Evan Longoria

Pos3B

B/TR/R

Born1985-10-07

Age (2027)41

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2023

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2021	35	MLBSF	291	.261/.353/.482	13	1	134	2.1
2022	36	AAASF	25	.333/.360/.417	0	0	66	—	.250/.280/.292
2022	36	MLBSF	298	.244/.319/.451	14	0	118	1.6
2023	37	MLBARI	237	.223/.299/.422	11	0	100	0.7
2024	38	No MLB games played
2025	39	No MLB games played
2026	40	No MLB games played

Last 3 seasons WAR 4.4

Contract

No contract data on file.

Skills · percentileStatcast 2021-2023

Avg EV90

Barrel%77

xwOBA74

Bat Spd56

Speed23

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)
2006	20	AA	Montgomery Biscuits	—	109	105	28	5	0	6	1	20	0	2	1	267	274	486	759	219	268	18	1	321	84	—	—	—	—	—	—
2006	20	A+	Visalia Oaks	—	128	110	36	8	0	8	13	19	2	1	1	327	408	618	1.026	291	329	15	10	434	145	—	—	—	—	—	—
2007	21	AAA	Durham Bulls	—	128	104	28	8	0	5	22	29	1	0	0	269	402	490	892	221	324	23	17	393	107	—	—	—	—	—	—
2007	21	AA	Montgomery Biscuits	—	447	381	117	21	0	21	51	81	12	4	0	307	405	528	933	220	340	18	11	409	124	—	—	—	—	—	—
2008	22	MLB	Tampa Bay Rays	—	508	448	122	31	2	27	46	122	6	7	0	272	348	531	879	259	309	24	9	378	138	+23	+1	+18	+2	+11	5.6
2008	22	AAA	Durham Bulls	—	30	25	5	0	0	0	4	5	1	0	0	200	333	200	533		250	17	13	268	42	—	—	—	—	—	—
2009	23	MLB	Tampa Bay Rays	—	671	584	164	44	0	33	72	140	8	9	0	281	367	526	893	245	313	21	11	388	141	+32	+2	+22	+2	+15	7.5
2010	24	MLB	Tampa Bay Rays	—	661	574	169	46	5	22	72	124	5	15	5	294	378	507	885	213	336	19	11	384	138	+30	+1	+14	+2	+14	6.3
2011	25	MLB	Tampa Bay Rays	—	574	483	118	26	1	31	80	93	6	3	2	244	359	495	853	251	239	16	14	373	135	+24	-0	+17	+2	+12	5.6
2011	25	AA	Montgomery Biscuits	—	17	15	4	0	0	3	1	2	1	0	0	267	353	867	1.220	600	100	12	6	505	156	—	—	—	—	—	—
2012	26	MLB	Tampa Bay Rays	—	312	273	79	14	0	17	33	61	3	2	3	289	372	527	900	238	313	20	11	391	143	+16	-1	-0	+1	+7	2.3
2012	26	AAA	Durham Bulls	—	39	30	6	0	0	0	7	9	1	0	0	200	368	200	568		273	23	18	283	51	—	—	—	—	—	—
2013	27	MLB	Tampa Bay Rays	—	693	614	165	39	3	32	70	162	3	1	0	269	346	498	845	230	312	23	10	368	129	+24	+0	+7	+2	+15	5.0
2014	28	MLB	Tampa Bay Rays	—	700	624	158	26	1	22	57	133	9	5	0	253	325	404	728	151	285	19	8	323	97	-2	+1	+5	+2	+15	2.2
2015	29	MLB	Tampa Bay Rays	—	670	604	163	35	1	21	51	132	6	3	1	270	333	435	768	166	309	20	8	338	109	+7	+0	+7	+2	+15	3.2
2016	30	MLB	Tampa Bay Rays	—	685	633	173	41	4	36	42	144	3	0	3	273	322	521	843	248	298	21	6	363	129	+24	-1	+0	+2	+15	4.1
2017	31	MLB	Tampa Bay Rays	—	677	613	160	36	2	20	46	109	6	6	1	261	319	424	743	163	282	16	7	325	104	+3	+1	+4	+2	+15	2.5
2018	32	MLB	San Francisco Giants	—	512	480	117	25	4	16	22	101	5	3	1	244	284	412	697	169	274	20	4	305	94	-3	+0	-3	+2	+11	0.7
2018	32	AAA	Sacramento River Cats	—	16	14	3	1	0	0	1	0	1	0	0	214	312	286	598	71	214	0	6	283	46	—	—	—	—	—	—
2019	33	MLB	San Francisco Giants	—	508	453	115	19	2	20	43	112	7	3	1	254	328	437	765	183	291	22	8	337	119	+12	+0	+6	+2	+11	3.1
2020	34	MLB	San Francisco Giants	—	209	193	49	10	1	7	11	39	2	0	1	254	301	425	726	171	280	19	5	317	106	+1	-0	+4	+1	+5	1.1
2021	35	MLB	San Francisco Giants	—	291	253	66	17	0	13	35	68	1	1	1	261	353	482	835	221	305	23	12	366	134	+12	-0	+2	+1	+6	2.1
2022	36	MLB	San Francisco Giants	—	298	266	65	13	0	14	27	83	2	0	0	244	319	451	770	207	297	28	9	337	118	+6	+0	+1	+1	+6	1.6
2022	36	AAA	Sacramento River Cats	—	25	24	8	2	0	0	1	8	0	0	0	333	360	417	777	83	500	32	4	348	66	—	—	—	—	—	—
2023	37	MLB	Arizona Diamondbacks	—	237	211	47	9	0	11	23	73	0	0	0	223	299	422	721	199	277	31	10	316	100	-0	+0	+1	+1	+5	0.7
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	8206	7306	1930	431	26	342	730	1696	72	58	19	264	337	471	808	207	296	21	9	353	125	+241	+4	+106	+30	+178	57.1
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2006	20	AA MLE	Montgomery Biscuits	—	109	105	27	5	0	5	1	24	0	2	1	257	264	448	712	190	261	22	1	293	84	—	—	—	—	—	—
2006	20	A+ MLE	Visalia Oaks	—	128	116	34	7	0	8	7	25	2	1	1	293	344	560	904	267	293	20	5	383	145	—	—	—	—	—	—
2007	21	AAA MLE	Durham Bulls	—	128	110	25	7	0	4	16	35	1	0	0	227	331	400	731	173	287	27	12	327	107	—	—	—	—	—	—
2007	21	AA MLE	Montgomery Biscuits	—	447	399	110	20	0	18	33	99	12	4	0	276	349	461	810	185	318	22	7	352	124	—	—	—	—	—	—
2008	22	MLB MLE	Tampa Bay Rays	—	508	452	122	31	2	27	42	122	6	7	0	270	340	527	867	257	309	24	8	372	138	+23	+1	+18	+2	+11	5.6
2008	22	AAA MLE	Durham Bulls	—	30	27	5	0	0	0	2	6	1	0	0	185	267	185	452		244	20	7	231	42	—	—	—	—	—	—
2009	23	MLB MLE	Tampa Bay Rays	—	671	595	164	44	0	33	61	140	8	9	0	276	351	516	867	240	313	21	9	376	141	+32	+2	+22	+2	+15	7.5
2010	24	MLB MLE	Tampa Bay Rays	—	661	586	169	47	5	23	60	124	5	15	5	288	359	503	863	215	336	19	9	372	138	+30	+1	+14	+2	+14	6.3
2011	25	MLB MLE	Tampa Bay Rays	—	574	489	119	27	1	32	74	93	6	3	2	243	350	499	849	256	239	16	13	368	135	+24	-0	+17	+2	+12	5.6
2011	25	AA MLE	Montgomery Biscuits	—	17	15	3	0	0	2	1	2	1	0	0	200	294	600	894	400	83	12	6	399	156	—	—	—	—	—	—
2012	26	MLB MLE	Tampa Bay Rays	—	312	279	78	14	0	17	27	61	3	2	3	280	350	513	862	233	313	20	9	379	143	+16	-1	-0	+1	+7	2.3
2012	26	AAA MLE	Durham Bulls	—	39	32	6	0	0	0	5	11	1	0	0	188	316	188	503		259	28	13	244	51	—	—	—	—	—	—
2013	27	MLB MLE	Tampa Bay Rays	—	693	624	165	40	3	33	60	162	3	1	0	264	332	497	829	232	312	23	9	359	129	+24	+0	+7	+2	+15	5.0
2014	28	MLB MLE	Tampa Bay Rays	—	700	635	158	27	1	23	46	133	9	5	0	249	309	403	712	154	285	19	7	312	97	-2	+1	+5	+2	+15	2.2
2015	29	MLB MLE	Tampa Bay Rays	—	670	612	163	36	1	22	43	132	6	3	1	266	321	436	757	170	309	20	6	330	109	+7	+0	+7	+2	+15	3.2
2016	30	MLB MLE	Tampa Bay Rays	—	685	639	173	42	4	37	36	144	3	0	3	271	313	523	835	252	298	21	5	359	129	+24	-1	+0	+2	+15	4.1
2017	31	MLB MLE	Tampa Bay Rays	—	677	616	160	37	2	21	43	109	6	6	1	260	314	429	743	169	282	16	6	322	104	+3	+1	+4	+2	+15	2.5
2018	32	MLB MLE	San Francisco Giants	—	512	483	119	25	4	17	19	101	5	3	1	246	282	420	702	174	274	20	4	308	94	-3	+0	-3	+2	+11	0.7
2018	32	AAA MLE	Sacramento River Cats	—	16	14	3	1	0	0	1	0	1	0	0	214	312	286	598	71	186	0	6	236	46	—	—	—	—	—	—
2019	33	MLB MLE	San Francisco Giants	—	508	454	118	19	2	22	42	112	7	3	1	260	332	456	788	196	291	22	8	344	119	+12	+0	+6	+2	+11	3.1
2020	34	MLB MLE	San Francisco Giants	—	209	193	50	10	1	8	11	39	2	0	1	259	306	446	751	187	280	19	5	324	106	+1	-0	+4	+1	+5	1.1
2021	35	MLB MLE	San Francisco Giants	—	291	257	68	17	0	14	31	68	1	1	1	265	346	494	840	230	305	23	11	366	134	+12	-0	+2	+1	+6	2.1
2022	36	MLB MLE	San Francisco Giants	—	298	268	67	13	0	15	25	83	2	0	0	250	319	466	785	216	297	28	8	342	118	+6	+0	+1	+1	+6	1.6
2022	36	AAA MLE	Sacramento River Cats	—	25	24	6	1	0	0	1	10	0	0	0	250	280	292	572	42	439	40	4	266	66	—	—	—	—	—	—
2023	37	MLB MLE	Arizona Diamondbacks	—	237	211	47	8	0	12	23	73	0	0	0	223	299	431	730	209	277	31	10	316	100	-0	+0	+1	+1	+5	0.7
2006	20	A+ MLE	2 teams	—	237	222	61	12	0	13	7	50	2	3	2	275	303	505	808	230	293	21	3	342	117	—	—	—	—	—	—
2006	20	A+ RAW	2 teams	—	237	215	64	13	0	14	14	39	2	3	2	298	346	553	900	256	299	16	6	382	117	—	—	—	—	—	—
2007	21	AA MLE	2 teams	—	575	509	136	27	0	22	49	134	13	4	0	267	347	450	797	183	319	23	9	346	121	—	—	—	—	—	—
2007	21	AA RAW	2 teams	—	575	485	145	29	0	26	73	110	13	4	0	299	405	520	924	221	337	19	13	406	121	—	—	—	—	—	—
2008	22	MLB MLE	2 teams	—	538	479	127	31	2	27	44	128	7	7	0	265	336	507	843	242	301	24	8	364	133	+21	+1	+18	+2	+12	5.5
2008	22	MLB RAW	2 teams	—	538	473	127	31	2	27	50	127	7	7	0	268	347	514	861	245	306	24	9	372	133	+21	+1	+18	+2	+12	5.5
2009	23	MLB MLE	Tampa Bay Rays	—	671	595	164	44	0	33	61	140	8	9	0	276	351	516	867	240	305	21	9	376	141	+32	+2	+22	+2	+15	7.5
2009	23	MLB RAW	Tampa Bay Rays	—	671	584	164	44	0	33	72	140	8	9	0	281	367	526	893	245	313	21	11	388	141	+32	+2	+22	+2	+15	7.5
2010	24	MLB MLE	Tampa Bay Rays	—	661	586	169	47	5	23	60	124	5	15	5	288	359	503	863	215	325	19	9	372	138	+30	+1	+14	+2	+14	6.3
2010	24	MLB RAW	Tampa Bay Rays	—	661	574	169	46	5	22	72	124	5	15	5	294	378	507	885	213	336	19	11	384	138	+30	+1	+14	+2	+14	6.3
2011	25	MLB MLE	2 teams	—	591	504	122	27	1	34	75	95	7	3	2	242	348	502	850	260	232	16	13	369	136	+25	-0	+17	+2	+13	5.8
2011	25	MLB RAW	2 teams	—	591	498	122	26	1	34	81	95	7	3	2	245	358	506	864	261	235	16	14	377	136	+25	-0	+17	+2	+13	5.8
2012	26	MLB MLE	2 teams	—	351	311	84	14	0	17	32	72	4	2	3	270	346	479	825	209	296	21	9	364	133	+14	-1	-0	+1	+8	2.2
2012	26	MLB RAW	2 teams	—	351	303	85	14	0	17	40	70	4	2	3	281	372	495	867	215	309	20	11	379	133	+14	-1	-0	+1	+8	2.2
2013	27	MLB MLE	Tampa Bay Rays	—	693	624	165	40	3	33	60	162	3	1	0	264	332	497	829	232	303	23	9	359	129	+24	+0	+7	+2	+15	5.0
2013	27	MLB RAW	Tampa Bay Rays	—	693	614	165	39	3	32	70	162	3	1	0	269	346	498	845	230	312	23	10	368	129	+24	+0	+7	+2	+15	5.0
2014	28	MLB MLE	Tampa Bay Rays	—	700	635	158	27	1	23	46	133	9	5	0	249	309	403	712	154	277	19	7	312	97	-2	+1	+5	+2	+15	2.2
2014	28	MLB RAW	Tampa Bay Rays	—	700	624	158	26	1	22	57	133	9	5	0	253	325	404	728	151	285	19	8	323	97	-2	+1	+5	+2	+15	2.2
2015	29	MLB MLE	Tampa Bay Rays	—	670	612	163	36	1	22	43	132	6	3	1	266	321	436	757	170	302	20	6	330	109	+7	+0	+7	+2	+15	3.2
2015	29	MLB RAW	Tampa Bay Rays	—	670	604	163	35	1	21	51	132	6	3	1	270	333	435	768	166	309	20	8	338	109	+7	+0	+7	+2	+15	3.2
2016	30	MLB MLE	Tampa Bay Rays	—	685	639	173	42	4	37	36	144	3	0	3	271	313	523	835	252	292	21	5	359	129	+24	-1	+0	+2	+15	4.1
2016	30	MLB RAW	Tampa Bay Rays	—	685	633	173	41	4	36	42	144	3	0	3	273	322	521	843	248	298	21	6	363	129	+24	-1	+0	+2	+15	4.1
2017	31	MLB MLE	Tampa Bay Rays	—	677	616	160	37	2	21	43	109	6	6	1	260	314	429	743	169	279	16	6	322	104	+3	+1	+4	+2	+15	2.5
2017	31	MLB RAW	Tampa Bay Rays	—	677	613	160	36	2	20	46	109	6	6	1	261	319	424	743	163	282	16	7	325	104	+3	+1	+4	+2	+15	2.5
2018	32	MLB MLE	2 teams	—	528	497	122	26	4	17	20	101	6	3	1	245	283	416	699	171	273	19	4	306	93	-4	+0	-3	+2	+11	0.6
2018	32	MLB RAW	2 teams	—	528	494	120	26	4	16	23	101	6	3	1	243	285	409	694	166	272	19	4	304	93	-4	+0	-3	+2	+11	0.6
2019	33	MLB MLE	San Francisco Giants	—	508	454	118	19	2	22	42	112	7	3	1	260	332	456	788	196	295	22	8	344	119	+12	+0	+6	+2	+11	3.1
2019	33	MLB RAW	San Francisco Giants	—	508	453	115	19	2	20	43	112	7	3	1	254	328	437	765	183	291	22	8	337	119	+12	+0	+6	+2	+11	3.1
2020	34	MLB MLE	San Francisco Giants	—	209	193	50	10	1	8	11	39	2	0	1	259	306	446	751	187	282	19	5	324	106	+1	-0	+4	+1	+5	1.1
2020	34	MLB RAW	San Francisco Giants	—	209	193	49	10	1	7	11	39	2	0	1	254	301	425	726	171	280	19	5	317	106	+1	-0	+4	+1	+5	1.1
2021	35	MLB MLE	San Francisco Giants	—	291	257	68	17	0	14	31	68	1	1	1	265	346	494	840	230	305	23	11	366	134	+12	-0	+2	+1	+6	2.1
2021	35	MLB RAW	San Francisco Giants	—	291	253	66	17	0	13	35	68	1	1	1	261	353	482	835	221	305	23	12	366	134	+12	-0	+2	+1	+6	2.1
2022	36	MLB MLE	2 teams	—	323	292	73	14	0	15	26	93	2	0	0	250	316	452	768	202	310	29	8	336	114	+5	+0	+1	+1	+7	1.5
2022	36	MLB RAW	2 teams	—	323	290	73	15	0	14	28	91	2	0	0	252	322	448	770	197	314	28	9	338	114	+5	+0	+1	+1	+7	1.5
2023	37	MLB MLE	Arizona Diamondbacks	—	237	211	47	8	0	12	23	73	0	0	0	223	299	431	730	209	271	31	10	316	100	-0	+0	+1	+1	+5	0.7
2023	37	MLB RAW	Arizona Diamondbacks	—	237	211	47	9	0	11	23	73	0	0	0	223	299	422	721	199	277	31	10	316	100	-0	+0	+1	+1	+5	0.7
2006	20	A+ MLE	2 teams	—	237	226	61	12	0	13	7	50	2	3	2	270	303	496	799	226	278	21	3	342	117	—	—	—	—	—	—
2007	21	AA MLE	2 teams	—	575	508	136	27	0	22	49	134	13	4	0	268	347	451	798	183	311	23	9	346	121	—	—	—	—	—	—
2008	22	MLB MLE	2 teams	—	538	482	127	31	2	27	44	128	7	7	0	263	336	504	840	241	306	24	8	364	133	—	—	—	—	—	—
2009	23	MLB MLE	Tampa Bay Rays	—	671	597	164	44	0	33	61	140	8	9	0	275	351	514	865	240	313	21	9	376	141	—	—	—	—	—	—
2010	24	MLB MLE	Tampa Bay Rays	—	661	591	169	47	5	23	60	124	5	15	5	286	359	499	859	213	336	19	9	372	138	—	—	—	—	—	—
2011	25	MLB MLE	2 teams	—	591	504	122	27	1	34	75	95	7	3	2	242	348	502	850	260	235	16	13	369	136	—	—	—	—	—	—
2012	26	MLB MLE	2 teams	—	351	312	84	14	0	17	32	72	4	2	3	269	346	478	823	208	307	21	9	364	133	—	—	—	—	—	—
2013	27	MLB MLE	Tampa Bay Rays	—	693	625	165	40	3	33	60	162	3	1	0	264	332	496	828	232	312	23	9	359	129	—	—	—	—	—	—
2014	28	MLB MLE	Tampa Bay Rays	—	700	639	158	27	1	23	46	133	9	5	0	247	309	401	709	153	285	19	7	312	97	—	—	—	—	—	—
2015	29	MLB MLE	Tampa Bay Rays	—	670	616	163	36	1	22	43	132	6	3	1	265	321	433	754	169	309	20	6	330	109	—	—	—	—	—	—
2016	30	MLB MLE	Tampa Bay Rays	—	685	641	173	42	4	37	36	144	3	0	3	270	313	521	834	251	298	21	5	359	129	—	—	—	—	—	—
2017	31	MLB MLE	Tampa Bay Rays	—	677	623	160	37	2	21	43	109	6	6	1	257	314	424	738	167	282	16	6	322	104	—	—	—	—	—	—
2018	32	MLB MLE	2 teams	—	528	497	122	26	4	17	20	101	6	3	1	245	283	416	699	171	272	19	4	306	93	—	—	—	—	—	—
2019	33	MLB MLE	San Francisco Giants	—	508	454	118	19	2	22	42	112	7	3	1	260	332	456	788	196	291	22	8	344	119	—	—	—	—	—	—
2020	34	MLB MLE	San Francisco Giants	—	209	194	50	10	1	8	11	39	2	0	1	258	306	443	749	186	280	19	5	324	106	—	—	—	—	—	—
2021	35	MLB MLE	San Francisco Giants	—	291	257	68	17	0	14	31	68	1	1	1	265	346	494	840	230	305	23	11	366	134	—	—	—	—	—	—
2022	36	MLB MLE	2 teams	—	323	292	73	14	0	15	26	93	2	0	0	250	316	452	768	202	308	29	8	336	114	—	—	—	—	—	—
2023	37	MLB MLE	Arizona Diamondbacks	—	237	212	47	8	0	12	23	73	0	0	0	222	299	429	728	208	277	31	10	316	100	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	8206	7306	1930	431	26	342	730	1696	72	58	19	264	337	471	808	207	296	21	9	353	120	+241	+4	+106	+30	+178	57.1
/ 162G	—	A+C		—	650	579	153	34	2	27	58	134	6	5	2	264	338	470	808	206	296	21	9	353	120	+19	+0	+8	+2	+14	4.5
Career	—	MLE		—	9145	8236	2160	478	26	393	709	1909	91	65	21	262	328	470	797	208	293	21	8	346	120	+241	+4	+106	+30	+178	57.1
/ 162G	—	MLE		—	650	585	154	34	2	28	50	136	6	5	1	263	327	472	799	209	294	21	8	346	120	+19	+0	+8	+2	+14	4.5

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.

2022 AAASacramento River Cats · 25 PAwOBA .338 → .266 (−73)K% 32.0% → 38.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	32.00%	÷0.986	×1.200	—	—	38.93%
BB% (unintentional) /PA	4.00%	÷0.984	×0.690	—	×0.940	2.64%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	37.50%	÷0.968	×0.930	—	×0.940	33.85%
Doubles /BIP	12.50%	÷0.967	×0.830	—	×0.940	10.09%
Triples /BIP	0.00%	÷0.900	×0.970	—	×0.940	0.00%
Home runs /BIP	0.00%	÷0.989	×0.700	—	×0.940	0.00%

Factors used: league row International League. Park (home-season share, regressed): HR 98, 1B 94, 2B 93, 3B 80, K 97, BB 97 — applied at half strength. Age: age 37 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (9.8 yr older than average).

Result: wOBA .338 → .266; ISO .083 → .061; BABIP .500 → .439. 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 AAASacramento River Cats · 16 PAwOBA .276 → .236 (−40)K% 0.0% → 0.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	0.00%	÷1.071	×1.290	—	—	0.00%
BB% (unintentional) /PA	6.25%	÷1.043	×0.710	—	×0.942	4.01%
HBP% /PA	6.25%	—	×0.980	—	×0.942	5.77%
Singles /BIP	14.29%	÷0.963	×0.900	—	×0.942	12.57%
Doubles /BIP	7.14%	÷0.910	×0.810	—	×0.942	5.99%
Triples /BIP	0.00%	÷1.150	×0.810	—	×0.942	0.00%
Home runs /BIP	0.00%	÷0.998	×0.780	—	×0.942	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 100, 1B 93, 2B 82, 3B 130, K 114, BB 109 — applied at half strength. Age: age 33 vs a level average of 27.2 → −5.8% on BB/HBP/hit rates (5.8 yr older than average).

Result: wOBA .276 → .236; ISO .071 → .062; BABIP .214 → .186. 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 AAADurham Bulls · 39 PAwOBA .285 → .244 (−41)K% 23.1% → 27.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.08%	÷1.008	×1.200	—	—	27.47%
BB% (unintentional) /PA	17.95%	÷1.046	×0.690	—	×1.003	11.87%
HBP% /PA	2.56%	—	×0.940	—	×1.003	2.42%
Singles /BIP	28.57%	÷0.987	×0.930	—	×1.003	26.99%
Doubles /BIP	0.00%	÷0.998	×0.830	—	×1.003	0.00%
Triples /BIP	0.00%	÷0.942	×0.970	—	×1.003	0.00%
Home runs /BIP	0.00%	÷1.093	×0.840	—	×1.003	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 119, 1B 97, 2B 100, 3B 88, K 102, BB 109 — 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 .285 → .244; ISO .000 → .000; BABIP .273 → .259. 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).

2011 AAMontgomery Biscuits · 17 PAwOBA .488 → .399 (−90)K% 11.8% → 13.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.76%	÷1.035	×1.220	—	—	13.87%
BB% (unintentional) /PA	5.88%	÷0.983	×0.680	—	×0.988	4.02%
HBP% /PA	5.88%	—	×0.840	—	×0.988	4.88%
Singles /BIP	7.69%	÷1.034	×0.920	—	×0.988	6.76%
Doubles /BIP	0.00%	÷0.902	×0.910	—	×0.988	0.00%
Triples /BIP	0.00%	÷0.994	×0.880	—	×0.988	0.00%
Home runs /BIP	23.08%	÷0.974	×0.800	—	×0.988	18.73%

Factors used: league row SL. Park (home-season share, regressed): HR 95, 1B 107, 2B 80, 3B 99, K 107, BB 97 — applied at half strength. Age: age 26 vs a level average of 24.8 → −1.2% on BB/HBP/hit rates (1.2 yr older than average).

Result: wOBA .488 → .399; ISO .600 → .492; BABIP .100 → .083. 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 AAADurham Bulls · 30 PAwOBA .248 → .231 (−17)K% 16.7% → 20.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.67%	÷1.000	×1.200	—	—	20.00%
BB% (unintentional) /PA	10.00%	÷1.000	×0.690	—	×1.050	7.25%
HBP% /PA	3.33%	—	×0.940	—	×1.050	3.29%
Singles /BIP	25.00%	÷1.000	×0.930	—	×1.050	24.41%
Doubles /BIP	0.00%	÷1.000	×0.830	—	×1.050	0.00%
Triples /BIP	0.00%	÷1.000	×0.970	—	×1.050	0.00%
Home runs /BIP	0.00%	÷1.000	×0.840	—	×1.050	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 23 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (4.2 yr younger than average).

Result: wOBA .248 → .231; ISO .000 → .000; BABIP .250 → .244. 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).

2007 AAMontgomery Biscuits · 447 PAwOBA .398 → .352 (−46)K% 18.1% → 22.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.12%	÷1.000	×1.220	—	—	22.11%
BB% (unintentional) /PA	10.29%	÷1.000	×0.680	—	×1.042	7.29%
HBP% /PA	2.68%	—	×0.840	—	×1.042	2.35%
Singles /BIP	25.00%	÷1.000	×0.920	×0.990	×1.042	23.72%
Doubles /BIP	7.00%	÷1.000	×0.910	×0.990	×1.042	6.57%
Triples /BIP	0.00%	÷1.000	×0.880	×0.990	×1.042	0.00%
Home runs /BIP	7.00%	÷1.000	×0.800	—	×1.042	5.84%

Factors used: league row SL. 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 24.8 → +4.2% on BB/HBP/hit rates (2.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.990.

Result: wOBA .398 → .352; ISO .220 → .193; BABIP .340 → .318. 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).

2007 AAADurham Bulls · 128 PAwOBA .386 → .327 (−59)K% 22.7% → 27.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.66%	÷1.000	×1.200	—	—	27.19%
BB% (unintentional) /PA	17.19%	÷1.000	×0.690	—	×1.050	12.45%
HBP% /PA	0.78%	—	×0.940	—	×1.050	0.77%
Singles /BIP	20.00%	÷1.000	×0.930	×0.950	×1.050	18.55%
Doubles /BIP	10.67%	÷1.000	×0.830	×0.950	×1.050	8.83%
Triples /BIP	0.00%	÷1.000	×0.970	×0.950	×1.050	0.00%
Home runs /BIP	6.67%	÷1.000	×0.840	—	×1.050	5.88%

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 ×0.950.

Result: wOBA .386 → .327; ISO .221 → .194; BABIP .324 → .287. 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 A+Visalia Oaks · 128 PAwOBA .420 → .383 (−37)K% 14.8% → 19.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.84%	÷1.000	×1.340	—	—	19.89%
BB% (unintentional) /PA	7.03%	÷1.000	×0.710	—	×1.027	5.13%
HBP% /PA	1.56%	—	×0.790	—	×1.027	1.27%
Singles /BIP	21.98%	÷1.000	×0.910	×0.984	×1.027	20.20%
Doubles /BIP	8.79%	÷1.000	×0.820	×0.984	×1.027	7.28%
Triples /BIP	0.00%	÷1.000	×0.760	×0.984	×1.027	0.00%
Home runs /BIP	8.79%	÷1.000	×0.920	—	×1.027	8.31%

Factors used: league row CAL. 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 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 ×0.984.

Result: wOBA .420 → .383; ISO .291 → .276; BABIP .329 → .293. 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).