Justin Smoak

Pos1B

B/TS/L

Born1986-12-05

Age (2027)40

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2020

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2018	31	MLBTOR	594	.242/.352/.457	25	0	127	2.1
2019	32	MLBTOR	500	.208/.343/.406	22	0	112	0.5
2020	33	MLBSF	132	.176/.252/.361	5	0	75	-0.6
2021	34	No MLB games played
2022	35	No MLB games played
2023	36	No MLB games played
2024	37	No MLB games played
2025	38	No MLB games played
2026	39	No MLB games played

Last 3 seasons WAR 2.0

Contract

No contract data on file.

Skills · percentileStatcast 2018-2020

xwOBA79

Barrel%63

Avg EV60

Speed10

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)
2008	21	A	Clinton LumberKings	—	62	56	17	3	0	3	5	10	0	0	0	304	361	518	879	214	318	16	8	380	92	—	—	—	—	—	—
2009	22	AAA	Oklahoma City RedHawks	—	237	197	48	11	0	4	35	45	3	0	0	244	366	360	726	117	293	19	15	333	79	—	—	—	—	—	—
2009	22	AA	Frisco RoughRiders	—	227	183	60	10	0	6	39	35	3	0	0	328	453	481	934	153	375	15	17	417	135	—	—	—	—	—	—
2010	23	MLB	Seattle Mariners	—	397	348	76	14	0	13	46	91	0	1	0	218	310	371	680	152	255	23	12	306	91	-4	+0	-5	-11	+9	-1.2
2010	23	AAA	Tacoma Rainiers	—	225	183	51	13	0	9	39	40	1	0	0	279	408	497	905	219	309	18	17	398	105	—	—	—	—	—	—
2011	24	MLB	Seattle Mariners	—	489	427	100	24	0	15	55	105	3	0	0	234	326	396	722	162	273	21	11	323	103	+2	+0	-7	-9	+11	-0.4
2011	24	AAA	Tacoma Rainiers	—	15	11	0	0	0	0	3	1	1	0	0		267		267			7	20	189	-11	—	—	—	—	—	—
2012	25	MLB	Seattle Mariners	—	535	483	105	14	0	19	49	111	1	1	0	217	291	364	655	147	242	21	9	295	86	-9	+0	-11	-11	+12	-2.0
2012	25	AAA	Tacoma Rainiers	—	82	66	16	6	1	0	16	16	0	1	0	242	390	364	754	121	320	20	20	349	72	—	—	—	—	—	—
2013	26	MLB	Seattle Mariners	—	521	454	108	19	0	20	64	119	2	0	0	238	335	412	747	174	278	23	12	335	114	+9	+0	-13	-10	+11	-0.2
2013	26	AAA	Tacoma Rainiers	—	22	21	5	2	0	0	0	5	1	0	0	238	273	333	606	95	312	23	0	274	36	—	—	—	—	—	—
2014	27	MLB	Seattle Mariners	—	276	248	50	13	0	7	24	66	2	0	1	202	277	339	616	137	243	24	9	278	76	-8	-0	-3	-9	+6	-1.5
2014	27	AAA	Tacoma Rainiers	—	249	205	69	13	0	7	33	41	3	0	2	337	436	502	938	166	376	16	13	405	119	—	—	—	—	—	—
2015	28	MLB	Toronto Blue Jays	—	328	296	67	16	1	18	29	86	2	0	0	226	300	470	769	243	254	26	9	336	113	+5	+0	-4	-6	+7	0.3
2016	29	MLB	Toronto Blue Jays	—	341	299	65	10	0	14	40	112	2	1	0	217	314	391	705	174	295	33	12	318	100	-0	+0	-8	-6	+7	-0.7
2017	30	MLB	Toronto Blue Jays	—	637	560	151	29	1	38	73	128	2	0	1	270	356	529	884	259	285	20	11	385	144	+34	-0	-5	-11	+14	3.1
2018	31	MLB	Toronto Blue Jays	—	594	505	122	34	0	25	83	156	3	0	1	242	352	457	809	216	297	26	14	358	127	+19	-0	-1	-11	+13	2.1
2019	32	MLB	Toronto Blue Jays	—	500	414	86	16	0	22	79	106	6	0	0	208	343	406	748	198	223	21	16	339	112	+7	+0	-4	-9	+11	0.5
2020	33	MLB	San Francisco Giants	—	132	119	21	7	0	5	10	42	2	0	0	176	252	361	613	185	219	32	8	273	75	-4	+0	-3	-2	+3	-0.6
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	4750	4153	951	196	2	196	552	1122	25	3	3	229	323	419	742	190	264	24	12	331	110	+56	-1	-63	-95	+103	-0.1
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2008	21	A MLE	Clinton LumberKings	—	62	58	14	2	0	2	3	14	0	0	0	241	279	379	658	138	276	23	5	304	92	—	—	—	—	—	—
2009	22	AAA MLE	Oklahoma City RedHawks	—	237	206	45	10	0	3	26	58	3	0	0	218	315	311	626	92	281	24	11	285	79	—	—	—	—	—	—
2009	22	AA MLE	Frisco RoughRiders	—	227	195	59	9	0	5	27	43	3	0	0	303	396	426	821	123	357	19	12	367	135	—	—	—	—	—	—
2010	23	MLB MLE	Seattle Mariners	—	397	352	76	14	0	13	42	91	0	1	0	216	299	366	666	151	255	23	11	302	91	-4	+0	-5	-11	+9	-1.2
2010	23	AAA MLE	Tacoma Rainiers	—	225	192	44	10	0	7	30	51	1	0	0	229	336	391	727	161	270	23	13	324	105	—	—	—	—	—	—
2011	24	MLB MLE	Seattle Mariners	—	489	431	100	24	0	15	51	105	3	0	0	232	318	392	710	160	273	21	10	320	103	+2	+0	-7	-9	+11	-0.4
2011	24	AAA MLE	Tacoma Rainiers	—	15	12	0	0	0	0	2	1	1	0	0		200		200			7	13	152	-11	—	—	—	—	—	—
2012	25	MLB MLE	Seattle Mariners	—	535	485	105	14	0	19	47	111	1	1	0	216	287	363	650	146	242	21	9	295	86	-9	+0	-11	-11	+12	-2.0
2012	25	AAA MLE	Tacoma Rainiers	—	82	72	14	5	1	0	10	20	0	1	0	194	293	292	584	97	278	24	12	275	72	—	—	—	—	—	—
2013	26	MLB MLE	Seattle Mariners	—	521	455	109	19	0	21	63	119	2	0	0	240	335	420	754	180	278	23	12	337	114	+9	+0	-13	-10	+11	-0.2
2013	26	AAA MLE	Tacoma Rainiers	—	22	21	5	2	0	0	0	6	1	0	0	238	273	333	606	95	278	27	0	222	36	—	—	—	—	—	—
2014	27	MLB MLE	Seattle Mariners	—	276	248	50	13	0	7	24	66	2	0	1	202	277	339	616	137	243	24	9	280	76	-8	-0	-3	-9	+6	-1.5
2014	27	AAA MLE	Tacoma Rainiers	—	249	219	63	12	0	6	19	52	3	0	2	288	353	425	777	137	320	21	8	344	119	—	—	—	—	—	—
2015	28	MLB MLE	Toronto Blue Jays	—	328	296	67	16	1	18	29	86	2	0	0	226	300	470	769	243	254	26	9	336	113	+5	+0	-4	-6	+7	0.3
2016	29	MLB MLE	Toronto Blue Jays	—	341	300	65	10	0	14	39	112	2	1	0	217	311	390	701	173	295	33	11	316	100	-0	+0	-8	-6	+7	-0.7
2017	30	MLB MLE	Toronto Blue Jays	—	637	563	151	29	1	38	70	128	2	0	1	268	351	526	877	258	285	20	11	382	144	+34	-0	-5	-11	+14	3.1
2018	31	MLB MLE	Toronto Blue Jays	—	594	507	122	34	0	25	81	156	3	0	1	241	349	456	804	215	297	26	14	356	127	+19	-0	-1	-11	+13	2.1
2019	32	MLB MLE	Toronto Blue Jays	—	500	417	86	16	0	22	76	106	6	0	0	206	337	403	740	197	223	21	15	334	112	+7	+0	-4	-9	+11	0.5
2020	33	MLB MLE	San Francisco Giants	—	132	119	21	7	0	5	10	42	2	0	0	176	252	361	613	185	219	32	8	280	75	-4	+0	-3	-2	+3	-0.6
2008	21	A MLE	Clinton LumberKings	—	62	58	14	2	0	2	3	14	0	0	0	241	279	379	658	138	279	23	5	304	92	—	—	—	—	—	—
2008	21	A RAW	Clinton LumberKings	—	62	56	17	3	0	3	5	10	0	0	0	304	361	518	879	214	318	16	8	380	92	—	—	—	—	—	—
2009	22	AAA MLE	2 teams	—	464	401	104	19	0	8	53	101	6	0	0	259	354	367	721	107	324	22	11	325	106	—	—	—	—	—	—
2009	22	AAA RAW	2 teams	—	464	380	108	21	0	10	74	80	6	0	0	284	409	418	827	134	333	17	16	374	106	—	—	—	—	—	—
2010	23	MLB MLE	2 teams	—	622	544	121	25	0	20	72	142	1	1	0	222	314	379	693	156	261	23	12	310	96	-3	+0	-5	-11	+13	-0.6
2010	23	MLB RAW	2 teams	—	622	531	127	27	0	22	85	131	1	1	0	239	345	414	760	175	274	21	14	339	96	-3	+0	-5	-11	+13	-0.6
2011	24	MLB MLE	2 teams	—	504	443	100	24	0	15	53	106	4	0	0	226	314	381	695	156	261	21	11	315	99	-0	+0	-7	-9	+11	-0.6
2011	24	MLB RAW	2 teams	—	504	438	100	24	0	15	58	106	4	0	0	228	324	386	710	158	265	21	12	319	99	-0	+0	-7	-9	+11	-0.6
2012	25	MLB MLE	2 teams	—	617	557	120	19	1	19	57	131	1	2	0	215	289	355	645	140	247	21	9	292	84	-12	+0	-11	-11	+13	-2.1
2012	25	MLB RAW	2 teams	—	617	549	121	20	1	19	65	127	1	2	0	220	304	364	668	144	252	21	11	302	84	-12	+0	-11	-11	+13	-2.1
2013	26	MLB MLE	2 teams	—	543	476	114	21	0	21	63	125	3	0	0	239	332	416	748	176	281	23	12	332	111	+7	+0	-13	-10	+12	-0.3
2013	26	MLB RAW	2 teams	—	543	475	113	21	0	20	64	124	3	0	0	238	332	408	741	171	280	23	12	333	111	+7	+0	-13	-10	+12	-0.3
2014	27	MLB MLE	2 teams	—	525	467	113	25	0	13	43	118	5	0	3	242	313	379	692	137	289	22	8	311	96	-2	-1	-3	-9	+11	-0.4
2014	27	MLB RAW	2 teams	—	525	453	119	26	0	14	57	107	5	0	3	263	351	413	764	150	307	20	11	338	96	-2	-1	-3	-9	+11	-0.4
2015	28	MLB MLE	Toronto Blue Jays	—	328	296	67	16	1	18	29	86	2	0	0	226	300	470	769	243	254	26	9	336	113	+5	+0	-4	-6	+7	0.3
2015	28	MLB RAW	Toronto Blue Jays	—	328	296	67	16	1	18	29	86	2	0	0	226	300	470	769	243	254	26	9	336	113	+5	+0	-4	-6	+7	0.3
2016	29	MLB MLE	Toronto Blue Jays	—	341	300	65	10	0	14	39	112	2	1	0	217	311	390	701	173	293	33	11	316	100	-0	+0	-8	-6	+7	-0.7
2016	29	MLB RAW	Toronto Blue Jays	—	341	299	65	10	0	14	40	112	2	1	0	217	314	391	705	174	295	33	12	318	100	-0	+0	-8	-6	+7	-0.7
2017	30	MLB MLE	Toronto Blue Jays	—	637	563	151	29	1	38	70	128	2	0	1	268	351	526	877	258	283	20	11	382	144	+34	-0	-5	-11	+14	3.1
2017	30	MLB RAW	Toronto Blue Jays	—	637	560	151	29	1	38	73	128	2	0	1	270	356	529	884	259	285	20	11	385	144	+34	-0	-5	-11	+14	3.1
2018	31	MLB MLE	Toronto Blue Jays	—	594	507	122	34	0	25	81	156	3	0	1	241	349	456	804	215	295	26	14	356	127	+19	-0	-1	-11	+13	2.1
2018	31	MLB RAW	Toronto Blue Jays	—	594	505	122	34	0	25	83	156	3	0	1	242	352	457	809	216	297	26	14	358	127	+19	-0	-1	-11	+13	2.1
2019	32	MLB MLE	Toronto Blue Jays	—	500	417	86	16	0	22	76	106	6	0	0	206	337	403	740	197	221	21	15	334	112	+7	+0	-4	-9	+11	0.5
2019	32	MLB RAW	Toronto Blue Jays	—	500	414	86	16	0	22	79	106	6	0	0	208	343	406	748	198	223	21	16	339	112	+7	+0	-4	-9	+11	0.5
2020	33	MLB MLE	San Francisco Giants	—	132	119	21	7	0	5	10	42	2	0	0	176	252	361	613	185	219	32	8	280	75	-4	+0	-3	-2	+3	-0.6
2020	33	MLB RAW	San Francisco Giants	—	132	119	21	7	0	5	10	42	2	0	0	176	252	361	613	185	219	32	8	273	75	-4	+0	-3	-2	+3	-0.6
2008	21	A MLE	Clinton LumberKings	—	62	59	14	2	0	2	3	14	0	0	0	237	279	373	652	136	276	23	5	304	92	—	—	—	—	—	—
2009	22	AAA MLE	2 teams	—	464	402	104	19	0	8	53	101	6	0	0	259	354	366	720	107	318	22	11	325	106	—	—	—	—	—	—
2010	23	MLB MLE	2 teams	—	622	544	121	25	0	20	72	142	1	1	0	222	314	379	693	156	260	23	12	310	96	—	—	—	—	—	—
2011	24	MLB MLE	2 teams	—	504	442	100	24	0	15	53	106	4	0	0	226	314	382	696	156	265	21	11	315	99	—	—	—	—	—	—
2012	25	MLB MLE	2 teams	—	617	554	120	19	1	19	57	131	1	2	0	217	289	357	647	141	247	21	9	292	84	—	—	—	—	—	—
2013	26	MLB MLE	2 teams	—	543	472	114	21	0	21	63	125	3	0	0	242	332	419	752	178	278	23	12	332	111	—	—	—	—	—	—
2014	27	MLB MLE	2 teams	—	525	472	113	25	0	13	43	118	5	0	3	239	313	375	688	136	279	22	8	311	96	—	—	—	—	—	—
2015	28	MLB MLE	Toronto Blue Jays	—	328	294	67	16	1	18	29	86	2	0	0	228	300	473	772	245	254	26	9	336	113	—	—	—	—	—	—
2016	29	MLB MLE	Toronto Blue Jays	—	341	297	65	10	0	14	39	112	2	1	0	219	311	394	705	175	295	33	11	316	100	—	—	—	—	—	—
2017	30	MLB MLE	Toronto Blue Jays	—	637	560	151	29	1	38	70	128	2	0	1	270	351	529	880	259	285	20	11	382	144	—	—	—	—	—	—
2018	31	MLB MLE	Toronto Blue Jays	—	594	505	122	34	0	25	81	156	3	0	1	242	349	457	806	216	297	26	14	356	127	—	—	—	—	—	—
2019	32	MLB MLE	Toronto Blue Jays	—	500	414	86	16	0	22	76	106	6	0	0	208	337	406	742	198	223	21	15	334	112	—	—	—	—	—	—
2020	33	MLB MLE	San Francisco Giants	—	132	119	21	7	0	5	10	42	2	0	0	176	252	361	613	185	219	32	8	280	75	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	4750	4153	951	196	2	196	552	1122	25	3	3	229	323	419	742	190	264	24	12	331	108	+56	-1	-63	-95	+103	-0.1
/ 162G	—	A+C		—	650	568	130	27	0	27	76	154	3	0	0	229	323	419	742	190	264	24	12	331	108	+8	-0	-9	-13	+14	-0.0
Career	—	MLE		—	5869	5148	1198	247	3	220	649	1367	37	4	5	233	323	410	733	177	272	23	11	327	108	+56	-1	-63	-95	+103	-0.1
/ 162G	—	MLE		—	650	570	132	27	0	24	72	151	4	0	1	232	322	405	727	174	271	23	11	327	108	+8	-0	-9	-13	+14	-0.0

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.

2014 AAATacoma Rainiers · 249 PAwOBA .403 → .344 (−59)K% 16.5% → 20.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.47%	÷1.017	×1.290	—	—	20.89%
BB% (unintentional) /PA	11.65%	÷1.052	×0.710	—	×0.992	7.80%
HBP% /PA	1.20%	—	×0.980	—	×0.992	1.17%
Singles /BIP	29.88%	÷1.016	×0.900	×0.980	×0.992	25.73%
Doubles /BIP	7.93%	÷0.937	×0.810	×0.980	×0.992	6.66%
Triples /BIP	0.00%	÷0.971	×0.810	×0.980	×0.992	0.00%
Home runs /BIP	4.27%	÷0.963	×0.780	—	×0.992	3.43%

Factors used: league row PCL. Park (home-season share, regressed): HR 93, 1B 103, 2B 87, 3B 94, K 103, BB 110 — applied at half strength. Age: age 28 vs a level average of 27.2 → −0.8% on BB/HBP/hit rates (0.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.980.

Result: wOBA .403 → .344; ISO .166 → .144; BABIP .376 → .320. 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 AAATacoma Rainiers · 22 PAwOBA .266 → .222 (−44)K% 22.7% → 28.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.73%	÷1.030	×1.290	—	—	28.48%
BB% (unintentional) /PA	0.00%	÷1.049	×0.710	—	×1.003	0.00%
HBP% /PA	4.55%	—	×0.980	—	×1.003	4.47%
Singles /BIP	18.75%	÷0.983	×0.900	—	×1.003	17.21%
Doubles /BIP	12.50%	÷0.957	×0.810	—	×1.003	10.61%
Triples /BIP	0.00%	÷0.900	×0.810	—	×1.003	0.00%
Home runs /BIP	0.00%	÷0.938	×0.780	—	×1.003	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 88, 1B 97, 2B 91, 3B 80, K 106, BB 110 — 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 .266 → .222; ISO .095 → .075; BABIP .312 → .278. 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 AAATacoma Rainiers · 82 PAwOBA .337 → .275 (−62)K% 19.5% → 24.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	19.51%	÷1.042	×1.290	—	—	24.16%
BB% (unintentional) /PA	18.29%	÷1.061	×0.710	—	×1.018	12.46%
HBP% /PA	0.00%	—	×0.980	—	×1.018	0.00%
Singles /BIP	18.00%	÷0.979	×0.900	×0.950	×1.018	16.01%
Doubles /BIP	12.00%	÷0.936	×0.810	×0.950	×1.018	10.05%
Triples /BIP	2.00%	÷0.900	×0.810	×0.950	×1.018	1.74%
Home runs /BIP	0.00%	÷0.963	×0.780	—	×1.018	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 93, 1B 96, 2B 87, 3B 80, K 108, BB 112 — applied at half strength. Age: age 26 vs a level average of 27.2 → +1.8% on BB/HBP/hit rates (1.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .337 → .275; ISO .121 → .107; BABIP .320 → .278. 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 AAATacoma Rainiers · 15 PAwOBA .186 → .152 (−34)K% 6.7% → 8.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	6.67%	÷0.990	×1.290	—	—	8.68%
BB% (unintentional) /PA	20.00%	÷0.976	×0.710	—	×1.033	15.03%
HBP% /PA	6.67%	—	×0.980	—	×1.033	6.75%
Singles /BIP	0.00%	÷0.960	×0.900	—	×1.033	0.00%
Doubles /BIP	0.00%	÷0.896	×0.810	—	×1.033	0.00%
Triples /BIP	0.00%	÷0.993	×0.810	—	×1.033	0.00%
Home runs /BIP	0.00%	÷1.004	×0.780	—	×1.033	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 101, 1B 92, 2B 79, 3B 99, K 98, BB 95 — applied at half strength. Age: age 25 vs a level average of 27.2 → +3.3% on BB/HBP/hit rates (2.2 yr younger than average).

Result: wOBA .186 → .152; ISO .000 → .000; BABIP .000 → .000. Both wOBAs use the same linear weights, so the change is the translation alone. The MLE line is not regressed to the mean — regression happens later, when seasons are blended into a projection (how).

2010 AAATacoma Rainiers · 225 PAwOBA .392 → .324 (−68)K% 17.8% → 22.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.78%	÷1.012	×1.290	—	—	22.67%
BB% (unintentional) /PA	17.33%	÷0.968	×0.710	—	×1.048	13.32%
HBP% /PA	0.44%	—	×0.980	—	×1.048	0.46%
Singles /BIP	20.28%	÷1.034	×0.900	×1.018	×1.048	18.83%
Doubles /BIP	9.09%	÷1.086	×0.810	×1.018	×1.048	7.24%
Triples /BIP	0.00%	÷0.934	×0.810	×1.018	×1.048	0.00%
Home runs /BIP	6.29%	÷1.031	×0.780	—	×1.048	4.99%

Factors used: league row PCL. Park (home-season share, regressed): HR 106, 1B 107, 2B 117, 3B 87, K 102, BB 94 — 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 ×1.018.

Result: wOBA .392 → .324; ISO .219 → .173; BABIP .309 → .270. 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).

2009 AAAOklahoma City RedHawks · 237 PAwOBA .328 → .285 (−43)K% 19.0% → 24.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.99%	÷1.000	×1.290	—	—	24.49%
BB% (unintentional) /PA	14.77%	÷1.000	×0.710	—	×1.050	11.01%
HBP% /PA	1.27%	—	×0.980	—	×1.050	1.30%
Singles /BIP	21.71%	÷1.000	×0.900	×1.045	×1.050	21.44%
Doubles /BIP	7.24%	÷1.000	×0.810	×1.045	×1.050	6.43%
Triples /BIP	0.00%	÷1.000	×0.810	×1.045	×1.050	0.00%
Home runs /BIP	2.63%	÷1.000	×0.780	—	×1.050	2.16%

Factors used: league row PCL. 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.045.

Result: wOBA .328 → .285; ISO .117 → .098; BABIP .293 → .281. 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).

2009 AAFrisco RoughRiders · 227 PAwOBA .408 → .367 (−41)K% 15.4% → 18.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.42%	÷1.000	×1.220	—	—	18.81%
BB% (unintentional) /PA	16.30%	÷1.000	×0.700	—	×1.027	11.72%
HBP% /PA	1.32%	—	×0.870	—	×1.027	1.18%
Singles /BIP	29.73%	÷1.000	×0.920	×1.031	×1.027	28.96%
Doubles /BIP	6.76%	÷1.000	×0.840	×1.031	×1.027	6.01%
Triples /BIP	0.00%	÷1.000	×0.940	×1.031	×1.027	0.00%
Home runs /BIP	4.05%	÷1.000	×0.800	—	×1.027	3.33%

Factors used: league row TL. 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 24.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.031.

Result: wOBA .408 → .367; ISO .153 → .136; BABIP .375 → .357. 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 AClinton LumberKings · 62 PAwOBA .375 → .304 (−71)K% 16.1% → 21.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.13%	÷1.000	×1.360	—	—	21.94%
BB% (unintentional) /PA	8.06%	÷1.000	×0.690	—	—	5.56%
HBP% /PA	0.00%	—	×0.780	—	—	0.00%
Singles /BIP	23.91%	÷1.000	×0.920	×0.978	—	21.51%
Doubles /BIP	6.52%	÷1.000	×0.830	×0.978	—	5.29%
Triples /BIP	0.00%	÷1.000	×0.910	×0.978	—	0.00%
Home runs /BIP	6.52%	÷1.000	×0.800	—	—	5.22%

Factors used: level-wide A 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 22 vs a level average of 22.0 → +0.0% on BB/HBP/hit rates (0.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.978.

Result: wOBA .375 → .304; ISO .214 → .168; BABIP .318 → .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).