Josh Thole

PosC

B/TL/R

Born1986-10-28

Age (2027)40

No MLB projection yet. Career stats below are historical.

Overview

AAALAST SEASON 2019

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2016	29	MLBTOR	136	.169/.258/.220	1	0	36	0.0
2018	31	AADET	76	.238/.333/.302	1	1	51	—	.227/.292/.288
2019	32	AAA/AA2 orgs	267	.243/.355/.327	3	1	45	—	.184/.264/.238
2020	33	No MLB games played
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 0.0

Contract

No contract data on file.

Skills · percentileStatcast 2015-2016

Barrel%10

xwOBA10

Avg EV10

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. Catcher framing: only 2027 Statcast framing is modeled (Baseball Savant server-caches the leaderboard to the current season); prior-year framing is unmodeled — projections for established framers may run 1-3 WAR low.

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	18	ROK	GCL Mets	—	130	104	28	2	1	1	20	11	4	1	1	269	406	337	743	67	293	8	15	345	64	—	—	—	—	—	—
2006	19	ROK	Kingsport Mets	—	110	98	23	4	0	1	7	25	3	1	1	235	306	306	612	71	297	23	6	278	23	—	—	—	—	—	—
2007	20	A	Savannah Sand Gnats	—	458	389	104	17	0	0	61	57	4	4	4	267	372	311	683	44	313	12	13	319	72	—	—	—	—	—	—
2008	21	A+	St. Lucie Mets	—	402	347	104	25	2	5	45	38	4	2	1	300	386	427	813	127	320	9	11	361	108	—	—	—	—	—	—
2009	22	MLB	New York Mets	—	59	53	17	2	1	0	4	5	0	1	0	321	368	396	765	75	340	8	7	333	111	+1	+0	-1	+9	+1	1.1
2009	22	AA	Binghamton Mets	—	442	384	126	29	2	1	42	34	6	8	4	328	403	422	825	94	349	8	10	365	128	—	—	—	—	—	—
2010	23	MLB	New York Mets	—	227	202	56	7	1	3	24	25	1	1	0	277	357	366	723	89	305	11	11	331	113	+3	+0	+1	+7	+5	1.8
2010	23	AAA	Buffalo Bisons	—	191	165	44	19	1	2	22	25	1	0	0	267	356	430	787	164	300	13	12	346	86	—	—	—	—	—	—
2011	24	MLB	New York Mets	—	386	340	91	17	0	3	38	47	4	0	2	268	348	344	692	76	300	12	10	315	98	-1	-1	-1	+7	+8	1.3
2012	25	MLB	New York Mets	—	354	321	75	15	0	1	27	50	1	0	0	234	295	290	585	56	273	14	8	266	63	-15	+0	+17	+6	+8	1.6
2013	26	MLB	Toronto Blue Jays	—	135	120	21	3	1	1	12	25	1	0	0	175	256	242	497	67	213	19	9	231	42	-9	+0	+2	+5	+3	0.1
2013	26	AAA	Buffalo Bisons	—	167	149	48	5	1	7	14	25	2	0	1	322	388	510	898	188	345	15	8	392	127	—	—	—	—	—	—
2014	27	MLB	Toronto Blue Jays	—	150	133	33	4	0	0	14	25	0	0	3	248	320	278	598	30	306	17	9	274	71	-5	-1	-1	+3	+3	-0.1
2015	28	MLB	Toronto Blue Jays	—	52	49	10	2	0	0	3	9	0	0	0	204	250	245	495	41	250	17	6	229	41	-4	+0	+1	+4	+1	0.2
2015	28	AAA	Buffalo Bisons	—	170	149	34	5	0	0	20	20	0	0	0	228	320	262	581	34	264	12	12	274	40	—	—	—	—	—	—
2016	29	MLB	Toronto Blue Jays	—	136	118	20	3	0	1	13	28	1	0	0	169	258	220	478	51	209	21	10	222	36	-10	+0	+5	+2	+3	0.0
2018	31	AA	Erie SeaWolves	—	76	63	15	1	0	1	8	14	1	1	0	238	333	302	635	63	275	18	11	282	51	—	—	—	—	—	—
2019	32	AAA	Salt Lake Bees	—	193	161	36	7	0	3	30	44	1	1	0	224	349	323	672	99	287	23	16	313	40	—	—	—	—	—	—
2019	32	AA	Tulsa Drillers	—	74	65	19	3	0	0	7	16	1	0	0	292	370	338	708	46	380	22	9	324	57	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1499	1336	323	53	3	9	135	214	8	2	5	242	315	306	621	64	280	14	9	284	78	-39	-2	+23	+44	+32	6.0
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2005	18	ROK MLE	GCL Mets	—	130	112	25	2	1	1	12	16	4	1	1	223	320	286	606	62	240	12	9	263	64	—	—	—	—	—	—
2006	19	ROK MLE	Kingsport Mets	—	110	101	18	3	0	1	4	36	3	1	1	178	231	238	469	59	242	33	4	202	23	—	—	—	—	—	—
2007	20	A MLE	Savannah Sand Gnats	—	458	410	102	15	0	0	40	76	4	4	4	249	322	285	607	37	302	17	9	274	72	—	—	—	—	—	—
2008	21	A+ MLE	St. Lucie Mets	—	402	359	101	25	2	4	33	54	4	2	1	281	348	396	744	114	310	13	8	328	108	—	—	—	—	—	—
2009	22	MLB MLE	New York Mets	—	59	53	17	2	1	0	4	5	0	1	0	321	368	396	765	75	340	8	7	333	111	+1	+0	-1	+9	+1	1.1
2009	22	AA MLE	Binghamton Mets	—	442	398	134	28	2	1	28	42	6	8	4	337	389	425	814	88	356	10	6	358	128	—	—	—	—	—	—
2010	23	MLB MLE	New York Mets	—	227	203	57	7	1	3	23	25	1	1	0	281	357	369	726	89	305	11	10	335	113	+3	+0	+1	+7	+5	1.8
2010	23	AAA MLE	Buffalo Bisons	—	191	170	41	15	1	2	17	31	1	0	0	241	314	376	690	135	271	16	9	296	86	—	—	—	—	—	—
2011	24	MLB MLE	New York Mets	—	386	346	94	18	0	3	32	47	4	0	2	272	340	350	690	78	300	12	8	312	98	-1	-1	-1	+7	+8	1.3
2012	25	MLB MLE	New York Mets	—	354	327	78	16	0	1	21	50	1	0	0	239	287	297	583	58	273	14	6	262	63	-15	+0	+17	+6	+8	1.6
2013	26	MLB MLE	Toronto Blue Jays	—	135	120	21	3	1	1	12	25	1	0	0	175	256	242	497	67	213	19	9	230	42	-9	+0	+2	+5	+3	0.1
2013	26	AAA MLE	Buffalo Bisons	—	167	154	46	4	1	7	9	30	2	0	1	299	345	474	819	175	325	18	5	356	127	—	—	—	—	—	—
2014	27	MLB MLE	Toronto Blue Jays	—	150	133	33	4	0	0	14	25	0	0	3	248	320	278	598	30	306	17	9	274	71	-5	-1	-1	+3	+3	-0.1
2015	28	MLB MLE	Toronto Blue Jays	—	52	49	10	2	0	0	3	9	0	0	0	204	250	245	495	41	250	17	6	228	41	-4	+0	+1	+4	+1	0.2
2015	28	AAA MLE	Buffalo Bisons	—	170	156	31	4	0	0	13	24	0	0	0	199	260	224	485	26	239	14	8	227	40	—	—	—	—	—	—
2016	29	MLB MLE	Toronto Blue Jays	—	136	118	20	3	0	1	13	28	1	0	0	169	258	220	478	51	209	21	10	222	36	-10	+0	+5	+2	+3	0.0
2018	31	AA MLE	Erie SeaWolves	—	76	66	15	1	0	1	5	17	1	1	0	227	292	288	580	61	245	22	7	244	51	—	—	—	—	—	—
2019	32	AAA MLE	Salt Lake Bees	—	193	171	28	5	0	2	20	56	1	1	0	164	255	228	483	64	227	29	10	227	40	—	—	—	—	—	—
2019	32	AA MLE	Tulsa Drillers	—	74	68	16	2	0	0	4	20	1	0	0	235	288	265	552	29	323	27	5	252	57	—	—	—	—	—	—
2005	18	ROK MLE	GCL Mets	—	130	112	25	2	1	1	12	16	4	1	1	223	320	286	606	62	253	12	9	263	64	—	—	—	—	—	—
2005	18	ROK RAW	GCL Mets	—	130	104	28	2	1	1	20	11	4	1	1	269	406	337	743	67	293	8	15	345	64	—	—	—	—	—	—
2006	19	ROK MLE	Kingsport Mets	—	110	101	18	3	0	1	4	36	3	1	1	178	231	238	469	59	258	33	4	202	23	—	—	—	—	—	—
2006	19	ROK RAW	Kingsport Mets	—	110	98	23	4	0	1	7	25	3	1	1	235	306	306	612	71	297	23	6	278	23	—	—	—	—	—	—
2007	20	A MLE	Savannah Sand Gnats	—	458	410	102	15	0	0	40	76	4	4	4	249	322	285	607	37	305	17	9	274	72	—	—	—	—	—	—
2007	20	A RAW	Savannah Sand Gnats	—	458	389	104	17	0	0	61	57	4	4	4	267	372	311	683	44	313	12	13	319	72	—	—	—	—	—	—
2008	21	A+ MLE	St. Lucie Mets	—	402	359	101	25	2	4	33	54	4	2	1	281	348	396	744	114	317	13	8	328	108	—	—	—	—	—	—
2008	21	A+ RAW	St. Lucie Mets	—	402	347	104	25	2	5	45	38	4	2	1	300	386	427	813	127	320	9	11	361	108	—	—	—	—	—	—
2009	22	AA MLE	2 teams	—	501	451	151	30	3	1	32	47	6	9	4	335	387	421	808	86	362	9	6	355	126	—	—	—	—	—	—
2009	22	AA RAW	2 teams	—	501	437	143	31	3	1	46	39	6	9	4	327	399	419	818	92	348	8	9	361	126	—	—	—	—	—	—
2010	23	MLB MLE	2 teams	—	418	373	98	22	2	5	40	56	2	1	0	263	337	373	710	110	296	13	10	317	101	+0	+0	+1	+7	+9	1.9
2010	23	MLB RAW	2 teams	—	418	367	100	26	2	5	46	50	2	1	0	272	357	395	752	123	303	12	11	338	101	+0	+0	+1	+7	+9	1.9
2011	24	MLB MLE	New York Mets	—	386	346	94	18	0	3	32	47	4	0	2	272	340	350	690	78	304	12	8	312	98	-1	-1	-1	+7	+8	1.3
2011	24	MLB RAW	New York Mets	—	386	340	91	17	0	3	38	47	4	0	2	268	348	344	692	76	300	12	10	315	98	-1	-1	-1	+7	+8	1.3
2012	25	MLB MLE	New York Mets	—	354	327	78	16	0	1	21	50	1	0	0	239	287	297	583	58	278	14	6	262	63	-15	+0	+17	+6	+8	1.6
2012	25	MLB RAW	New York Mets	—	354	321	75	15	0	1	27	50	1	0	0	234	295	290	585	56	273	14	8	266	63	-15	+0	+17	+6	+8	1.6
2013	26	AAA MLE	2 teams	—	302	274	67	7	2	8	21	55	3	0	1	245	305	372	678	128	277	18	7	300	89	—	—	—	—	—	—
2013	26	AAA RAW	2 teams	—	302	269	69	8	2	8	26	50	3	0	1	257	329	390	719	134	286	17	9	320	89	—	—	—	—	—	—
2014	27	MLB MLE	Toronto Blue Jays	—	150	133	33	4	0	0	14	25	0	0	3	248	320	278	598	30	306	17	9	274	71	-5	-1	-1	+3	+3	-0.1
2014	27	MLB RAW	Toronto Blue Jays	—	150	133	33	4	0	0	14	25	0	0	3	248	320	278	598	30	306	17	9	274	71	-5	-1	-1	+3	+3	-0.1
2015	28	AAA MLE	2 teams	—	222	205	41	6	0	0	16	33	0	0	0	200	258	229	487	29	238	15	7	227	40	—	—	—	—	—	—
2015	28	AAA RAW	2 teams	—	222	198	44	7	0	0	23	29	0	0	0	222	303	258	561	35	260	13	10	264	40	—	—	—	—	—	—
2016	29	MLB MLE	Toronto Blue Jays	—	136	118	20	3	0	1	13	28	1	0	0	169	258	220	478	51	209	21	10	222	36	-10	+0	+5	+2	+3	0.0
2016	29	MLB RAW	Toronto Blue Jays	—	136	118	20	3	0	1	13	28	1	0	0	169	258	220	478	51	209	21	10	222	36	-10	+0	+5	+2	+3	0.0
2018	31	AA MLE	Erie SeaWolves	—	76	66	15	1	0	1	5	17	1	1	0	227	292	288	580	61	275	22	7	244	51	—	—	—	—	—	—
2018	31	AA RAW	Erie SeaWolves	—	76	63	15	1	0	1	8	14	1	1	0	238	333	302	635	63	275	18	11	282	51	—	—	—	—	—	—
2019	32	AAA MLE	2 teams	—	267	238	44	7	0	2	25	77	2	1	0	185	268	239	507	55	261	29	9	234	44	—	—	—	—	—	—
2019	32	AAA RAW	2 teams	—	267	226	55	10	0	3	37	60	2	1	0	243	355	327	682	84	315	22	14	316	44	—	—	—	—	—	—
2005	18	ROK MLE	GCL Mets	—	130	113	25	2	1	1	12	16	4	1	1	221	320	283	603	62	240	12	9	263	64	—	—	—	—	—	—
2006	19	ROK MLE	Kingsport Mets	—	110	102	18	3	0	1	4	36	3	1	1	176	231	235	467	59	242	33	4	202	23	—	—	—	—	—	—
2007	20	A MLE	Savannah Sand Gnats	—	458	411	102	15	0	0	40	76	4	4	4	248	322	285	606	36	302	17	9	274	72	—	—	—	—	—	—
2008	21	A+ MLE	St. Lucie Mets	—	402	362	101	25	2	4	33	54	4	2	1	279	348	392	741	113	310	13	8	328	108	—	—	—	—	—	—
2009	22	AA MLE	2 teams	—	501	458	151	30	3	1	32	47	6	9	4	330	387	415	801	85	354	9	6	355	126	—	—	—	—	—	—
2010	23	MLB MLE	2 teams	—	418	373	98	22	2	5	40	56	2	1	0	263	337	373	710	110	289	13	10	317	101	—	—	—	—	—	—
2011	24	MLB MLE	New York Mets	—	386	347	94	18	0	3	32	47	4	0	2	271	340	349	689	78	300	12	8	312	98	—	—	—	—	—	—
2012	25	MLB MLE	New York Mets	—	354	329	78	16	0	1	21	50	1	0	0	237	287	295	581	58	273	14	6	262	63	—	—	—	—	—	—
2013	26	AAA MLE	2 teams	—	302	275	67	7	2	8	21	55	3	0	1	244	305	371	676	127	275	18	7	300	89	—	—	—	—	—	—
2014	27	MLB MLE	Toronto Blue Jays	—	150	135	33	4	0	0	14	25	0	0	3	244	320	274	594	30	306	17	9	274	71	—	—	—	—	—	—
2015	28	AAA MLE	2 teams	—	222	204	41	6	0	0	16	33	0	0	0	201	258	230	488	29	242	15	7	227	40	—	—	—	—	—	—
2016	29	MLB MLE	Toronto Blue Jays	—	136	121	20	3	0	1	13	28	1	0	0	165	258	215	472	50	209	21	10	222	36	—	—	—	—	—	—
2018	31	AA MLE	Erie SeaWolves	—	76	70	15	1	0	1	5	17	1	1	0	214	292	271	563	57	245	22	7	244	51	—	—	—	—	—	—
2019	32	AAA MLE	2 teams	—	267	238	44	7	0	2	25	77	2	1	0	185	268	239	507	55	254	29	9	234	44	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1499	1336	323	53	3	9	135	214	8	2	5	242	315	306	621	64	280	14	9	284	82	-39	-2	+23	+44	+32	6.0
/ 162G	—	A+C		—	650	579	140	23	1	4	59	93	3	1	2	242	315	306	620	64	280	14	9	284	82	-17	-1	+10	+19	+14	2.6
Career	—	MLE		—	3912	3513	887	159	10	28	308	617	35	20	17	252	319	327	646	75	296	16	8	289	82	-39	-2	+23	+44	+32	6.0
/ 162G	—	MLE		—	650	584	148	26	2	5	51	103	6	3	3	253	320	330	650	77	297	16	8	289	82	-17	-1	+10	+19	+14	2.6

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.

2019 AAASalt Lake Bees · 193 PAwOBA .307 → .227 (−81)K% 22.8% → 29.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.80%	÷1.009	×1.290	—	—	29.15%
BB% (unintentional) /PA	15.54%	÷0.980	×0.710	—	×0.942	10.61%
HBP% /PA	0.52%	—	×0.980	—	×0.942	0.48%
Singles /BIP	22.22%	÷1.007	×0.900	×0.978	×0.942	18.31%
Doubles /BIP	5.98%	÷1.062	×0.810	×0.978	×0.942	4.21%
Triples /BIP	0.00%	÷0.900	×0.810	×0.978	×0.942	0.00%
Home runs /BIP	2.56%	÷1.059	×0.780	—	×0.942	1.78%

Factors used: league row PCL. Park (home-season share, regressed): HR 112, 1B 101, 2B 112, 3B 80, K 102, 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.978.

Result: wOBA .307 → .227; ISO .099 → .068; BABIP .287 → .227. 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).

2019 AATulsa Drillers · 74 PAwOBA .320 → .252 (−68)K% 21.6% → 27.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.62%	÷0.954	×1.220	—	—	27.66%
BB% (unintentional) /PA	9.46%	÷1.063	×0.700	—	×0.940	5.85%
HBP% /PA	1.35%	—	×0.870	—	×0.940	1.11%
Singles /BIP	32.65%	÷0.997	×0.920	—	×0.940	28.31%
Doubles /BIP	6.12%	÷1.048	×0.840	—	×0.940	4.61%
Triples /BIP	0.00%	÷0.925	×0.940	—	×0.940	0.00%
Home runs /BIP	0.00%	÷0.909	×0.800	—	×0.940	0.00%

Factors used: league row TL. Park (home-season share, regressed): HR 82, 1B 99, 2B 110, 3B 85, K 91, BB 113 — applied at half strength. Age: age 33 vs a level average of 24.8 → −6.0% on BB/HBP/hit rates (8.2 yr older than average).

Result: wOBA .320 → .252; ISO .046 → .034; BABIP .380 → .323. 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 AAErie SeaWolves · 76 PAwOBA .287 → .244 (−43)K% 18.4% → 22.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.42%	÷0.999	×1.220	—	—	22.51%
BB% (unintentional) /PA	10.53%	÷1.007	×0.700	—	×0.940	6.88%
HBP% /PA	1.32%	—	×0.850	—	×0.940	1.05%
Singles /BIP	26.53%	÷1.016	×0.970	×1.002	×0.940	23.84%
Doubles /BIP	2.04%	÷0.974	×0.860	×1.002	×0.940	1.70%
Triples /BIP	0.00%	÷0.901	×0.950	×1.002	×0.940	0.00%
Home runs /BIP	2.04%	÷1.112	×0.780	—	×0.940	1.35%

Factors used: league row EL. Park (home-season share, regressed): HR 122, 1B 103, 2B 95, 3B 80, K 100, BB 101 — applied at half strength. Age: age 32 vs a level average of 24.8 → −6.0% on BB/HBP/hit rates (7.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.002.

Result: wOBA .287 → .244; ISO .063 → .048; BABIP .275 → .245. 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 AAABuffalo Bisons · 170 PAwOBA .268 → .227 (−41)K% 11.8% → 14.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.76%	÷1.008	×1.200	—	—	14.01%
BB% (unintentional) /PA	11.76%	÷1.040	×0.690	—	×0.982	7.66%
HBP% /PA	0.00%	—	×0.940	—	×0.982	0.00%
Singles /BIP	22.48%	÷0.981	×0.930	×0.985	×0.982	20.62%
Doubles /BIP	3.88%	÷0.944	×0.830	×0.985	×0.982	3.30%
Triples /BIP	0.00%	÷0.900	×0.970	×0.985	×0.982	0.00%
Home runs /BIP	0.00%	÷0.966	×0.840	—	×0.982	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 93, 1B 96, 2B 89, 3B 80, K 102, BB 108 — applied at half strength. Age: age 29 vs a level average of 27.2 → −1.8% on BB/HBP/hit rates (1.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.985.

Result: wOBA .268 → .227; ISO .034 → .029; BABIP .264 → .239. 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 AAABuffalo Bisons · 167 PAwOBA .384 → .356 (−29)K% 15.0% → 18.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.97%	÷0.987	×1.200	—	—	18.20%
BB% (unintentional) /PA	7.78%	÷1.015	×0.690	—	×1.003	5.31%
HBP% /PA	1.20%	—	×0.940	—	×1.003	1.13%
Singles /BIP	28.23%	÷0.976	×0.930	×1.010	×1.003	27.26%
Doubles /BIP	4.03%	÷1.094	×0.830	×1.010	×1.003	3.10%
Triples /BIP	0.81%	÷0.900	×0.970	×1.010	×1.003	0.88%
Home runs /BIP	5.65%	÷0.853	×0.840	—	×1.003	5.58%

Factors used: league row IL. Park (home-season share, regressed): HR 71, 1B 95, 2B 119, 3B 80, K 97, BB 103 — 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.010.

Result: wOBA .384 → .356; ISO .188 → .182; BABIP .345 → .325. 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 AAABuffalo Bisons · 191 PAwOBA .341 → .296 (−45)K% 13.1% → 16.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.09%	÷0.984	×1.200	—	—	15.96%
BB% (unintentional) /PA	11.52%	÷0.958	×0.690	—	×1.048	8.69%
HBP% /PA	0.52%	—	×0.940	—	×1.048	0.52%
Singles /BIP	15.71%	÷0.969	×0.930	×1.010	×1.048	15.97%
Doubles /BIP	13.57%	÷1.133	×0.830	×1.010	×1.048	10.53%
Triples /BIP	0.71%	÷1.150	×0.970	×1.010	×1.048	0.64%
Home runs /BIP	1.43%	÷1.026	×0.840	—	×1.048	1.23%

Factors used: league row IL. Park (home-season share, regressed): HR 105, 1B 94, 2B 127, 3B 130, K 97, BB 92 — 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 ×1.010.

Result: wOBA .341 → .296; ISO .164 → .134; BABIP .300 → .271. 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 AABinghamton Mets · 442 PAwOBA .361 → .358 (−3)K% 7.7% → 9.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	7.69%	÷1.000	×1.220	—	—	9.38%
BB% (unintentional) /PA	8.82%	÷1.000	×0.700	—	×1.027	6.34%
HBP% /PA	1.36%	—	×0.850	—	×1.027	1.19%
Singles /BIP	26.86%	÷1.000	×0.970	×1.050	×1.027	28.09%
Doubles /BIP	8.29%	÷1.000	×0.860	×1.050	×1.027	7.68%
Triples /BIP	0.57%	÷1.000	×0.950	×1.050	×1.027	0.59%
Home runs /BIP	0.29%	÷1.000	×0.780	—	×1.027	0.23%

Factors used: league row EL. 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 ×1.050.

Result: wOBA .361 → .358; ISO .094 → .091; BABIP .349 → .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).

2008 A+St. Lucie Mets · 402 PAwOBA .355 → .328 (−27)K% 9.5% → 13.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.45%	÷1.000	×1.420	—	—	13.42%
BB% (unintentional) /PA	10.70%	÷1.000	×0.750	—	×1.012	8.12%
HBP% /PA	1.00%	—	×0.930	—	×1.012	0.94%
Singles /BIP	23.30%	÷1.000	×0.920	×1.031	×1.012	22.37%
Doubles /BIP	8.09%	÷1.000	×0.960	×1.031	×1.012	8.10%
Triples /BIP	0.65%	÷1.000	×0.810	×1.031	×1.012	0.55%
Home runs /BIP	1.62%	÷1.000	×0.880	—	×1.012	1.44%

Factors used: league row FSL. 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 22.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.031.

Result: wOBA .355 → .328; ISO .127 → .121; BABIP .320 → .310. 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).