Conor Gillaspie

Pos3B

B/TL/R

Born1987-07-18

Age (2027)40

No MLB projection yet. Career stats below are historical.

Overview

AAALAST SEASON 2017

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2015	28	MLBLAA	253	.228/.271/.359	4	0	73	-0.9
2016	29	AAASF	52	.314/.327/.431	1	1	72	—	.275/.288/.373
2016	29	MLBSF	205	.262/.309/.440	6	1	106	0.6
2017	30	AAA/A+SF	108	.308/.333/.385	0	3	55	—	.238/.259/.295
2017	30	MLBSF	87	.162/.221/.288	2	0	44	-0.3
2018	31	No MLB games played
2019	32	No MLB games played
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 -0.6

Contract

No contract data on file.

Skills · percentileStatcast 2015-2017

Avg EV28

xwOBA23

Barrel%10

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.

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)
2008	21	ROK	AZL Giants	—	25	22	6	3	0	0	3	1	0	0	1	273	360	409	769	136	286	4	12	346	54	—	—	—	—	—	—
2009	22	A+	San Jose Giants	—	530	469	134	31	2	4	55	68	3	2	3	286	364	386	750	100	327	13	10	339	71	—	—	—	—	—	—
2010	23	AA	Richmond Flying Squirrels	—	540	491	141	25	8	8	37	67	2	0	4	287	340	420	759	132	314	12	7	332	86	—	—	—	—	—	—
2010	23	INTL	Scottsdale Scorpions	—	80	72	22	6	0	5	4	5	2	1	1	306	359	597	956	292	266	6	5	404	160	—	—	—	—	—	—
2011	24	MLB	San Francisco Giants	—	21	19	5	0	0	1	2	1	0	0	0	263	333	421	754	158	235	5	10	338	122	+1	+0	+0	+2	+0	0.3
2011	24	AAA	Fresno Grizzlies	—	503	428	127	22	6	11	66	79	1	9	9	297	392	453	845	157	339	16	13	372	98	—	—	—	—	—	—
2012	25	MLB	San Francisco Giants	—	20	20	3	1	0	0	0	2	0	0	0	150	150	200	350	50	167	10	0	155	-8	-3	+0	-1	+2	+0	-0.1
2012	25	AAA	Fresno Grizzlies	—	465	413	116	18	3	14	41	54	2	0	0	281	349	441	789	160	291	12	9	345	86	—	—	—	—	—	—
2013	26	MLB	Chicago White Sox	—	452	408	100	14	3	13	37	79	1	0	1	245	309	390	699	145	270	17	8	309	90	-5	-0	-2	+2	+10	0.4
2014	27	MLB	Chicago White Sox	—	506	464	131	31	5	7	36	78	3	0	4	282	338	416	754	134	325	15	7	335	108	+5	-2	-5	+2	+11	1.1
2015	28	MLB	Los Angeles Angels	—	253	237	54	15	2	4	13	47	1	0	1	228	271	359	630	131	266	19	5	279	73	-8	-0	-6	+1	+5	-0.9
2016	29	MLB	San Francisco Giants	—	205	191	50	8	4	6	12	28	1	1	2	262	309	440	749	178	278	14	6	328	106	+2	-1	-0	+1	+4	0.6
2016	29	AAA	Sacramento River Cats	—	52	51	16	3	0	1	1	6	0	1	0	314	327	431	758	118	341	12	2	336	72	—	—	—	—	—	—
2017	30	MLB	San Francisco Giants	—	87	80	13	4	0	2	5	10	1	0	0	163	221	287	508	125	159	11	6	228	44	-6	+0	+0	+1	+2	-0.3
2017	30	AAA	Sacramento River Cats	—	93	90	28	7	0	0	3	8	0	3	2	311	333	389	722	78	341	9	3	324	57	—	—	—	—	—	—
2017	30	A+	San Jose Giants	—	15	14	4	1	0	0	1	3	0	0	0	286	333	357	690	71	364	20	7	313	45	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1544	1419	356	73	14	33	105	245	7	1	8	251	306	392	698	141	280	16	7	309	95	-9	-3	-14	+10	+33	1.8
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2008	21	ROK MLE	AZL Giants	—	25	23	5	2	0	0	2	1	0	0	1	217	280	304	584	87	217	4	8	248	54	—	—	—	—	—	—
2009	22	A+ MLE	San Jose Giants	—	530	486	116	25	1	4	38	91	3	2	3	239	298	319	617	80	283	17	7	273	71	—	—	—	—	—	—
2010	23	AA MLE	Richmond Flying Squirrels	—	540	505	133	22	8	8	23	83	2	0	4	263	298	386	684	123	289	15	4	296	86	—	—	—	—	—	—
2011	24	MLB MLE	San Francisco Giants	—	21	19	5	0	0	1	2	1	0	0	0	263	333	421	754	158	235	5	10	349	122	+1	+0	+0	+2	+0	0.3
2011	24	AAA MLE	Fresno Grizzlies	—	503	447	118	19	4	8	47	99	1	9	9	264	335	378	713	114	313	20	9	313	98	—	—	—	—	—	—
2012	25	MLB MLE	San Francisco Giants	—	20	20	3	1	0	0	0	2	0	0	0	150	150	200	350	50	167	10	0	156	-8	-3	+0	-1	+2	+0	-0.1
2012	25	AAA MLE	Fresno Grizzlies	—	465	428	106	15	3	14	26	74	2	0	0	248	294	395	689	147	258	16	6	295	86	—	—	—	—	—	—
2013	26	MLB MLE	Chicago White Sox	—	452	412	100	14	3	13	33	79	1	0	1	243	300	386	686	143	270	17	7	302	90	-5	-0	-2	+2	+10	0.4
2014	27	MLB MLE	Chicago White Sox	—	506	468	131	31	5	7	32	78	3	0	4	280	330	412	742	132	325	15	6	328	108	+5	-2	-5	+2	+11	1.1
2015	28	MLB MLE	Los Angeles Angels	—	253	239	55	16	2	4	11	47	1	0	1	230	267	364	631	134	266	19	4	276	73	-8	-0	-6	+1	+5	-0.9
2016	29	MLB MLE	San Francisco Giants	—	205	194	50	8	4	6	9	28	1	1	2	258	294	433	727	175	278	14	4	325	106	+2	-1	-0	+1	+4	0.6
2016	29	AAA MLE	Sacramento River Cats	—	52	51	14	2	0	1	1	7	0	1	0	275	288	373	661	98	305	13	2	275	72	—	—	—	—	—	—
2017	30	MLB MLE	San Francisco Giants	—	87	80	13	4	0	2	5	10	1	0	0	163	221	287	508	125	159	11	6	233	44	-6	+0	+0	+1	+2	-0.3
2017	30	AAA MLE	Sacramento River Cats	—	93	91	22	5	0	0	2	10	0	3	2	242	258	297	555	55	282	11	2	252	57	—	—	—	—	—	—
2017	30	A+ MLE	San Jose Giants	—	15	14	3	1	0	0	1	4	0	0	0	214	267	286	552	71	305	27	7	235	45	—	—	—	—	—	—
2008	21	ROK MLE	AZL Giants	—	25	23	5	2	0	0	2	1	0	0	1	217	280	304	584	87	227	4	8	248	54	—	—	—	—	—	—
2008	21	ROK RAW	AZL Giants	—	25	22	6	3	0	0	3	1	0	0	1	273	360	409	769	136	286	4	12	346	54	—	—	—	—	—	—
2009	22	A+ MLE	San Jose Giants	—	530	486	116	25	1	4	38	91	3	2	3	239	298	319	617	80	286	17	7	273	71	—	—	—	—	—	—
2009	22	A+ RAW	San Jose Giants	—	530	469	134	31	2	4	55	68	3	2	3	286	364	386	750	100	327	13	10	339	71	—	—	—	—	—	—
2010	23	AA MLE	2 teams	—	620	577	152	25	9	9	27	95	4	1	5	263	301	385	686	121	296	15	4	296	86	—	—	—	—	—	—
2010	23	AA RAW	2 teams	—	620	563	163	31	8	13	41	72	4	1	5	290	342	442	784	153	307	12	7	342	86	—	—	—	—	—	—
2011	24	AAA MLE	2 teams	—	524	466	123	19	4	9	49	100	1	9	9	264	335	380	715	116	316	19	9	314	99	—	—	—	—	—	—
2011	24	AAA RAW	2 teams	—	524	447	132	22	6	12	68	80	1	9	9	295	390	452	841	157	334	15	13	371	99	—	—	—	—	—	—
2012	25	AAA MLE	2 teams	—	485	448	109	16	3	14	26	76	2	0	0	243	288	386	674	143	262	16	5	289	82	—	—	—	—	—	—
2012	25	AAA RAW	2 teams	—	485	433	119	19	3	14	41	56	2	0	0	275	340	430	770	155	285	12	8	337	82	—	—	—	—	—	—
2013	26	MLB MLE	Chicago White Sox	—	452	412	100	14	3	13	33	79	1	0	1	243	300	386	686	143	267	17	7	302	90	-5	-0	-2	+2	+10	0.4
2013	26	MLB RAW	Chicago White Sox	—	452	408	100	14	3	13	37	79	1	0	1	245	309	390	699	145	270	17	8	309	90	-5	-0	-2	+2	+10	0.4
2014	27	MLB MLE	Chicago White Sox	—	506	468	131	31	5	7	32	78	3	0	4	280	330	412	742	132	321	15	6	328	108	+5	-2	-5	+2	+11	1.1
2014	27	MLB RAW	Chicago White Sox	—	506	464	131	31	5	7	36	78	3	0	4	282	338	416	754	134	325	15	7	335	108	+5	-2	-5	+2	+11	1.1
2015	28	MLB MLE	Los Angeles Angels	—	253	239	55	16	2	4	11	47	1	0	1	230	267	364	631	134	268	19	4	276	73	-8	-0	-6	+1	+5	-0.9
2015	28	MLB RAW	Los Angeles Angels	—	253	237	54	15	2	4	13	47	1	0	1	228	271	359	630	131	266	19	5	279	73	-8	-0	-6	+1	+5	-0.9
2016	29	MLB MLE	2 teams	—	257	245	64	10	4	7	10	35	1	2	2	261	293	420	713	159	279	14	4	315	99	-0	-0	-0	+1	+6	0.6
2016	29	MLB RAW	2 teams	—	257	242	66	11	4	7	13	34	1	2	2	273	312	438	751	165	292	13	5	330	99	-0	-0	-0	+1	+6	0.6
2017	30	AAA MLE	3 teams	—	195	185	39	10	0	2	8	24	1	3	2	211	247	297	545	86	231	12	4	242	50	—	—	—	—	—	—
2017	30	AAA RAW	3 teams	—	195	184	45	12	0	2	9	21	1	3	2	245	284	342	626	98	265	11	5	280	50	—	—	—	—	—	—
2008	21	ROK MLE	AZL Giants	—	25	23	5	2	0	0	2	1	0	0	1	217	280	304	584	87	217	4	8	248	54	—	—	—	—	—	—
2009	22	A+ MLE	San Jose Giants	—	530	484	116	25	1	4	38	91	3	2	3	240	298	320	618	81	283	17	7	273	71	—	—	—	—	—	—
2010	23	AA MLE	2 teams	—	620	584	152	25	9	9	27	95	4	1	5	260	301	380	681	120	289	15	4	296	86	—	—	—	—	—	—
2011	24	AAA MLE	2 teams	—	524	469	123	19	4	9	49	100	1	9	9	262	335	377	713	115	310	19	9	314	99	—	—	—	—	—	—
2012	25	AAA MLE	2 teams	—	485	454	109	16	3	14	26	76	2	0	0	240	288	381	669	141	254	16	5	289	82	—	—	—	—	—	—
2013	26	MLB MLE	Chicago White Sox	—	452	415	100	14	3	13	33	79	1	0	1	241	300	383	684	142	270	17	7	302	90	—	—	—	—	—	—
2014	27	MLB MLE	Chicago White Sox	—	506	466	131	31	5	7	32	78	3	0	4	281	330	414	744	133	325	15	6	328	108	—	—	—	—	—	—
2015	28	MLB MLE	Los Angeles Angels	—	253	239	55	16	2	4	11	47	1	0	1	230	267	364	631	134	266	19	4	276	73	—	—	—	—	—	—
2016	29	MLB MLE	2 teams	—	257	244	64	10	4	7	10	35	1	2	2	262	293	422	715	160	284	14	4	315	99	—	—	—	—	—	—
2017	30	AAA MLE	3 teams	—	195	184	39	10	0	2	8	24	1	3	2	212	247	299	546	87	229	12	4	242	50	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1544	1419	356	73	14	33	105	245	7	1	8	251	306	392	698	141	280	16	7	309	87	-9	-3	-14	+10	+33	1.8
/ 162G	—	A+C		—	650	597	150	31	6	14	44	103	3	0	3	251	305	394	699	142	280	16	7	309	87	-4	-1	-6	+4	+14	0.8
Career	—	MLE		—	3847	3549	894	168	31	69	236	626	17	17	28	252	302	375	677	123	286	16	6	296	87	-9	-3	-14	+10	+33	1.8
/ 162G	—	MLE		—	650	600	151	28	5	12	40	106	3	3	5	252	302	375	677	123	285	16	6	296	87	-4	-1	-6	+4	+14	0.8

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 AAASacramento River Cats · 93 PAwOBA .315 → .252 (−63)K% 8.6% → 11.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	8.60%	÷0.989	×1.290	—	—	11.22%
BB% (unintentional) /PA	3.23%	÷0.952	×0.710	—	×0.972	2.34%
HBP% /PA	0.00%	—	×0.980	—	×0.972	0.00%
Singles /BIP	25.61%	÷1.027	×0.900	×0.983	×0.972	21.44%
Doubles /BIP	8.54%	÷0.975	×0.810	×0.983	×0.972	6.78%
Triples /BIP	0.00%	÷0.920	×0.810	×0.983	×0.972	0.00%
Home runs /BIP	0.00%	÷0.889	×0.780	—	×0.972	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 78, 1B 105, 2B 95, 3B 84, K 98, BB 90 — applied at half strength. Age: age 30 vs a level average of 27.2 → −2.8% on BB/HBP/hit rates (2.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.983.

Result: wOBA .315 → .252; ISO .078 → .061; BABIP .341 → .282. 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 A+San Jose Giants · 15 PAwOBA .305 → .235 (−70)K% 20.0% → 26.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.00%	÷1.018	×1.340	—	—	26.32%
BB% (unintentional) /PA	6.67%	÷0.968	×0.710	—	×0.940	4.60%
HBP% /PA	0.00%	—	×0.790	—	×0.940	0.00%
Singles /BIP	27.27%	÷0.990	×0.910	—	×0.940	23.56%
Doubles /BIP	9.09%	÷1.008	×0.820	—	×0.940	6.95%
Triples /BIP	0.00%	÷0.900	×0.760	—	×0.940	0.00%
Home runs /BIP	0.00%	÷1.010	×0.920	—	×0.940	0.00%

Factors used: league row CAL. Park (home-season share, regressed): HR 102, 1B 98, 2B 102, 3B 80, K 104, BB 94 — applied at half strength. Age: age 30 vs a level average of 22.8 → −6.0% on BB/HBP/hit rates (7.2 yr older than average).

Result: wOBA .305 → .235; ISO .071 → .051; BABIP .364 → .305. 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 AAASacramento River Cats · 52 PAwOBA .327 → .275 (−52)K% 11.5% → 14.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.54%	÷1.034	×1.290	—	—	14.39%
BB% (unintentional) /PA	1.92%	÷1.052	×0.710	—	×0.982	1.27%
HBP% /PA	0.00%	—	×0.980	—	×0.982	0.00%
Singles /BIP	26.67%	÷0.960	×0.900	—	×0.982	24.55%
Doubles /BIP	6.67%	÷0.973	×0.810	—	×0.982	5.45%
Triples /BIP	0.00%	÷0.900	×0.810	—	×0.982	0.00%
Home runs /BIP	2.22%	÷1.082	×0.780	—	×0.982	1.57%

Factors used: league row PCL. Park (home-season share, regressed): HR 116, 1B 92, 2B 95, 3B 80, K 107, BB 110 — 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 .327 → .275; ISO .118 → .087; BABIP .341 → .305. 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 AAAFresno Grizzlies · 465 PAwOBA .337 → .295 (−42)K% 11.6% → 15.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.61%	÷0.945	×1.290	—	—	15.86%
BB% (unintentional) /PA	8.17%	÷1.068	×0.710	—	×1.033	5.61%
HBP% /PA	0.43%	—	×0.980	—	×1.033	0.44%
Singles /BIP	22.56%	÷1.001	×0.900	×0.968	×1.033	20.27%
Doubles /BIP	5.01%	÷0.964	×0.810	×0.968	×1.033	4.21%
Triples /BIP	0.84%	÷0.940	×0.810	×0.968	×1.033	0.72%
Home runs /BIP	3.90%	÷0.834	×0.780	—	×1.033	3.77%

Factors used: league row PCL. Park (home-season share, regressed): HR 67, 1B 100, 2B 93, 3B 88, K 89, BB 114 — 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 ×0.968.

Result: wOBA .337 → .295; ISO .160 → .149; BABIP .291 → .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).

2011 AAAFresno Grizzlies · 503 PAwOBA .366 → .313 (−52)K% 15.7% → 19.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.71%	÷1.027	×1.290	—	—	19.74%
BB% (unintentional) /PA	13.12%	÷1.043	×0.710	—	×1.048	9.36%
HBP% /PA	0.20%	—	×0.980	—	×1.048	0.20%
Singles /BIP	25.21%	÷0.960	×0.900	×0.988	×1.048	24.45%
Doubles /BIP	6.30%	÷1.013	×0.810	×0.988	×1.048	5.22%
Triples /BIP	1.72%	÷1.150	×0.810	×0.988	×1.048	1.25%
Home runs /BIP	3.15%	÷1.112	×0.780	—	×1.048	2.32%

Factors used: league row PCL. Park (home-season share, regressed): HR 122, 1B 92, 2B 103, 3B 130, K 105, BB 109 — 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 ×0.988.

Result: wOBA .366 → .313; ISO .157 → .122; BABIP .339 → .313. 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 AARichmond Flying Squirrels · 540 PAwOBA .326 → .296 (−29)K% 12.4% → 15.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	12.41%	÷0.983	×1.220	—	—	15.40%
BB% (unintentional) /PA	6.11%	÷1.018	×0.700	—	×1.027	4.32%
HBP% /PA	0.37%	—	×0.850	—	×1.027	0.32%
Singles /BIP	23.58%	÷1.035	×0.970	×0.969	×1.027	22.01%
Doubles /BIP	5.90%	÷0.992	×0.860	×0.969	×1.027	5.09%
Triples /BIP	1.89%	÷1.008	×0.950	×0.969	×1.027	1.77%
Home runs /BIP	1.89%	÷0.813	×0.780	—	×1.027	1.86%

Factors used: league row EL. Park (home-season share, regressed): HR 63, 1B 107, 2B 98, 3B 102, K 97, BB 104 — 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 ×0.969.

Result: wOBA .326 → .296; ISO .132 → .125; BABIP .314 → .289. 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 A+San Jose Giants · 530 PAwOBA .330 → .273 (−57)K% 12.8% → 17.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	12.83%	÷1.000	×1.340	—	—	17.19%
BB% (unintentional) /PA	10.00%	÷1.000	×0.710	—	×1.012	7.19%
HBP% /PA	0.57%	—	×0.790	—	×1.012	0.45%
Singles /BIP	24.19%	÷1.000	×0.910	×0.966	×1.012	21.52%
Doubles /BIP	7.73%	÷1.000	×0.820	×0.966	×1.012	6.20%
Triples /BIP	0.50%	÷1.000	×0.760	×0.966	×1.012	0.37%
Home runs /BIP	1.00%	÷1.000	×0.920	—	×1.012	0.93%

Factors used: league row CAL. 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 ×0.966.

Result: wOBA .330 → .273; ISO .100 → .083; BABIP .327 → .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).

2008 ROKAZL Giants · 25 PAwOBA .338 → .248 (−89)K% 4.0% → 5.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	4.00%	÷1.000	×1.440	—	—	5.76%
BB% (unintentional) /PA	12.00%	÷1.000	×0.620	—	×0.985	7.33%
HBP% /PA	0.00%	—	×0.720	—	×0.985	0.00%
Singles /BIP	14.29%	÷1.000	×0.820	—	×0.985	11.54%
Doubles /BIP	14.29%	÷1.000	×0.720	—	×0.985	10.13%
Triples /BIP	0.00%	÷1.000	×0.720	—	×0.985	0.00%
Home runs /BIP	0.00%	÷1.000	×0.780	—	×0.985	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 21 vs a level average of 19.5 → −1.5% on BB/HBP/hit rates (1.5 yr older than average).

Result: wOBA .338 → .248; ISO .136 → .100; BABIP .286 → .217. 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 — .