Tom Murphy

PosC

B/TR/R

Born1991-04-03

Age (2027)36

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2024

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2022	31	MLBSEA	42	.303/.429/.455	1	0	155	0.5
2023	32	MLBSEA	159	.290/.338/.538	8	0	143	1.1
2024	33	MLBSF	38	.118/.211/.235	1	0	31	-0.4
2025	34	No MLB games played
2026	35	No MLB games played

Last 3 seasons WAR 1.1

Contract

No contract data on file.

Skills · percentileStatcast 2022-2024

Barrel%90

xwOBA82

Avg EV63

Bat Spd60

Speed22

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)
2013	22	AA	Tulsa Drillers	—	74	69	20	5	0	3	4	16	1	0	0	290	338	493	831	203	340	22	5	366	86	—	—	—	—	—	—
2013	22	A	Asheville Tourists	—	341	288	83	26	2	19	37	87	10	4	5	288	388	590	978	302	346	26	11	416	70	—	—	—	—	—	—
2014	23	AA	Tulsa Drillers	—	109	94	20	4	0	5	14	27	1	0	0	213	321	415	736	202	242	25	13	330	66	—	—	—	—	—	—
2015	24	MLB	Colorado Rockies	—	39	35	9	1	0	3	4	10	0	0	0	257	333	543	876	286	273	26	10	381	117	+1	+0	-2	+8	+1	0.8
2015	24	AAA	Albuquerque Isotopes	—	136	129	35	9	2	7	5	43	1	0	1	271	304	535	839	264	350	32	4	360	58	—	—	—	—	—	—
2015	24	AA	New Britain Rock Cats	—	294	265	65	17	1	13	23	80	5	5	2	245	317	464	782	219	301	27	8	343	60	—	—	—	—	—	—
2016	25	MLB	Colorado Rockies	—	49	44	12	2	0	5	4	19	1	1	0	273	347	659	1.006	386	350	39	8	429	158	+3	+0	+1	+7	+1	1.3
2016	25	AAA	Albuquerque Isotopes	—	322	303	99	26	7	19	16	78	1	1	1	327	361	647	1.008	320	386	24	5	431	88	—	—	—	—	—	—
2017	26	MLB	Colorado Rockies	—	26	24	1	1	0	0	2	9	0	0	0	42	115	83	199	42	67	35	8	103	-63	-5	+0	+1	+3	+1	-0.1
2017	26	AAA	Albuquerque Isotopes	—	154	141	36	10	1	4	9	56	3	0	0	255	314	426	739	170	390	36	6	326	32	—	—	—	—	—	—
2018	27	MLB	Colorado Rockies	—	96	93	21	7	1	2	3	44	0	0	1	226	250	387	637	161	404	46	3	279	59	-5	-0	+1	+6	+2	0.5
2018	27	AAA	Albuquerque Isotopes	—	264	236	61	16	3	17	22	76	5	4	2	258	335	568	902	309	306	29	8	388	63	—	—	—	—	—	—
2019	28	MLB	Seattle Mariners	—	281	260	71	12	1	18	19	87	1	2	0	273	325	535	860	262	340	31	7	372	142	+14	+0	+12	+5	+6	3.8
2021	30	MLB	Seattle Mariners	—	325	277	56	8	0	11	40	99	2	0	0	202	304	350	655	148	265	30	12	295	88	-5	+0	+1	+6	+7	0.9
2022	31	MLB	Seattle Mariners	—	42	33	10	2	0	1	8	13	0	0	0	303	429	455	883	152	474	31	19	394	155	+3	+0	+1	+1	+1	0.5
2023	32	MLB	Seattle Mariners	—	159	145	42	12	0	8	10	44	1	0	0	290	338	538	876	248	358	28	6	375	143	+8	+0	-4	+3	+3	1.1
2024	33	MLB	San Francisco Giants	—	38	34	4	1	0	1	4	14	0	0	0	118	211	235	446	118	158	37	11	210	31	-3	+0	-3	+1	+1	-0.4
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1055	945	226	46	2	49	94	339	5	3	1	239	310	448	757	208	314	32	9	332	111	+14	+0	+8	+40	+23	8.6
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2013	22	AA MLE	Tulsa Drillers	—	74	70	18	5	0	2	3	20	1	0	0	257	297	414	712	157	316	27	4	295	86	—	—	—	—	—	—
2013	22	A MLE	Asheville Tourists	—	341	301	58	15	2	11	24	124	10	4	5	193	275	365	640	173	264	36	7	271	70	—	—	—	—	—	—
2014	23	AA MLE	Tulsa Drillers	—	109	98	17	3	0	4	10	33	1	0	0	173	257	327	583	153	216	30	9	266	66	—	—	—	—	—	—
2015	24	MLB MLE	Colorado Rockies	—	39	36	9	1	0	3	3	10	0	0	0	250	308	528	835	278	273	26	8	341	117	+1	+0	-2	+8	+1	0.8
2015	24	AAA MLE	Albuquerque Isotopes	—	136	131	26	6	2	4	3	58	1	0	1	198	222	366	589	168	305	43	2	254	58	—	—	—	—	—	—
2015	24	AA MLE	New Britain Rock Cats	—	294	271	51	13	1	8	17	100	5	5	2	188	249	332	581	144	262	34	6	257	60	—	—	—	—	—	—
2016	25	MLB MLE	Colorado Rockies	—	49	44	11	2	0	4	4	19	1	1	0	250	327	568	895	318	350	39	8	402	158	+3	+0	+1	+7	+1	1.3
2016	25	AAA MLE	Albuquerque Isotopes	—	322	309	74	19	5	12	11	106	1	1	1	239	268	450	718	210	320	33	3	299	88	—	—	—	—	—	—
2017	26	MLB MLE	Colorado Rockies	—	26	25	1	1	0	0	1	9	0	0	0	40	77	80	157	40	67	35	4	75	-63	-5	+0	+1	+3	+1	-0.1
2017	26	AAA MLE	Albuquerque Isotopes	—	154	144	24	6	1	2	6	76	3	0	0	167	216	264	480	97	323	49	4	215	32	—	—	—	—	—	—
2018	27	MLB MLE	Colorado Rockies	—	96	94	21	7	1	2	2	44	0	0	1	223	240	383	623	160	404	46	2	256	59	-5	-0	+1	+6	+2	0.5
2018	27	AAA MLE	Albuquerque Isotopes	—	264	243	43	12	2	10	15	100	5	4	2	177	240	366	606	189	240	38	6	261	63	—	—	—	—	—	—
2019	28	MLB MLE	Seattle Mariners	—	281	260	71	12	1	18	19	87	1	2	0	273	325	535	860	262	340	31	7	378	142	+14	+0	+12	+5	+6	3.8
2021	30	MLB MLE	Seattle Mariners	—	325	280	56	8	0	11	40	99	2	0	0	200	304	346	651	146	265	30	12	298	88	-5	+0	+1	+6	+7	0.9
2022	31	MLB MLE	Seattle Mariners	—	42	34	10	2	0	1	8	13	0	0	0	294	429	441	870	147	474	31	19	397	155	+3	+0	+1	+1	+1	0.5
2023	32	MLB MLE	Seattle Mariners	—	159	146	42	12	0	8	10	44	1	0	0	288	338	534	872	247	358	28	6	380	143	+8	+0	-4	+3	+3	1.1
2024	33	MLB MLE	San Francisco Giants	—	38	34	4	1	0	1	4	14	0	0	0	118	211	235	446	118	158	37	11	215	31	-3	+0	-3	+1	+1	-0.4
2013	22	A MLE	2 teams	—	415	371	76	20	2	13	27	144	11	4	5	205	279	375	653	170	290	35	7	275	72	—	—	—	—	—	—
2013	22	A RAW	2 teams	—	415	357	103	31	2	22	41	103	11	4	5	289	379	571	950	283	345	25	10	407	72	—	—	—	—	—	—
2014	23	AA MLE	Tulsa Drillers	—	109	98	17	3	0	4	10	33	1	0	0	173	257	327	583	153	213	30	9	266	66	—	—	—	—	—	—
2014	23	AA RAW	Tulsa Drillers	—	109	94	20	4	0	5	14	27	1	0	0	213	321	415	736	202	242	25	13	330	66	—	—	—	—	—	—
2015	24	AA MLE	3 teams	—	469	438	87	20	3	16	23	168	6	5	3	199	248	368	616	169	277	36	5	263	64	—	—	—	—	—	—
2015	24	AA RAW	3 teams	—	469	429	109	27	3	23	32	133	6	5	3	254	315	492	807	238	313	28	7	351	64	—	—	—	—	—	—
2016	25	AAA MLE	2 teams	—	371	353	85	21	5	16	15	125	2	2	1	241	276	465	740	224	324	34	4	313	98	—	—	—	—	—	—
2016	25	AAA RAW	2 teams	—	371	347	111	28	7	24	20	97	2	2	1	320	359	648	1.008	329	383	26	5	430	98	—	—	—	—	—	—
2017	26	AAA MLE	2 teams	—	180	169	25	7	1	2	7	85	3	0	0	148	196	237	432	89	277	47	4	195	18	—	—	—	—	—	—
2017	26	AAA RAW	2 teams	—	180	165	37	11	1	4	11	65	3	0	0	224	285	376	661	152	340	36	6	294	18	—	—	—	—	—	—
2018	27	AAA MLE	2 teams	—	360	337	62	18	3	12	17	144	5	4	3	184	234	362	596	178	275	40	5	260	62	—	—	—	—	—	—
2018	27	AAA RAW	2 teams	—	360	329	82	23	4	19	25	120	5	4	3	249	312	517	829	267	330	33	7	359	62	—	—	—	—	—	—
2019	28	MLB MLE	Seattle Mariners	—	281	260	71	12	1	18	19	87	1	2	0	273	325	535	860	262	340	31	7	378	142	+14	+0	+12	+5	+6	3.8
2019	28	MLB RAW	Seattle Mariners	—	281	260	71	12	1	18	19	87	1	2	0	273	325	535	860	262	340	31	7	372	142	+14	+0	+12	+5	+6	3.8
2021	30	MLB MLE	Seattle Mariners	—	325	280	56	8	0	11	40	99	2	0	0	200	304	346	651	146	260	30	12	298	88	-5	+0	+1	+6	+7	0.9
2021	30	MLB RAW	Seattle Mariners	—	325	277	56	8	0	11	40	99	2	0	0	202	304	350	655	148	265	30	12	295	88	-5	+0	+1	+6	+7	0.9
2022	31	MLB MLE	Seattle Mariners	—	42	34	10	2	0	1	8	13	0	0	0	294	429	441	870	147	450	31	19	397	155	+3	+0	+1	+1	+1	0.5
2022	31	MLB RAW	Seattle Mariners	—	42	33	10	2	0	1	8	13	0	0	0	303	429	455	883	152	474	31	19	394	155	+3	+0	+1	+1	+1	0.5
2023	32	MLB MLE	Seattle Mariners	—	159	146	42	12	0	8	10	44	1	0	0	288	338	534	872	247	354	28	6	380	143	+8	+0	-4	+3	+3	1.1
2023	32	MLB RAW	Seattle Mariners	—	159	145	42	12	0	8	10	44	1	0	0	290	338	538	876	248	358	28	6	375	143	+8	+0	-4	+3	+3	1.1
2024	33	MLB MLE	San Francisco Giants	—	38	34	4	1	0	1	4	14	0	0	0	118	211	235	446	118	158	37	11	215	31	-3	+0	-3	+1	+1	-0.4
2024	33	MLB RAW	San Francisco Giants	—	38	34	4	1	0	1	4	14	0	0	0	118	211	235	446	118	158	37	11	210	31	-3	+0	-3	+1	+1	-0.4
2013	22	A MLE	2 teams	—	415	374	76	20	2	13	27	144	11	4	5	203	279	372	650	168	273	35	7	275	72	—	—	—	—	—	—
2014	23	AA MLE	Tulsa Drillers	—	109	97	17	3	0	4	10	33	1	0	0	175	257	330	587	155	216	30	9	266	66	—	—	—	—	—	—
2015	24	AA MLE	3 teams	—	469	437	87	20	3	16	23	168	6	5	3	199	248	368	617	169	275	36	5	263	64	—	—	—	—	—	—
2016	25	AAA MLE	2 teams	—	371	351	85	21	5	16	15	125	2	2	1	242	276	467	743	225	324	34	4	313	98	—	—	—	—	—	—
2017	26	AAA MLE	2 teams	—	180	168	25	7	1	2	7	85	3	0	0	149	196	238	434	89	286	47	4	195	18	—	—	—	—	—	—
2018	27	AAA MLE	2 teams	—	360	335	62	18	3	12	17	144	5	4	3	185	234	364	598	179	284	40	5	260	62	—	—	—	—	—	—
2019	28	MLB MLE	Seattle Mariners	—	281	259	71	12	1	18	19	87	1	2	0	274	325	537	862	263	340	31	7	378	142	—	—	—	—	—	—
2021	30	MLB MLE	Seattle Mariners	—	325	280	56	8	0	11	40	99	2	0	0	200	304	346	651	146	265	30	12	298	88	—	—	—	—	—	—
2022	31	MLB MLE	Seattle Mariners	—	42	34	10	2	0	1	8	13	0	0	0	294	429	441	870	147	474	31	19	397	155	—	—	—	—	—	—
2023	32	MLB MLE	Seattle Mariners	—	159	147	42	12	0	8	10	44	1	0	0	286	338	531	868	245	358	28	6	380	143	—	—	—	—	—	—
2024	33	MLB MLE	San Francisco Giants	—	38	34	4	1	0	1	4	14	0	0	0	118	211	235	446	118	158	37	11	215	31	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	33	MLB MLE	San Francisco Giants	—	38	34	4	1	0	1	4	14	0	0	0	118	211	235	446	118	158	37	11	215	31	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1055	945	226	46	2	49	94	339	5	3	1	239	310	448	757	208	314	32	9	332	83	+14	+0	+8	+40	+23	8.6
/ 162G	—	A+C		—	650	582	138	28	1	30	58	209	3	2	1	237	308	443	751	206	311	32	9	332	83	+8	+0	+5	+24	+14	5.3
Career	—	MLE		—	2749	2520	535	124	15	102	180	956	32	17	12	212	273	395	668	183	293	35	7	291	83	+14	+0	+8	+40	+23	8.6
/ 162G	—	MLE		—	650	596	127	29	4	24	43	226	8	4	3	213	276	396	672	183	295	35	7	291	83	+8	+0	+5	+24	+14	5.3

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.

2018 AAAAlbuquerque Isotopes · 264 PAwOBA .378 → .261 (−117)K% 28.8% → 38.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	28.79%	÷0.978	×1.290	—	—	37.99%
BB% (unintentional) /PA	8.33%	÷1.024	×0.710	—	×1.003	5.79%
HBP% /PA	1.89%	—	×0.980	—	×1.003	1.86%
Singles /BIP	15.62%	÷1.034	×0.900	×0.950	×1.003	12.96%
Doubles /BIP	10.00%	÷0.960	×0.810	×0.950	×1.003	8.04%
Triples /BIP	1.88%	÷0.973	×0.810	×0.950	×1.003	1.49%
Home runs /BIP	10.62%	÷1.162	×0.780	—	×1.003	7.15%

Factors used: league row PCL. Park (home-season share, regressed): HR 132, 1B 107, 2B 92, 3B 95, K 96, BB 105 — applied at half strength. Age: age 27 vs a level average of 27.2 → +0.3% on BB/HBP/hit rates (0.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .378 → .261; ISO .309 → .197; BABIP .306 → .240. 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 AAAAlbuquerque Isotopes · 154 PAwOBA .320 → .215 (−105)K% 36.4% → 49.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	36.36%	÷0.956	×1.290	—	—	49.06%
BB% (unintentional) /PA	5.84%	÷0.996	×0.710	—	×1.018	4.24%
HBP% /PA	1.95%	—	×0.980	—	×1.018	1.94%
Singles /BIP	24.71%	÷1.019	×0.900	×0.982	×1.018	21.80%
Doubles /BIP	11.76%	÷1.045	×0.810	×0.982	×1.018	9.11%
Triples /BIP	1.18%	÷1.150	×0.810	×0.982	×1.018	0.83%
Home runs /BIP	4.71%	÷1.202	×0.780	—	×1.018	3.11%

Factors used: league row PCL. Park (home-season share, regressed): HR 140, 1B 104, 2B 109, 3B 130, K 91, BB 99 — 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 ×0.982.

Result: wOBA .320 → .215; ISO .170 → .098; BABIP .390 → .323. 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 AAAAlbuquerque Isotopes · 322 PAwOBA .419 → .299 (−121)K% 24.2% → 33.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.22%	÷0.948	×1.290	—	—	32.95%
BB% (unintentional) /PA	4.97%	÷1.083	×0.710	—	×1.033	3.36%
HBP% /PA	0.31%	—	×0.980	—	×1.033	0.31%
Singles /BIP	20.89%	÷1.032	×0.900	×1.001	×1.033	18.84%
Doubles /BIP	11.56%	÷1.050	×0.810	×1.001	×1.033	9.21%
Triples /BIP	3.11%	÷1.150	×0.810	×1.001	×1.033	2.27%
Home runs /BIP	8.44%	÷1.202	×0.780	—	×1.033	5.66%

Factors used: league row PCL. Park (home-season share, regressed): HR 140, 1B 106, 2B 110, 3B 130, K 90, BB 117 — 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 ×1.001.

Result: wOBA .419 → .299; ISO .320 → .207; BABIP .386 → .320. 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 AANew Britain Rock Cats · 294 PAwOBA .334 → .257 (−77)K% 27.2% → 34.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.21%	÷0.974	×1.220	—	—	34.07%
BB% (unintentional) /PA	7.48%	÷0.935	×0.700	—	×1.012	5.67%
HBP% /PA	1.70%	—	×0.850	—	×1.012	1.46%
Singles /BIP	18.38%	÷1.024	×0.970	×0.960	×1.012	16.91%
Doubles /BIP	9.19%	÷1.016	×0.860	×0.960	×1.012	7.56%
Triples /BIP	0.54%	÷0.900	×0.950	×0.960	×1.012	0.55%
Home runs /BIP	7.03%	÷1.133	×0.780	—	×1.012	4.89%

Factors used: league row EL. Park (home-season share, regressed): HR 127, 1B 105, 2B 103, 3B 80, K 95, BB 87 — 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.960.

Result: wOBA .334 → .257; ISO .219 → .152; BABIP .301 → .262. 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 AAAAlbuquerque Isotopes · 136 PAwOBA .349 → .254 (−95)K% 31.6% → 42.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	31.62%	÷0.960	×1.290	—	—	42.49%
BB% (unintentional) /PA	2.94%	÷1.056	×0.710	—	×1.048	2.07%
HBP% /PA	0.74%	—	×0.980	—	×1.048	0.76%
Singles /BIP	19.77%	÷1.010	×0.900	×1.008	×1.048	18.61%
Doubles /BIP	10.47%	÷1.075	×0.810	×1.008	×1.048	8.33%
Triples /BIP	2.33%	÷0.933	×0.810	×1.008	×1.048	2.13%
Home runs /BIP	8.14%	÷1.105	×0.780	—	×1.048	6.02%

Factors used: league row PCL. Park (home-season share, regressed): HR 121, 1B 102, 2B 115, 3B 87, K 92, BB 111 — 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 ×1.008.

Result: wOBA .349 → .254; ISO .264 → .177; BABIP .350 → .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).

2014 AATulsa Drillers · 109 PAwOBA .322 → .266 (−56)K% 24.8% → 30.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.77%	÷0.998	×1.220	—	—	30.29%
BB% (unintentional) /PA	12.84%	÷1.038	×0.700	—	×1.027	8.89%
HBP% /PA	0.92%	—	×0.870	—	×1.027	0.82%
Singles /BIP	16.42%	÷0.995	×0.920	×0.988	×1.027	15.41%
Doubles /BIP	5.97%	÷1.087	×0.840	×0.988	×1.027	4.68%
Triples /BIP	0.00%	÷0.901	×0.940	×0.988	×1.027	0.00%
Home runs /BIP	7.46%	÷0.894	×0.800	—	×1.027	6.86%

Factors used: league row TL. Park (home-season share, regressed): HR 79, 1B 99, 2B 117, 3B 80, K 100, BB 108 — 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 ×0.988.

Result: wOBA .322 → .266; ISO .202 → .176; BABIP .242 → .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).

2013 AAsheville Tourists · 341 PAwOBA .408 → .271 (−137)K% 25.5% → 36.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.51%	÷0.940	×1.340	—	—	36.36%
BB% (unintentional) /PA	10.85%	÷1.043	×0.660	—	—	6.86%
HBP% /PA	2.93%	—	×0.770	—	—	2.26%
Singles /BIP	17.91%	÷0.992	×0.920	×0.982	—	16.31%
Doubles /BIP	12.94%	÷1.305	×0.820	×0.982	—	7.98%
Triples /BIP	1.00%	÷0.900	×0.940	×0.982	—	1.02%
Home runs /BIP	9.45%	÷1.331	×0.820	—	—	5.83%

Factors used: league row SAL. Park (home-season share, regressed): HR 166, 1B 98, 2B 161, 3B 80, K 88, BB 109 — applied at half strength. Age: age 22 vs a level average of 22.0 → +0.0% on BB/HBP/hit rates (0.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.982.

Result: wOBA .408 → .271; ISO .302 → .178; BABIP .346 → .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).

2013 AATulsa Drillers · 74 PAwOBA .355 → .295 (−60)K% 21.6% → 26.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.62%	÷0.999	×1.220	—	—	26.41%
BB% (unintentional) /PA	5.41%	÷0.967	×0.700	—	×1.042	4.08%
HBP% /PA	1.35%	—	×0.870	—	×1.042	1.23%
Singles /BIP	22.64%	÷1.049	×0.920	×1.022	×1.042	21.15%
Doubles /BIP	9.43%	÷0.911	×0.840	×1.022	×1.042	9.26%
Triples /BIP	0.00%	÷1.150	×0.940	×1.022	×1.042	0.00%
Home runs /BIP	5.66%	÷1.239	×0.800	—	×1.042	3.81%

Factors used: league row TL. Park (home-season share, regressed): HR 148, 1B 110, 2B 82, 3B 130, K 100, BB 93 — 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 ×1.022.

Result: wOBA .355 → .295; ISO .203 → .151; BABIP .340 → .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).