Joey Wendle

Pos2B

B/TL/R

Born1990-04-26

Age (2027)37

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	32	MLBMIA	371	.259/.300/.360	3	12	92	1.1
2023	33	AAAMIA	37	.189/.189/.432	2	1	12	—	.135/.135/.297
2023	33	MLBMIA	318	.212/.250/.306	2	7	59	-0.4
2024	34	MLBNYM	37	.222/.243/.250	0	1	43	-0.2
2025	35	No MLB games played
2026	36	No MLB games played

Last 3 seasons WAR 0.4

Contract

No contract data on file.

Skills · percentileStatcast 2022-2024

Speed52

Avg EV39

xwOBA19

Bat Spd19

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)
2013	23	A+	Carolina Mudcats	—	474	413	122	32	5	16	44	79	10	10	2	295	377	513	890	218	327	17	9	386	101	—	—	—	—	—	—
2013	23	INTL	Surprise Saguaros	—	70	61	19	4	2	1	6	11	1	0	0	311	382	492	874	180	353	16	9	375	140	—	—	—	—	—	—
2014	24	AA	Akron RubberDucks	—	370	336	85	20	5	8	26	56	4	4	2	253	314	414	728	161	279	15	7	320	68	—	—	—	—	—	—
2014	24	ROK	AZL Indians	—	26	22	10	1	1	0	4	4	0	1	1	455	538	591	1.129	136	556	15	15	497	121	—	—	—	—	—	—
2015	25	AAA	Nashville Sounds	—	618	577	167	42	8	10	22	114	9	12	2	289	326	442	768	153	343	18	4	334	84	—	—	—	—	—	—
2016	26	MLB	Oakland Athletics	—	104	96	25	1	0	1	6	16	0	2	0	260	304	302	606	42	296	15	6	273	72	-3	+0	+2	+3	+2	0.4
2016	26	AAA	Nashville Sounds	—	526	491	137	31	9	12	26	112	7	14	4	279	324	452	777	173	340	21	5	341	72	—	—	—	—	—	—
2016	26	INTL	Yaquis de Obregon	—	150	137	42	11	4	3	8	16	1	5	1	307	349	511	860	204	325	11	5	366	134	—	—	—	—	—	—
2017	27	AAA	Nashville Sounds	—	510	478	136	29	8	8	19	82	12	13	4	285	328	429	757	144	329	16	4	336	76	—	—	—	—	—	—
2018	28	MLB	Tampa Bay Rays	—	545	487	146	33	6	7	37	96	9	16	4	300	360	435	796	136	353	18	7	348	117	+11	+2	+5	+2	+12	3.2
2019	29	MLB	Tampa Bay Rays	—	263	238	55	13	2	3	14	47	8	8	3	231	296	340	636	109	272	18	5	286	79	-7	+0	+3	+1	+6	0.4
2019	29	AAA	Durham Bulls	—	21	19	5	2	0	0	2	4	0	1	0	263	333	368	702	105	333	19	10	318	59	—	—	—	—	—	—
2020	30	MLB	Tampa Bay Rays	—	184	168	48	9	2	4	10	35	5	8	2	286	344	435	779	149	338	19	5	346	117	+4	+1	+1	+1	+4	1.0
2021	31	MLB	Tampa Bay Rays	—	501	460	122	31	4	11	28	113	10	8	6	265	321	422	743	157	327	23	6	329	105	+3	-1	+8	+2	+11	2.3
2022	32	MLB	Miami Marlins	—	371	347	90	24	1	3	15	50	5	12	3	259	300	360	660	101	293	13	4	294	92	-3	+1	+3	+1	+8	1.1
2022	32	AAA	Jacksonville Jumbo Shrimp	—	17	15	5	1	0	1	0	4	1	0	0	333	375	600	975	267	364	24	0	401	111	—	—	—	—	—	—
2023	33	MLB	Miami Marlins	—	318	297	63	16	3	2	13	67	2	7	1	212	250	306	556	94	264	21	4	246	59	-16	+1	-0	+4	+7	-0.4
2023	33	AAA	Jacksonville Jumbo Shrimp	—	37	37	7	1	1	2	0	9	0	1	0	189	189	432	622	243	192	24	0	264	12	—	—	—	—	—	—
2024	34	MLB	New York Mets	—	37	36	8	1	0	0	1	9	0	1	0	222	243	250	493	28	296	24	3	225	43	-3	+0	-1	+0	+1	-0.2
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	2323	2129	557	128	18	31	124	433	39	62	19	262	314	382	696	121	311	19	5	309	95	-14	+5	+22	+14	+50	7.8
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2013	23	A+ MLE	Carolina Mudcats	—	474	430	108	27	3	14	27	113	10	10	2	251	310	426	736	174	299	24	6	317	101	—	—	—	—	—	—
2014	24	AA MLE	Akron RubberDucks	—	370	345	75	17	5	7	17	71	4	4	2	217	262	357	619	139	246	19	5	269	68	—	—	—	—	—	—
2014	24	ROK MLE	AZL Indians	—	26	24	8	1	1	0	2	6	0	1	1	333	385	458	843	125	424	23	8	347	121	—	—	—	—	—	—
2015	25	AAA MLE	Nashville Sounds	—	618	584	153	33	7	9	15	142	9	12	2	262	291	389	680	127	321	23	2	292	84	—	—	—	—	—	—
2016	26	MLB MLE	Oakland Athletics	—	104	96	25	1	0	1	6	16	0	2	0	260	304	302	606	42	296	15	6	275	72	-3	+0	+2	+3	+2	0.4
2016	26	AAA MLE	Nashville Sounds	—	526	499	113	26	7	10	18	144	7	14	4	226	263	367	630	140	296	27	3	274	72	—	—	—	—	—	—
2017	27	AAA MLE	Nashville Sounds	—	510	485	119	23	7	7	12	106	12	13	4	245	281	365	646	120	299	21	2	281	76	—	—	—	—	—	—
2018	28	MLB MLE	Tampa Bay Rays	—	545	491	145	34	6	7	33	96	9	16	4	295	351	432	783	136	353	18	6	341	117	+11	+2	+5	+2	+12	3.2
2019	29	MLB MLE	Tampa Bay Rays	—	263	238	54	13	2	3	14	47	8	8	3	227	292	336	628	109	272	18	5	285	79	-7	+0	+3	+1	+6	0.4
2019	29	AAA MLE	Durham Bulls	—	21	20	5	2	0	0	1	5	0	1	0	250	286	350	636	100	293	24	5	256	59	—	—	—	—	—	—
2020	30	MLB MLE	Tampa Bay Rays	—	184	169	47	9	2	4	9	35	5	8	2	278	333	426	759	148	338	19	5	341	117	+4	+1	+1	+1	+4	1.0
2021	31	MLB MLE	Tampa Bay Rays	—	501	464	122	32	4	11	24	113	10	8	6	263	313	420	734	157	327	23	5	323	105	+3	-1	+8	+2	+11	2.3
2022	32	MLB MLE	Miami Marlins	—	371	347	93	26	1	3	15	50	5	12	3	268	308	375	683	107	293	13	4	305	92	-3	+1	+3	+1	+8	1.1
2022	32	AAA MLE	Jacksonville Jumbo Shrimp	—	17	15	5	1	0	1	0	5	1	0	0	333	375	600	975	267	300	29	0	332	111	—	—	—	—	—	—
2023	33	MLB MLE	Miami Marlins	—	318	297	65	17	3	2	13	67	2	7	1	219	256	316	573	98	264	21	4	255	59	-16	+1	-0	+4	+7	-0.4
2023	33	AAA MLE	Jacksonville Jumbo Shrimp	—	37	37	5	1	1	1	0	10	0	1	0	135	135	297	432	162	160	27	0	186	12	—	—	—	—	—	—
2024	34	MLB MLE	New York Mets	—	37	36	8	1	0	0	1	9	0	1	0	222	243	250	493	28	296	24	3	231	43	-3	+0	-1	+0	+1	-0.2
2013	23	A+ MLE	2 teams	—	544	494	125	31	4	16	30	129	11	10	2	253	310	429	739	176	305	24	6	317	101	—	—	—	—	—	—
2013	23	A+ RAW	2 teams	—	544	474	141	36	7	17	50	90	11	10	2	297	378	511	888	213	331	17	9	385	101	—	—	—	—	—	—
2014	24	AA MLE	2 teams	—	396	368	82	17	6	7	20	76	4	5	3	223	270	359	629	136	260	19	5	274	72	—	—	—	—	—	—
2014	24	AA RAW	2 teams	—	396	358	95	21	6	8	30	60	4	5	3	265	329	425	754	159	296	15	8	332	72	—	—	—	—	—	—
2015	25	AAA MLE	Nashville Sounds	—	618	584	153	33	7	9	15	142	9	12	2	262	291	389	680	127	329	23	2	292	84	—	—	—	—	—	—
2015	25	AAA RAW	Nashville Sounds	—	618	577	167	42	8	10	22	114	9	12	2	289	326	442	768	153	343	18	4	334	84	—	—	—	—	—	—
2016	26	AAA MLE	3 teams	—	780	734	171	34	8	14	30	198	8	21	5	233	271	358	629	125	298	25	4	274	72	—	—	—	—	—	—
2016	26	AAA RAW	3 teams	—	780	724	204	43	13	16	40	144	8	21	5	282	326	443	770	162	330	18	5	337	72	—	—	—	—	—	—
2017	27	AAA MLE	Nashville Sounds	—	510	485	119	23	7	7	12	106	12	13	4	245	281	365	646	120	300	21	2	281	76	—	—	—	—	—	—
2017	27	AAA RAW	Nashville Sounds	—	510	478	136	29	8	8	19	82	12	13	4	285	328	429	757	144	329	16	4	336	76	—	—	—	—	—	—
2018	28	MLB MLE	Tampa Bay Rays	—	545	491	145	34	6	7	33	96	9	16	4	295	351	432	783	136	347	18	6	341	117	+11	+2	+5	+2	+12	3.2
2018	28	MLB RAW	Tampa Bay Rays	—	545	487	146	33	6	7	37	96	9	16	4	300	360	435	796	136	353	18	7	348	117	+11	+2	+5	+2	+12	3.2
2019	29	MLB MLE	2 teams	—	284	258	59	15	2	3	15	52	8	9	3	229	292	337	629	109	272	18	5	283	78	-8	+1	+3	+1	+6	0.3
2019	29	MLB RAW	2 teams	—	284	257	60	15	2	3	16	51	8	9	3	233	299	342	641	109	277	18	6	289	78	-8	+1	+3	+1	+6	0.3
2020	30	MLB MLE	Tampa Bay Rays	—	184	169	47	9	2	4	9	35	5	8	2	278	333	426	759	148	328	19	5	341	117	+4	+1	+1	+1	+4	1.0
2020	30	MLB RAW	Tampa Bay Rays	—	184	168	48	9	2	4	10	35	5	8	2	286	344	435	779	149	338	19	5	346	117	+4	+1	+1	+1	+4	1.0
2021	31	MLB MLE	Tampa Bay Rays	—	501	464	122	32	4	11	24	113	10	8	6	263	313	420	734	157	324	23	5	323	105	+3	-1	+8	+2	+11	2.3
2021	31	MLB RAW	Tampa Bay Rays	—	501	460	122	31	4	11	28	113	10	8	6	265	321	422	743	157	327	23	6	329	105	+3	-1	+8	+2	+11	2.3
2022	32	MLB MLE	2 teams	—	388	362	97	27	1	4	15	55	6	12	3	268	308	381	689	113	303	14	4	306	93	-3	+1	+3	+1	+8	1.2
2022	32	MLB RAW	2 teams	—	388	362	95	25	1	4	15	54	6	12	3	262	303	370	673	108	295	14	4	298	93	-3	+1	+3	+1	+8	1.2
2023	33	MLB MLE	2 teams	—	355	334	70	18	4	3	13	77	2	8	1	210	244	314	558	105	261	22	4	248	54	-19	+1	-0	+4	+8	-0.7
2023	33	MLB RAW	2 teams	—	355	334	70	17	4	4	13	76	2	8	1	210	244	320	564	111	257	21	4	248	54	-19	+1	-0	+4	+8	-0.7
2024	34	MLB MLE	New York Mets	—	37	36	8	1	0	0	1	9	0	1	0	222	243	250	493	28	296	24	3	231	43	-3	+0	-1	+0	+1	-0.2
2024	34	MLB RAW	New York Mets	—	37	36	8	1	0	0	1	9	0	1	0	222	243	250	493	28	296	24	3	225	43	-3	+0	-1	+0	+1	-0.2
2013	23	A+ MLE	2 teams	—	544	498	125	31	4	16	30	129	11	10	2	251	310	426	736	175	299	24	6	317	101	—	—	—	—	—	—
2014	24	AA MLE	2 teams	—	396	369	82	17	6	7	20	76	4	5	3	222	270	358	628	136	258	19	5	274	72	—	—	—	—	—	—
2015	25	AAA MLE	Nashville Sounds	—	618	589	153	33	7	9	15	142	9	12	2	260	291	385	677	126	321	23	2	292	84	—	—	—	—	—	—
2016	26	AAA MLE	3 teams	—	780	736	171	34	8	14	30	198	8	21	5	232	271	357	628	125	296	25	4	274	72	—	—	—	—	—	—
2017	27	AAA MLE	Nashville Sounds	—	510	481	119	23	7	7	12	106	12	13	4	247	281	368	649	121	299	21	2	281	76	—	—	—	—	—	—
2018	28	MLB MLE	Tampa Bay Rays	—	545	498	145	34	6	7	33	96	9	16	4	291	351	426	777	135	353	18	6	341	117	—	—	—	—	—	—
2019	29	MLB MLE	2 teams	—	284	259	59	15	2	3	15	52	8	9	3	228	292	336	628	108	274	18	5	283	78	—	—	—	—	—	—
2020	30	MLB MLE	Tampa Bay Rays	—	184	168	47	9	2	4	9	35	5	8	2	280	333	429	762	149	338	19	5	341	117	—	—	—	—	—	—
2021	31	MLB MLE	Tampa Bay Rays	—	501	462	122	32	4	11	24	113	10	8	6	264	313	422	735	158	327	23	5	323	105	—	—	—	—	—	—
2022	32	MLB MLE	2 teams	—	388	364	97	27	1	4	15	55	6	12	3	266	308	379	687	113	293	14	4	306	93	—	—	—	—	—	—
2023	33	MLB MLE	2 teams	—	355	337	70	18	4	3	13	77	2	8	1	208	244	312	555	104	253	22	4	248	54	—	—	—	—	—	—
2024	34	MLB MLE	New York Mets	—	37	36	8	1	0	0	1	9	0	1	0	222	243	250	493	28	296	24	3	231	43	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	34	MLB MLE	New York Mets	—	37	36	8	1	0	0	1	9	0	1	0	222	243	250	493	28	296	24	3	231	43	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	2323	2129	557	128	18	31	124	433	39	62	19	262	314	382	696	121	311	19	5	309	87	-14	+5	+22	+14	+50	7.8
/ 162G	—	A+C		—	650	596	156	36	5	9	35	121	11	17	5	262	315	384	699	122	311	19	5	309	87	-4	+1	+6	+4	+14	2.2
Career	—	MLE		—	5142	4779	1198	274	51	85	217	1088	84	123	35	251	295	383	678	132	305	21	4	297	87	-14	+5	+22	+14	+50	7.8
/ 162G	—	MLE		—	650	604	152	35	6	11	27	138	11	16	4	252	296	384	680	132	306	21	4	297	87	-4	+1	+6	+4	+14	2.2

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 AAAJacksonville Jumbo Shrimp · 37 PAwOBA .256 → .186 (−70)K% 24.3% → 28.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.32%	÷1.040	×1.200	—	—	28.06%
BB% (unintentional) /PA	0.00%	÷1.008	×0.690	—	×0.942	0.00%
HBP% /PA	0.00%	—	×0.940	—	×0.942	0.00%
Singles /BIP	10.71%	÷1.016	×0.930	—	×0.942	9.24%
Doubles /BIP	3.57%	÷0.955	×0.830	—	×0.942	2.92%
Triples /BIP	3.57%	÷1.039	×0.970	—	×0.942	3.14%
Home runs /BIP	7.14%	÷1.031	×0.700	—	×0.942	4.57%

Factors used: league row International League. Park (home-season share, regressed): HR 106, 1B 103, 2B 91, 3B 108, K 108, BB 102 — 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 .256 → .186; ISO .243 → .165; BABIP .192 → .160. 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 AAAJacksonville Jumbo Shrimp · 17 PAwOBA .413 → .332 (−81)K% 23.5% → 29.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.53%	÷0.966	×1.200	—	—	29.24%
BB% (unintentional) /PA	0.00%	÷0.978	×0.690	—	×0.952	0.00%
HBP% /PA	5.88%	—	×0.940	—	×0.952	5.26%
Singles /BIP	27.27%	÷1.054	×0.930	—	×0.952	22.91%
Doubles /BIP	9.09%	÷0.898	×0.830	—	×0.952	8.00%
Triples /BIP	0.00%	÷0.930	×0.970	—	×0.952	0.00%
Home runs /BIP	9.09%	÷1.032	×0.700	—	×0.952	5.87%

Factors used: league row International League. Park (home-season share, regressed): HR 106, 1B 111, 2B 80, 3B 86, K 93, BB 96 — 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 .413 → .332; ISO .267 → .190; BABIP .364 → .300. 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 AAADurham Bulls · 21 PAwOBA .310 → .256 (−54)K% 19.0% → 22.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	19.05%	÷1.020	×1.200	—	—	22.40%
BB% (unintentional) /PA	9.52%	÷0.998	×0.690	—	×0.982	6.47%
HBP% /PA	0.00%	—	×0.940	—	×0.982	0.00%
Singles /BIP	20.00%	÷0.986	×0.930	—	×0.982	18.52%
Doubles /BIP	13.33%	÷1.006	×0.830	—	×0.982	10.80%
Triples /BIP	0.00%	÷1.025	×0.970	—	×0.982	0.00%
Home runs /BIP	0.00%	÷1.128	×0.840	—	×0.982	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 126, 1B 97, 2B 101, 3B 105, K 104, BB 100 — 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).

Result: wOBA .310 → .256; ISO .105 → .085; BABIP .333 → .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).

2017 AAANashville Sounds · 510 PAwOBA .326 → .281 (−45)K% 16.1% → 20.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.08%	÷1.003	×1.290	—	—	20.68%
BB% (unintentional) /PA	3.53%	÷1.029	×0.710	—	×1.003	2.44%
HBP% /PA	2.35%	—	×0.980	—	×1.003	2.31%
Singles /BIP	22.98%	÷0.984	×0.900	×1.025	×1.003	21.62%
Doubles /BIP	7.32%	÷1.021	×0.810	×1.025	×1.003	5.98%
Triples /BIP	2.02%	÷0.900	×0.810	×1.025	×1.003	1.87%
Home runs /BIP	2.02%	÷0.868	×0.780	—	×1.003	1.82%

Factors used: league row PCL. Park (home-season share, regressed): HR 74, 1B 97, 2B 104, 3B 80, K 101, BB 106 — 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 ×1.025.

Result: wOBA .326 → .281; ISO .144 → .121; BABIP .329 → .299. 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 AAANashville Sounds · 526 PAwOBA .331 → .274 (−57)K% 21.3% → 27.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.29%	÷1.004	×1.290	—	—	27.35%
BB% (unintentional) /PA	4.56%	÷0.979	×0.710	—	×1.018	3.37%
HBP% /PA	1.33%	—	×0.980	—	×1.018	1.33%
Singles /BIP	22.43%	÷1.035	×0.900	×0.985	×1.018	19.56%
Doubles /BIP	8.18%	÷0.898	×0.810	×0.985	×1.018	7.39%
Triples /BIP	2.37%	÷1.015	×0.810	×0.985	×1.018	1.90%
Home runs /BIP	3.17%	÷0.856	×0.780	—	×1.018	2.94%

Factors used: league row PCL. Park (home-season share, regressed): HR 71, 1B 107, 2B 80, 3B 103, K 101, 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 ×0.985.

Result: wOBA .331 → .274; ISO .173 → .146; BABIP .340 → .296. 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 AAANashville Sounds · 618 PAwOBA .327 → .292 (−35)K% 18.4% → 23.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.45%	÷1.034	×1.290	—	—	23.00%
BB% (unintentional) /PA	3.40%	÷1.018	×0.710	—	×1.033	2.45%
HBP% /PA	1.46%	—	×0.980	—	×1.033	1.47%
Singles /BIP	23.11%	÷0.953	×0.900	×1.018	×1.033	22.94%
Doubles /BIP	9.07%	÷1.063	×0.810	×1.018	×1.033	7.27%
Triples /BIP	1.73%	÷0.952	×0.810	×1.018	×1.033	1.55%
Home runs /BIP	2.16%	÷0.843	×0.780	—	×1.033	2.06%

Factors used: league row PCL. Park (home-season share, regressed): HR 69, 1B 91, 2B 113, 3B 90, K 107, BB 104 — 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.018.

Result: wOBA .327 → .292; ISO .153 → .130; BABIP .343 → .321. 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 AAAkron RubberDucks · 370 PAwOBA .314 → .269 (−45)K% 15.1% → 19.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.14%	÷0.965	×1.220	—	—	19.13%
BB% (unintentional) /PA	6.76%	÷1.008	×0.700	—	×1.012	4.75%
HBP% /PA	1.08%	—	×0.850	—	×1.012	0.93%
Singles /BIP	18.57%	÷1.064	×0.970	×0.963	×1.012	16.49%
Doubles /BIP	7.14%	÷0.992	×0.860	×0.963	×1.012	6.04%
Triples /BIP	1.79%	÷0.900	×0.950	×0.963	×1.012	1.84%
Home runs /BIP	2.86%	÷0.881	×0.780	—	×1.012	2.56%

Factors used: league row EL. Park (home-season share, regressed): HR 76, 1B 113, 2B 98, 3B 80, K 93, BB 102 — 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 ×0.963.

Result: wOBA .314 → .269; ISO .161 → .144; BABIP .279 → .246. 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 ROKAZL Indians · 26 PAwOBA .485 → .347 (−138)K% 15.4% → 22.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.38%	÷1.000	×1.440	—	—	22.15%
BB% (unintentional) /PA	15.38%	÷1.000	×0.620	—	×0.955	9.11%
HBP% /PA	0.00%	—	×0.720	—	×0.955	0.00%
Singles /BIP	44.44%	÷1.000	×0.820	—	×0.955	34.80%
Doubles /BIP	5.56%	÷1.000	×0.720	—	×0.955	3.82%
Triples /BIP	5.56%	÷1.000	×0.720	—	×0.955	3.82%
Home runs /BIP	0.00%	÷1.000	×0.780	—	×0.955	0.00%

Factors used: level-wide ROK 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 24 vs a level average of 19.5 → −4.5% on BB/HBP/hit rates (4.5 yr older than average).

Result: wOBA .485 → .347; ISO .136 → .093; BABIP .556 → .424. 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 — .