Jack Murphy

PosC

B/TS/R

Born1988-04-06

Age (2027)39

No MLB projection yet. Career stats below are historical.

Overview

INTLLAST SEASON 2019

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	wRC+	WAR	MLE AVG/OBP/SLG
2017	29	AAA/AA2 orgs	187	.141/.239/.184	0	-9	—	.112/.179/.141
2018	30	INTLSydney Blue S…	165	.187/.302/.281	3	69	—
2019	31	INTLSydney Blue S…	138	.158/.263/.250	2	49	—

Contract

No contract data on file.

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)
2009	21	ROK	GCL Blue Jays	—	99	88	23	8	0	1	11	17	0	1	1	261	343	386	730	125	314	17	11	330	36	—	—	—	—	—	—
2011	23	A	Lansing Lugnuts	—	185	162	36	11	0	3	21	43	1	0	2	222	315	346	661	123	282	23	11	301	37	—	—	—	—	—	—
2012	24	AA	New Hampshire Fisher Cats	—	28	24	8	1	0	2	3	7	0	0	0	333	407	625	1.032	292	375	25	11	431	146	—	—	—	—	—	—
2012	24	A+	Dunedin Blue Jays	—	323	278	62	13	1	10	35	67	3	0	0	223	316	385	701	162	252	21	11	310	54	—	—	—	—	—	—
2012	24	INTL	Canberra Cavalry	—	167	148	45	11	0	5	14	22	2	0	0	304	372	480	852	176	323	13	8	371	137	—	—	—	—	—	—
2013	25	AA	New Hampshire Fisher Cats	—	214	197	43	11	1	3	14	37	0	0	0	218	270	330	600	112	253	17	7	267	38	—	—	—	—	—	—
2013	25	INTL	Canberra Cavalry	—	180	160	49	9	0	2	14	21	2	0	0	306	369	400	769	94	333	12	8	341	117	—	—	—	—	—	—
2014	26	AAA	Buffalo Bisons	—	39	34	7	3	0	0	5	7	0	0	0	206	308	294	602	88	259	18	13	281	40	—	—	—	—	—	—
2014	26	AA	New Hampshire Fisher Cats	—	146	129	29	4	0	6	15	30	1	0	0	225	310	395	706	171	247	21	10	315	53	—	—	—	—	—	—
2014	26	INTL	Canberra Cavalry	—	172	153	54	11	0	6	17	22	0	0	0	353	418	542	960	190	378	13	10	418	169	—	—	—	—	—	—
2015	27	AA	New Hampshire Fisher Cats	—	328	286	63	14	2	3	37	58	1	0	1	220	312	315	626	94	263	18	11	286	40	—	—	—	—	—	—
2015	27	INTL	Canberra Cavalry	—	182	149	32	6	0	7	27	34	1	1	1	215	339	396	735	181	223	19	15	324	106	—	—	—	—	—	—
2016	28	AAA	Oklahoma City Dodgers	—	250	208	52	7	0	3	36	55	0	0	0	250	361	327	688	77	322	22	14	313	57	—	—	—	—	—	—
2016	28	INTL	Leones del Escogido	—	21	19	5	0	0	1	2	5	0	0	0	263	333	421	754	158	308	24	10	338	115	—	—	—	—	—	—
2017	29	AAA	Oklahoma City Dodgers	—	160	139	19	7	0	0	17	38	1	0	1	137	236	187	423	50	184	24	11	203	-10	—	—	—	—	—	—
2017	29	AA	Tulsa Drillers	—	27	24	4	0	0	0	3	6	0	0	0	167	259	167	426		222	22	11	212	-6	—	—	—	—	—	—
2018	30	INTL	Sydney Blue Sox	—	165	139	26	4	0	3	22	29	1	0	0	187	302	281	583	94	211	18	13	271	69	—	—	—	—	—	—
2019	31	INTL	Sydney Blue Sox	—	138	120	19	5	0	2	17	30	0	0	0	158	263	250	513	92	191	22	12	241	49	—	—	—	—	—	—
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2009	21	ROK MLE	GCL Blue Jays	—	99	93	17	5	0	1	6	24	0	1	1	183	232	269	501	86	237	24	6	222	36	—	—	—	—	—	—
2011	23	A MLE	Lansing Lugnuts	—	185	169	29	8	0	2	14	61	1	0	2	172	239	254	494	83	243	33	8	223	37	—	—	—	—	—	—
2012	24	AA MLE	New Hampshire Fisher Cats	—	28	25	8	1	0	2	2	8	0	0	0	320	370	600	970	280	354	29	7	384	146	—	—	—	—	—	—
2012	24	A+ MLE	Dunedin Blue Jays	—	323	289	53	11	1	9	24	95	3	0	0	183	253	322	575	138	218	29	7	248	54	—	—	—	—	—	—
2013	25	AA MLE	New Hampshire Fisher Cats	—	214	201	39	9	1	2	10	47	0	0	0	194	232	279	511	85	237	22	5	225	38	—	—	—	—	—	—
2014	26	AAA MLE	Buffalo Bisons	—	39	36	7	3	0	0	3	9	0	0	0	194	256	278	534	83	240	23	8	228	40	—	—	—	—	—	—
2014	26	AA MLE	New Hampshire Fisher Cats	—	146	134	26	4	0	4	10	38	1	0	0	194	255	313	569	119	227	26	7	246	53	—	—	—	—	—	—
2015	27	AA MLE	New Hampshire Fisher Cats	—	328	298	56	11	2	2	25	74	1	0	1	188	253	258	511	70	232	23	8	227	40	—	—	—	—	—	—
2016	28	AAA MLE	Oklahoma City Dodgers	—	250	220	46	6	0	2	24	73	0	0	0	209	287	264	551	55	289	29	10	252	57	—	—	—	—	—	—
2017	29	AAA MLE	Oklahoma City Dodgers	—	160	145	16	5	0	0	11	49	1	0	1	110	178	145	323	34	157	31	7	154	-10	—	—	—	—	—	—
2017	29	AA MLE	Tulsa Drillers	—	27	25	3	0	0	0	2	8	0	0	0	120	185	120	305		195	30	7	159	-6	—	—	—	—	—	—
2009	21	ROK MLE	GCL Blue Jays	—	99	93	17	5	0	1	6	24	0	1	1	183	232	269	501	86	235	24	6	222	36	—	—	—	—	—	—
2009	21	ROK RAW	GCL Blue Jays	—	99	88	23	8	0	1	11	17	0	1	1	261	343	386	730	125	314	17	11	330	36	—	—	—	—	—	—
2011	23	A MLE	Lansing Lugnuts	—	185	169	29	8	0	2	14	61	1	0	2	172	239	254	494	83	252	33	8	223	37	—	—	—	—	—	—
2011	23	A RAW	Lansing Lugnuts	—	185	162	36	11	0	3	21	43	1	0	2	222	315	346	661	123	282	23	11	301	37	—	—	—	—	—	—
2012	24	A+ MLE	3 teams	—	518	463	87	17	1	15	39	152	5	0	0	188	258	326	585	138	236	29	8	259	61	—	—	—	—	—	—
2012	24	A+ RAW	3 teams	—	518	450	115	25	1	17	52	96	5	0	0	256	339	429	768	173	283	19	10	336	61	—	—	—	—	—	—
2013	25	AA MLE	2 teams	—	394	367	73	17	2	4	18	86	2	0	0	199	240	289	529	90	245	22	5	225	38	—	—	—	—	—	—
2013	25	AA RAW	2 teams	—	394	357	92	20	1	5	28	58	2	0	0	258	315	361	677	104	291	15	7	301	38	—	—	—	—	—	—
2014	26	INTL MLE	3 teams	—	357	328	62	12	0	8	25	90	1	0	0	189	249	299	547	110	233	25	7	242	50	—	—	—	—	—	—
2014	26	INTL RAW	3 teams	—	357	316	90	18	0	12	37	59	1	0	0	285	362	456	817	171	316	17	10	361	50	—	—	—	—	—	—
2015	27	AA MLE	2 teams	—	510	461	86	17	2	3	38	115	2	1	2	187	251	252	503	65	237	23	7	227	40	—	—	—	—	—	—
2015	27	AA RAW	2 teams	—	510	435	95	20	2	10	64	92	2	1	2	218	321	343	664	124	250	18	13	300	40	—	—	—	—	—	—
2016	28	AAA MLE	2 teams	—	271	239	50	6	0	3	26	79	0	0	0	209	287	272	559	63	296	29	10	252	57	—	—	—	—	—	—
2016	28	AAA RAW	2 teams	—	271	227	57	7	0	4	38	60	0	0	0	251	358	335	693	84	321	22	14	315	57	—	—	—	—	—	—
2017	29	AAA MLE	2 teams	—	187	170	19	5	0	0	13	57	1	0	1	112	179	141	321	29	165	30	7	155	-9	—	—	—	—	—	—
2017	29	AAA RAW	2 teams	—	187	163	23	7	0	0	20	44	1	0	1	141	239	184	423	43	190	24	11	204	-9	—	—	—	—	—	—
2018	30	INTL MLE	Sydney Blue Sox	—	165	139	26	4	0	3	22	29	1	0	0	187	302	281	583	94	211	18	13	—	—	—	—	—	—	—	—
2018	30	INTL RAW	Sydney Blue Sox	—	165	139	26	4	0	3	22	29	1	0	0	187	302	281	583	94	211	18	13	271	69	—	—	—	—	—	—
2019	31	INTL MLE	Sydney Blue Sox	—	138	120	19	5	0	2	17	30	0	0	0	158	263	250	513	92	191	22	12	—	—	—	—	—	—	—	—
2019	31	INTL RAW	Sydney Blue Sox	—	138	120	19	5	0	2	17	30	0	0	0	158	263	250	513	92	191	22	12	241	49	—	—	—	—	—	—
2009	21	ROK MLE	GCL Blue Jays	—	99	93	17	5	0	1	6	24	0	1	1	183	232	269	501	86	237	24	6	222	36	—	—	—	—	—	—
2011	23	A MLE	Lansing Lugnuts	—	185	168	29	8	0	2	14	61	1	0	2	173	239	256	495	83	243	33	8	223	37	—	—	—	—	—	—
2012	24	A+ MLE	3 teams	—	518	469	87	17	1	15	39	152	5	0	0	186	258	322	580	136	229	29	8	259	61	—	—	—	—	—	—
2013	25	AA MLE	2 teams	—	394	371	73	17	2	4	18	86	2	0	0	197	240	286	526	89	237	22	5	225	38	—	—	—	—	—	—
2014	26	INTL MLE	3 teams	—	357	328	62	12	0	8	25	90	1	0	0	189	249	299	547	110	230	25	7	242	50	—	—	—	—	—	—
2015	27	AA MLE	2 teams	—	510	465	86	17	2	3	38	115	2	1	2	185	251	249	501	65	232	23	7	227	40	—	—	—	—	—	—
2016	28	AAA MLE	2 teams	—	271	243	50	6	0	3	26	79	0	0	0	206	287	267	554	62	289	29	10	252	57	—	—	—	—	—	—
2017	29	AAA MLE	2 teams	—	187	171	19	5	0	0	13	57	1	0	1	111	179	140	320	29	162	30	7	155	-9	—	—	—	—	—	—
▸ Career Totals
Career	—	MLE		—	2824	2549	468	96	5	41	218	723	13	2	6	184	251	273	525	90	235	26	8	207	26	-247	-2	+0	+50	+61	-14.0
/ 162G	—	MLE		—	650	587	107	22	1	9	50	166	3	0	1	182	250	269	519	87	234	26	8	207	26	-57	-0	+0	+12	+14	-3.2

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 AAAOklahoma City Dodgers · 160 PAwOBA .198 → .154 (−44)K% 23.8% → 30.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.75%	÷1.000	×1.290	—	—	30.64%
BB% (unintentional) /PA	10.00%	÷1.017	×0.710	—	×0.982	6.85%
HBP% /PA	0.62%	—	×0.980	—	×0.982	0.60%
Singles /BIP	11.88%	÷0.978	×0.900	×0.997	×0.982	10.70%
Doubles /BIP	6.93%	÷1.038	×0.810	×0.997	×0.982	5.29%
Triples /BIP	0.00%	÷1.150	×0.810	×0.997	×0.982	0.00%
Home runs /BIP	0.00%	÷0.989	×0.780	—	×0.982	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 98, 1B 96, 2B 108, 3B 130, K 100, BB 103 — 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.997.

Result: wOBA .198 → .154; ISO .050 → .038; BABIP .184 → .157. 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 AATulsa Drillers · 27 PAwOBA .207 → .159 (−48)K% 22.2% → 28.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.22%	÷0.955	×1.220	—	—	28.38%
BB% (unintentional) /PA	11.11%	÷1.058	×0.700	—	×0.958	7.04%
HBP% /PA	0.00%	—	×0.870	—	×0.958	0.00%
Singles /BIP	22.22%	÷1.005	×0.920	—	×0.958	19.49%
Doubles /BIP	0.00%	÷1.000	×0.840	—	×0.958	0.00%
Triples /BIP	0.00%	÷1.150	×0.940	—	×0.958	0.00%
Home runs /BIP	0.00%	÷1.295	×0.800	—	×0.958	0.00%

Factors used: league row TL. Park (home-season share, regressed): HR 159, 1B 101, 2B 100, 3B 130, K 91, BB 112 — applied at half strength. Age: age 29 vs a level average of 24.8 → −4.2% on BB/HBP/hit rates (4.2 yr older than average).

Result: wOBA .207 → .159; ISO .000 → .000; BABIP .222 → .195. 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 AAAOklahoma City Dodgers · 250 PAwOBA .309 → .252 (−57)K% 22.0% → 29.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.00%	÷0.972	×1.290	—	—	29.18%
BB% (unintentional) /PA	14.40%	÷1.059	×0.710	—	×0.992	9.58%
HBP% /PA	0.00%	—	×0.980	—	×0.992	0.00%
Singles /BIP	27.45%	÷1.017	×0.900	×1.034	×0.992	24.92%
Doubles /BIP	4.58%	÷0.975	×0.810	×1.034	×0.992	3.90%
Triples /BIP	0.00%	÷1.104	×0.810	×1.034	×0.992	0.00%
Home runs /BIP	1.96%	÷0.947	×0.780	—	×0.992	1.60%

Factors used: league row PCL. Park (home-season share, regressed): HR 89, 1B 103, 2B 95, 3B 121, K 94, BB 112 — applied at half strength. Age: age 28 vs a level average of 27.2 → −0.8% on BB/HBP/hit rates (0.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.034.

Result: wOBA .309 → .252; ISO .077 → .064; BABIP .322 → .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).

2015 AANew Hampshire Fisher Cats · 328 PAwOBA .282 → .227 (−55)K% 17.7% → 22.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.68%	÷0.960	×1.220	—	—	22.48%
BB% (unintentional) /PA	11.28%	÷1.026	×0.700	—	×0.978	7.53%
HBP% /PA	0.30%	—	×0.850	—	×0.978	0.25%
Singles /BIP	19.30%	÷0.996	×0.970	×0.974	×0.978	17.90%
Doubles /BIP	6.14%	÷1.064	×0.860	×0.974	×0.978	4.73%
Triples /BIP	0.88%	÷1.150	×0.950	×0.974	×0.978	0.69%
Home runs /BIP	1.32%	÷1.130	×0.780	—	×0.978	0.89%

Factors used: league row EL. Park (home-season share, regressed): HR 126, 1B 99, 2B 113, 3B 130, K 92, BB 105 — applied at half strength. Age: age 27 vs a level average of 24.8 → −2.2% on BB/HBP/hit rates (2.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.974.

Result: wOBA .282 → .227; ISO .094 → .070; BABIP .263 → .232. 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 AANew Hampshire Fisher Cats · 146 PAwOBA .307 → .246 (−60)K% 20.5% → 25.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.55%	÷0.976	×1.220	—	—	25.67%
BB% (unintentional) /PA	10.27%	÷1.062	×0.700	—	×0.988	6.69%
HBP% /PA	0.68%	—	×0.850	—	×0.988	0.58%
Singles /BIP	19.19%	÷1.012	×0.970	×0.999	×0.988	18.15%
Doubles /BIP	4.04%	÷0.952	×0.860	×0.999	×0.988	3.60%
Triples /BIP	0.00%	÷0.921	×0.950	×0.999	×0.988	0.00%
Home runs /BIP	6.06%	÷1.066	×0.780	—	×0.988	4.38%

Factors used: league row EL. Park (home-season share, regressed): HR 113, 1B 102, 2B 90, 3B 84, K 95, BB 112 — applied at half strength. Age: age 26 vs a level average of 24.8 → −1.2% on BB/HBP/hit rates (1.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.999.

Result: wOBA .307 → .246; ISO .171 → .127; BABIP .247 → .227. 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 AAABuffalo Bisons · 39 PAwOBA .274 → .228 (−46)K% 17.9% → 22.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.95%	÷0.942	×1.200	—	—	22.87%
BB% (unintentional) /PA	12.82%	÷1.041	×0.690	—	×1.018	8.65%
HBP% /PA	0.00%	—	×0.940	—	×1.018	0.00%
Singles /BIP	14.81%	÷0.968	×0.930	—	×1.018	14.49%
Doubles /BIP	11.11%	÷0.985	×0.830	—	×1.018	9.53%
Triples /BIP	0.00%	÷1.150	×0.970	—	×1.018	0.00%
Home runs /BIP	0.00%	÷1.006	×0.840	—	×1.018	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 101, 1B 94, 2B 97, 3B 130, K 88, BB 108 — 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).

Result: wOBA .274 → .228; ISO .088 → .075; BABIP .259 → .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).

2013 AANew Hampshire Fisher Cats · 214 PAwOBA .261 → .225 (−36)K% 17.3% → 21.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.29%	÷0.969	×1.220	—	—	21.78%
BB% (unintentional) /PA	6.54%	÷1.007	×0.700	—	×0.998	4.54%
HBP% /PA	0.00%	—	×0.850	—	×0.998	0.00%
Singles /BIP	17.50%	÷0.986	×0.970	×1.005	×0.998	17.27%
Doubles /BIP	6.88%	÷1.042	×0.860	×1.005	×0.998	5.69%
Triples /BIP	0.62%	÷1.068	×0.950	×1.005	×0.998	0.56%
Home runs /BIP	1.88%	÷0.975	×0.780	—	×0.998	1.50%

Factors used: league row EL. Park (home-season share, regressed): HR 95, 1B 97, 2B 108, 3B 114, K 94, BB 101 — applied at half strength. Age: age 25 vs a level average of 24.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.005.

Result: wOBA .261 → .225; ISO .112 → .090; BABIP .253 → .237. 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 A+Dunedin Blue Jays · 323 PAwOBA .304 → .248 (−56)K% 20.7% → 29.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.74%	÷1.001	×1.420	—	—	29.41%
BB% (unintentional) /PA	10.22%	÷1.020	×0.750	—	×0.988	7.42%
HBP% /PA	0.93%	—	×0.930	—	×0.988	0.85%
Singles /BIP	18.01%	÷1.034	×0.920	×0.996	×0.988	15.76%
Doubles /BIP	6.16%	÷1.111	×0.960	×0.996	×0.988	5.24%
Triples /BIP	0.47%	÷1.014	×0.810	×0.996	×0.988	0.37%
Home runs /BIP	4.74%	÷0.944	×0.880	—	×0.988	4.37%

Factors used: league row FSL. Park (home-season share, regressed): HR 89, 1B 107, 2B 122, 3B 103, K 100, BB 104 — applied at half strength. Age: age 24 vs a level average of 22.8 → −1.2% on BB/HBP/hit rates (1.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.996.

Result: wOBA .304 → .248; ISO .162 → .138; BABIP .252 → .218. 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 — .