Daniel Murphy

Pos2B

B/TL/R

Born1985-04-01

Age (2027)42

No MLB projection yet. Career stats below are historical.

Overview

AAALAST SEASON 2023

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2019	34	MLBCOL	478	.279/.329/.452	13	1	107	-0.3
2020	35	MLBCOL	132	.236/.275/.333	3	0	55	-1.0
2023	38	AAALAA	169	.295/.379/.362	1	0	65	—	.244/.302/.301
2024	39	No MLB games played
2025	40	No MLB games played
2026	41	No MLB games played

Last 3 seasons WAR -1.4

Contract

No contract data on file.

Skills · percentileStatcast 2018-2020

xwOBA48

Speed30

Avg EV17

Barrel%14

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)
2006	21	ROK	Kingsport Mets	—	59	51	10	0	0	2	8	4	0	0	0	196	305	314	619	118	178	7	14	288	33	—	—	—	—	—	—
2007	22	A+	St. Lucie Mets	—	559	502	143	34	3	11	42	61	4	6	3	285	345	430	775	145	299	11	8	339	91	—	—	—	—	—	—
2008	23	MLB	New York Mets	—	151	131	41	9	3	2	18	28	1	0	2	313	400	473	873	160	382	19	12	386	144	+8	-1	-2	-6	+3	0.3
2008	23	AA	Binghamton Mets	—	407	357	110	26	1	13	39	46	3	14	5	308	381	496	877	188	318	11	10	380	120	—	—	—	—	—	—
2009	24	MLB	New York Mets	—	556	508	135	38	4	12	38	69	0	4	2	266	317	427	744	161	284	12	7	324	102	+1	+0	-6	-10	+12	-0.2
2010	25	AAA	Buffalo Bisons	—	35	34	10	3	0	1	1	2	0	1	0	294	314	471	785	176	290	6	3	345	84	—	—	—	—	—	—
2010	25	INTL	Aguilas Cibaenas	—	119	103	33	6	1	4	13	11	1	5	1	320	402	515	916	194	322	9	11	398	155	—	—	—	—	—	—
2011	26	MLB	New York Mets	—	423	391	125	28	2	6	24	42	3	5	5	320	364	448	811	128	345	10	6	357	133	+17	-1	+1	-8	+9	1.8
2012	27	MLB	New York Mets	—	612	571	166	40	3	6	36	82	1	10	2	291	334	403	737	112	329	13	6	327	111	+8	+1	-7	+2	+13	1.8
2013	28	MLB	New York Mets	—	697	658	188	38	4	13	32	95	2	23	3	286	321	415	736	129	315	14	5	325	111	+9	+3	-4	+2	+15	2.7
2014	29	MLB	New York Mets	—	642	596	172	37	2	9	39	86	2	13	5	289	334	403	737	114	322	13	6	328	112	+9	+1	-4	+2	+14	2.3
2015	30	MLB	New York Mets	—	538	499	140	38	2	14	31	38	2	2	2	281	325	449	774	168	278	7	6	338	113	+8	-0	-1	+2	+12	2.1
2015	30	A+	St. Lucie Mets	—	17	15	7	2	0	0	2	0	0	0	0	467	529	600	1.129	133	467	0	12	499	163	—	—	—	—	—	—
2016	31	MLB	Washington Nationals	—	582	531	184	47	5	25	35	57	8	5	3	347	395	595	991	249	348	10	6	425	167	+46	-0	-12	+2	+13	5.0
2017	32	MLB	Washington Nationals	—	593	534	172	43	3	23	52	77	4	2	0	322	386	543	930	221	341	13	9	405	149	+35	+0	-9	+2	+13	4.2
2018	33	MLB	Chicago Cubs	—	351	328	98	15	0	12	20	40	0	3	0	299	339	454	793	155	308	11	6	349	120	+8	+1	-9	+1	+8	0.9
2018	33	AA	Harrisburg Senators	—	44	37	9	2	0	2	6	4	1	0	0	243	364	459	823	216	226	9	14	368	82	—	—	—	—	—	—
2019	34	MLB	Colorado Rockies	—	478	438	122	35	1	13	32	74	2	1	1	279	329	452	781	174	307	15	7	342	107	+4	-0	-9	-9	+10	-0.3
2019	34	AAA	Albuquerque Isotopes	—	16	13	2	1	0	0	3	3	0	0	0	154	312	231	543	77	200	19	19	268	15	—	—	—	—	—	—
2020	35	MLB	Colorado Rockies	—	132	123	29	3	0	3	7	21	0	0	0	236	275	333	608	98	260	16	5	271	55	-7	+0	-3	-2	+3	-1.0
2023	38	AAA	Salt Lake Bees	—	169	149	44	7	0	1	20	18	0	0	0	295	379	362	741	67	331	11	12	341	65	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	5755	5308	1572	371	29	138	364	709	25	68	25	296	344	455	799	159	318	12	6	351	123	+161	+4	-64	-20	+125	20.9
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2006	21	ROK MLE	Kingsport Mets	—	59	54	9	0	0	2	5	6	0	0	0	167	237	278	515	111	149	10	8	217	33	—	—	—	—	—	—
2007	22	A+ MLE	St. Lucie Mets	—	559	516	133	33	2	10	28	87	4	6	3	258	301	388	689	130	279	16	5	302	91	—	—	—	—	—	—
2008	23	MLB MLE	New York Mets	—	151	132	41	9	3	2	17	28	1	0	2	311	393	470	863	159	382	19	11	381	144	+8	-1	-2	-6	+3	0.3
2008	23	AA MLE	Binghamton Mets	—	407	373	110	24	1	11	23	56	3	14	5	295	341	453	794	158	308	14	6	346	120	—	—	—	—	—	—
2009	24	MLB MLE	New York Mets	—	556	512	135	38	4	12	34	69	0	4	2	264	310	424	733	160	284	12	6	319	102	+1	+0	-6	-10	+12	-0.2
2010	25	AAA MLE	Buffalo Bisons	—	35	34	9	2	0	1	1	2	0	1	0	265	286	412	697	147	264	6	3	292	84	—	—	—	—	—	—
2011	26	MLB MLE	New York Mets	—	423	393	129	30	2	6	22	42	3	5	5	328	368	461	829	132	345	10	5	365	133	+17	-1	+1	-8	+9	1.8
2012	27	MLB MLE	New York Mets	—	612	576	171	42	3	6	31	82	1	10	2	297	334	411	745	115	329	13	5	332	111	+8	+1	-7	+2	+13	1.8
2013	28	MLB MLE	New York Mets	—	697	660	193	40	4	13	30	95	2	23	3	292	325	424	749	132	315	14	4	332	111	+9	+3	-4	+2	+15	2.7
2014	29	MLB MLE	New York Mets	—	642	599	177	39	2	9	36	86	2	13	5	295	338	412	750	117	322	13	6	334	112	+9	+1	-4	+2	+14	2.3
2015	30	MLB MLE	New York Mets	—	538	509	144	40	2	14	21	38	2	2	2	283	314	452	766	169	278	7	4	335	113	+8	-0	-1	+2	+12	2.1
2015	30	A+ MLE	St. Lucie Mets	—	17	16	6	2	0	0	1	0	0	0	0	375	412	500	912	125	389	0	6	409	163	—	—	—	—	—	—
2016	31	MLB MLE	Washington Nationals	—	582	541	184	46	6	25	25	57	8	5	3	340	378	586	964	246	348	10	4	415	167	+46	-0	-12	+2	+13	5.0
2017	32	MLB MLE	Washington Nationals	—	593	548	172	42	4	23	38	77	4	2	0	314	363	531	894	217	341	13	6	389	149	+35	+0	-9	+2	+13	4.2
2018	33	MLB MLE	Chicago Cubs	—	351	330	98	15	0	12	18	40	0	3	0	297	333	452	785	155	308	11	5	346	120	+8	+1	-9	+1	+8	0.9
2018	33	AA MLE	Harrisburg Senators	—	44	39	9	2	0	2	4	5	1	0	0	231	318	436	754	205	194	11	9	289	82	—	—	—	—	—	—
2019	34	MLB MLE	Colorado Rockies	—	478	441	118	34	1	12	31	74	2	1	1	268	319	431	749	163	307	15	6	327	107	+4	-0	-9	-9	+10	-0.3
2019	34	AAA MLE	Albuquerque Isotopes	—	16	14	2	1	0	0	2	4	0	0	0	143	250	214	464	71	158	25	12	191	15	—	—	—	—	—	—
2020	35	MLB MLE	Colorado Rockies	—	132	126	28	3	0	3	5	21	0	0	0	222	252	317	569	95	260	16	4	250	55	-7	+0	-3	-2	+3	-1.0
2023	38	AAA MLE	Salt Lake Bees	—	169	156	38	6	0	1	13	21	0	0	0	244	302	301	603	58	275	12	8	265	65	—	—	—	—	—	—
2006	21	ROK MLE	Kingsport Mets	—	59	54	9	0	0	2	5	6	0	0	0	167	237	278	515	111	152	10	8	217	33	—	—	—	—	—	—
2006	21	ROK RAW	Kingsport Mets	—	59	51	10	0	0	2	8	4	0	0	0	196	305	314	619	118	178	7	14	288	33	—	—	—	—	—	—
2007	22	A+ MLE	St. Lucie Mets	—	559	516	133	33	2	10	28	87	4	6	3	258	301	388	689	130	286	16	5	302	91	—	—	—	—	—	—
2007	22	A+ RAW	St. Lucie Mets	—	559	502	143	34	3	11	42	61	4	6	3	285	345	430	775	145	299	11	8	339	91	—	—	—	—	—	—
2008	23	AA MLE	2 teams	—	558	505	151	33	4	13	40	84	4	14	7	299	355	457	813	158	332	15	7	356	127	—	—	—	—	—	—
2008	23	AA RAW	2 teams	—	558	488	151	35	4	15	57	74	4	14	7	309	386	490	876	180	334	13	10	382	127	—	—	—	—	—	—
2009	24	MLB MLE	New York Mets	—	556	512	135	38	4	12	34	69	0	4	2	264	310	424	733	160	281	12	6	319	102	+1	+0	-6	-10	+12	-0.2
2009	24	MLB RAW	New York Mets	—	556	508	135	38	4	12	38	69	0	4	2	266	317	427	744	161	284	12	7	324	102	+1	+0	-6	-10	+12	-0.2
2010	25	INTL MLE	2 teams	—	154	148	40	10	0	4	3	11	1	6	1	270	289	419	708	149	267	7	2	292	84	—	—	—	—	—	—
2010	25	INTL RAW	2 teams	—	154	137	43	9	1	5	14	13	1	6	1	314	382	504	885	190	314	8	9	386	84	—	—	—	—	—	—
2011	26	MLB MLE	New York Mets	—	423	393	129	30	2	6	22	42	3	5	5	328	368	461	829	132	354	10	5	365	133	+17	-1	+1	-8	+9	1.8
2011	26	MLB RAW	New York Mets	—	423	391	125	28	2	6	24	42	3	5	5	320	364	448	811	128	345	10	6	357	133	+17	-1	+1	-8	+9	1.8
2012	27	MLB MLE	New York Mets	—	612	576	171	42	3	6	31	82	1	10	2	297	334	411	745	115	335	13	5	332	111	+8	+1	-7	+2	+13	1.8
2012	27	MLB RAW	New York Mets	—	612	571	166	40	3	6	36	82	1	10	2	291	334	403	737	112	329	13	6	327	111	+8	+1	-7	+2	+13	1.8
2013	28	MLB MLE	New York Mets	—	697	660	193	40	4	13	30	95	2	23	3	292	325	424	749	132	323	14	4	332	111	+9	+3	-4	+2	+15	2.7
2013	28	MLB RAW	New York Mets	—	697	658	188	38	4	13	32	95	2	23	3	286	321	415	736	129	315	14	5	325	111	+9	+3	-4	+2	+15	2.7
2014	29	MLB MLE	New York Mets	—	642	599	177	39	2	9	36	86	2	13	5	295	338	412	750	117	330	13	6	334	112	+9	+1	-4	+2	+14	2.3
2014	29	MLB RAW	New York Mets	—	642	596	172	37	2	9	39	86	2	13	5	289	334	403	737	114	322	13	6	328	112	+9	+1	-4	+2	+14	2.3
2015	30	MLB MLE	2 teams	—	555	525	150	42	2	14	22	38	2	2	2	286	317	453	770	168	284	7	4	337	114	+10	-0	-1	+2	+12	2.2
2015	30	MLB RAW	2 teams	—	555	514	147	40	2	14	33	38	2	2	2	286	332	453	785	167	284	7	6	343	114	+10	-0	-1	+2	+12	2.2
2016	31	MLB MLE	Washington Nationals	—	582	541	184	46	6	25	25	57	8	5	3	340	378	586	964	246	340	10	4	415	167	+46	-0	-12	+2	+13	5.0
2016	31	MLB RAW	Washington Nationals	—	582	531	184	47	5	25	35	57	8	5	3	347	395	595	991	249	348	10	6	425	167	+46	-0	-12	+2	+13	5.0
2017	32	MLB MLE	Washington Nationals	—	593	548	172	42	4	23	38	77	4	2	0	314	363	531	894	217	330	13	6	389	149	+35	+0	-9	+2	+13	4.2
2017	32	MLB RAW	Washington Nationals	—	593	534	172	43	3	23	52	77	4	2	0	322	386	543	930	221	341	13	9	405	149	+35	+0	-9	+2	+13	4.2
2018	33	MLB MLE	2 teams	—	395	369	106	16	0	14	22	45	1	3	0	287	329	444	774	157	294	11	6	339	116	+7	+1	-9	+1	+9	0.9
2018	33	MLB RAW	2 teams	—	395	365	107	17	0	14	26	44	1	3	0	293	342	455	797	162	300	11	7	351	116	+7	+1	-9	+1	+9	0.9
2019	34	MLB MLE	2 teams	—	494	455	119	34	1	12	33	78	2	1	1	262	314	420	734	158	290	16	7	322	104	+2	-0	-9	-9	+11	-0.5
2019	34	MLB RAW	2 teams	—	494	451	124	36	1	13	35	77	2	1	1	275	329	446	774	171	304	16	7	340	104	+2	-0	-9	-9	+11	-0.5
2020	35	MLB MLE	Colorado Rockies	—	132	126	28	3	0	3	5	21	0	0	0	222	252	317	569	95	243	16	4	250	55	-7	+0	-3	-2	+3	-1.0
2020	35	MLB RAW	Colorado Rockies	—	132	123	29	3	0	3	7	21	0	0	0	236	275	333	608	98	260	16	5	271	55	-7	+0	-3	-2	+3	-1.0
2023	38	AAA MLE	Salt Lake Bees	—	169	156	38	6	0	1	13	21	0	0	0	244	302	301	603	58	276	12	8	265	65	—	—	—	—	—	—
2023	38	AAA RAW	Salt Lake Bees	—	169	149	44	7	0	1	20	18	0	0	0	295	379	362	741	67	331	11	12	341	65	—	—	—	—	—	—
2006	21	ROK MLE	Kingsport Mets	—	59	54	9	0	0	2	5	6	0	0	0	167	237	278	515	111	149	10	8	217	33	—	—	—	—	—	—
2007	22	A+ MLE	St. Lucie Mets	—	559	522	133	33	2	10	28	87	4	6	3	255	301	383	684	128	279	16	5	302	91	—	—	—	—	—	—
2008	23	AA MLE	2 teams	—	558	509	151	33	4	13	40	84	4	14	7	297	355	454	809	157	328	15	7	356	127	—	—	—	—	—	—
2009	24	MLB MLE	New York Mets	—	556	517	135	38	4	12	34	69	0	4	2	261	310	420	729	159	284	12	6	319	102	—	—	—	—	—	—
2010	25	INTL MLE	2 teams	—	154	149	40	10	0	4	3	11	1	6	1	268	289	416	706	148	264	7	2	292	84	—	—	—	—	—	—
2011	26	MLB MLE	New York Mets	—	423	395	129	30	2	6	22	42	3	5	5	327	368	458	827	132	345	10	5	365	133	—	—	—	—	—	—
2012	27	MLB MLE	New York Mets	—	612	575	171	42	3	6	31	82	1	10	2	297	334	412	746	115	329	13	5	332	111	—	—	—	—	—	—
2013	28	MLB MLE	New York Mets	—	697	660	193	40	4	13	30	95	2	23	3	292	325	424	749	132	315	14	4	332	111	—	—	—	—	—	—
2014	29	MLB MLE	New York Mets	—	642	599	177	39	2	9	36	86	2	13	5	295	338	412	750	117	322	13	6	334	112	—	—	—	—	—	—
2015	30	MLB MLE	2 teams	—	555	526	150	42	2	14	22	38	2	2	2	285	317	452	769	167	282	7	4	337	114	—	—	—	—	—	—
2016	31	MLB MLE	Washington Nationals	—	582	544	184	46	6	25	25	57	8	5	3	338	378	583	961	244	348	10	4	415	167	—	—	—	—	—	—
2017	32	MLB MLE	Washington Nationals	—	593	546	172	42	4	23	38	77	4	2	0	315	363	533	896	218	341	13	6	389	149	—	—	—	—	—	—
2018	33	MLB MLE	2 teams	—	395	369	106	16	0	14	22	45	1	3	0	287	329	444	774	157	296	11	6	339	116	—	—	—	—	—	—
2019	34	MLB MLE	2 teams	—	494	456	119	34	1	12	33	78	2	1	1	261	314	419	733	158	302	16	7	322	104	—	—	—	—	—	—
2020	35	MLB MLE	Colorado Rockies	—	132	126	28	3	0	3	5	21	0	0	0	222	252	317	569	95	260	16	4	250	55	—	—	—	—	—	—
2023	38	AAA MLE	Salt Lake Bees	—	169	154	38	6	0	1	13	21	0	0	0	247	302	305	607	58	275	12	8	265	65	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	5755	5308	1572	371	29	138	364	709	25	68	25	296	344	455	799	159	318	12	6	351	116	+161	+4	-64	-20	+125	20.9
/ 162G	—	A+C		—	650	600	178	42	3	16	41	80	3	8	3	297	345	457	801	160	318	12	6	351	116	+18	+0	-7	-2	+14	2.4
Career	—	MLE		—	7180	6683	1935	454	34	167	387	899	34	94	34	290	332	443	774	153	311	13	5	339	116	+161	+4	-64	-20	+125	20.9
/ 162G	—	MLE		—	650	605	175	41	3	15	35	81	3	9	3	289	331	441	773	152	311	12	5	339	116	+18	+0	-7	-2	+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.

2023 AAASalt Lake Bees · 169 PAwOBA .333 → .265 (−67)K% 10.7% → 12.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.65%	÷1.010	×1.200	—	—	12.66%
BB% (unintentional) /PA	11.83%	÷1.005	×0.690	—	×0.940	7.64%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	27.48%	÷1.058	×0.930	×1.012	×0.940	22.98%
Doubles /BIP	5.34%	÷0.967	×0.830	×1.012	×0.940	4.36%
Triples /BIP	0.00%	÷0.900	×0.970	×1.012	×0.940	0.00%
Home runs /BIP	0.76%	÷1.003	×0.700	—	×0.940	0.50%

Factors used: league row International League. Park (home-season share, regressed): HR 101, 1B 112, 2B 93, 3B 80, K 102, BB 101 — applied at half strength. Age: age 38 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (10.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.012.

Result: wOBA .333 → .265; ISO .067 → .053; BABIP .331 → .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).

2019 AAAAlbuquerque Isotopes · 16 PAwOBA .262 → .191 (−71)K% 18.8% → 25.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.75%	÷0.957	×1.290	—	—	25.28%
BB% (unintentional) /PA	18.75%	÷0.974	×0.710	—	×0.940	12.85%
HBP% /PA	0.00%	—	×0.980	—	×0.940	0.00%
Singles /BIP	10.00%	÷0.974	×0.900	—	×0.940	8.69%
Doubles /BIP	10.00%	÷1.071	×0.810	—	×0.940	7.11%
Triples /BIP	0.00%	÷1.150	×0.810	—	×0.940	0.00%
Home runs /BIP	0.00%	÷1.136	×0.780	—	×0.940	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 127, 1B 95, 2B 114, 3B 130, K 91, BB 95 — applied at half strength. Age: age 34 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (6.8 yr older than average).

Result: wOBA .262 → .191; ISO .077 → .054; BABIP .200 → .158. 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).

2018 AAHarrisburg Senators · 44 PAwOBA .358 → .289 (−69)K% 9.1% → 11.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.09%	÷0.987	×1.220	—	—	11.24%
BB% (unintentional) /PA	13.64%	÷1.037	×0.700	—	×0.940	8.65%
HBP% /PA	2.27%	—	×0.850	—	×0.940	1.82%
Singles /BIP	15.15%	÷1.017	×0.970	—	×0.940	13.59%
Doubles /BIP	6.06%	÷1.012	×0.860	—	×0.940	4.84%
Triples /BIP	0.00%	÷1.070	×0.950	—	×0.940	0.00%
Home runs /BIP	6.06%	÷0.925	×0.780	—	×0.940	4.80%

Factors used: league row EL. Park (home-season share, regressed): HR 85, 1B 103, 2B 102, 3B 114, K 97, BB 107 — applied at half strength. Age: age 33 vs a level average of 24.8 → −6.0% on BB/HBP/hit rates (8.2 yr older than average).

Result: wOBA .358 → .289; ISO .216 → .179; BABIP .226 → .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).

2015 A+St. Lucie Mets · 17 PAwOBA .486 → .409 (−78)K% 0.0% → 0.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	0.00%	÷0.992	×1.420	—	—	0.00%
BB% (unintentional) /PA	11.76%	÷1.023	×0.750	—	×0.940	8.11%
HBP% /PA	0.00%	—	×0.930	—	×0.940	0.00%
Singles /BIP	33.33%	÷1.049	×0.920	—	×0.940	27.47%
Doubles /BIP	13.33%	÷1.055	×0.960	—	×0.940	11.41%
Triples /BIP	0.00%	÷0.900	×0.810	—	×0.940	0.00%
Home runs /BIP	0.00%	÷1.269	×0.880	—	×0.940	0.00%

Factors used: league row FSL. Park (home-season share, regressed): HR 154, 1B 110, 2B 111, 3B 80, K 98, BB 105 — 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 .486 → .409; ISO .133 → .119; BABIP .467 → .389. 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 AAABuffalo Bisons · 35 PAwOBA .335 → .292 (−43)K% 5.7% → 7.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	5.71%	÷0.984	×1.200	—	—	6.97%
BB% (unintentional) /PA	2.86%	÷0.958	×0.690	—	×1.033	2.13%
HBP% /PA	0.00%	—	×0.940	—	×1.033	0.00%
Singles /BIP	18.75%	÷0.969	×0.930	—	×1.033	18.59%
Doubles /BIP	9.38%	÷1.133	×0.830	—	×1.033	7.10%
Triples /BIP	0.00%	÷1.150	×0.970	—	×1.033	0.00%
Home runs /BIP	3.12%	÷1.026	×0.840	—	×1.033	2.64%

Factors used: league row IL. Park (home-season share, regressed): HR 105, 1B 94, 2B 127, 3B 130, K 97, BB 92 — 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).

Result: wOBA .335 → .292; ISO .176 → .141; BABIP .290 → .264. 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 AABinghamton Mets · 407 PAwOBA .371 → .346 (−25)K% 11.3% → 13.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.30%	÷1.000	×1.220	—	—	13.79%
BB% (unintentional) /PA	7.86%	÷1.000	×0.700	—	×1.027	5.65%
HBP% /PA	0.74%	—	×0.850	—	×1.027	0.64%
Singles /BIP	22.51%	÷1.000	×0.970	×1.009	×1.027	22.63%
Doubles /BIP	8.36%	÷1.000	×0.860	×1.009	×1.027	7.45%
Triples /BIP	0.32%	÷1.000	×0.950	×1.009	×1.027	0.32%
Home runs /BIP	4.18%	÷1.000	×0.780	—	×1.027	3.35%

Factors used: league row EL. 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 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 ×1.009.

Result: wOBA .371 → .346; ISO .188 → .165; BABIP .318 → .308. 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 A+St. Lucie Mets · 559 PAwOBA .334 → .302 (−32)K% 10.9% → 15.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.91%	÷1.000	×1.420	—	—	15.50%
BB% (unintentional) /PA	6.62%	÷1.000	×0.750	—	×1.012	5.02%
HBP% /PA	0.72%	—	×0.930	—	×1.012	0.67%
Singles /BIP	21.54%	÷1.000	×0.920	×0.997	×1.012	19.99%
Doubles /BIP	7.71%	÷1.000	×0.960	×0.997	×1.012	7.46%
Triples /BIP	0.68%	÷1.000	×0.810	×0.997	×1.012	0.56%
Home runs /BIP	2.49%	÷1.000	×0.880	—	×1.012	2.22%

Factors used: league row FSL. 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 ×0.997.

Result: wOBA .334 → .302; ISO .145 → .134; BABIP .299 → .279. 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 ROKKingsport Mets · 59 PAwOBA .281 → .217 (−63)K% 6.8% → 9.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	6.78%	÷1.000	×1.420	—	—	9.63%
BB% (unintentional) /PA	13.56%	÷1.000	×0.640	—	×0.985	8.55%
HBP% /PA	0.00%	—	×0.740	—	×0.985	0.00%
Singles /BIP	17.02%	÷1.000	×0.840	×1.020	×0.985	14.37%
Doubles /BIP	0.00%	÷1.000	×0.740	×1.020	×0.985	0.00%
Triples /BIP	0.00%	÷1.000	×0.740	×1.020	×0.985	0.00%
Home runs /BIP	4.26%	÷1.000	×0.800	—	×0.985	3.35%

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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.020.

Result: wOBA .281 → .217; ISO .118 → .095; BABIP .178 → .149. 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 — .