Jarrett Parker

PosRF

B/TL/L

Born1989-01-01

Age (2027)38

No MLB projection yet. Career stats below are historical.

Overview

AAALAST SEASON 2019

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2016	27	AAASF	222	.273/.365/.582	16	1	87	—	.208/.279/.421
2016	27	MLBSF	151	.236/.358/.394	5	0	120	0.1
2017	28	AAASF	133	.232/.353/.357	3	1	61	—	.188/.286/.274
2017	28	MLBSF	177	.247/.294/.416	4	2	97	-0.0
2019	30	AAALAA	424	.266/.397/.535	24	2	80	—	.186/.285/.365
2019	30	MLBLAA	15	.000/.200/.000	0	0	-19	-0.8
2020	31	No MLB games played
2021	32	No MLB games played
2022	33	No MLB games played
2023	34	No MLB games played
2024	35	No MLB games played
2025	36	No MLB games played
2026	37	No MLB games played

Last 3 seasons WAR -0.7

Contract

No contract data on file.

Skills · percentileStatcast 2016-2019

Speed55

Barrel%50

xwOBA47

Avg EV22

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)
2011	22	A+	San Jose Giants	—	571	486	123	25	3	13	74	144	8	20	5	253	361	397	758	144	333	25	13	343	74	—	—	—	—	—	—
2012	23	A+	San Jose Giants	—	491	409	101	21	7	15	70	175	8	28	6	247	368	443	810	196	389	36	14	360	70	—	—	—	—	—	—
2013	24	AA	Richmond Flying Squirrels	—	524	443	109	18	5	18	62	161	15	13	11	246	358	431	789	185	345	31	12	352	79	—	—	—	—	—	—
2013	24	INTL	Scottsdale Scorpions	—	71	60	18	0	1	0	8	19	0	1	0	300	382	333	716	33	409	27	11	318	101	—	—	—	—	—	—
2014	25	AAA	Fresno Grizzlies	—	89	79	22	5	0	3	9	23	1	1	2	278	360	456	815	177	358	26	10	363	76	—	—	—	—	—	—
2014	25	AA	Richmond Flying Squirrels	—	419	363	100	20	6	12	45	103	10	11	4	275	371	463	834	187	353	25	11	370	93	—	—	—	—	—	—
2015	26	MLB	San Francisco Giants	—	54	49	17	2	0	6	5	21	0	1	1	347	407	755	1.163	408	500	39	9	494	235	+9	-0	-1	-6	+1	0.2
2015	26	AAA	Sacramento River Cats	—	504	434	123	25	3	23	62	164	4	20	7	283	378	514	892	230	398	33	12	389	95	—	—	—	—	—	—
2016	27	MLB	San Francisco Giants	—	151	127	30	3	1	5	19	44	5	0	1	236	358	394	751	157	321	29	13	343	120	+4	-0	-2	-4	+3	0.1
2016	27	AAA	Sacramento River Cats	—	222	194	53	8	2	16	26	66	2	1	1	273	365	582	947	309	330	30	12	409	87	—	—	—	—	—	—
2017	28	MLB	San Francisco Giants	—	177	166	41	12	2	4	10	54	1	2	1	247	294	416	709	169	343	31	6	313	97	-1	+0	+0	-3	+4	-0.0
2017	28	AAA	Sacramento River Cats	—	133	112	26	5	0	3	21	31	0	1	1	232	353	357	711	125	295	23	16	328	61	—	—	—	—	—	—
2019	30	MLB	Los Angeles Angels	—	15	12	0	0	0	0	3	8	0	0	0		200		200			53	20	140	-19	-2	+0	-1	-5	+0	-0.8
2019	30	AAA	Salt Lake Bees	—	424	346	92	19	1	24	72	125	3	2	1	266	397	535	931	269	340	29	17	405	80	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	397	354	88	17	3	15	37	127	6	3	3	249	330	441	771	192	344	32	9	343	118	+9	-1	-4	-18	+9	-0.5
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2011	22	A+ MLE	San Jose Giants	—	571	503	104	20	2	11	57	197	8	20	5	207	298	320	618	113	311	35	10	277	74	—	—	—	—	—	—
2012	23	A+ MLE	San Jose Giants	—	491	434	82	17	5	12	45	234	8	28	6	189	277	334	611	145	359	48	9	271	70	—	—	—	—	—	—
2013	24	AA MLE	Richmond Flying Squirrels	—	524	463	98	15	5	14	42	203	15	13	11	212	298	356	654	145	330	39	8	285	79	—	—	—	—	—	—
2014	25	AAA MLE	Fresno Grizzlies	—	89	81	18	4	0	2	7	30	1	1	2	222	292	346	638	123	323	34	8	281	76	—	—	—	—	—	—
2014	25	AA MLE	Richmond Flying Squirrels	—	419	380	91	17	5	10	28	124	10	11	4	239	309	389	698	150	327	30	7	306	93	—	—	—	—	—	—
2015	26	MLB MLE	San Francisco Giants	—	54	49	17	2	0	6	5	21	0	1	1	347	407	755	1.163	408	500	39	9	515	235	+9	-0	-1	-6	+1	0.2
2015	26	AAA MLE	Sacramento River Cats	—	504	452	104	20	3	17	44	202	4	20	7	230	304	400	704	170	360	40	9	309	95	—	—	—	—	—	—
2016	27	MLB MLE	San Francisco Giants	—	151	128	30	3	1	5	18	44	5	0	1	234	351	391	742	156	321	29	12	346	120	+4	-0	-2	-4	+3	0.1
2016	27	AAA MLE	Sacramento River Cats	—	222	202	42	6	2	11	18	82	2	1	1	208	279	421	700	213	283	37	8	297	87	—	—	—	—	—	—
2017	28	MLB MLE	San Francisco Giants	—	177	168	41	12	2	4	8	54	1	2	1	244	282	411	693	167	343	31	5	311	97	-1	+0	+0	-3	+4	-0.0
2017	28	AAA MLE	Sacramento River Cats	—	133	117	22	4	0	2	16	40	0	1	1	188	286	274	559	85	263	30	12	259	61	—	—	—	—	—	—
2019	30	MLB MLE	Los Angeles Angels	—	15	12	0	0	0	0	3	8	0	0	0		200		200			53	20	140	-19	-2	+0	-1	-5	+0	-0.8
2019	30	AAA MLE	Salt Lake Bees	—	424	370	69	13	1	17	48	160	3	2	1	186	285	365	650	178	260	38	11	286	80	—	—	—	—	—	—
2011	22	A+ MLE	San Jose Giants	—	571	503	104	20	2	11	57	197	8	20	5	207	298	320	618	113	314	35	10	277	74	—	—	—	—	—	—
2011	22	A+ RAW	San Jose Giants	—	571	486	123	25	3	13	74	144	8	20	5	253	361	397	758	144	333	25	13	343	74	—	—	—	—	—	—
2012	23	A+ MLE	San Jose Giants	—	491	434	82	17	5	12	45	234	8	28	6	189	277	334	611	145	368	48	9	271	70	—	—	—	—	—	—
2012	23	A+ RAW	San Jose Giants	—	491	409	101	21	7	15	70	175	8	28	6	247	368	443	810	196	389	36	14	360	70	—	—	—	—	—	—
2013	24	AA MLE	2 teams	—	595	525	111	17	5	16	48	230	15	14	11	211	296	354	650	143	337	39	8	285	79	—	—	—	—	—	—
2013	24	AA RAW	2 teams	—	595	503	127	18	6	18	70	180	15	14	11	252	361	419	780	167	354	30	12	348	79	—	—	—	—	—	—
2014	25	AA MLE	2 teams	—	508	461	109	21	5	12	35	154	11	12	6	236	306	382	687	145	328	30	7	302	90	—	—	—	—	—	—
2014	25	AA RAW	2 teams	—	508	442	122	25	6	15	54	126	11	12	6	276	369	462	830	186	354	25	11	369	90	—	—	—	—	—	—
2015	26	AAA MLE	2 teams	—	558	501	122	22	3	24	49	223	4	21	8	244	316	443	759	200	380	40	9	329	109	—	—	—	—	—	—
2015	26	AAA RAW	2 teams	—	558	483	140	27	3	29	67	185	4	21	8	290	381	538	919	248	407	33	12	399	109	—	—	—	—	—	—
2016	27	AAA MLE	2 teams	—	373	330	72	9	3	16	36	126	7	1	2	218	308	409	717	191	298	34	10	317	101	—	—	—	—	—	—
2016	27	AAA RAW	2 teams	—	373	321	83	11	3	21	45	110	7	1	2	259	362	508	870	249	326	29	12	382	101	—	—	—	—	—	—
2017	28	MLB MLE	2 teams	—	310	285	64	16	2	7	24	94	1	3	2	225	287	368	656	144	310	30	8	289	82	-7	-0	+0	-3	+7	-0.4
2017	28	MLB RAW	2 teams	—	310	278	67	17	2	7	31	85	1	3	2	241	319	392	711	151	323	27	10	320	82	-7	-0	+0	-3	+7	-0.4
2019	30	AAA MLE	2 teams	—	439	382	69	13	1	17	51	168	3	2	1	181	282	353	636	173	260	38	12	281	76	—	—	—	—	—	—
2019	30	AAA RAW	2 teams	—	439	358	92	19	1	24	75	133	3	2	1	257	390	517	907	260	333	30	17	396	76	—	—	—	—	—	—
2011	22	A+ MLE	San Jose Giants	—	571	501	104	20	2	11	57	197	8	20	5	208	298	321	619	114	311	35	10	277	74	—	—	—	—	—	—
2012	23	A+ MLE	San Jose Giants	—	491	435	82	17	5	12	45	234	8	28	6	189	277	333	611	145	359	48	9	271	70	—	—	—	—	—	—
2013	24	AA MLE	2 teams	—	595	527	111	17	5	16	48	230	15	14	11	211	296	353	649	142	330	39	8	285	79	—	—	—	—	—	—
2014	25	AA MLE	2 teams	—	508	457	109	21	5	12	35	154	11	12	6	239	306	385	691	147	326	30	7	302	90	—	—	—	—	—	—
2015	26	AAA MLE	2 teams	—	558	500	122	22	3	24	49	223	4	21	8	244	316	444	760	200	374	40	9	329	109	—	—	—	—	—	—
2016	27	AAA MLE	2 teams	—	373	327	72	9	3	16	36	126	7	1	2	220	308	413	721	193	298	34	10	317	101	—	—	—	—	—	—
2017	28	MLB MLE	2 teams	—	310	282	64	16	2	7	24	94	1	3	2	227	287	372	659	145	308	30	8	289	82	—	—	—	—	—	—
2019	30	AAA MLE	2 teams	—	439	382	69	13	1	17	51	168	3	2	1	181	282	353	636	173	251	38	12	281	76	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	397	354	88	17	3	15	37	127	6	3	3	249	330	441	771	192	344	32	9	343	85	+9	-1	-4	-18	+9	-0.5
/ 162G	—	A+C		—	650	580	145	28	5	25	61	208	10	5	5	250	332	445	777	195	346	32	9	343	85	+14	-1	-6	-29	+14	-0.9
Career	—	MLE		—	3845	3421	733	135	26	115	345	1426	57	101	41	214	297	370	667	156	326	37	9	294	85	+9	-1	-4	-18	+9	-0.5
/ 162G	—	MLE		—	650	578	123	23	4	19	58	241	10	17	7	213	295	365	660	152	325	37	9	294	85	+14	-1	-6	-29	+14	-0.9

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.

2019 AAASalt Lake Bees · 424 PAwOBA .395 → .286 (−109)K% 29.5% → 37.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	29.48%	÷1.009	×1.290	—	—	37.69%
BB% (unintentional) /PA	16.04%	÷0.980	×0.710	—	×0.972	11.30%
HBP% /PA	0.71%	—	×0.980	—	×0.972	0.67%
Singles /BIP	21.72%	÷1.007	×0.900	×0.950	×0.972	17.93%
Doubles /BIP	8.60%	÷1.062	×0.810	×0.950	×0.972	6.06%
Triples /BIP	0.45%	÷0.900	×0.810	×0.950	×0.972	0.38%
Home runs /BIP	10.86%	÷1.059	×0.780	—	×0.972	7.78%

Factors used: league row PCL. Park (home-season share, regressed): HR 112, 1B 101, 2B 112, 3B 80, K 102, BB 96 — 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 ×0.950.

Result: wOBA .395 → .286; ISO .269 → .186; BABIP .340 → .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).

2017 AAASacramento River Cats · 133 PAwOBA .320 → .259 (−61)K% 23.3% → 30.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.31%	÷0.989	×1.290	—	—	30.40%
BB% (unintentional) /PA	15.79%	÷0.952	×0.710	—	×0.992	11.68%
HBP% /PA	0.00%	—	×0.980	—	×0.992	0.00%
Singles /BIP	22.22%	÷1.027	×0.900	×1.043	×0.992	20.15%
Doubles /BIP	6.17%	÷0.975	×0.810	×1.043	×0.992	5.31%
Triples /BIP	0.00%	÷0.920	×0.810	×1.043	×0.992	0.00%
Home runs /BIP	3.70%	÷0.889	×0.780	—	×0.992	3.22%

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 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 ×1.043.

Result: wOBA .320 → .259; ISO .125 → .103; BABIP .295 → .263. 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 · 222 PAwOBA .398 → .297 (−101)K% 29.7% → 37.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	29.73%	÷1.034	×1.290	—	—	37.09%
BB% (unintentional) /PA	11.71%	÷1.052	×0.710	—	×1.003	7.93%
HBP% /PA	0.90%	—	×0.980	—	×1.003	0.89%
Singles /BIP	21.09%	÷0.960	×0.900	×0.973	×1.003	19.30%
Doubles /BIP	6.25%	÷0.973	×0.810	×0.973	×1.003	5.08%
Triples /BIP	1.56%	÷0.900	×0.810	×0.973	×1.003	1.37%
Home runs /BIP	12.50%	÷1.082	×0.780	—	×1.003	9.03%

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 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 ×0.973.

Result: wOBA .398 → .297; ISO .309 → .216; BABIP .330 → .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).

2015 AAASacramento River Cats · 504 PAwOBA .380 → .309 (−71)K% 32.5% → 40.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	32.54%	÷1.047	×1.290	—	—	40.09%
BB% (unintentional) /PA	11.71%	÷0.960	×0.710	—	×1.018	8.82%
HBP% /PA	0.79%	—	×0.980	—	×1.018	0.79%
Singles /BIP	26.67%	÷1.003	×0.900	×1.038	×1.018	25.29%
Doubles /BIP	9.26%	÷1.024	×0.810	×1.038	×1.018	7.74%
Triples /BIP	1.11%	÷0.925	×0.810	×1.038	×1.018	1.03%
Home runs /BIP	8.52%	÷0.982	×0.780	—	×1.018	6.89%

Factors used: league row PCL. Park (home-season share, regressed): HR 96, 1B 101, 2B 105, 3B 85, K 109, BB 92 — 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 ×1.038.

Result: wOBA .380 → .309; ISO .230 → .178; BABIP .398 → .360. 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 AARichmond Flying Squirrels · 419 PAwOBA .360 → .306 (−54)K% 24.6% → 29.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.58%	÷1.012	×1.220	—	—	29.62%
BB% (unintentional) /PA	10.26%	÷1.075	×0.700	—	×0.998	6.67%
HBP% /PA	2.39%	—	×0.850	—	×0.998	2.02%
Singles /BIP	23.85%	÷1.014	×0.970	×0.999	×0.998	22.75%
Doubles /BIP	7.69%	÷0.983	×0.860	×0.999	×0.998	6.71%
Triples /BIP	2.31%	÷1.061	×0.950	×0.999	×0.998	2.06%
Home runs /BIP	4.62%	÷0.918	×0.780	—	×0.998	3.91%

Factors used: league row EL. Park (home-season share, regressed): HR 84, 1B 103, 2B 97, 3B 112, K 102, BB 115 — 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 ×0.999.

Result: wOBA .360 → .306; ISO .187 → .161; BABIP .353 → .327. 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 AAAFresno Grizzlies · 89 PAwOBA .354 → .281 (−73)K% 25.8% → 33.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.84%	÷0.994	×1.290	—	—	33.53%
BB% (unintentional) /PA	10.11%	÷1.005	×0.710	—	×1.033	7.38%
HBP% /PA	1.12%	—	×0.980	—	×1.033	1.14%
Singles /BIP	25.00%	÷1.002	×0.900	×1.012	×1.033	23.48%
Doubles /BIP	8.93%	÷1.014	×0.810	×1.012	×1.033	7.45%
Triples /BIP	0.00%	÷0.900	×0.810	×1.012	×1.033	0.00%
Home runs /BIP	5.36%	÷1.045	×0.780	—	×1.033	4.13%

Factors used: league row PCL. Park (home-season share, regressed): HR 109, 1B 100, 2B 103, 3B 80, K 99, BB 101 — 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 ×1.012.

Result: wOBA .354 → .281; ISO .177 → .130; BABIP .358 → .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).

2013 AARichmond Flying Squirrels · 524 PAwOBA .342 → .285 (−58)K% 30.7% → 38.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	30.73%	÷0.969	×1.220	—	—	38.68%
BB% (unintentional) /PA	11.64%	÷1.036	×0.700	—	×1.012	7.96%
HBP% /PA	2.86%	—	×0.850	—	×1.012	2.46%
Singles /BIP	24.11%	÷1.018	×0.970	×1.030	×1.012	23.95%
Doubles /BIP	6.38%	÷1.030	×0.860	×1.030	×1.012	5.56%
Triples /BIP	1.77%	÷1.011	×0.950	×1.030	×1.012	1.74%
Home runs /BIP	6.38%	÷0.934	×0.780	—	×1.012	5.39%

Factors used: league row EL. Park (home-season share, regressed): HR 87, 1B 104, 2B 106, 3B 102, K 94, BB 107 — 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 ×1.030.

Result: wOBA .342 → .285; ISO .185 → .152; BABIP .345 → .330. 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+San Jose Giants · 491 PAwOBA .350 → .271 (−79)K% 35.6% → 47.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	35.64%	÷1.001	×1.340	—	—	47.70%
BB% (unintentional) /PA	13.65%	÷1.047	×0.710	—	×0.998	9.23%
HBP% /PA	1.63%	—	×0.790	—	×0.998	1.28%
Singles /BIP	24.79%	÷0.994	×0.910	×1.029	×0.998	23.31%
Doubles /BIP	8.97%	÷0.926	×0.820	×1.029	×0.998	8.16%
Triples /BIP	2.99%	÷0.900	×0.760	×1.029	×0.998	2.59%
Home runs /BIP	6.41%	÷0.984	×0.920	—	×0.998	5.98%

Factors used: league row CAL. Park (home-season share, regressed): HR 97, 1B 99, 2B 85, 3B 80, K 100, BB 109 — applied at half strength. Age: age 23 vs a level average of 22.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.029.

Result: wOBA .350 → .271; ISO .196 → .157; BABIP .389 → .359. 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 — .