Christian Villanueva

Pos3B

B/TR/R

Born1991-06-19

Age (2027)36

No MLB projection yet. Career stats below are historical.

Overview

INTLLAST SEASON 2024

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2022	31	INTLCharros de Ja…	279	.288/.378/.421	6	6	127	—
2023	32	INTLCharros de Ja…	190	.283/.330/.428	5	4	109	—
2024	33	INTLYaquis de Obr…	133	.181/.275/.328	5	1	70	—

Contract

No contract data on file.

Skills · percentileStatcast 2017-2018

Barrel%60

xwOBA40

Avg EV17

Speed13

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)
2009	18	ROK	DSL Rangers 2	—	33	24	5	1	0	0	6	5	1	1	0	208	387	250	637	42	250	15	18	298	36	—	—	—	—	—	—
2010	19	ROK	AZL Rangers	—	210	188	59	14	1	2	13	42	4	6	2	314	371	431	802	117	388	20	6	351	52	—	—	—	—	—	—
2011	20	A	Hickory Crawdads	—	529	467	130	30	3	17	37	86	12	32	6	278	347	465	812	186	300	16	7	351	75	—	—	—	—	—	—
2012	21	A+	Daytona Cubs	—	520	459	128	24	1	14	34	107	21	14	11	279	356	427	783	148	332	21	7	349	77	—	—	—	—	—	—
2012	21	INTL	Yaquis de Obregon	—	60	51	9	3	0	2	6	25	2	0	1	176	288	353	641	176	280	42	10	288	81	—	—	—	—	—	—
2013	22	AA	Tennessee Smokies	—	542	490	128	41	2	19	34	117	9	5	7	261	321	469	790	208	303	22	6	342	84	—	—	—	—	—	—
2013	22	INTL	Yaquis de Obregon	—	110	97	22	6	0	3	12	26	0	1	1	227	312	381	693	155	279	24	11	310	96	—	—	—	—	—	—
2014	23	AAA	Iowa Cubs	—	248	223	47	18	0	6	21	64	2	2	1	211	285	372	657	161	266	26	8	292	32	—	—	—	—	—	—
2014	23	AA	Tennessee Smokies	—	259	234	58	20	0	4	19	42	3	0	1	248	312	385	697	137	284	16	7	310	59	—	—	—	—	—	—
2014	23	INTL	Yaquis de Obregon	—	196	170	41	4	0	10	19	27	2	1	1	241	325	441	766	200	228	14	10	332	111	—	—	—	—	—	—
2015	24	AAA	Iowa Cubs	—	508	455	118	23	2	18	35	80	5	2	3	259	319	437	757	178	273	16	7	327	76	—	—	—	—	—	—
2015	24	AA	Tennessee Smokies	—	28	24	5	0	0	2	4	5	0	0	0	208	321	458	780	250	176	18	14	345	65	—	—	—	—	—	—
2015	24	INTL	Yaquis de Obregon	—	280	227	73	9	0	9	41	31	7	3	2	322	440	480	920	159	335	11	15	406	161	—	—	—	—	—	—
2016	25	INTL	Yaquis de Obregon	—	64	54	15	0	0	3	6	13	2	0	0	278	371	444	815	167	300	20	9	356	127	—	—	—	—	—	—
2017	26	MLB	San Diego Padres	—	32	32	11	1	0	4	0	10	0	0	0	344	344	750	1.094	406	389	31	0	470	203	+4	+0	+1	+2	+1	0.7
2017	26	AAA	El Paso Chihuahuas	—	454	398	118	28	2	20	43	83	6	4	2	296	374	528	901	231	326	18	9	389	89	—	—	—	—	—	—
2018	27	MLB	San Diego Padres	—	384	351	83	15	0	20	23	104	9	3	0	236	300	450	750	214	276	27	6	331	111	+5	+1	+2	+2	+8	1.8
2018	27	INTL	Yaquis de Obregon	—	82	66	15	4	0	3	15	13	1	1	0	227	378	424	802	197	240	16	18	364	132	—	—	—	—	—	—
2020	29	INTL	Charros de Jalisco	—	135	111	29	6	0	6	23	17	0	1	0	261	388	477	866	216	258	13	17	382	145	—	—	—	—	—	—
2021	30	INTL	Charros de Jalisco	—	114	94	32	6	2	4	17	18	1	0	1	340	446	574	1.021	234	378	16	15	438	183	—	—	—	—	—	—
2022	31	INTL	Charros de Jalisco	—	279	240	69	14	0	6	34	44	1	6	1	287	378	421	799	133	325	16	12	356	127	—	—	—	—	—	—
2023	32	INTL	Charros de Jalisco	—	190	173	49	10	0	5	10	19	2	4	2	283	330	428	757	145	286	10	5	329	109	—	—	—	—	—	—
2024	33	INTL	Yaquis de Obregon	—	133	116	21	2	0	5	12	30	3	1	1	181	275	328	602	147	195	23	9	272	70	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	416	383	94	16	0	24	23	114	9	3	0	245	304	475	779	230	285	27	6	341	117	+9	+1	+2	+3	+9	2.4
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2009	18	ROK MLE	DSL Rangers 2	—	33	26	4	1	0	0	4	6	1	1	0	154	290	192	483	38	168	18	12	221	36	—	—	—	—	—	—
2010	19	ROK MLE	AZL Rangers	—	210	193	42	9	1	1	8	60	4	6	2	218	263	290	554	73	293	29	4	245	52	—	—	—	—	—	—
2011	20	A MLE	Hickory Crawdads	—	529	480	109	26	3	11	24	118	12	32	6	227	281	362	644	135	258	22	5	279	75	—	—	—	—	—	—
2012	21	A+ MLE	Daytona Cubs	—	520	469	108	19	1	10	24	145	21	14	11	230	298	339	637	109	301	28	5	282	77	—	—	—	—	—	—
2013	22	AA MLE	Tennessee Smokies	—	542	502	116	37	2	17	22	147	9	5	7	231	276	414	690	183	280	27	4	293	84	—	—	—	—	—	—
2014	23	AAA MLE	Iowa Cubs	—	248	233	38	13	0	5	11	83	2	2	1	163	207	283	491	120	222	33	4	216	32	—	—	—	—	—	—
2014	23	AA MLE	Tennessee Smokies	—	259	241	51	16	0	3	12	52	3	0	1	212	258	315	573	104	253	20	5	256	59	—	—	—	—	—	—
2015	24	AAA MLE	Iowa Cubs	—	508	465	105	19	2	16	25	98	5	2	3	226	273	378	651	153	238	19	5	281	76	—	—	—	—	—	—
2015	24	AA MLE	Tennessee Smokies	—	28	25	5	0	0	2	3	6	0	0	0	200	286	440	726	240	156	21	11	265	65	—	—	—	—	—	—
2017	26	MLB MLE	San Diego Padres	—	32	32	11	1	0	4	0	10	0	0	0	344	344	750	1.094	406	389	31	0	468	203	+4	+0	+1	+2	+1	0.7
2017	26	AAA MLE	El Paso Chihuahuas	—	454	407	93	22	1	14	34	107	6	4	2	229	298	391	688	162	263	24	7	300	89	—	—	—	—	—	—
2018	27	MLB MLE	San Diego Padres	—	384	351	84	16	0	20	23	104	9	3	0	239	303	456	759	217	276	27	6	332	111	+5	+1	+2	+2	+8	1.8
2009	18	ROK MLE	DSL Rangers 2	—	33	26	4	1	0	0	4	6	1	1	0	154	290	192	483	38	190	18	12	221	36	—	—	—	—	—	—
2009	18	ROK RAW	DSL Rangers 2	—	33	24	5	1	0	0	6	5	1	1	0	208	387	250	637	42	250	15	18	298	36	—	—	—	—	—	—
2010	19	ROK MLE	AZL Rangers	—	210	193	42	9	1	1	8	60	4	6	2	218	263	290	554	73	304	29	4	245	52	—	—	—	—	—	—
2010	19	ROK RAW	AZL Rangers	—	210	188	59	14	1	2	13	42	4	6	2	314	371	431	802	117	388	20	6	351	52	—	—	—	—	—	—
2011	20	A MLE	Hickory Crawdads	—	529	480	109	26	3	11	24	118	12	32	6	227	281	362	644	135	269	22	5	279	75	—	—	—	—	—	—
2011	20	A RAW	Hickory Crawdads	—	529	467	130	30	3	17	37	86	12	32	6	278	347	465	812	186	300	16	7	351	75	—	—	—	—	—	—
2012	21	A+ MLE	2 teams	—	580	523	120	21	1	11	27	161	23	14	12	229	297	337	633	107	305	28	5	282	77	—	—	—	—	—	—
2012	21	A+ RAW	2 teams	—	580	510	137	27	1	16	40	132	23	14	12	269	349	420	769	151	329	23	7	342	77	—	—	—	—	—	—
2013	22	AA MLE	2 teams	—	652	606	139	45	2	20	27	177	9	6	8	229	273	409	682	180	287	27	4	293	84	—	—	—	—	—	—
2013	22	AA RAW	2 teams	—	652	587	150	47	2	22	46	143	9	6	8	256	319	455	774	199	299	22	7	337	84	—	—	—	—	—	—
2014	23	AA MLE	3 teams	—	703	653	125	40	0	12	33	187	7	3	3	191	238	308	546	116	246	27	5	236	46	—	—	—	—	—	—
2014	23	AA RAW	3 teams	—	703	627	146	42	0	20	59	133	7	3	3	233	306	396	701	163	263	19	8	310	46	—	—	—	—	—	—
2015	24	AAA MLE	3 teams	—	816	743	166	29	3	27	43	159	12	5	5	223	277	380	656	156	244	19	5	280	76	—	—	—	—	—	—
2015	24	AAA RAW	3 teams	—	816	706	196	32	2	29	80	116	12	5	5	278	361	452	813	174	291	14	10	355	76	—	—	—	—	—	—
2016	25	INTL MLE	Yaquis de Obregon	—	64	54	15	0	0	3	6	13	2	0	0	278	371	444	815	167	300	20	9	—	—	—	—	—	—	—	—
2016	25	INTL RAW	Yaquis de Obregon	—	64	54	15	0	0	3	6	13	2	0	0	278	371	444	815	167	300	20	9	356	127	—	—	—	—	—	—
2017	26	AAA MLE	2 teams	—	486	439	104	23	1	18	34	117	6	4	2	237	301	417	717	180	277	24	7	311	97	—	—	—	—	—	—
2017	26	AAA RAW	2 teams	—	486	430	129	29	2	24	43	93	6	4	2	300	372	544	916	244	329	19	9	395	97	—	—	—	—	—	—
2018	27	MLB MLE	2 teams	—	466	427	102	19	0	24	28	126	10	4	0	239	301	452	753	213	281	27	6	332	111	+6	+1	+2	+2	+10	2.1
2018	27	MLB RAW	2 teams	—	466	417	98	19	0	23	38	117	10	4	0	235	314	446	760	211	270	25	8	336	111	+6	+1	+2	+2	+10	2.1
2020	29	INTL MLE	Charros de Jalisco	—	135	111	29	6	0	6	23	17	0	1	0	261	388	477	866	216	258	13	17	—	—	—	—	—	—	—	—
2020	29	INTL RAW	Charros de Jalisco	—	135	111	29	6	0	6	23	17	0	1	0	261	388	477	866	216	258	13	17	382	145	—	—	—	—	—	—
2021	30	INTL MLE	Charros de Jalisco	—	114	94	32	6	2	4	17	18	1	0	1	340	446	574	1.021	234	378	16	15	—	—	—	—	—	—	—	—
2021	30	INTL RAW	Charros de Jalisco	—	114	94	32	6	2	4	17	18	1	0	1	340	446	574	1.021	234	378	16	15	438	183	—	—	—	—	—	—
2022	31	INTL MLE	Charros de Jalisco	—	279	240	69	14	0	6	34	44	1	6	1	287	378	421	799	133	325	16	12	—	—	—	—	—	—	—	—
2022	31	INTL RAW	Charros de Jalisco	—	279	240	69	14	0	6	34	44	1	6	1	287	378	421	799	133	325	16	12	356	127	—	—	—	—	—	—
2023	32	INTL MLE	Charros de Jalisco	—	190	173	49	10	0	5	10	19	2	4	2	283	330	428	757	145	286	10	5	—	—	—	—	—	—	—	—
2023	32	INTL RAW	Charros de Jalisco	—	190	173	49	10	0	5	10	19	2	4	2	283	330	428	757	145	286	10	5	329	109	—	—	—	—	—	—
2024	33	INTL MLE	Yaquis de Obregon	—	133	116	21	2	0	5	12	30	3	1	1	181	275	328	602	147	195	23	9	—	—	—	—	—	—	—	—
2024	33	INTL RAW	Yaquis de Obregon	—	133	116	21	2	0	5	12	30	3	1	1	181	275	328	602	147	195	23	9	272	70	—	—	—	—	—	—
2009	18	ROK MLE	DSL Rangers 2	—	33	28	4	1	0	0	4	6	1	1	0	143	290	179	469	36	168	18	12	221	36	—	—	—	—	—	—
2010	19	ROK MLE	AZL Rangers	—	210	196	42	9	1	1	8	60	4	6	2	214	263	286	549	71	293	29	4	245	52	—	—	—	—	—	—
2011	20	A MLE	Hickory Crawdads	—	529	488	109	26	3	11	24	118	12	32	6	223	281	357	638	133	258	22	5	279	75	—	—	—	—	—	—
2012	21	A+ MLE	2 teams	—	580	525	120	21	1	11	27	161	23	14	12	229	297	335	632	107	301	28	5	282	77	—	—	—	—	—	—
2013	22	AA MLE	2 teams	—	652	611	139	45	2	20	27	177	9	6	8	227	273	406	678	178	280	27	4	293	84	—	—	—	—	—	—
2014	23	AA MLE	3 teams	—	703	657	125	40	0	12	33	187	7	3	3	190	238	306	544	116	238	27	5	236	46	—	—	—	—	—	—
2015	24	AAA MLE	3 teams	—	816	755	166	29	3	27	43	159	12	5	5	220	277	374	650	154	234	19	5	280	76	—	—	—	—	—	—
2017	26	AAA MLE	2 teams	—	486	443	104	23	1	18	34	117	6	4	2	235	301	413	714	178	271	24	7	311	97	—	—	—	—	—	—
2018	27	MLB MLE	2 teams	—	466	425	102	19	0	24	28	126	10	4	0	240	301	454	755	214	276	27	6	332	111	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	416	383	94	16	0	24	23	114	9	3	0	245	304	475	779	230	285	27	6	341	45	+9	+1	+2	+3	+9	2.4
/ 162G	—	A+C		—	650	598	147	25	0	38	36	178	14	5	0	246	304	478	782	232	284	27	6	341	45	+13	+1	+4	+5	+14	3.8
Career	—	MLE		—	5390	4878	1126	251	13	153	330	1252	93	87	43	231	292	382	674	151	275	23	6	234	45	+9	+1	+2	+3	+9	2.4
/ 162G	—	MLE		—	650	588	136	30	2	18	40	151	11	10	5	231	292	381	673	150	276	23	6	234	45	+13	+1	+4	+5	+14	3.8

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.

2017 AAAEl Paso Chihuahuas · 454 PAwOBA .384 → .300 (−83)K% 18.3% → 23.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.28%	÷1.005	×1.290	—	—	23.47%
BB% (unintentional) /PA	9.47%	÷0.921	×0.710	—	×1.018	7.43%
HBP% /PA	1.32%	—	×0.980	—	×1.018	1.32%
Singles /BIP	21.59%	÷1.040	×0.900	×0.950	×1.018	18.07%
Doubles /BIP	8.89%	÷0.983	×0.810	×0.950	×1.018	7.08%
Triples /BIP	0.63%	÷1.049	×0.810	×0.950	×1.018	0.47%
Home runs /BIP	6.35%	÷1.087	×0.780	—	×1.018	4.64%

Factors used: league row PCL. Park (home-season share, regressed): HR 117, 1B 108, 2B 97, 3B 110, K 101, BB 84 — 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 .384 → .300; ISO .231 → .170; BABIP .326 → .263. 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 AAAIowa Cubs · 508 PAwOBA .323 → .281 (−42)K% 15.7% → 19.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.75%	÷1.051	×1.290	—	—	19.33%
BB% (unintentional) /PA	6.69%	÷0.996	×0.710	—	×1.048	5.00%
HBP% /PA	0.98%	—	×0.980	—	×1.048	1.01%
Singles /BIP	20.00%	÷1.008	×0.900	×0.955	×1.048	17.88%
Doubles /BIP	6.13%	÷0.984	×0.810	×0.955	×1.048	5.05%
Triples /BIP	0.53%	÷0.948	×0.810	×0.955	×1.048	0.46%
Home runs /BIP	4.80%	÷0.916	×0.780	—	×1.048	4.28%

Factors used: league row PCL. Park (home-season share, regressed): HR 83, 1B 102, 2B 97, 3B 90, K 110, BB 99 — 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 ×0.955.

Result: wOBA .323 → .281; ISO .178 → .157; BABIP .273 → .238. 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 AATennessee Smokies · 28 PAwOBA .336 → .265 (−71)K% 17.9% → 22.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.86%	÷0.967	×1.220	—	—	22.53%
BB% (unintentional) /PA	14.29%	÷0.959	×0.680	—	×1.012	10.25%
HBP% /PA	0.00%	—	×0.840	—	×1.012	0.00%
Singles /BIP	15.79%	÷1.026	×0.920	—	×1.012	14.33%
Doubles /BIP	0.00%	÷0.998	×0.910	—	×1.012	0.00%
Triples /BIP	0.00%	÷1.058	×0.880	—	×1.012	0.00%
Home runs /BIP	10.53%	÷1.048	×0.800	—	×1.012	8.13%

Factors used: league row SL. Park (home-season share, regressed): HR 110, 1B 105, 2B 100, 3B 112, K 93, BB 92 — 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).

Result: wOBA .336 → .265; ISO .250 → .191; BABIP .176 → .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).

2014 AATennessee Smokies · 259 PAwOBA .301 → .256 (−45)K% 16.2% → 20.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.22%	÷0.980	×1.220	—	—	20.18%
BB% (unintentional) /PA	6.56%	÷0.948	×0.680	—	×1.027	4.83%
HBP% /PA	1.16%	—	×0.840	—	×1.027	1.00%
Singles /BIP	17.71%	÷0.958	×0.920	×0.968	×1.027	16.91%
Doubles /BIP	10.42%	÷1.140	×0.910	×0.968	×1.027	8.27%
Triples /BIP	0.00%	÷1.065	×0.880	×0.968	×1.027	0.00%
Home runs /BIP	2.08%	÷1.009	×0.800	—	×1.027	1.70%

Factors used: league row SL. Park (home-season share, regressed): HR 102, 1B 92, 2B 128, 3B 113, K 96, BB 90 — 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 ×0.968.

Result: wOBA .301 → .256; ISO .137 → .109; BABIP .284 → .253. 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 AAAIowa Cubs · 248 PAwOBA .283 → .216 (−66)K% 25.8% → 33.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.81%	÷0.996	×1.290	—	—	33.42%
BB% (unintentional) /PA	7.66%	÷1.249	×0.710	—	×1.050	4.57%
HBP% /PA	0.81%	—	×0.980	—	×1.050	0.83%
Singles /BIP	14.47%	÷1.021	×0.900	×0.980	×1.050	13.12%
Doubles /BIP	11.32%	÷1.111	×0.810	×0.980	×1.050	8.49%
Triples /BIP	0.00%	÷1.150	×0.810	×0.980	×1.050	0.00%
Home runs /BIP	3.77%	÷0.929	×0.780	—	×1.050	3.33%

Factors used: league row PCL. Park (home-season share, regressed): HR 86, 1B 104, 2B 122, 3B 130, K 99, BB 150 — 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 ×0.980.

Result: wOBA .283 → .216; ISO .161 → .126; BABIP .266 → .222. 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 AATennessee Smokies · 542 PAwOBA .336 → .293 (−43)K% 21.6% → 27.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.59%	÷0.968	×1.220	—	—	27.20%
BB% (unintentional) /PA	6.09%	÷1.060	×0.680	—	×1.042	4.07%
HBP% /PA	1.66%	—	×0.840	—	×1.042	1.45%
Singles /BIP	17.69%	÷0.990	×0.920	×0.957	×1.042	16.40%
Doubles /BIP	10.99%	÷0.970	×0.910	×0.957	×1.042	10.28%
Triples /BIP	0.54%	÷0.935	×0.880	×0.957	×1.042	0.50%
Home runs /BIP	5.09%	÷0.920	×0.800	—	×1.042	4.62%

Factors used: league row SL. Park (home-season share, regressed): HR 84, 1B 98, 2B 94, 3B 87, K 94, BB 112 — 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 ×0.957.

Result: wOBA .336 → .293; ISO .208 → .187; BABIP .303 → .280. 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 A+Daytona Cubs · 520 PAwOBA .341 → .282 (−59)K% 20.6% → 27.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.58%	÷1.051	×1.420	—	—	27.79%
BB% (unintentional) /PA	5.96%	÷0.995	×0.750	—	×1.027	4.62%
HBP% /PA	4.04%	—	×0.930	—	×1.027	3.86%
Singles /BIP	25.28%	÷0.970	×0.920	×0.960	×1.027	23.66%
Doubles /BIP	6.82%	÷1.112	×0.960	×0.960	×1.027	5.81%
Triples /BIP	0.28%	÷1.150	×0.810	×0.960	×1.027	0.20%
Home runs /BIP	3.98%	÷1.240	×0.880	—	×1.027	2.90%

Factors used: league row FSL. Park (home-season share, regressed): HR 148, 1B 94, 2B 122, 3B 130, K 110, BB 99 — 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 ×0.960.

Result: wOBA .341 → .282; ISO .148 → .108; BABIP .332 → .301. 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 AHickory Crawdads · 529 PAwOBA .350 → .279 (−71)K% 16.3% → 22.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.26%	÷0.974	×1.340	—	—	22.37%
BB% (unintentional) /PA	6.81%	÷1.037	×0.660	—	×1.030	4.46%
HBP% /PA	2.27%	—	×0.770	—	×1.030	1.80%
Singles /BIP	21.00%	÷1.034	×0.920	×0.950	×1.030	18.27%
Doubles /BIP	7.87%	÷0.921	×0.820	×0.950	×1.030	6.86%
Triples /BIP	0.79%	÷0.946	×0.940	×0.950	×1.030	0.77%
Home runs /BIP	4.46%	÷1.247	×0.820	—	×1.030	3.02%

Factors used: league row SAL. Park (home-season share, regressed): HR 149, 1B 107, 2B 84, 3B 89, K 95, BB 107 — applied at half strength. Age: age 20 vs a level average of 22.0 → +3.0% on BB/HBP/hit rates (2.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .350 → .279; ISO .186 → .141; BABIP .300 → .258. 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 — .