Jonathan Schoop

Pos2B

B/TR/R

Born1991-10-16

Age (2027)35

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2023

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2021	29	MLBDET	674	.278/.324/.435	22	2	116	1.6
2022	30	INTLCuracao	28	.136/.321/.182	0	0	62	—
2022	30	MLBDET	510	.202/.241/.322	11	5	58	1.0
2023	31	INTLCuracao	32	.219/.219/.469	2	0	86	—
2023	31	MLBDET	151	.213/.282/.272	0	0	60	-0.3
2024	32	No MLB games played
2025	33	No MLB games played
2026	34	No MLB games played

Last 3 seasons WAR 2.2

Contract

No contract data on file.

Skills · percentileStatcast 2021-2023

xwOBA37

Speed37

Avg EV33

Barrel%31

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	17	ROK	DSL Orioles	—	284	247	59	7	3	0	24	39	6	11	3	239	321	291	613	53	282	14	8	279	15	—	—	—	—	—	—
2010	18	A+	Frederick Keys	—	22	21	5	3	0	0	1	4	0	0	0	238	273	381	654	143	294	18	5	289	46	—	—	—	—	—	—
2010	18	ROK	Bluefield Orioles	—	218	193	57	15	1	5	19	21	1	1	1	295	362	461	823	166	308	10	9	357	74	—	—	—	—	—	—
2011	19	A+	Frederick Keys	—	329	299	81	12	2	5	22	44	4	6	3	271	329	375	704	104	304	13	7	314	71	—	—	—	—	—	—
2011	19	A	Delmarva Shorebirds	—	238	212	67	12	3	8	20	32	2	6	4	316	380	514	894	198	337	13	8	387	111	—	—	—	—	—	—
2012	20	AA	Bowie Baysox	—	555	485	119	24	1	14	50	103	9	5	3	245	327	386	713	140	282	19	9	316	77	—	—	—	—	—	—
2012	20	INTL	Mesa Solar Sox	—	83	63	17	4	0	2	16	18	4	2	0	270	446	429	874	159	349	22	19	402	158	—	—	—	—	—	—
2013	21	MLB	Baltimore Orioles	—	15	14	4	0	0	1	1	2	0	0	0	286	333	500	833	214	273	13	7	366	124	+0	+0	-1	+1	+0	0.2
2013	21	AAA	Norfolk Tides	—	289	270	69	11	0	9	13	55	5	1	2	256	302	396	698	141	290	19	4	311	70	—	—	—	—	—	—
2013	21	ROK	GCL Orioles	—	32	25	9	2	0	3	6	6	0	0	0	360	484	800	1.284	440	353	19	19	519	148	—	—	—	—	—	—
2013	21	INTL	Surprise Saguaros	—	70	62	11	0	0	3	8	17	0	0	0	177	271	323	594	145	190	24	11	272	70	—	—	—	—	—	—
2014	22	MLB	Baltimore Orioles	—	481	455	95	18	0	16	13	122	8	2	0	209	244	354	598	145	249	25	3	263	60	-23	+0	+6	+2	+10	-0.5
2015	23	MLB	Baltimore Orioles	—	321	305	85	17	0	15	9	79	4	2	0	279	308	482	790	203	329	25	3	343	112	+5	+0	+0	+1	+7	1.3
2015	23	AA	Bowie Baysox	—	26	25	6	2	0	3	1	6	0	0	0	240	269	680	949	440	188	23	4	401	70	—	—	—	—	—	—
2016	24	MLB	Baltimore Orioles	—	647	615	164	38	1	25	21	137	8	1	2	267	300	454	753	187	305	21	3	330	104	+3	-1	+11	+2	+14	3.0
2017	25	MLB	Baltimore Orioles	—	675	622	182	35	0	32	35	142	11	1	0	293	341	503	845	211	330	21	5	368	128	+23	+0	+7	+2	+15	4.7
2018	26	MLB	Milwaukee Brewers	—	501	473	110	22	1	21	19	115	4	1	1	233	268	416	685	184	261	23	4	298	86	-8	-0	+6	+2	+11	1.0
2019	27	MLB	Minnesota Twins	—	464	433	111	23	1	23	20	116	10	1	1	256	305	473	778	217	298	25	4	341	116	+9	-0	+4	+2	+10	2.5
2020	28	MLB	Detroit Tigers	—	177	162	45	4	2	8	8	39	4	0	0	278	326	475	801	198	316	22	5	348	126	+6	+0	+4	+1	+4	1.4
2021	29	MLB	Detroit Tigers	—	674	623	173	30	1	22	37	133	6	2	0	278	324	435	759	157	317	20	5	333	116	+13	+0	-1	-12	+15	1.6
2022	30	MLB	Detroit Tigers	—	510	481	97	23	1	11	19	107	6	5	0	202	241	322	563	121	234	21	4	251	58	-25	+1	+21	+2	+11	1.0
2022	30	INTL	Curacao	—	28	22	3	1	0	0	6	4	0	0	0	136	321	182	503	45	167	14	21	260	62	—	—	—	—	—	—
2023	31	MLB	Detroit Tigers	—	151	136	29	8	0	0	13	37	0	0	0	213	282	272	554	59	287	25	9	254	60	-7	+0	+0	+1	+3	-0.3
2023	31	INTL	Curacao	—	32	32	7	2	0	2	0	6	0	0	0	219	219	469	688	250	208	19	0	295	86	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	4616	4319	1095	218	7	174	195	1029	61	15	4	254	295	428	723	175	292	22	4	317	101	+4	+1	+58	+4	+100	17.1
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2009	17	ROK MLE	DSL Orioles	—	284	254	41	4	2	0	17	48	6	11	3	161	231	193	424	31	191	17	6	191	15	—	—	—	—	—	—
2010	18	A+ MLE	Frederick Keys	—	22	21	5	3	0	0	1	6	0	0	0	238	273	381	654	143	273	27	5	236	46	—	—	—	—	—	—
2010	18	ROK MLE	Bluefield Orioles	—	218	200	47	11	1	4	12	30	1	1	1	235	282	360	642	125	249	14	6	277	74	—	—	—	—	—	—
2011	19	A+ MLE	Frederick Keys	—	329	306	76	11	2	4	15	61	4	6	3	248	292	337	629	88	293	19	5	273	71	—	—	—	—	—	—
2011	19	A MLE	Delmarva Shorebirds	—	238	219	63	12	3	6	13	42	2	6	4	288	333	452	785	164	318	18	5	332	111	—	—	—	—	—	—
2012	20	AA MLE	Bowie Baysox	—	555	498	115	23	1	11	37	126	9	5	3	231	296	347	643	116	275	23	7	282	77	—	—	—	—	—	—
2013	21	MLB MLE	Baltimore Orioles	—	15	14	4	0	0	1	1	2	0	0	0	286	333	500	833	214	273	13	7	351	124	+0	+0	-1	+1	+0	0.2
2013	21	AAA MLE	Norfolk Tides	—	289	274	65	10	0	8	9	68	5	1	2	237	274	361	636	124	283	24	3	272	70	—	—	—	—	—	—
2013	21	ROK MLE	GCL Orioles	—	32	27	6	1	0	2	4	9	0	0	0	222	323	481	804	259	260	28	12	387	148	—	—	—	—	—	—
2014	22	MLB MLE	Baltimore Orioles	—	481	455	92	18	0	14	13	122	8	2	0	202	237	334	571	132	249	25	3	256	60	-23	+0	+6	+2	+10	-0.5
2015	23	MLB MLE	Baltimore Orioles	—	321	305	83	17	0	14	9	79	4	2	0	272	302	466	767	193	329	25	3	334	112	+5	+0	+0	+1	+7	1.3
2015	23	AA MLE	Bowie Baysox	—	26	25	5	2	0	2	1	7	0	0	0	200	231	520	751	320	159	27	4	271	70	—	—	—	—	—	—
2016	24	MLB MLE	Baltimore Orioles	—	647	615	162	39	1	23	21	137	8	1	2	263	297	442	739	179	305	21	3	322	104	+3	-1	+11	+2	+14	3.0
2017	25	MLB MLE	Baltimore Orioles	—	675	622	179	36	0	29	35	142	11	1	0	288	337	486	822	198	330	21	5	358	128	+23	+0	+7	+2	+15	4.7
2018	26	MLB MLE	Milwaukee Brewers	—	501	475	111	24	1	20	17	115	4	1	1	234	266	415	681	181	261	23	3	296	86	-8	-0	+6	+2	+11	1.0
2019	27	MLB MLE	Minnesota Twins	—	464	434	111	23	1	23	19	116	10	1	1	256	302	472	775	217	298	25	4	339	116	+9	-0	+4	+2	+10	2.5
2020	28	MLB MLE	Detroit Tigers	—	177	163	45	4	2	8	8	39	4	0	0	276	326	472	798	196	316	22	5	355	126	+6	+0	+4	+1	+4	1.4
2021	29	MLB MLE	Detroit Tigers	—	674	623	177	31	1	23	37	133	6	2	0	284	330	448	778	164	317	20	5	340	116	+13	+0	-1	-12	+15	1.6
2022	30	MLB MLE	Detroit Tigers	—	510	482	99	24	1	11	18	107	6	5	0	205	243	328	571	122	234	21	4	254	58	-25	+1	+21	+2	+11	1.0
2023	31	MLB MLE	Detroit Tigers	—	151	136	29	8	0	0	13	37	0	0	0	213	282	272	554	59	287	25	9	257	60	-7	+0	+0	+1	+3	-0.3
2009	17	ROK MLE	DSL Orioles	—	284	254	41	4	2	0	17	48	6	11	3	161	231	193	424	31	198	17	6	191	15	—	—	—	—	—	—
2009	17	ROK RAW	DSL Orioles	—	284	247	59	7	3	0	24	39	6	11	3	239	321	291	613	53	282	14	8	279	15	—	—	—	—	—	—
2010	18	ROK MLE	2 teams	—	240	222	52	14	1	4	12	35	1	1	1	234	277	360	637	126	259	15	5	273	71	—	—	—	—	—	—
2010	18	ROK RAW	2 teams	—	240	214	62	18	1	5	20	25	1	1	1	290	353	453	806	164	306	10	8	351	71	—	—	—	—	—	—
2011	19	A+ MLE	2 teams	—	567	525	138	23	4	10	28	103	6	12	7	263	308	379	687	116	308	18	5	298	88	—	—	—	—	—	—
2011	19	A+ RAW	2 teams	—	567	511	148	24	5	13	42	76	6	12	7	290	351	432	783	143	318	13	7	345	88	—	—	—	—	—	—
2012	20	AA MLE	2 teams	—	638	572	133	27	1	13	42	145	13	7	3	233	300	351	651	119	286	23	7	282	77	—	—	—	—	—	—
2012	20	AA RAW	2 teams	—	638	548	136	28	1	16	66	121	13	7	3	248	343	391	733	142	288	19	10	327	77	—	—	—	—	—	—
2013	21	AAA MLE	4 teams	—	406	383	91	13	0	13	16	95	5	1	2	238	277	373	651	136	282	23	4	286	80	—	—	—	—	—	—
2013	21	AAA RAW	4 teams	—	406	371	93	13	0	16	28	80	5	1	2	251	312	415	727	164	278	20	7	323	80	—	—	—	—	—	—
2014	22	MLB MLE	Baltimore Orioles	—	481	455	92	18	0	14	13	122	8	2	0	202	237	334	571	132	245	25	3	256	60	-23	+0	+6	+2	+10	-0.5
2014	22	MLB RAW	Baltimore Orioles	—	481	455	95	18	0	16	13	122	8	2	0	209	244	354	598	145	249	25	3	263	60	-23	+0	+6	+2	+10	-0.5
2015	23	MLB MLE	2 teams	—	347	330	87	19	0	15	10	86	4	2	0	264	294	458	751	194	312	25	3	329	109	+4	+0	+0	+1	+8	1.3
2015	23	MLB RAW	2 teams	—	347	330	91	19	0	18	10	85	4	2	0	276	305	497	802	221	319	24	3	348	109	+4	+0	+0	+1	+8	1.3
2016	24	MLB MLE	Baltimore Orioles	—	647	615	162	39	1	23	21	137	8	1	2	263	297	442	739	179	303	21	3	322	104	+3	-1	+11	+2	+14	3.0
2016	24	MLB RAW	Baltimore Orioles	—	647	615	164	38	1	25	21	137	8	1	2	267	300	454	753	187	305	21	3	330	104	+3	-1	+11	+2	+14	3.0
2017	25	MLB MLE	Baltimore Orioles	—	675	622	179	36	0	29	35	142	11	1	0	288	337	486	822	198	328	21	5	358	128	+23	+0	+7	+2	+15	4.7
2017	25	MLB RAW	Baltimore Orioles	—	675	622	182	35	0	32	35	142	11	1	0	293	341	503	845	211	330	21	5	368	128	+23	+0	+7	+2	+15	4.7
2018	26	MLB MLE	Milwaukee Brewers	—	501	475	111	24	1	20	17	115	4	1	1	234	266	415	681	181	265	23	3	296	86	-8	-0	+6	+2	+11	1.0
2018	26	MLB RAW	Milwaukee Brewers	—	501	473	110	22	1	21	19	115	4	1	1	233	268	416	685	184	261	23	4	298	86	-8	-0	+6	+2	+11	1.0
2019	27	MLB MLE	Minnesota Twins	—	464	434	111	23	1	23	19	116	10	1	1	256	302	472	775	217	297	25	4	339	116	+9	-0	+4	+2	+10	2.5
2019	27	MLB RAW	Minnesota Twins	—	464	433	111	23	1	23	20	116	10	1	1	256	305	473	778	217	298	25	4	341	116	+9	-0	+4	+2	+10	2.5
2020	28	MLB MLE	Detroit Tigers	—	177	163	45	4	2	8	8	39	4	0	0	276	326	472	798	196	314	22	5	355	126	+6	+0	+4	+1	+4	1.4
2020	28	MLB RAW	Detroit Tigers	—	177	162	45	4	2	8	8	39	4	0	0	278	326	475	801	198	316	22	5	348	126	+6	+0	+4	+1	+4	1.4
2021	29	MLB MLE	Detroit Tigers	—	674	623	177	31	1	23	37	133	6	2	0	284	330	448	778	164	324	20	5	340	116	+13	+0	-1	-12	+15	1.6
2021	29	MLB RAW	Detroit Tigers	—	674	623	173	30	1	22	37	133	6	2	0	278	324	435	759	157	317	20	5	333	116	+13	+0	-1	-12	+15	1.6
2022	30	MLB MLE	2 teams	—	538	509	104	25	1	12	19	113	6	5	0	204	242	328	570	124	237	21	4	254	58	-27	+1	+21	+2	+12	0.9
2022	30	MLB RAW	2 teams	—	538	503	100	24	1	11	25	111	6	5	0	199	245	316	561	117	231	21	5	251	58	-27	+1	+21	+2	+12	0.9
2023	31	MLB MLE	2 teams	—	183	165	36	10	0	0	16	45	0	0	0	218	287	279	566	61	295	25	9	257	60	-9	+0	+0	+1	+4	-0.4
2023	31	MLB RAW	2 teams	—	183	168	36	10	0	2	13	43	0	0	0	214	271	310	580	95	272	23	7	261	60	-9	+0	+0	+1	+4	-0.4
2009	17	ROK MLE	DSL Orioles	—	284	259	41	4	2	0	17	48	6	11	3	158	231	189	420	31	191	17	6	191	15	—	—	—	—	—	—
2010	18	ROK MLE	2 teams	—	240	225	52	14	1	4	12	35	1	1	1	231	277	356	632	124	251	15	5	273	71	—	—	—	—	—	—
2011	19	A+ MLE	2 teams	—	567	528	138	23	4	10	28	103	6	12	7	261	308	377	685	116	303	18	5	298	88	—	—	—	—	—	—
2012	20	AA MLE	2 teams	—	638	578	133	27	1	13	42	145	13	7	3	230	300	348	648	118	275	23	7	282	77	—	—	—	—	—	—
2013	21	AAA MLE	4 teams	—	406	382	91	13	0	13	16	95	5	1	2	238	277	374	652	136	280	23	4	286	80	—	—	—	—	—	—
2014	22	MLB MLE	Baltimore Orioles	—	481	457	92	18	0	14	13	122	8	2	0	201	237	333	570	131	249	25	3	256	60	—	—	—	—	—	—
2015	23	MLB MLE	2 teams	—	347	330	87	19	0	15	10	86	4	2	0	264	294	458	751	194	316	25	3	329	109	—	—	—	—	—	—
2016	24	MLB MLE	Baltimore Orioles	—	647	613	162	39	1	23	21	137	8	1	2	264	297	444	740	179	305	21	3	322	104	—	—	—	—	—	—
2017	25	MLB MLE	Baltimore Orioles	—	675	624	179	36	0	29	35	142	11	1	0	287	337	484	821	197	330	21	5	358	128	—	—	—	—	—	—
2018	26	MLB MLE	Milwaukee Brewers	—	501	475	111	24	1	20	17	115	4	1	1	234	266	415	681	181	261	23	3	296	86	—	—	—	—	—	—
2019	27	MLB MLE	Minnesota Twins	—	464	432	111	23	1	23	19	116	10	1	1	257	302	475	777	218	298	25	4	339	116	—	—	—	—	—	—
2020	28	MLB MLE	Detroit Tigers	—	177	163	45	4	2	8	8	39	4	0	0	276	326	472	798	196	316	22	5	355	126	—	—	—	—	—	—
2021	29	MLB MLE	Detroit Tigers	—	674	626	177	31	1	23	37	133	6	2	0	283	330	446	776	163	317	20	5	340	116	—	—	—	—	—	—
2022	30	MLB MLE	2 teams	—	538	508	104	25	1	12	19	113	6	5	0	205	242	329	570	124	234	21	4	254	58	—	—	—	—	—	—
2023	31	MLB MLE	2 teams	—	183	165	36	10	0	0	16	45	0	0	0	218	287	279	566	61	287	25	9	257	60	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	4616	4319	1095	218	7	174	195	1029	61	15	4	254	295	428	723	175	292	22	4	317	90	+4	+1	+58	+4	+100	17.1
/ 162G	—	A+C		—	650	608	155	31	1	25	27	145	9	2	1	255	297	433	729	178	293	22	4	317	90	+1	+0	+8	+1	+14	2.4
Career	—	MLE		—	6822	6347	1559	310	15	207	310	1474	92	47	20	246	291	397	688	151	287	22	5	301	90	+4	+1	+58	+4	+100	17.1
/ 162G	—	MLE		—	650	605	149	30	1	20	30	140	9	4	2	246	292	398	691	152	287	22	5	301	90	+1	+0	+8	+1	+14	2.4

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.

2015 AABowie Baysox · 26 PAwOBA .387 → .271 (−116)K% 23.1% → 28.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.08%	÷0.989	×1.220	—	—	28.47%
BB% (unintentional) /PA	3.85%	÷1.014	×0.700	—	×1.012	2.69%
HBP% /PA	0.00%	—	×0.850	—	×1.012	0.00%
Singles /BIP	5.26%	÷1.023	×0.970	—	×1.012	5.05%
Doubles /BIP	10.53%	÷0.999	×0.860	—	×1.012	9.17%
Triples /BIP	0.00%	÷1.123	×0.950	—	×1.012	0.00%
Home runs /BIP	15.79%	÷1.197	×0.780	—	×1.012	10.41%

Factors used: league row EL. Park (home-season share, regressed): HR 139, 1B 105, 2B 100, 3B 125, K 98, BB 103 — 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 .387 → .271; ISO .440 → .289; BABIP .188 → .159. 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 AAANorfolk Tides · 289 PAwOBA .302 → .272 (−30)K% 19.0% → 23.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	19.03%	÷0.967	×1.200	—	—	23.62%
BB% (unintentional) /PA	4.15%	÷1.001	×0.690	—	×1.050	3.00%
HBP% /PA	1.73%	—	×0.940	—	×1.050	1.71%
Singles /BIP	22.79%	÷0.970	×0.930	×0.992	×1.050	22.75%
Doubles /BIP	5.12%	÷0.956	×0.830	×0.992	×1.050	4.62%
Triples /BIP	0.00%	÷0.900	×0.970	×0.992	×1.050	0.00%
Home runs /BIP	4.19%	÷1.006	×0.840	—	×1.050	3.67%

Factors used: league row IL. Park (home-season share, regressed): HR 101, 1B 94, 2B 91, 3B 80, K 93, BB 100 — 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 ×0.992.

Result: wOBA .302 → .272; ISO .141 → .120; BABIP .290 → .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).

2013 ROKGCL Orioles · 32 PAwOBA .521 → .387 (−135)K% 18.8% → 27.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.75%	÷1.000	×1.440	—	—	27.00%
BB% (unintentional) /PA	18.75%	÷1.000	×0.620	—	×0.975	11.33%
HBP% /PA	0.00%	—	×0.720	—	×0.975	0.00%
Singles /BIP	21.05%	÷1.000	×0.820	—	×0.975	16.83%
Doubles /BIP	10.53%	÷1.000	×0.720	—	×0.975	7.39%
Triples /BIP	0.00%	÷1.000	×0.720	—	×0.975	0.00%
Home runs /BIP	15.79%	÷1.000	×0.780	—	×0.975	12.01%

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 22 vs a level average of 19.5 → −2.5% on BB/HBP/hit rates (2.5 yr older than average).

Result: wOBA .521 → .387; ISO .440 → .343; BABIP .353 → .260. 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 AABowie Baysox · 555 PAwOBA .310 → .282 (−28)K% 18.6% → 22.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.56%	÷1.000	×1.220	—	—	22.65%
BB% (unintentional) /PA	8.83%	÷0.978	×0.700	—	×1.050	6.64%
HBP% /PA	1.62%	—	×0.850	—	×1.050	1.45%
Singles /BIP	20.94%	÷1.027	×0.970	×0.997	×1.050	20.71%
Doubles /BIP	6.28%	÷0.932	×0.860	×0.997	×1.050	6.07%
Triples /BIP	0.26%	÷0.900	×0.950	×0.997	×1.050	0.29%
Home runs /BIP	3.66%	÷1.018	×0.780	—	×1.050	2.95%

Factors used: league row EL. Park (home-season share, regressed): HR 104, 1B 105, 2B 86, 3B 80, K 100, BB 96 — applied at half strength. Age: age 21 vs a level average of 24.8 → +5.0% on BB/HBP/hit rates (3.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.997.

Result: wOBA .310 → .282; ISO .140 → .123; BABIP .282 → .275. 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 A+Frederick Keys · 329 PAwOBA .305 → .273 (−32)K% 13.4% → 18.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.37%	÷1.027	×1.420	—	—	18.48%
BB% (unintentional) /PA	6.38%	÷0.936	×0.630	—	×1.042	4.48%
HBP% /PA	1.22%	—	×0.860	—	×1.042	1.09%
Singles /BIP	24.31%	÷1.002	×0.940	×0.996	×1.042	23.67%
Doubles /BIP	4.71%	÷0.904	×0.830	×0.996	×1.042	4.49%
Triples /BIP	0.78%	÷0.900	×0.800	×0.996	×1.042	0.72%
Home runs /BIP	1.96%	÷1.173	×0.900	—	×1.042	1.57%

Factors used: league row CAR. Park (home-season share, regressed): HR 135, 1B 100, 2B 81, 3B 80, K 105, BB 87 — applied at half strength. Age: age 20 vs a level average of 22.8 → +4.2% on BB/HBP/hit rates (2.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.996.

Result: wOBA .305 → .273; ISO .104 → .089; BABIP .304 → .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).

2011 ADelmarva Shorebirds · 238 PAwOBA .381 → .332 (−49)K% 13.4% → 17.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.45%	÷1.016	×1.340	—	—	17.73%
BB% (unintentional) /PA	8.40%	÷1.034	×0.660	—	×1.030	5.52%
HBP% /PA	0.84%	—	×0.770	—	×1.030	0.67%
Singles /BIP	24.44%	÷0.985	×0.920	×0.991	×1.030	23.29%
Doubles /BIP	6.67%	÷0.850	×0.820	×0.991	×1.030	6.56%
Triples /BIP	1.67%	÷1.150	×0.940	×0.991	×1.030	1.39%
Home runs /BIP	4.44%	÷1.135	×0.820	—	×1.030	3.31%

Factors used: league row SAL. Park (home-season share, regressed): HR 127, 1B 97, 2B 70, 3B 130, K 103, 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 ×0.991.

Result: wOBA .381 → .332; ISO .198 → .165; BABIP .337 → .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).

2010 ROKBluefield Orioles · 218 PAwOBA .349 → .277 (−72)K% 9.6% → 13.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.63%	÷1.000	×1.420	—	—	13.68%
BB% (unintentional) /PA	8.26%	÷1.000	×0.640	—	×1.008	5.32%
HBP% /PA	0.46%	—	×0.740	—	×1.008	0.34%
Singles /BIP	20.93%	÷1.000	×0.840	×0.998	×1.008	17.67%
Doubles /BIP	8.72%	÷1.000	×0.740	×0.998	×1.008	6.49%
Triples /BIP	0.58%	÷1.000	×0.740	×0.998	×1.008	0.43%
Home runs /BIP	2.91%	÷1.000	×0.800	—	×1.008	2.34%

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 19 vs a level average of 19.5 → +0.8% on BB/HBP/hit rates (0.5 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.998.

Result: wOBA .349 → .277; ISO .166 → .131; BABIP .308 → .249. 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 A+Frederick Keys · 22 PAwOBA .281 → .236 (−46)K% 18.2% → 25.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.18%	÷1.024	×1.420	—	—	25.20%
BB% (unintentional) /PA	4.55%	÷0.958	×0.630	—	×1.050	3.14%
HBP% /PA	0.00%	—	×0.860	—	×1.050	0.00%
Singles /BIP	11.76%	÷1.031	×0.940	—	×1.050	11.27%
Doubles /BIP	17.65%	÷0.960	×0.830	—	×1.050	16.02%
Triples /BIP	0.00%	÷0.900	×0.800	—	×1.050	0.00%
Home runs /BIP	0.00%	÷1.142	×0.900	—	×1.050	0.00%

Factors used: league row CAR. Park (home-season share, regressed): HR 128, 1B 106, 2B 92, 3B 80, K 105, BB 92 — applied at half strength. Age: age 19 vs a level average of 22.8 → +5.0% on BB/HBP/hit rates (3.8 yr younger than average).

Result: wOBA .281 → .236; ISO .143 → .120; BABIP .294 → .273. 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 — .