Melky Cabrera

PosLF

B/TS/L

Born1984-08-11

Age (2027)42

No MLB projection yet. Career stats below are historical.

Overview

INTLLAST SEASON 2022

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	wRC+	WAR
2020	35	INTLDominican Rep…	128	.308/.346/.433	3	120	—
2021	36	INTLAguilas Cibae…	107	.260/.295/.300	0	67	—
2022	37	INTLAguilas Cibae…	91	.188/.261/.212	0	35	—

Contract

No contract data on file.

Skills · percentileStatcast 2017-2019

xwOBA61

Avg EV45

Speed17

Barrel%10

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)
2005	20	MLB	New York Yankees	—	19	19	4	0	0	0	0	2	0	0	0	211	211	211	421		235	11	0	191	15	-2	+0	-4	+2	+0	-0.3
2005	20	AAA	Columbus Clippers	—	112	101	25	3	0	3	9	15	0	2	0	248	309	366	675	119	265	13	8	300	72	—	—	—	—	—	—
2005	20	AA	Trenton Thunder	—	464	426	117	22	3	10	28	72	4	11	2	275	325	411	736	136	307	16	6	325	92	—	—	—	—	—	—
2006	21	MLB	New York Yankees	—	524	460	129	26	2	7	56	59	2	12	5	280	361	391	752	111	309	11	11	337	112	+7	+0	-3	-7	+11	1.0
2006	21	AAA	Columbus Clippers	—	135	122	47	6	2	4	10	9	1	3	1	385	436	566	1.002	180	387	7	7	434	168	—	—	—	—	—	—
2007	22	MLB	New York Yankees	—	612	545	149	24	8	8	43	68	5	13	5	273	332	391	723	117	295	11	7	315	99	-1	+1	-7	+2	+13	0.9
2008	23	MLB	New York Yankees	—	453	414	103	12	1	8	29	58	3	9	2	249	303	341	643	92	271	13	6	288	76	-13	+1	+4	+2	+10	0.4
2008	23	AAA	Scranton/Wilkes-Barre Yankees	—	66	57	19	2	0	0	8	9	0	1	3	333	415	368	784	35	388	14	12	357	109	—	—	—	—	—	—
2009	24	MLB	New York Yankees	—	540	485	133	28	1	13	43	59	4	10	2	274	338	416	755	142	288	11	8	333	108	+5	+1	+1	+2	+12	2.1
2010	25	MLB	Atlanta Braves	—	509	458	117	27	3	4	42	64	1	7	1	255	319	354	673	98	288	13	8	301	78	-13	+1	-16	-5	+11	-2.3
2011	26	MLB	Kansas City Royals	—	706	658	201	44	5	18	35	94	1	20	10	305	341	470	811	164	332	13	5	352	126	+22	+0	-5	+3	+15	3.5
2012	27	MLB	San Francisco Giants	—	501	459	159	25	10	11	36	63	0	13	5	346	394	516	910	170	379	13	7	396	155	+33	+1	-6	-5	+11	3.4
2013	28	MLB	Toronto Blue Jays	—	372	344	96	15	2	3	23	47	0	2	2	279	324	360	685	81	313	13	6	305	92	-4	-0	-5	-4	+8	-0.5
2014	29	MLB	Toronto Blue Jays	—	621	568	171	35	3	16	43	67	3	6	2	301	353	458	811	157	316	11	7	356	124	+18	+0	-6	-7	+13	2.0
2015	30	MLB	Chicago White Sox	—	683	629	172	36	2	12	40	88	2	3	0	273	319	394	713	121	297	13	6	314	96	-3	+1	-13	-7	+15	-0.8
2016	31	MLB	Chicago White Sox	—	646	591	175	42	5	14	47	69	0	2	0	296	348	455	803	159	314	11	7	352	121	+17	+0	-12	-7	+14	1.2
2017	32	MLB	Kansas City Royals	—	666	620	177	30	2	17	36	74	2	1	2	285	327	423	749	137	299	11	5	330	112	+10	-1	-12	-7	+14	0.4
2018	33	MLB	Cleveland Indians	—	278	250	70	17	0	6	20	38	3	1	1	280	341	420	761	140	303	14	7	335	112	+4	-0	-7	-4	+6	-0.1
2018	33	AAA	Columbus Clippers	—	80	78	25	6	1	0	2	10	0	2	0	321	338	423	761	103	368	12	2	338	78	—	—	—	—	—	—
2019	34	MLB	Pittsburgh Pirates	—	397	378	106	22	1	7	17	41	1	2	0	280	313	399	713	119	300	10	4	317	98	-1	+0	-5	-4	+9	-0.2
2020	35	INTL	Dominican Republic	—	128	120	37	6	0	3	7	14	0	0	0	308	346	433	780	125	327	11	5	345	120	—	—	—	—	—	—
2021	36	INTL	Aguilas Cibaenas	—	107	100	26	4	0	0	5	15	0	0	0	260	295	300	595	40	299	14	5	267	67	—	—	—	—	—	—
2022	37	INTL	Aguilas Cibaenas	—	91	80	15	2	0	0	6	12	2	0	0	188	261	212	474	25	211	13	7	220	35	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	7527	6878	1962	383	45	144	510	891	27	101	37	285	337	417	754	132	308	12	7	332	111	+96	+5	-95	-47	+163	12.5
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2005	20	MLB MLE	New York Yankees	—	19	19	4	0	0	0	0	2	0	0	0	211	211	211	421		235	11	0	191	15	-2	+0	-4	+2	+0	-0.3
2005	20	AAA MLE	Columbus Clippers	—	112	104	26	3	0	3	6	18	0	2	0	250	291	365	656	115	267	16	5	275	72	—	—	—	—	—	—
2005	20	AA MLE	Trenton Thunder	—	464	435	119	21	3	8	19	88	4	11	2	274	310	391	701	117	316	19	4	304	92	—	—	—	—	—	—
2006	21	MLB MLE	New York Yankees	—	524	463	129	26	2	7	53	59	2	12	5	279	355	389	744	110	309	11	10	333	112	+7	+0	-3	-7	+11	1.0
2006	21	AAA MLE	Columbus Clippers	—	135	125	48	6	2	4	7	11	1	3	1	384	421	560	981	176	386	8	5	416	168	—	—	—	—	—	—
2007	22	MLB MLE	New York Yankees	—	612	545	149	24	8	8	43	68	5	13	5	273	332	391	723	117	295	11	7	315	99	-1	+1	-7	+2	+13	0.9
2008	23	MLB MLE	New York Yankees	—	453	419	103	12	1	8	24	58	3	9	2	246	291	337	628	91	271	13	5	280	76	-13	+1	+4	+2	+10	0.4
2008	23	AAA MLE	Scranton/Wilkes-Barre Yankees	—	66	59	19	2	0	0	6	11	0	1	3	322	385	356	741	34	379	17	9	330	109	—	—	—	—	—	—
2009	24	MLB MLE	New York Yankees	—	540	489	133	28	1	13	39	59	4	10	2	272	331	413	744	141	288	11	7	328	108	+5	+1	+1	+2	+12	2.1
2010	25	MLB MLE	Atlanta Braves	—	509	469	116	26	3	4	31	64	1	7	1	247	295	341	637	94	288	13	6	283	78	-13	+1	-16	-5	+11	-2.3
2011	26	MLB MLE	Kansas City Royals	—	706	661	203	44	5	19	32	94	1	20	10	307	340	475	815	168	332	13	5	354	126	+22	+0	-5	+3	+15	3.5
2012	27	MLB MLE	San Francisco Giants	—	501	463	162	25	10	12	32	63	0	13	5	350	392	525	917	175	379	13	6	398	155	+33	+1	-6	-5	+11	3.4
2013	28	MLB MLE	Toronto Blue Jays	—	372	344	96	15	2	3	23	47	0	2	2	279	324	360	685	81	313	13	6	304	92	-4	-0	-5	-4	+8	-0.5
2014	29	MLB MLE	Toronto Blue Jays	—	621	571	170	35	3	16	40	67	3	6	2	298	347	454	800	156	316	11	6	352	124	+18	+0	-6	-7	+13	2.0
2015	30	MLB MLE	Chicago White Sox	—	683	631	172	36	2	12	38	88	2	3	0	273	316	393	709	120	297	13	6	310	96	-3	+1	-13	-7	+15	-0.8
2016	31	MLB MLE	Chicago White Sox	—	646	593	175	42	5	14	45	69	0	2	0	295	345	454	798	159	314	11	7	348	121	+17	+0	-12	-7	+14	1.2
2017	32	MLB MLE	Kansas City Royals	—	666	621	179	30	2	18	35	74	2	1	2	288	328	430	758	142	299	11	5	334	112	+10	-1	-12	-7	+14	0.4
2018	33	MLB MLE	Cleveland Indians	—	278	250	70	17	0	6	20	38	3	1	1	280	341	420	761	140	303	14	7	333	112	+4	-0	-7	-4	+6	-0.1
2018	33	AAA MLE	Columbus Clippers	—	80	79	22	5	1	0	1	12	0	2	0	278	287	367	655	89	329	15	1	284	78	—	—	—	—	—	—
2019	34	MLB MLE	Pittsburgh Pirates	—	397	381	106	21	1	8	14	41	1	2	0	278	306	402	707	123	300	10	4	312	98	-1	+0	-5	-4	+9	-0.2
2005	20	AA MLE	3 teams	—	595	557	148	23	3	11	26	108	4	13	2	266	303	377	680	111	309	18	4	295	86	—	—	—	—	—	—
2005	20	AA RAW	3 teams	—	595	546	146	25	3	13	37	89	4	13	2	267	319	396	714	128	296	15	6	316	86	—	—	—	—	—	—
2006	21	MLB MLE	2 teams	—	659	588	177	32	4	11	60	70	3	15	6	301	369	425	794	124	325	11	9	350	123	+18	+1	-3	-7	+14	2.4
2006	21	MLB RAW	2 teams	—	659	582	176	32	4	11	66	68	3	15	6	302	376	428	804	125	326	10	10	357	123	+18	+1	-3	-7	+14	2.4
2007	22	MLB MLE	New York Yankees	—	612	545	149	24	8	8	43	68	5	13	5	273	332	391	723	117	295	11	7	315	99	-1	+1	-7	+2	+13	0.9
2007	22	MLB RAW	New York Yankees	—	612	545	149	24	8	8	43	68	5	13	5	273	332	391	723	117	295	11	7	315	99	-1	+1	-7	+2	+13	0.9
2008	23	MLB MLE	2 teams	—	519	478	122	14	1	8	30	69	3	10	5	255	303	339	642	84	281	13	6	286	80	-12	+0	+4	+2	+11	0.5
2008	23	MLB RAW	2 teams	—	519	471	122	14	1	8	37	67	3	10	5	259	317	344	661	85	285	13	7	296	80	-12	+0	+4	+2	+11	0.5
2009	24	MLB MLE	New York Yankees	—	540	489	133	28	1	13	39	59	4	10	2	272	331	413	744	141	285	11	7	328	108	+5	+1	+1	+2	+12	2.1
2009	24	MLB RAW	New York Yankees	—	540	485	133	28	1	13	43	59	4	10	2	274	338	416	755	142	288	11	8	333	108	+5	+1	+1	+2	+12	2.1
2010	25	MLB MLE	Atlanta Braves	—	509	469	116	26	3	4	31	64	1	7	1	247	295	341	637	94	277	13	6	283	78	-13	+1	-16	-5	+11	-2.3
2010	25	MLB RAW	Atlanta Braves	—	509	458	117	27	3	4	42	64	1	7	1	255	319	354	673	98	288	13	8	301	78	-13	+1	-16	-5	+11	-2.3
2011	26	MLB MLE	Kansas City Royals	—	706	661	203	44	5	19	32	94	1	20	10	307	340	475	815	168	333	13	5	354	126	+22	+0	-5	+3	+15	3.5
2011	26	MLB RAW	Kansas City Royals	—	706	658	201	44	5	18	35	94	1	20	10	305	341	470	811	164	332	13	5	352	126	+22	+0	-5	+3	+15	3.5
2012	27	MLB MLE	San Francisco Giants	—	501	463	162	25	10	12	32	63	0	13	5	350	392	525	917	175	382	13	6	398	155	+33	+1	-6	-5	+11	3.4
2012	27	MLB RAW	San Francisco Giants	—	501	459	159	25	10	11	36	63	0	13	5	346	394	516	910	170	379	13	7	396	155	+33	+1	-6	-5	+11	3.4
2013	28	MLB MLE	Toronto Blue Jays	—	372	344	96	15	2	3	23	47	0	2	2	279	324	360	685	81	313	13	6	304	92	-4	-0	-5	-4	+8	-0.5
2013	28	MLB RAW	Toronto Blue Jays	—	372	344	96	15	2	3	23	47	0	2	2	279	324	360	685	81	313	13	6	305	92	-4	-0	-5	-4	+8	-0.5
2014	29	MLB MLE	Toronto Blue Jays	—	621	571	170	35	3	16	40	67	3	6	2	298	347	454	800	156	312	11	6	352	124	+18	+0	-6	-7	+13	2.0
2014	29	MLB RAW	Toronto Blue Jays	—	621	568	171	35	3	16	43	67	3	6	2	301	353	458	811	157	316	11	7	356	124	+18	+0	-6	-7	+13	2.0
2015	30	MLB MLE	Chicago White Sox	—	683	631	172	36	2	12	38	88	2	3	0	273	316	393	709	120	296	13	6	310	96	-3	+1	-13	-7	+15	-0.8
2015	30	MLB RAW	Chicago White Sox	—	683	629	172	36	2	12	40	88	2	3	0	273	319	394	713	121	297	13	6	314	96	-3	+1	-13	-7	+15	-0.8
2016	31	MLB MLE	Chicago White Sox	—	646	593	175	42	5	14	45	69	0	2	0	295	345	454	798	159	313	11	7	348	121	+17	+0	-12	-7	+14	1.2
2016	31	MLB RAW	Chicago White Sox	—	646	591	175	42	5	14	47	69	0	2	0	296	348	455	803	159	314	11	7	352	121	+17	+0	-12	-7	+14	1.2
2017	32	MLB MLE	Kansas City Royals	—	666	621	179	30	2	18	35	74	2	1	2	288	328	430	758	142	301	11	5	334	112	+10	-1	-12	-7	+14	0.4
2017	32	MLB RAW	Kansas City Royals	—	666	620	177	30	2	17	36	74	2	1	2	285	327	423	749	137	299	11	5	330	112	+10	-1	-12	-7	+14	0.4
2018	33	MLB MLE	2 teams	—	358	329	92	22	1	6	21	50	3	3	1	280	329	407	736	128	309	14	6	322	104	+2	+0	-7	-4	+8	-0.1
2018	33	MLB RAW	2 teams	—	358	328	95	23	1	6	22	48	3	3	1	290	340	421	761	131	319	13	6	335	104	+2	+0	-7	-4	+8	-0.1
2019	34	MLB MLE	Pittsburgh Pirates	—	397	381	106	21	1	8	14	41	1	2	0	278	306	402	707	123	295	10	4	312	98	-1	+0	-5	-4	+9	-0.2
2019	34	MLB RAW	Pittsburgh Pirates	—	397	378	106	22	1	7	17	41	1	2	0	280	313	399	713	119	300	10	4	317	98	-1	+0	-5	-4	+9	-0.2
2020	35	INTL MLE	Dominican Republic	—	128	120	37	6	0	3	7	14	0	0	0	308	346	433	780	125	327	11	5	—	—	—	—	—	—	—	—
2020	35	INTL RAW	Dominican Republic	—	128	120	37	6	0	3	7	14	0	0	0	308	346	433	780	125	327	11	5	345	120	—	—	—	—	—	—
2021	36	INTL MLE	Aguilas Cibaenas	—	107	100	26	4	0	0	5	15	0	0	0	260	295	300	595	40	299	14	5	—	—	—	—	—	—	—	—
2021	36	INTL RAW	Aguilas Cibaenas	—	107	100	26	4	0	0	5	15	0	0	0	260	295	300	595	40	299	14	5	267	67	—	—	—	—	—	—
2022	37	INTL MLE	Aguilas Cibaenas	—	91	80	15	2	0	0	6	12	2	0	0	188	261	212	474	25	211	13	7	—	—	—	—	—	—	—	—
2022	37	INTL RAW	Aguilas Cibaenas	—	91	80	15	2	0	0	6	12	2	0	0	188	261	212	474	25	211	13	7	220	35	—	—	—	—	—	—
2005	20	AA MLE	3 teams	—	595	560	148	23	3	11	26	108	4	13	2	264	303	375	678	111	304	18	4	295	86	—	—	—	—	—	—
2006	21	MLB MLE	2 teams	—	659	591	177	32	4	11	60	70	3	15	6	299	369	423	792	124	325	11	9	350	123	—	—	—	—	—	—
2007	22	MLB MLE	New York Yankees	—	612	559	149	24	8	8	43	68	5	13	5	267	332	381	713	114	295	11	7	315	99	—	—	—	—	—	—
2008	23	MLB MLE	2 teams	—	519	481	122	14	1	8	30	69	3	10	5	254	303	337	640	83	284	13	6	286	80	—	—	—	—	—	—
2009	24	MLB MLE	New York Yankees	—	540	492	133	28	1	13	39	59	4	10	2	270	331	411	741	140	288	11	7	328	108	—	—	—	—	—	—
2010	25	MLB MLE	Atlanta Braves	—	509	472	116	26	3	4	31	64	1	7	1	246	295	339	634	93	288	13	6	283	78	—	—	—	—	—	—
2011	26	MLB MLE	Kansas City Royals	—	706	667	203	44	5	19	32	94	1	20	10	304	340	471	811	166	332	13	5	354	126	—	—	—	—	—	—
2012	27	MLB MLE	San Francisco Giants	—	501	464	162	25	10	12	32	63	0	13	5	349	392	524	916	175	379	13	6	398	155	—	—	—	—	—	—
2013	28	MLB MLE	Toronto Blue Jays	—	372	346	96	15	2	3	23	47	0	2	2	277	324	358	683	81	313	13	6	304	92	—	—	—	—	—	—
2014	29	MLB MLE	Toronto Blue Jays	—	621	573	170	35	3	16	40	67	3	6	2	297	347	452	799	155	316	11	6	352	124	—	—	—	—	—	—
2015	30	MLB MLE	Chicago White Sox	—	683	638	172	36	2	12	38	88	2	3	0	270	316	389	705	119	297	13	6	310	96	—	—	—	—	—	—
2016	31	MLB MLE	Chicago White Sox	—	646	596	175	42	5	14	45	69	0	2	0	294	345	451	796	158	314	11	7	348	121	—	—	—	—	—	—
2017	32	MLB MLE	Kansas City Royals	—	666	624	179	30	2	18	35	74	2	1	2	287	328	428	756	141	299	11	5	334	112	—	—	—	—	—	—
2018	33	MLB MLE	2 teams	—	358	331	92	22	1	6	21	50	3	3	1	278	329	405	733	127	309	14	6	322	104	—	—	—	—	—	—
2019	34	MLB MLE	Pittsburgh Pirates	—	397	379	106	21	1	8	14	41	1	2	0	280	306	404	709	124	300	10	4	312	98	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	7527	6878	1962	383	45	144	510	891	27	101	37	285	337	417	754	132	308	12	7	332	100	+96	+5	-95	-47	+163	12.5
/ 162G	—	A+C		—	650	594	169	33	4	12	44	77	2	9	3	285	336	414	750	130	307	12	7	332	100	+8	+0	-8	-4	+14	1.1
Career	—	MLE		—	8710	8020	2278	429	51	166	527	1072	34	120	43	284	331	412	743	128	308	12	6	316	100	+96	+5	-95	-47	+163	12.5
/ 162G	—	MLE		—	650	599	170	32	4	12	39	80	3	9	3	284	331	411	742	127	308	12	6	316	100	+8	+0	-8	-4	+14	1.1

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.

2018 AAAColumbus Clippers · 80 PAwOBA .328 → .284 (−44)K% 12.5% → 14.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	12.50%	÷1.017	×1.200	—	—	14.75%
BB% (unintentional) /PA	2.50%	÷0.972	×0.690	—	×0.940	1.67%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	26.47%	÷0.974	×0.930	×1.014	×0.940	24.10%
Doubles /BIP	8.82%	÷0.958	×0.830	×1.014	×0.940	7.29%
Triples /BIP	1.47%	÷0.900	×0.970	×1.014	×0.940	1.51%
Home runs /BIP	0.00%	÷1.135	×0.840	—	×0.940	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 127, 1B 95, 2B 92, 3B 80, K 103, BB 94 — applied at half strength. Age: age 34 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (6.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.014.

Result: wOBA .328 → .284; ISO .103 → .088; BABIP .368 → .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).

2008 AAAScranton/Wilkes-Barre Yankees · 66 PAwOBA .353 → .330 (−23)K% 13.6% → 16.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.64%	÷1.000	×1.200	—	—	16.36%
BB% (unintentional) /PA	12.12%	÷1.000	×0.690	—	×1.048	8.77%
HBP% /PA	0.00%	—	×0.940	—	×1.048	0.00%
Singles /BIP	35.42%	÷1.000	×0.930	×1.014	×1.048	34.99%
Doubles /BIP	4.17%	÷1.000	×0.830	×1.014	×1.048	3.67%
Triples /BIP	0.00%	÷1.000	×0.970	×1.014	×1.048	0.00%
Home runs /BIP	0.00%	÷1.000	×0.840	—	×1.048	0.00%

Factors used: league row IL. Park (home-season share, regressed): no park row on file (treated as neutral) — 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 ×1.014.

Result: wOBA .353 → .330; ISO .035 → .032; BABIP .388 → .379. 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).

2006 AAAColumbus Clippers · 135 PAwOBA .429 → .416 (−13)K% 6.7% → 8.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	6.67%	÷1.000	×1.200	—	—	8.00%
BB% (unintentional) /PA	7.41%	÷1.000	×0.690	—	×1.050	5.37%
HBP% /PA	0.74%	—	×0.940	—	×1.050	0.73%
Singles /BIP	30.97%	÷1.000	×0.930	×1.038	×1.050	31.39%
Doubles /BIP	5.31%	÷1.000	×0.830	×1.038	×1.050	4.80%
Triples /BIP	1.77%	÷1.000	×0.970	×1.038	×1.050	1.87%
Home runs /BIP	3.54%	÷1.000	×0.840	—	×1.050	3.12%

Factors used: league row IL. Park (home-season share, regressed): no park row on file (treated as neutral) — 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 ×1.038.

Result: wOBA .429 → .416; ISO .180 → .170; BABIP .387 → .386. 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).

2005 AATrenton Thunder · 464 PAwOBA .318 → .304 (−13)K% 15.5% → 18.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.52%	÷1.000	×1.220	—	—	18.93%
BB% (unintentional) /PA	5.60%	÷1.000	×0.700	—	×1.050	4.12%
HBP% /PA	0.86%	—	×0.850	—	×1.050	0.77%
Singles /BIP	23.16%	÷1.000	×0.970	×1.041	×1.050	24.55%
Doubles /BIP	6.21%	÷1.000	×0.860	×1.041	×1.050	5.84%
Triples /BIP	0.85%	÷1.000	×0.950	×1.041	×1.050	0.88%
Home runs /BIP	2.82%	÷1.000	×0.780	—	×1.050	2.31%

Factors used: league row EL. Park (home-season share, regressed): no park row on file (treated as neutral) — 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 ×1.041.

Result: wOBA .318 → .304; ISO .136 → .121; BABIP .307 → .316. 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).

2005 AAAColumbus Clippers · 112 PAwOBA .292 → .275 (−17)K% 13.4% → 16.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.39%	÷1.000	×1.200	—	—	16.07%
BB% (unintentional) /PA	8.04%	÷1.000	×0.690	—	×1.050	5.82%
HBP% /PA	0.00%	—	×0.940	—	×1.050	0.00%
Singles /BIP	22.09%	÷1.000	×0.930	×1.050	×1.050	22.65%
Doubles /BIP	3.49%	÷1.000	×0.830	×1.050	×1.050	3.19%
Triples /BIP	0.00%	÷1.000	×0.970	×1.050	×1.050	0.00%
Home runs /BIP	3.49%	÷1.000	×0.840	—	×1.050	3.08%

Factors used: league row IL. Park (home-season share, regressed): no park row on file (treated as neutral) — 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 ×1.050.

Result: wOBA .292 → .275; ISO .119 → .108; BABIP .265 → .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).

Not traceable per stint: some stints lack the counting stats the component translation needs. Level-wide factors that apply instead — .