Mitch Haniger

PosRF

B/TR/R

Born1990-12-23

Age (2027)36

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2024

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2022	31	AAASEA	33	.238/.515/.524	2	0	116	—	.160/.364/.280
2022	31	MLBSEA	247	.246/.310/.429	11	0	107	0.4
2023	32	AAASF	35	.167/.286/.367	2	0	36	—	.129/.229/.226
2023	32	MLBSF	229	.209/.268/.365	6	1	80	-0.7
2024	33	MLBSEA	423	.208/.287/.334	12	0	79	-1.2
2025	34	No MLB games played
2026	35	No MLB games played

Last 3 seasons WAR -1.5

Contract

No contract data on file.

Skills · percentileStatcast 2022-2024

Avg EV79

Bat Spd65

Barrel%64

xwOBA52

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)
2012	21	A	Wisconsin Timber Rattlers	—	58	49	14	4	0	1	7	13	1	1	0	286	386	429	815	143	361	22	12	362	82	—	—	—	—	—	—
2013	22	A+	Brevard County Manatees	—	365	328	82	24	3	6	32	68	4	2	2	250	324	396	721	146	298	19	9	323	57	—	—	—	—	—	—
2013	22	A	Wisconsin Timber Rattlers	—	178	145	43	12	2	5	25	24	3	7	0	297	410	510	921	214	314	13	14	396	102	—	—	—	—	—	—
2013	22	INTL	Surprise Saguaros	—	113	100	28	8	0	4	11	18	1	1	0	280	357	480	837	200	304	16	10	367	135	—	—	—	—	—	—
2014	23	AA	Mobile BayBears	—	301	267	70	10	1	10	22	45	6	4	0	262	332	419	752	157	278	15	7	330	76	—	—	—	—	—	—
2014	23	ROK	ACL D-backs	—	16	15	3	1	0	1	1	6	0	0	0	200	250	467	717	267	250	38	6	310	0	—	—	—	—	—	—
2015	24	AA	Mobile BayBears	—	174	153	43	10	1	1	16	32	2	4	4	281	357	379	736	98	341	18	9	329	70	—	—	—	—	—	—
2015	24	A+	Visalia Rawhide	—	226	202	67	16	3	12	17	39	2	8	2	332	389	619	1.008	287	353	17	8	426	117	—	—	—	—	—	—
2016	25	MLB	Arizona Diamondbacks	—	123	109	25	2	1	5	12	27	1	0	0	229	311	404	715	174	256	22	10	318	90	-2	+0	+1	+2	+3	0.4
2016	25	AAA	Reno Aces	—	312	261	89	20	3	20	39	62	5	8	1	341	436	670	1.107	330	373	20	12	464	127	—	—	—	—	—	—
2016	25	AA	Mobile BayBears	—	236	197	58	14	2	5	30	37	8	4	3	294	409	462	870	168	340	16	13	389	92	—	—	—	—	—	—
2017	26	MLB	Seattle Mariners	—	410	369	104	25	2	16	31	93	9	5	4	282	352	491	843	209	338	23	8	371	140	+20	-1	-2	-5	+9	2.1
2017	26	AAA	Tacoma Rainiers	—	48	39	10	2	0	3	7	5	1	0	0	256	383	538	921	282	219	10	15	395	113	—	—	—	—	—	—
2018	27	MLB	Seattle Mariners	—	683	596	170	38	4	26	70	148	10	8	2	285	370	493	863	208	336	22	10	378	142	+34	+1	-5	-7	+15	3.8
2019	28	MLB	Seattle Mariners	—	283	246	54	13	1	15	30	81	5	4	0	220	317	463	780	244	257	29	11	342	119	+6	+1	+1	-3	+6	1.1
2021	30	MLB	Seattle Mariners	—	691	620	157	23	2	39	54	169	9	1	0	253	322	485	808	232	281	24	8	351	125	+21	+0	-11	-7	+15	1.8
2022	31	MLB	Seattle Mariners	—	247	224	55	8	0	11	20	65	1	0	0	246	310	429	739	183	293	26	8	326	107	+2	+0	-0	-3	+5	0.4
2022	31	AAA	Tacoma Rainiers	—	33	21	5	0	0	2	10	3	2	0	0	238	515	524	1.039	286	188	9	30	464	116	—	—	—	—	—	—
2023	32	MLB	San Francisco Giants	—	229	211	44	13	1	6	15	65	2	1	0	209	268	365	632	156	270	28	7	281	80	-5	+0	-4	-3	+5	-0.7
2023	32	AAA	Sacramento River Cats	—	35	30	5	0	0	2	2	4	3	0	0	167	286	367	652	200	125	11	6	299	36	—	—	—	—	—	—
2024	33	MLB	Seattle Mariners	—	423	380	79	12	0	12	40	126	2	0	0	208	287	334	621	126	276	30	9	283	79	-10	+0	-6	-5	+9	-1.2
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	3089	2755	688	134	11	130	272	774	39	19	6	250	326	448	774	198	298	25	9	341	117	+62	+1	-25	-31	+67	7.5
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2012	21	A MLE	Wisconsin Timber Rattlers	—	58	51	12	3	0	1	5	18	1	1	0	235	316	353	669	118	318	31	9	289	82	—	—	—	—	—	—
2013	22	A+ MLE	Brevard County Manatees	—	365	336	67	20	2	5	24	100	4	2	2	199	261	315	576	116	263	27	7	252	57	—	—	—	—	—	—
2013	22	A MLE	Wisconsin Timber Rattlers	—	178	155	38	10	2	4	15	33	3	7	0	245	324	413	737	168	263	19	8	319	102	—	—	—	—	—	—
2014	23	AA MLE	Mobile BayBears	—	301	275	63	9	1	9	14	55	6	4	0	229	281	367	649	138	244	18	5	281	76	—	—	—	—	—	—
2014	23	ROK MLE	ACL D-backs	—	16	15	3	1	0	1	1	9	0	0	0	200	250	467	717	267	178	56	6	168	0	—	—	—	—	—	—
2015	24	AA MLE	Mobile BayBears	—	174	159	38	9	1	1	10	40	2	4	4	239	292	327	619	88	297	23	6	271	70	—	—	—	—	—	—
2015	24	A+ MLE	Visalia Rawhide	—	226	208	54	12	2	11	11	52	2	8	2	260	303	495	798	236	281	23	5	341	117	—	—	—	—	—	—
2016	25	MLB MLE	Arizona Diamondbacks	—	123	111	25	2	1	5	10	27	1	0	0	225	295	396	691	171	256	22	8	301	90	-2	+0	+1	+2	+3	0.4
2016	25	AAA MLE	Reno Aces	—	312	271	70	15	2	14	29	81	5	8	1	258	341	483	824	225	298	26	9	356	127	—	—	—	—	—	—
2016	25	AA MLE	Mobile BayBears	—	236	206	48	11	2	3	21	46	8	4	3	233	328	350	677	117	283	19	9	304	92	—	—	—	—	—	—
2017	26	MLB MLE	Seattle Mariners	—	410	369	104	25	2	16	31	93	9	5	4	282	352	491	843	209	338	23	8	375	140	+20	-1	-2	-5	+9	2.1
2017	26	AAA MLE	Tacoma Rainiers	—	48	41	10	2	0	3	5	6	1	0	0	244	340	512	853	268	193	12	10	335	113	—	—	—	—	—	—
2018	27	MLB MLE	Seattle Mariners	—	683	600	173	39	4	27	66	148	10	8	2	288	368	502	870	213	336	22	10	378	142	+34	+1	-5	-7	+15	3.8
2019	28	MLB MLE	Seattle Mariners	—	283	247	54	13	1	15	29	81	5	4	0	219	313	462	775	243	257	29	10	344	119	+6	+1	+1	-3	+6	1.1
2021	30	MLB MLE	Seattle Mariners	—	691	622	158	23	2	40	52	169	9	1	0	254	321	490	811	236	281	24	8	353	125	+21	+0	-11	-7	+15	1.8
2022	31	MLB MLE	Seattle Mariners	—	247	225	55	8	0	11	19	65	1	0	0	244	306	427	733	182	293	26	8	327	107	+2	+0	-0	-3	+5	0.4
2022	31	AAA MLE	Tacoma Rainiers	—	33	25	4	0	0	1	6	4	2	0	0	160	364	280	644	120	156	12	18	340	116	—	—	—	—	—	—
2023	32	MLB MLE	San Francisco Giants	—	229	211	44	13	1	6	15	65	2	1	0	209	268	365	632	156	270	28	7	287	80	-5	+0	-4	-3	+5	-0.7
2023	32	AAA MLE	Sacramento River Cats	—	35	31	4	0	0	1	1	4	3	0	0	129	229	226	454	97	107	11	3	222	36	—	—	—	—	—	—
2024	33	MLB MLE	Seattle Mariners	—	423	380	79	12	0	12	40	126	2	0	0	208	287	334	621	126	276	30	9	285	79	-10	+0	-6	-5	+9	-1.2
2012	21	A MLE	Wisconsin Timber Rattlers	—	58	51	12	3	0	1	5	18	1	1	0	235	316	353	669	118	333	31	9	289	82	—	—	—	—	—	—
2012	21	A RAW	Wisconsin Timber Rattlers	—	58	49	14	4	0	1	7	13	1	1	0	286	386	429	815	143	361	22	12	362	82	—	—	—	—	—	—
2013	22	A+ MLE	3 teams	—	656	594	126	36	4	11	47	161	8	10	2	212	279	342	621	130	268	25	7	274	72	—	—	—	—	—	—
2013	22	A+ RAW	3 teams	—	656	573	153	44	5	15	68	110	8	10	2	267	353	440	793	173	303	17	10	350	72	—	—	—	—	—	—
2014	23	AA MLE	2 teams	—	317	290	66	10	1	10	15	64	6	4	0	228	280	372	652	145	255	20	5	275	72	—	—	—	—	—	—
2014	23	AA RAW	2 teams	—	317	282	73	11	1	11	23	51	6	4	0	259	328	422	750	163	277	16	7	329	72	—	—	—	—	—	—
2015	24	A+ MLE	2 teams	—	400	367	92	21	3	12	21	92	4	12	6	251	298	422	721	172	295	23	5	311	96	—	—	—	—	—	—
2015	24	A+ RAW	2 teams	—	400	355	110	26	4	13	33	71	4	12	6	310	375	515	890	206	348	18	8	384	96	—	—	—	—	—	—
2016	25	AAA MLE	3 teams	—	671	589	144	28	5	23	59	154	14	12	4	244	328	426	754	182	288	23	9	328	108	—	—	—	—	—	—
2016	25	AAA RAW	3 teams	—	671	567	172	36	6	30	81	126	14	12	4	303	403	547	950	243	339	19	12	411	108	—	—	—	—	—	—
2017	26	MLB MLE	2 teams	—	458	410	114	27	2	19	36	99	10	5	4	278	351	493	844	215	324	22	8	371	137	+20	-1	-2	-5	+10	2.3
2017	26	MLB RAW	2 teams	—	458	408	114	27	2	19	38	98	10	5	4	279	355	495	850	216	325	21	8	373	137	+20	-1	-2	-5	+10	2.3
2018	27	MLB MLE	Seattle Mariners	—	683	600	173	39	4	27	66	148	10	8	2	288	368	502	870	213	338	22	10	378	142	+34	+1	-5	-7	+15	3.8
2018	27	MLB RAW	Seattle Mariners	—	683	596	170	38	4	26	70	148	10	8	2	285	370	493	863	208	336	22	10	378	142	+34	+1	-5	-7	+15	3.8
2019	28	MLB MLE	Seattle Mariners	—	283	247	54	13	1	15	29	81	5	4	0	219	313	462	775	243	255	29	10	344	119	+6	+1	+1	-3	+6	1.1
2019	28	MLB RAW	Seattle Mariners	—	283	246	54	13	1	15	30	81	5	4	0	220	317	463	780	244	257	29	11	342	119	+6	+1	+1	-3	+6	1.1
2021	30	MLB MLE	Seattle Mariners	—	691	622	158	23	2	40	52	169	9	1	0	254	321	490	811	236	280	24	8	353	125	+21	+0	-11	-7	+15	1.8
2021	30	MLB RAW	Seattle Mariners	—	691	620	157	23	2	39	54	169	9	1	0	253	322	485	808	232	281	24	8	351	125	+21	+0	-11	-7	+15	1.8
2022	31	MLB MLE	2 teams	—	280	250	60	8	0	13	25	69	3	0	0	240	317	428	745	188	276	25	9	328	108	+3	+0	-0	-3	+6	0.6
2022	31	MLB RAW	2 teams	—	280	245	60	8	0	13	30	68	3	0	0	245	335	437	771	192	283	24	11	342	108	+3	+0	-0	-3	+6	0.6
2023	32	MLB MLE	2 teams	—	264	242	49	13	1	8	16	69	5	1	0	202	266	364	630	161	247	26	6	278	75	-8	+0	-4	-3	+6	-0.9
2023	32	MLB RAW	2 teams	—	264	241	49	13	1	8	17	69	5	1	0	203	270	365	635	162	248	26	6	284	75	-8	+0	-4	-3	+6	-0.9
2024	33	MLB MLE	Seattle Mariners	—	423	380	79	12	0	12	40	126	2	0	0	208	287	334	621	126	276	30	9	285	79	-10	+0	-6	-5	+9	-1.2
2024	33	MLB RAW	Seattle Mariners	—	423	380	79	12	0	12	40	126	2	0	0	208	287	334	621	126	276	30	9	283	79	-10	+0	-6	-5	+9	-1.2
2012	21	A MLE	Wisconsin Timber Rattlers	—	58	52	12	3	0	1	5	18	1	1	0	231	316	346	662	115	318	31	9	289	82	—	—	—	—	—	—
2013	22	A+ MLE	3 teams	—	656	596	126	36	4	11	47	161	8	10	2	211	279	341	619	129	263	25	7	274	72	—	—	—	—	—	—
2014	23	AA MLE	2 teams	—	317	293	66	10	1	10	15	64	6	4	0	225	280	369	648	143	241	20	5	275	72	—	—	—	—	—	—
2015	24	A+ MLE	2 teams	—	400	372	92	21	3	12	21	92	4	12	6	247	298	417	715	169	288	23	5	311	96	—	—	—	—	—	—
2016	25	AAA MLE	3 teams	—	671	593	144	28	5	23	59	154	14	12	4	243	328	423	751	180	285	23	9	328	108	—	—	—	—	—	—
2017	26	MLB MLE	2 teams	—	458	409	114	27	2	19	36	99	10	5	4	279	351	494	845	215	323	22	8	371	137	—	—	—	—	—	—
2018	27	MLB MLE	Seattle Mariners	—	683	602	173	39	4	27	66	148	10	8	2	287	368	500	868	213	336	22	10	378	142	—	—	—	—	—	—
2019	28	MLB MLE	Seattle Mariners	—	283	247	54	13	1	15	29	81	5	4	0	219	313	462	775	243	257	29	10	344	119	—	—	—	—	—	—
2021	30	MLB MLE	Seattle Mariners	—	691	625	158	23	2	40	52	169	9	1	0	253	321	488	809	235	281	24	8	353	125	—	—	—	—	—	—
2022	31	MLB MLE	2 teams	—	280	250	60	8	0	13	25	69	3	0	0	240	317	428	745	188	277	25	9	328	108	—	—	—	—	—	—
2023	32	MLB MLE	2 teams	—	264	241	49	13	1	8	16	69	5	1	0	203	266	365	631	162	248	26	6	278	75	—	—	—	—	—	—
2024	33	MLB MLE	Seattle Mariners	—	423	378	79	12	0	12	40	126	2	0	0	209	287	336	623	127	276	30	9	285	79	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	33	MLB MLE	Seattle Mariners	—	423	378	79	12	0	12	40	126	2	0	0	209	287	336	623	127	276	30	9	285	79	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	3089	2755	688	134	11	130	272	774	39	19	6	250	326	448	774	198	298	25	9	341	106	+62	+1	-25	-31	+67	7.5
/ 162G	—	A+C		—	650	580	144	28	2	27	57	163	8	4	1	248	324	443	767	195	296	25	9	341	106	+13	+0	-5	-6	+14	1.6
Career	—	MLE		—	5184	4642	1127	233	23	191	411	1250	77	58	18	243	315	426	741	184	288	24	8	325	106	+62	+1	-25	-31	+67	7.5
/ 162G	—	MLE		—	650	582	141	29	3	24	52	157	10	7	2	242	316	426	742	184	287	24	8	325	106	+13	+0	-5	-6	+14	1.6

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.

2023 AAASacramento River Cats · 35 PAwOBA .291 → .222 (−69)K% 11.4% → 12.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.43%	÷1.087	×1.200	—	—	12.62%
BB% (unintentional) /PA	5.71%	÷0.981	×0.690	—	×0.942	3.79%
HBP% /PA	8.57%	—	×0.940	—	×0.942	7.59%
Singles /BIP	11.54%	÷0.992	×0.930	—	×0.942	10.19%
Doubles /BIP	0.00%	÷0.992	×0.830	—	×0.942	0.00%
Triples /BIP	0.00%	÷0.956	×0.970	—	×0.942	0.00%
Home runs /BIP	7.69%	÷1.057	×0.700	—	×0.942	4.80%

Factors used: league row International League. Park (home-season share, regressed): HR 111, 1B 98, 2B 98, 3B 91, K 117, BB 96 — 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 .291 → .222; ISO .200 → .128; BABIP .125 → .107. 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).

2022 AAATacoma Rainiers · 33 PAwOBA .447 → .340 (−107)K% 9.1% → 10.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.09%	÷1.020	×1.200	—	—	10.70%
BB% (unintentional) /PA	27.27%	÷1.048	×0.690	—	×0.952	17.09%
HBP% /PA	6.06%	—	×0.940	—	×0.952	5.42%
Singles /BIP	16.67%	÷1.013	×0.930	—	×0.952	14.56%
Doubles /BIP	0.00%	÷0.839	×0.830	—	×0.952	0.00%
Triples /BIP	0.00%	÷0.902	×0.970	—	×0.952	0.00%
Home runs /BIP	11.11%	÷1.144	×0.700	—	×0.952	6.47%

Factors used: league row International League. Park (home-season share, regressed): HR 129, 1B 103, 2B 68, 3B 80, K 104, BB 110 — applied at half strength. Age: age 32 vs a level average of 27.2 → −4.8% on BB/HBP/hit rates (4.8 yr older than average).

Result: wOBA .447 → .340; ISO .286 → .204; BABIP .188 → .156. 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).

2017 AAATacoma Rainiers · 48 PAwOBA .393 → .335 (−57)K% 10.4% → 13.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.42%	÷1.028	×1.290	—	—	13.07%
BB% (unintentional) /PA	14.58%	÷0.979	×0.710	—	×1.003	10.61%
HBP% /PA	2.08%	—	×0.980	—	×1.003	2.05%
Singles /BIP	14.71%	÷0.978	×0.900	—	×1.003	13.58%
Doubles /BIP	5.88%	÷0.988	×0.810	—	×1.003	4.84%
Triples /BIP	0.00%	÷0.900	×0.810	—	×1.003	0.00%
Home runs /BIP	8.82%	÷0.963	×0.780	—	×1.003	7.17%

Factors used: league row PCL. Park (home-season share, regressed): HR 93, 1B 96, 2B 98, 3B 80, K 106, BB 96 — 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 .393 → .335; ISO .282 → .241; BABIP .219 → .193. 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).

2016 AAAReno Aces · 312 PAwOBA .460 → .356 (−104)K% 19.9% → 25.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	19.87%	÷0.991	×1.290	—	—	25.87%
BB% (unintentional) /PA	12.50%	÷0.979	×0.710	—	×1.018	9.23%
HBP% /PA	1.60%	—	×0.980	—	×1.018	1.60%
Singles /BIP	23.12%	÷1.052	×0.900	×0.982	×1.018	19.78%
Doubles /BIP	10.05%	÷1.060	×0.810	×0.982	×1.018	7.68%
Triples /BIP	1.51%	÷1.150	×0.810	×0.982	×1.018	1.06%
Home runs /BIP	10.05%	÷1.092	×0.780	—	×1.018	7.31%

Factors used: league row PCL. Park (home-season share, regressed): HR 118, 1B 110, 2B 112, 3B 130, K 98, BB 96 — 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 ×0.982.

Result: wOBA .460 → .356; ISO .330 → .240; BABIP .373 → .298. 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).

2016 AAMobile BayBears · 236 PAwOBA .381 → .304 (−78)K% 15.7% → 19.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.68%	÷0.977	×1.220	—	—	19.58%
BB% (unintentional) /PA	12.71%	÷0.975	×0.680	—	×0.988	8.76%
HBP% /PA	3.39%	—	×0.840	—	×0.988	2.81%
Singles /BIP	23.12%	÷1.065	×0.920	×1.004	×0.988	19.81%
Doubles /BIP	8.75%	÷1.146	×0.910	×1.004	×0.988	6.89%
Triples /BIP	1.25%	÷0.900	×0.880	×1.004	×0.988	1.21%
Home runs /BIP	3.12%	÷1.160	×0.800	—	×0.988	2.13%

Factors used: league row SL. Park (home-season share, regressed): HR 132, 1B 113, 2B 129, 3B 80, K 95, BB 95 — 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.004.

Result: wOBA .381 → .304; ISO .168 → .130; BABIP .340 → .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).

2015 A+Visalia Rawhide · 226 PAwOBA .423 → .341 (−83)K% 17.3% → 23.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.26%	÷1.002	×1.340	—	—	23.07%
BB% (unintentional) /PA	7.52%	÷1.057	×0.710	—	×0.978	4.94%
HBP% /PA	0.88%	—	×0.790	—	×0.978	0.68%
Singles /BIP	22.09%	÷1.058	×0.910	×0.970	×0.978	18.04%
Doubles /BIP	9.82%	÷1.007	×0.820	×0.970	×0.978	7.59%
Triples /BIP	1.84%	÷0.913	×0.760	×0.970	×0.978	1.45%
Home runs /BIP	7.36%	÷0.973	×0.920	—	×0.978	6.81%

Factors used: league row CAL. Park (home-season share, regressed): HR 95, 1B 112, 2B 101, 3B 83, K 100, BB 111 — applied at half strength. Age: age 25 vs a level average of 22.8 → −2.2% on BB/HBP/hit rates (2.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.970.

Result: wOBA .423 → .341; ISO .287 → .247; BABIP .353 → .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).

2015 AAMobile BayBears · 174 PAwOBA .324 → .271 (−53)K% 18.4% → 23.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.39%	÷0.975	×1.220	—	—	23.00%
BB% (unintentional) /PA	8.62%	÷1.043	×0.680	—	×0.998	5.61%
HBP% /PA	1.15%	—	×0.840	—	×0.998	0.96%
Singles /BIP	25.62%	÷1.011	×0.920	×0.950	×0.998	22.10%
Doubles /BIP	8.26%	÷0.961	×0.910	×0.950	×0.998	7.42%
Triples /BIP	0.83%	÷0.921	×0.880	×0.950	×0.998	0.75%
Home runs /BIP	0.83%	÷1.098	×0.800	—	×0.998	0.60%

Factors used: league row SL. Park (home-season share, regressed): HR 120, 1B 102, 2B 92, 3B 84, K 95, BB 109 — applied at half strength. Age: age 25 vs a level average of 24.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .324 → .271; ISO .098 → .086; BABIP .341 → .297. 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 AAMobile BayBears · 301 PAwOBA .324 → .281 (−43)K% 15.0% → 18.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.95%	÷0.993	×1.220	—	—	18.36%
BB% (unintentional) /PA	6.98%	÷1.030	×0.680	—	×1.012	4.66%
HBP% /PA	1.99%	—	×0.840	—	×1.012	1.69%
Singles /BIP	22.07%	÷1.018	×0.920	×0.960	×1.012	19.38%
Doubles /BIP	4.50%	÷0.965	×0.910	×0.960	×1.012	4.13%
Triples /BIP	0.45%	÷1.038	×0.880	×0.960	×1.012	0.37%
Home runs /BIP	4.50%	÷0.902	×0.800	—	×1.012	4.04%

Factors used: league row SL. Park (home-season share, regressed): HR 80, 1B 104, 2B 93, 3B 108, K 99, BB 106 — applied at half strength. Age: age 24 vs a level average of 24.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.960.

Result: wOBA .324 → .281; ISO .157 → .144; BABIP .278 → .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).