÷0.993	×0.910	×1.003	×1.050	12.58%
Doubles /BIP	2.90%	÷1.038	×0.820	×1.003	×1.050	2.41%
Triples /BIP	1.45%	÷1.027	×0.760	×1.003	×1.050	1.13%
Home runs /BIP	5.80%	÷0.980	×0.920	—	×1.050	5.71%

Factors used: league row CAL. Park (home-season share, regressed): HR 96, 1B 99, 2B 108, 3B 105, K 104, BB 94 — applied at half strength. Age: age 19 vs a level average of 22.8 → +5.0% on BB/HBP/hit rates (3.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.003.

Result: wOBA .252 → .213; ISO .160 → .140; BABIP .179 → .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).

2012 ROKOgden Raptors · 202 PAwOBA .386 → .301 (−85)K% 16.3% → 23.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.34%	÷1.000	×1.440	—	—	23.52%
BB% (unintentional) /PA	9.90%	÷1.000	×0.610	—	×1.022	6.18%
HBP% /PA	0.99%	—	×0.720	—	×1.022	0.73%
Singles /BIP	24.65%	÷1.000	×0.820	×1.050	×1.022	21.70%
Doubles /BIP	6.34%	÷1.000	×0.720	×1.050	×1.022	4.90%
Triples /BIP	1.41%	÷1.000	×0.720	×1.050	×1.022	1.09%
Home runs /BIP	5.63%	÷1.000	×0.750	—	×1.022	4.32%

Factors used: league row PIO. Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 18 vs a level average of 19.5 → +2.2% on BB/HBP/hit rates (1.5 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .386 → .301; ISO .211 → .161; BABIP .336 → .283. 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
#1Robinson Canólast yr '22 AS×8GG×2SS×5 career totals — 17 seasons, 9,550 PA	14.4	9,550	335	51	301	351	488	+34	+8	70.1	58.3
/162	664	23	4	+2.4	+0.6	4.9	pk7: 47
#2Scott Rolen'96-'12 * career totals — 17 seasons, 8,518 PA	12.8	8,518	316	118	271*	352*	479*	+30	+75	64.8	53.9
/162	664	25	9	+2.4	+5.8	5.1	pk7: 43
#3Bobby Grich'70-'86 * career totals — 16 seasons, 8,220 PA	12.4	8,220	224	104	264*	372*	459*	+27	+57	63.4	51.7
/162	664	18	8	+2.1	+4.6	5.1	pk7: 40
#4Josh Donaldsonlast yr '23 career totals — 12 seasons, 5,822 PA	8.8	5,822	278	40	262	359	491	+21	+53	51.1	48.3
/162	664	32	5	+2.4	+6.0	5.8	pk7: 46
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 15 projected seasons	9.3	6,206	284	28	282	354	502	+63	+49	52.8	44.5
/162	664	30	3	+6.7	+5.3	5.6	pk7: 36
#5Nomar Garciaparra'96-'09 * career totals — 14 seasons, 6,116 PA	9.2	6,116	229	95	299*	348*	504*	+32	-3	44.5	43.1
/162	664	25	10	+3.5	-0.3	4.8	pk7: 42
#6Anthony Rendonlast yr '24 career totals — 12 seasons, 5,022 PA	7.6	5,022	158	55	280	364	464	+18	+21	38.5	37.2
/162	664	21	7	+2.4	+2.8	5.1	pk7: 36

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
Corey Seager (OGILVIE)	33	—	—	—	—	267/345/491	224	301	20	3	22%	10%	135	+2.6	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
●Dan Uggla 2012 · MLB	32	47→57	62→65	40→45	54	257/337/483	227	285	32	3	23%	10%	130	+3.5	215/320/399	184	265	24	4	27%	12%	105	+1.7	193/314/358	165	247	21	3	29%	13%	94	+0.7
✕Charlie Blackmon 2020 · MLB	34	56→59	58→60	45→55	49	287/346/501	214	315	25	7	18%	7%	137	+4.0	267/328/425	158	303	17	5	17%	7%	113	+2.3	253/324/385	132	288	13	4	16%	8%	104	+1.4
✕Ty Wigginton 2009 · MLB	31	53→55	59→60	38→50	49	276/336/485	209	298	27	5	18%	7%	130	+3.5	257/313/418	161	283	20	4	17%	7%	105	+1.7	245/303/390	146	271	17	4	17%	7%	96	+0.9
●Salvador Perez 2023 · MLB	33	48→58	67→70	47→30	39	270/308/522	251	298	38	2	24%	4%	130	+3.5	258/299/458	200	289	28	1	22%	4%	111	+2.1	253/296/448	195	279	28	0	21%	4%	108	+2.0
●Jeromy Burnitz 2002 · MLB	33	44→51	61→65	38→45	57	241/336/472	231	260	29	3	21%	12%	127	+3.3	240/313/455	215	267	29	7	22%	9%	115	+2.4	256/317/483	227	273	31	5	19%	7%	124	+3.0
●Robinson Canó 2017 · MLB	34	56→66	57→60	38→45	48	293/338/488	195	306	25	3	14%	6%	131	+3.6	283/334/467	184	302	22	2	14%	7%	125	+3.2	279/333/451	172	300	20	0	14%	7%	121	+2.3
✕David Dellucci 2007 · MLB	33	50→46	61→65	45→50	57	264/358/497	233	300	28	7	22%	12%	140	+4.2	244/316/416	172	288	18	7	21%	8%	106	+1.8	229/294/374	145	276	14	6	22%	6%	89	+0.2
✕Matt Joyce 2018 · MLB	33	45→52	59→60	43→50	60	240/342/458	218	267	27	5	22%	13%	126	+3.2	249/348/420	171	296	20	4	21%	13%	119	+2.7	250/355/382	132	307	15	1	22%	14%	114	+0.9
✕Brad Hawpe 2010 · MLB	31	55→57	59→60	38→40	56	279/367/502	222	338	25	2	24%	12%	144	+4.5	249/329/414	165	327	17	4	27%	10%	111	+2.1	231/310/366	135	313	13	2	28%	10%	94	+0.3
★Bret Boone 2002 · MLB	33	56→61	59→65	43→50	48	291/342/504	212	311	28	7	17%	6%	137	+4.0	280/340/484	204	305	25	10	17%	8%	131	+3.6	270/336/467	196	297	24	11	18%	8%	126	+3.7
✕Matt Kemp 2019 · MLB	34	53→63	57→65	40→55	44	277/318/471	195	320	26	2	22%	6%	120	+2.8	250/305/411	161	313	21	5	26%	7%	101	+1.4	226/308/385	160	297	24	5	31%	11%	99	+0.3
✕Mitch Garver 2024 · MLB	33	47→46	59→70	43→40	55	250/343/459	210	295	28	3	25%	12%	126	+3.2	210/309/375	165	269	21	4	29%	12%	95	+1.0	184/291/329	146	239	19	3	30%	12%	80	-0.1
★Ken Griffey Jr. 2003 · MLB	33	49→54	60→70	43→45	55	264/344/482	217	278	29	5	18%	10%	132	+3.6	262/345/507	245	278	32	4	19%	10%	138	+4.1	270/341/506	236	275	33	2	16%	9%	137	+3.7
●Ray Lankford 2001 · MLB	34	49	63→60	42→45	57	255/362/494	240	332	31	8	31%	14%	141	+4.3	243/336/444	201	322	23	10	29%	12%	120	+2.8	238/330/425	188	310	20	9	28%	11%	114	+1.2
✕Derek Dietrich 2021 · MLB	32	43→53	59→55	45→40	47	231/332/442	210	269	27	5	25%	8%	120	+2.8	188/291/335	147	265	17	4	34%	7%	81	-0.1	151/259/274	123	226	14	0	37%	7%	53	-0.9
✕Ryan Zimmerman 2019 · MLB	34	51→59	60→65	45	49	269/330/493	224	296	28	4	20%	8%	130	+3.5	257/319/446	189	299	23	4	22%	8%	114	+2.4	248/303/432	184	295	23	0	24%	7%	106	+0.8
✕Danny Valencia 2017 · MLB	32	55→59	54→60	41→40	47	282/336/464	182	330	23	3	22%	7%	124	+3.1	260/319/418	158	312	19	4	22%	8%	107	+1.9	253/312/400	147	304	18	2	22%	8%	102	+0.9
✕Mendy López 2007 · MLB	33	52→54	58→55	45→30	54	269/351/469	201	315	26	5	23%	11%	132	+3.6	249/320/423	174	300	21	3	23%	9%	109	+2.0	241/312/403	162	289	19	0	23%	9%	101	+0.8
✕Preston Wilson 2006 · MLB	32	50→52	58→60	42→55	47	262/325/467	205	308	25	8	24%	8%	122	+2.9	254/305/418	164	308	20	12	23%	6%	103	+1.5	260/302/418	158	312	19	13	23%	5%	102	+1.3
✕Josh Hamilton 2013 · MLB	32	52→62	65→70	48→50	46	282/333/529	247	311	33	8	22%	7%	139	+4.2	256/310/443	187	317	22	5	26%	7%	111	+2.1	251/304/417	166	319	19	3	26%	6%	103	+1.1

Corey Seager

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps