Geovany Soto

PosC

B/TR/R

Born1983-01-20

Age (2027)44

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2017

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2015	32	MLBCWS	210	.219/.301/.406	9	0	98	1.6
2016	33	AAA/INTL2 orgs	60	.214/.241/.339	1	0	22	—	.167/.189/.333
2016	33	MLBLAA	86	.269/.321/.487	4	0	121	0.7
2017	34	MLBCWS	48	.190/.277/.405	3	0	86	-0.0
2018	35	No MLB games played
2019	36	No MLB games played
2020	37	No MLB games played
2021	38	No MLB games played
2022	39	No MLB games played
2023	40	No MLB games played
2024	41	No MLB games played
2025	42	No MLB games played
2026	43	No MLB games played

Last 3 seasons WAR 2.3

Contract

No contract data on file.

Skills · percentileStatcast 2015-2017

xwOBA78

Avg EV57

Barrel%50

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	22	AAA	Iowa Cubs	—	345	292	74	14	0	4	48	77	0	0	1	253	359	342	701	89	329	22	14	320	72	—	—	—	—	—	—
2006	23	MLB	Chicago Cubs	—	26	25	5	1	0	0	0	5	1	0	0	200	231	240	471	40	250	19	0	217	33	-2	+0	+0	+7	+1	0.7
2006	23	AAA	Iowa Cubs	—	391	342	93	21	0	6	41	74	3	0	1	272	355	386	741	114	330	19	10	332	80	—	—	—	—	—	—
2007	24	MLB	Chicago Cubs	—	60	54	21	6	0	3	5	14	0	0	0	389	441	667	1.107	278	474	23	8	472	205	+8	+0	+1	+9	+1	1.9
2007	24	AAA	Iowa Cubs	—	449	385	136	31	3	26	53	94	1	0	0	353	433	652	1.085	299	401	21	12	457	148	—	—	—	—	—	—
2008	25	MLB	Chicago Cubs	—	563	494	141	35	2	23	62	121	2	0	1	285	367	504	871	219	332	21	11	380	138	+26	-0	-1	+10	+12	4.7
2009	26	MLB	Chicago Cubs	—	389	331	72	19	1	11	50	77	3	1	0	218	326	381	706	163	246	20	13	316	97	-2	+0	+12	+7	+8	2.7
2010	27	MLB	Chicago Cubs	—	387	322	90	19	0	17	62	83	0	0	1	280	396	497	893	217	324	21	16	392	148	+22	-0	+11	+7	+8	4.9
2011	28	MLB	Chicago Cubs	—	474	421	96	26	0	17	45	124	6	0	0	228	311	411	722	183	280	26	9	322	101	+1	+0	+23	+8	+10	4.4
2012	29	MLB	Texas Rangers	—	361	324	64	12	1	11	30	76	3	1	0	198	272	343	614	145	222	21	8	275	64	-16	+0	+14	+7	+8	1.3
2012	29	AAA	Iowa Cubs	—	17	16	3	2	0	0	0	5	1	0	0	188	235	312	548	125	273	29	0	248	6	—	—	—	—	—	—
2013	30	MLB	Texas Rangers	—	184	163	40	9	0	9	20	60	0	1	2	245	328	466	794	221	330	33	11	349	112	+3	-1	+5	+3	+4	1.5
2014	31	MLB	Oakland Athletics	—	87	80	20	6	0	1	6	19	0	0	0	250	302	362	665	112	317	22	7	296	86	-1	+0	+0	+3	+2	0.3
2014	31	AAA	Round Rock Express	—	33	32	6	2	0	1	1	10	0	0	0	188	212	344	556	156	238	30	3	244	-1	—	—	—	—	—	—
2014	31	AA	Frisco RoughRiders	—	22	19	7	2	0	0	3	6	0	0	0	368	455	474	928	105	538	27	14	418	103	—	—	—	—	—	—
2015	32	MLB	Chicago White Sox	—	210	187	41	8	0	9	21	63	1	0	1	219	301	406	708	187	278	30	10	315	98	-1	-0	+9	+4	+5	1.6
2016	33	MLB	Los Angeles Angels	—	86	78	21	5	0	4	6	21	0	0	0	269	321	487	809	218	321	24	7	346	121	+2	+0	+0	+3	+2	0.7
2016	33	AAA	Salt Lake Bees	—	38	36	7	4	0	1	1	7	0	0	0	194	216	389	605	194	214	18	3	256	8	—	—	—	—	—	—
2016	33	INTL	Gigantes de Carolina	—	22	20	5	0	0	0	1	4	0	0	0	250	286	250	536		294	18	5	237	47	—	—	—	—	—	—
2017	34	MLB	Chicago White Sox	—	48	42	8	0	0	3	4	10	1	0	0	190	277	405	681	214	167	21	8	298	86	-1	+0	-1	+1	+1	-0.0
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	2875	2521	619	146	4	108	311	673	17	3	5	246	332	435	768	190	291	23	11	339	116	+53	-1	+74	+69	+62	26.2
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2005	22	AAA MLE	Iowa Cubs	—	345	304	69	12	0	3	36	99	0	0	1	227	309	296	605	69	318	29	10	274	72	—	—	—	—	—	—
2006	23	MLB MLE	Chicago Cubs	—	26	25	5	1	0	0	0	5	1	0	0	200	231	240	471	40	250	19	0	217	33	-2	+0	+0	+7	+1	0.7
2006	23	AAA MLE	Iowa Cubs	—	391	354	87	18	0	5	29	95	3	0	1	246	308	339	647	93	316	24	7	287	80	—	—	—	—	—	—
2007	24	MLB MLE	Chicago Cubs	—	60	54	21	6	0	3	5	14	0	0	0	389	441	667	1.107	278	474	23	8	472	205	+8	+0	+1	+9	+1	1.9
2007	24	AAA MLE	Iowa Cubs	—	449	398	122	26	3	21	40	121	1	0	0	307	371	545	917	239	367	27	9	387	148	—	—	—	—	—	—
2008	25	MLB MLE	Chicago Cubs	—	563	500	141	35	2	23	56	121	2	0	1	282	357	498	855	216	332	21	10	373	138	+26	-0	-1	+10	+12	4.7
2009	26	MLB MLE	Chicago Cubs	—	389	334	72	19	1	11	47	77	3	1	0	216	318	377	695	162	246	20	12	311	97	-2	+0	+12	+7	+8	2.7
2010	27	MLB MLE	Chicago Cubs	—	387	326	90	18	0	18	58	83	0	0	1	276	385	497	882	221	324	21	15	387	148	+22	-0	+11	+7	+8	4.9
2011	28	MLB MLE	Chicago Cubs	—	474	424	96	25	0	18	42	124	6	0	0	226	305	413	718	186	280	26	9	318	101	+1	+0	+23	+8	+10	4.4
2012	29	MLB MLE	Texas Rangers	—	361	325	63	11	1	11	29	76	3	1	0	194	266	335	601	142	222	21	8	263	64	-16	+0	+14	+7	+8	1.3
2012	29	AAA MLE	Iowa Cubs	—	17	16	2	1	0	0	0	6	1	0	0	125	176	188	364	62	216	35	0	177	6	—	—	—	—	—	—
2013	30	MLB MLE	Texas Rangers	—	184	163	39	8	0	9	20	60	0	1	2	239	322	454	776	215	330	33	11	334	112	+3	-1	+5	+3	+4	1.5
2014	31	MLB MLE	Oakland Athletics	—	87	80	20	6	0	1	6	19	0	0	0	250	302	362	665	112	317	22	7	296	86	-1	+0	+0	+3	+2	0.3
2014	31	AAA MLE	Round Rock Express	—	33	32	4	1	0	1	1	13	0	0	0	125	152	250	402	125	194	39	3	166	-1	—	—	—	—	—	—
2014	31	AA MLE	Frisco RoughRiders	—	22	20	6	2	0	0	2	7	0	0	0	300	364	400	764	100	459	32	9	321	103	—	—	—	—	—	—
2015	32	MLB MLE	Chicago White Sox	—	210	187	41	8	0	9	21	63	1	0	1	219	301	406	708	187	278	30	10	313	98	-1	-0	+9	+4	+5	1.6
2016	33	MLB MLE	Los Angeles Angels	—	86	78	21	5	0	4	6	21	0	0	0	269	321	487	809	218	321	24	7	348	121	+2	+0	+0	+3	+2	0.7
2016	33	AAA MLE	Salt Lake Bees	—	38	36	6	3	0	1	1	9	0	0	0	167	189	333	523	167	164	24	3	180	8	—	—	—	—	—	—
2017	34	MLB MLE	Chicago White Sox	—	48	42	8	0	0	3	4	10	1	0	0	190	277	405	681	214	167	21	8	296	86	-1	+0	-1	+1	+1	-0.0
2005	22	AAA MLE	Iowa Cubs	—	345	304	69	12	0	3	36	99	0	0	1	227	309	296	605	69	324	29	10	274	72	—	—	—	—	—	—
2005	22	AAA RAW	Iowa Cubs	—	345	292	74	14	0	4	48	77	0	0	1	253	359	342	701	89	329	22	14	320	72	—	—	—	—	—	—
2006	23	AAA MLE	2 teams	—	417	379	92	19	0	5	29	100	4	0	1	243	303	332	636	90	315	24	7	283	77	—	—	—	—	—	—
2006	23	AAA RAW	2 teams	—	417	367	98	22	0	6	41	79	4	0	1	267	347	376	723	109	324	19	10	325	77	—	—	—	—	—	—
2007	24	AAA MLE	2 teams	—	509	452	143	32	3	24	45	135	1	0	0	316	380	560	939	243	393	27	9	397	155	—	—	—	—	—	—
2007	24	AAA RAW	2 teams	—	509	439	157	37	3	29	58	108	1	0	0	358	434	654	1.087	296	410	21	11	459	155	—	—	—	—	—	—
2008	25	MLB MLE	Chicago Cubs	—	563	500	141	35	2	23	56	121	2	0	1	282	357	498	855	216	327	21	10	373	138	+26	-0	-1	+10	+12	4.7
2008	25	MLB RAW	Chicago Cubs	—	563	494	141	35	2	23	62	121	2	0	1	285	367	504	871	219	332	21	11	380	138	+26	-0	-1	+10	+12	4.7
2009	26	MLB MLE	Chicago Cubs	—	389	334	72	19	1	11	47	77	3	1	0	216	318	377	695	162	243	20	12	311	97	-2	+0	+12	+7	+8	2.7
2009	26	MLB RAW	Chicago Cubs	—	389	331	72	19	1	11	50	77	3	1	0	218	326	381	706	163	246	20	13	316	97	-2	+0	+12	+7	+8	2.7
2010	27	MLB MLE	Chicago Cubs	—	387	326	90	18	0	18	58	83	0	0	1	276	385	497	882	221	316	21	15	387	148	+22	-0	+11	+7	+8	4.9
2010	27	MLB RAW	Chicago Cubs	—	387	322	90	19	0	17	62	83	0	0	1	280	396	497	893	217	324	21	16	392	148	+22	-0	+11	+7	+8	4.9
2011	28	MLB MLE	Chicago Cubs	—	474	424	96	25	0	18	42	124	6	0	0	226	305	413	718	186	275	26	9	318	101	+1	+0	+23	+8	+10	4.4
2011	28	MLB RAW	Chicago Cubs	—	474	421	96	26	0	17	45	124	6	0	0	228	311	411	722	183	280	26	9	322	101	+1	+0	+23	+8	+10	4.4
2012	29	MLB MLE	2 teams	—	378	341	66	13	1	11	29	82	4	1	0	194	265	334	599	141	220	22	8	259	61	-17	+0	+14	+7	+8	1.2
2012	29	MLB RAW	2 teams	—	378	340	67	14	1	11	30	81	4	1	0	197	270	341	611	144	224	21	8	274	61	-17	+0	+14	+7	+8	1.2
2013	30	MLB MLE	Texas Rangers	—	184	163	39	8	0	9	20	60	0	1	2	239	322	454	776	215	319	33	11	334	112	+3	-1	+5	+3	+4	1.5
2013	30	MLB RAW	Texas Rangers	—	184	163	40	9	0	9	20	60	0	1	2	245	328	466	794	221	330	33	11	349	112	+3	-1	+5	+3	+4	1.5
2014	31	MLB MLE	3 teams	—	142	132	31	9	0	2	9	39	0	0	0	235	284	348	632	114	319	27	6	270	69	-5	+0	+0	+3	+3	0.0
2014	31	MLB RAW	3 teams	—	142	131	33	10	0	2	10	35	0	0	0	252	305	374	679	122	330	25	7	303	69	-5	+0	+0	+3	+3	0.0
2015	32	MLB MLE	Chicago White Sox	—	210	187	41	8	0	9	21	63	1	0	1	219	301	406	708	187	278	30	10	313	98	-1	-0	+9	+4	+5	1.6
2015	32	MLB RAW	Chicago White Sox	—	210	187	41	8	0	9	21	63	1	0	1	219	301	406	708	187	278	30	10	315	98	-1	-0	+9	+4	+5	1.6
2016	33	MLB MLE	3 teams	—	146	134	31	10	0	5	8	36	0	0	0	231	275	418	693	187	277	25	5	296	87	-2	+0	+0	+3	+3	0.4
2016	33	MLB RAW	3 teams	—	146	134	33	9	0	5	8	32	0	0	0	246	289	425	714	179	286	22	5	306	87	-2	+0	+0	+3	+3	0.4
2017	34	MLB MLE	Chicago White Sox	—	48	42	8	0	0	3	4	10	1	0	0	190	277	405	681	214	167	21	8	296	86	-1	+0	-1	+1	+1	-0.0
2017	34	MLB RAW	Chicago White Sox	—	48	42	8	0	0	3	4	10	1	0	0	190	277	405	681	214	167	21	8	298	86	-1	+0	-1	+1	+1	-0.0
2005	22	AAA MLE	Iowa Cubs	—	345	306	69	12	0	3	36	99	0	0	1	225	309	294	603	69	318	29	10	274	72	—	—	—	—	—	—
2006	23	AAA MLE	2 teams	—	417	381	92	19	0	5	29	100	4	0	1	241	303	331	634	89	312	24	7	283	77	—	—	—	—	—	—
2007	24	AAA MLE	2 teams	—	509	458	143	32	3	24	45	135	1	0	0	312	380	552	932	240	380	27	9	397	155	—	—	—	—	—	—
2008	25	MLB MLE	Chicago Cubs	—	563	500	141	35	2	23	56	121	2	0	1	282	357	498	855	216	332	21	10	373	138	—	—	—	—	—	—
2009	26	MLB MLE	Chicago Cubs	—	389	336	72	19	1	11	47	77	3	1	0	214	318	375	693	161	246	20	12	311	97	—	—	—	—	—	—
2010	27	MLB MLE	Chicago Cubs	—	387	326	90	18	0	18	58	83	0	0	1	276	385	497	882	221	324	21	15	387	148	—	—	—	—	—	—
2011	28	MLB MLE	Chicago Cubs	—	474	423	96	25	0	18	42	124	6	0	0	227	305	414	719	187	280	26	9	318	101	—	—	—	—	—	—
2012	29	MLB MLE	2 teams	—	378	342	66	13	1	11	29	82	4	1	0	193	265	333	598	140	221	22	8	259	61	—	—	—	—	—	—
2013	30	MLB MLE	Texas Rangers	—	184	162	39	8	0	9	20	60	0	1	2	241	322	457	779	216	330	33	11	334	112	—	—	—	—	—	—
2014	31	MLB MLE	3 teams	—	142	132	31	9	0	2	9	39	0	0	0	235	284	348	632	114	310	27	6	270	69	—	—	—	—	—	—
2015	32	MLB MLE	Chicago White Sox	—	210	186	41	8	0	9	21	63	1	0	1	220	301	409	710	188	278	30	10	313	98	—	—	—	—	—	—
2016	33	MLB MLE	3 teams	—	146	137	31	10	0	5	8	36	0	0	0	226	275	409	683	182	273	25	5	296	87	—	—	—	—	—	—
2017	34	MLB MLE	Chicago White Sox	—	48	43	8	0	0	3	4	10	1	0	0	186	277	395	672	209	167	21	8	296	86	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	2875	2521	619	146	4	108	311	673	17	3	5	246	332	435	768	190	291	23	11	339	107	+53	-1	+74	+69	+62	26.2
/ 162G	—	A+C		—	650	570	140	33	1	24	70	152	4	1	1	246	332	433	766	188	291	23	11	339	107	+12	-0	+17	+16	+14	5.9
Career	—	MLE		—	4192	3718	919	208	7	141	404	1029	22	3	7	247	325	421	745	173	301	25	10	326	107	+53	-1	+74	+69	+62	26.2
/ 162G	—	MLE		—	650	577	142	32	1	22	63	160	3	0	1	246	323	419	743	173	300	25	10	326	107	+12	-0	+17	+16	+14	5.9

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.

2016 AAASalt Lake Bees · 38 PAwOBA .248 → .180 (−69)K% 18.4% → 24.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.42%	÷0.969	×1.290	—	—	24.53%
BB% (unintentional) /PA	2.63%	÷1.021	×0.710	—	×0.942	1.72%
HBP% /PA	0.00%	—	×0.980	—	×0.942	0.00%
Singles /BIP	6.90%	÷1.033	×0.900	—	×0.942	5.66%
Doubles /BIP	13.79%	÷1.020	×0.810	—	×0.942	10.32%
Triples /BIP	0.00%	÷1.137	×0.810	—	×0.942	0.00%
Home runs /BIP	3.45%	÷1.035	×0.780	—	×0.942	2.45%

Factors used: league row PCL. Park (home-season share, regressed): HR 107, 1B 107, 2B 104, 3B 127, K 94, BB 104 — 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 .248 → .180; ISO .194 → .137; BABIP .214 → .164. 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 AAARound Rock Express · 33 PAwOBA .237 → .166 (−71)K% 30.3% → 38.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	30.30%	÷1.015	×1.290	—	—	38.53%
BB% (unintentional) /PA	3.03%	÷1.033	×0.710	—	×0.962	2.00%
HBP% /PA	0.00%	—	×0.980	—	×0.962	0.00%
Singles /BIP	13.64%	÷0.984	×0.900	—	×0.962	12.00%
Doubles /BIP	9.09%	÷1.055	×0.810	—	×0.962	6.71%
Triples /BIP	0.00%	÷0.953	×0.810	—	×0.962	0.00%
Home runs /BIP	4.55%	÷1.027	×0.780	—	×0.962	3.32%

Factors used: league row PCL. Park (home-season share, regressed): HR 105, 1B 97, 2B 111, 3B 91, K 103, BB 107 — applied at half strength. Age: age 31 vs a level average of 27.2 → −3.8% on BB/HBP/hit rates (3.8 yr older than average).

Result: wOBA .237 → .166; ISO .156 → .102; BABIP .238 → .194. 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 AAFrisco RoughRiders · 22 PAwOBA .407 → .321 (−87)K% 27.3% → 33.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.27%	÷0.983	×1.220	—	—	33.84%
BB% (unintentional) /PA	13.64%	÷1.055	×0.700	—	×0.940	8.50%
HBP% /PA	0.00%	—	×0.870	—	×0.940	0.00%
Singles /BIP	38.46%	÷1.030	×0.920	—	×0.940	32.30%
Doubles /BIP	15.38%	÷0.890	×0.840	—	×0.940	13.65%
Triples /BIP	0.00%	÷0.921	×0.940	—	×0.940	0.00%
Home runs /BIP	0.00%	÷1.322	×0.800	—	×0.940	0.00%

Factors used: league row TL. Park (home-season share, regressed): HR 164, 1B 106, 2B 78, 3B 84, K 97, BB 111 — applied at half strength. Age: age 31 vs a level average of 24.8 → −6.0% on BB/HBP/hit rates (6.2 yr older than average).

Result: wOBA .407 → .321; ISO .105 → .091; BABIP .538 → .459. 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 AAAIowa Cubs · 17 PAwOBA .241 → .177 (−64)K% 29.4% → 37.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	29.41%	÷1.004	×1.290	—	—	37.80%
BB% (unintentional) /PA	0.00%	÷0.938	×0.710	—	×0.982	0.00%
HBP% /PA	5.88%	—	×0.980	—	×0.982	5.66%
Singles /BIP	9.09%	÷1.028	×0.900	—	×0.982	7.82%
Doubles /BIP	18.18%	÷1.049	×0.810	—	×0.982	13.78%
Triples /BIP	0.00%	÷0.953	×0.810	—	×0.982	0.00%
Home runs /BIP	0.00%	÷0.863	×0.780	—	×0.982	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 73, 1B 106, 2B 110, 3B 91, K 101, BB 88 — 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 .241 → .177; ISO .125 → .083; BABIP .273 → .216. 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).

2007 AAAIowa Cubs · 449 PAwOBA .454 → .387 (−66)K% 20.9% → 27.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.94%	÷1.000	×1.290	—	—	27.01%
BB% (unintentional) /PA	11.80%	÷1.000	×0.710	—	×1.048	8.78%
HBP% /PA	0.22%	—	×0.980	—	×1.048	0.23%
Singles /BIP	26.12%	÷1.000	×0.900	×1.019	×1.048	25.10%
Doubles /BIP	10.65%	÷1.000	×0.810	×1.019	×1.048	9.22%
Triples /BIP	1.03%	÷1.000	×0.810	×1.019	×1.048	0.89%
Home runs /BIP	8.93%	÷1.000	×0.780	—	×1.048	7.30%

Factors used: league row PCL. 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 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 ×1.019.

Result: wOBA .454 → .387; ISO .299 → .246; BABIP .401 → .367. 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).

2006 AAAIowa Cubs · 391 PAwOBA .324 → .287 (−36)K% 18.9% → 24.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.93%	÷1.000	×1.290	—	—	24.41%
BB% (unintentional) /PA	9.97%	÷1.000	×0.710	—	×1.050	7.44%
HBP% /PA	0.77%	—	×0.980	—	×1.050	0.79%
Singles /BIP	24.63%	÷1.000	×0.900	×1.046	×1.050	24.33%
Doubles /BIP	7.84%	÷1.000	×0.810	×1.046	×1.050	6.97%
Triples /BIP	0.00%	÷1.000	×0.810	×1.046	×1.050	0.00%
Home runs /BIP	2.24%	÷1.000	×0.780	—	×1.050	1.83%

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 ×1.046.

Result: wOBA .324 → .287; ISO .114 → .096; BABIP .330 → .316. 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).

2005 AAAIowa Cubs · 345 PAwOBA .314 → .274 (−40)K% 22.3% → 28.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.32%	÷1.000	×1.290	—	—	28.79%
BB% (unintentional) /PA	13.91%	÷1.000	×0.710	—	×1.050	10.37%
HBP% /PA	0.00%	—	×0.980	—	×1.050	0.00%
Singles /BIP	26.05%	÷1.000	×0.900	×1.050	×1.050	25.84%
Doubles /BIP	6.51%	÷1.000	×0.810	×1.050	×1.050	5.82%
Triples /BIP	0.00%	÷1.000	×0.810	×1.050	×1.050	0.00%
Home runs /BIP	1.86%	÷1.000	×0.780	—	×1.050	1.52%

Factors used: league row PCL. 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 27.2 → +5.0% on BB/HBP/hit rates (5.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .314 → .274; ISO .089 → .075; BABIP .329 → .318. 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 — .