Steven Duggar

PosCF

B/TL/R

Born1993-11-04

Age (2027)33

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
2021	27	AAASF	70	.279/.371/.393	1	8	74	—	.234/.300/.344
2021	27	MLBSF	297	.257/.330/.437	8	7	116	1.8
2022	28	AAALAA	140	.182/.288/.314	4	6	29	—	.150/.223/.244
2022	28	MLBLAA	80	.153/.228/.222	0	5	29	-0.5
2023	29	AAAOklahoma City…	272	.242/.363/.410	9	5	57	—	.179/.270/.288
2024	30	No MLB games played
2025	31	No MLB games played
2026	32	No MLB games played

Last 3 seasons WAR 1.4

Contract

No contract data on file.

Skills · percentileStatcast 2020-2022

Speed55

Avg EV43

Barrel%42

xwOBA27

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)
2016	22	AA	Richmond Flying Squirrels	—	276	243	78	16	4	1	28	51	2	9	7	321	396	432	828	111	397	18	10	368	111	—	—	—	—	—	—
2016	22	A+	San Jose Giants	—	311	264	75	12	4	9	44	66	1	6	7	284	388	462	850	178	346	21	14	377	93	—	—	—	—	—	—
2017	23	AAA	Sacramento River Cats	—	54	46	12	1	0	2	8	12	0	3	2	261	370	413	783	152	312	22	15	355	82	—	—	—	—	—	—
2017	23	A+	San Jose Giants	—	133	115	31	11	0	4	17	42	0	7	0	270	364	470	833	200	386	32	13	367	70	—	—	—	—	—	—
2017	23	INTL	Scottsdale Scorpions	—	90	76	20	3	0	3	12	21	1	9	1	263	371	421	792	158	321	23	13	354	126	—	—	—	—	—	—
2018	24	MLB	San Francisco Giants	—	152	141	36	11	1	2	10	44	0	5	1	255	305	390	695	135	354	29	7	308	94	-1	+1	+2	+2	+3	0.7
2018	24	AAA	Sacramento River Cats	—	356	316	86	27	4	4	39	103	1	11	4	272	354	421	775	149	392	29	11	347	72	—	—	—	—	—	—
2019	25	MLB	San Francisco Giants	—	281	261	61	12	2	4	16	78	1	1	4	234	281	341	622	107	313	28	6	277	76	-8	-1	+1	+1	+6	-0.1
2019	25	AAA	Sacramento River Cats	—	102	83	28	6	1	3	18	21	1	2	3	337	461	542	1.003	205	424	21	18	443	124	—	—	—	—	—	—
2020	26	MLB	San Francisco Giants	—	36	34	6	2	0	0	1	11	1	1	0	176	222	235	458	59	261	31	3	212	30	-3	+0	-1	-0	+1	-0.3
2021	27	MLB	San Francisco Giants	—	297	268	69	14	5	8	27	88	2	7	0	257	330	437	767	179	355	30	9	340	116	+6	+1	+3	+1	+6	1.8
2021	27	AAA	Sacramento River Cats	—	70	61	17	2	1	1	9	22	0	8	0	279	371	393	765	115	421	31	13	347	74	—	—	—	—	—	—
2022	28	MLB	Los Angeles Angels	—	80	72	11	3	1	0	7	41	0	5	1	153	228	222	450	69	344	51	9	209	29	-7	+1	-1	+1	+2	-0.5
2022	28	AAA	Salt Lake Bees	—	140	121	22	4	0	4	17	45	1	6	0	182	288	314	602	132	247	32	12	277	29	—	—	—	—	—	—
2023	29	AAA	Oklahoma City Dodgers	—	272	227	55	11	0	9	42	92	1	5	1	242	363	410	773	167	359	34	15	348	57	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	846	776	183	42	9	14	61	262	4	19	6	236	295	367	662	131	335	31	7	295	86	-14	+1	+4	+5	+18	1.5
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2016	22	AA MLE	Richmond Flying Squirrels	—	276	254	78	15	3	1	17	64	2	9	7	307	355	402	757	94	395	23	6	332	111	—	—	—	—	—	—
2016	22	A+ MLE	San Jose Giants	—	311	277	66	10	3	8	31	89	1	6	7	238	317	383	700	144	313	29	10	306	93	—	—	—	—	—	—
2017	23	AAA MLE	Sacramento River Cats	—	54	48	11	1	0	2	6	16	0	3	2	229	315	375	690	146	284	30	11	290	82	—	—	—	—	—	—
2017	23	A+ MLE	San Jose Giants	—	133	120	24	8	0	3	12	55	0	7	0	200	273	342	614	142	322	41	9	271	70	—	—	—	—	—	—
2018	24	MLB MLE	San Francisco Giants	—	152	142	36	11	1	2	9	44	0	5	1	254	298	387	685	134	354	29	6	307	94	-1	+1	+2	+2	+3	0.7
2018	24	AAA MLE	Sacramento River Cats	—	356	329	74	23	3	3	26	124	1	11	4	225	284	340	624	116	349	35	7	274	72	—	—	—	—	—	—
2019	25	MLB MLE	San Francisco Giants	—	281	261	62	12	2	4	16	78	1	1	4	238	284	345	629	107	313	28	6	281	76	-8	-1	+1	+1	+6	-0.1
2019	25	AAA MLE	Sacramento River Cats	—	102	88	24	5	1	2	13	28	1	2	3	273	373	420	793	148	380	27	13	352	124	—	—	—	—	—	—
2020	26	MLB MLE	San Francisco Giants	—	36	34	6	2	0	0	1	11	1	1	0	176	222	235	458	59	261	31	3	213	30	-3	+0	-1	-0	+1	-0.3
2021	27	MLB MLE	San Francisco Giants	—	297	271	71	14	5	9	24	88	2	7	0	262	327	450	777	188	355	30	8	340	116	+6	+1	+3	+1	+6	1.8
2021	27	AAA MLE	Sacramento River Cats	—	70	64	15	2	1	1	6	26	0	8	0	234	300	344	644	109	372	37	9	277	74	—	—	—	—	—	—
2022	28	MLB MLE	Los Angeles Angels	—	80	72	11	3	1	0	7	41	0	5	1	153	228	222	450	69	344	51	9	211	29	-7	+1	-1	+1	+2	-0.5
2022	28	AAA MLE	Salt Lake Bees	—	140	127	19	3	0	3	11	55	1	6	0	150	223	244	467	94	227	39	8	211	29	—	—	—	—	—	—
2023	29	AAA MLE	Oklahoma City Dodgers	—	272	240	43	8	0	6	29	110	1	5	1	179	270	287	558	108	290	40	11	253	57	—	—	—	—	—	—
2016	22	A+ MLE	2 teams	—	587	531	143	25	7	8	48	152	3	15	14	269	333	388	721	119	359	26	8	318	102	—	—	—	—	—	—
2016	22	A+ RAW	2 teams	—	587	507	153	28	8	10	72	117	3	15	14	302	392	448	839	146	371	20	12	373	102	—	—	—	—	—	—
2017	23	A+ MLE	3 teams	—	277	246	50	13	0	7	28	105	1	19	3	203	287	341	629	138	316	38	10	276	73	—	—	—	—	—	—
2017	23	A+ RAW	3 teams	—	277	237	63	15	0	9	37	75	1	19	3	266	367	443	810	177	348	27	13	360	73	—	—	—	—	—	—
2018	24	AAA MLE	2 teams	—	508	471	110	34	4	5	35	168	1	16	5	234	288	355	643	121	351	33	7	284	78	—	—	—	—	—	—
2018	24	AAA RAW	2 teams	—	508	457	122	38	5	6	49	147	1	16	5	267	339	411	751	144	380	29	10	335	78	—	—	—	—	—	—
2019	25	MLB MLE	2 teams	—	383	349	87	17	3	7	29	106	2	3	7	249	311	375	686	126	335	28	8	300	89	-5	-2	+1	+1	+8	0.3
2019	25	MLB RAW	2 teams	—	383	344	89	18	3	7	34	99	2	3	7	259	329	390	718	131	340	26	9	321	89	-5	-2	+1	+1	+8	0.3
2020	26	MLB MLE	San Francisco Giants	—	36	34	6	2	0	0	1	11	1	1	0	176	222	235	458	59	261	31	3	213	30	-3	+0	-1	-0	+1	-0.3
2020	26	MLB RAW	San Francisco Giants	—	36	34	6	2	0	0	1	11	1	1	0	176	222	235	458	59	261	31	3	212	30	-3	+0	-1	-0	+1	-0.3
2021	27	MLB MLE	2 teams	—	367	335	85	16	6	9	30	114	2	15	0	254	319	418	737	164	358	31	8	328	108	+3	+3	+3	+1	+8	1.9
2021	27	MLB RAW	2 teams	—	367	329	86	16	6	9	36	110	2	15	0	261	338	429	766	167	367	30	10	341	108	+3	+3	+3	+1	+8	1.9
2022	28	AAA MLE	2 teams	—	220	199	31	7	1	3	18	96	1	11	1	156	229	246	476	90	275	44	8	211	29	—	—	—	—	—	—
2022	28	AAA RAW	2 teams	—	220	193	33	7	1	4	24	86	1	11	1	171	266	280	546	109	276	39	11	252	29	—	—	—	—	—	—
2023	29	AAA MLE	Oklahoma City Dodgers	—	272	240	43	8	0	6	29	110	1	5	1	179	270	287	558	108	294	40	11	253	57	—	—	—	—	—	—
2023	29	AAA RAW	Oklahoma City Dodgers	—	272	227	55	11	0	9	42	92	1	5	1	242	363	410	773	167	359	34	15	348	57	—	—	—	—	—	—
2016	22	A+ MLE	2 teams	—	587	531	143	25	7	8	48	152	3	15	14	269	333	388	721	119	352	26	8	318	102	—	—	—	—	—	—
2017	23	A+ MLE	3 teams	—	277	246	50	13	0	7	28	105	1	19	3	203	287	341	629	138	311	38	10	276	73	—	—	—	—	—	—
2018	24	AAA MLE	2 teams	—	508	467	110	34	4	5	35	168	1	16	5	236	288	358	646	122	351	33	7	284	78	—	—	—	—	—	—
2019	25	MLB MLE	2 teams	—	383	349	87	17	3	7	29	106	2	3	7	249	311	375	686	126	331	28	8	300	89	—	—	—	—	—	—
2020	26	MLB MLE	San Francisco Giants	—	36	34	6	2	0	0	1	11	1	1	0	176	222	235	458	59	261	31	3	213	30	—	—	—	—	—	—
2021	27	MLB MLE	2 teams	—	367	332	85	16	6	9	30	114	2	15	0	256	319	422	740	166	358	31	8	328	108	—	—	—	—	—	—
2022	28	AAA MLE	2 teams	—	220	199	31	7	1	3	18	96	1	11	1	156	229	246	476	90	269	44	8	211	29	—	—	—	—	—	—
2023	29	AAA MLE	Oklahoma City Dodgers	—	272	240	43	8	0	6	29	110	1	5	1	179	270	287	558	108	290	40	11	253	57	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	846	776	183	42	9	14	61	262	4	19	6	236	295	367	662	131	335	31	7	295	82	-14	+1	+4	+5	+18	1.5
/ 162G	—	A+C		—	650	596	141	32	7	11	47	201	3	15	5	237	296	369	665	133	335	31	7	295	82	-11	+1	+3	+4	+14	1.1
Career	—	MLE		—	2650	2405	555	122	21	45	218	862	12	85	31	231	298	355	653	124	337	33	8	289	82	-14	+1	+4	+5	+18	1.5
/ 162G	—	MLE		—	650	590	136	30	5	11	53	211	3	21	8	231	297	354	651	124	336	32	8	289	82	-11	+1	+3	+4	+14	1.1

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 AAAOklahoma City Dodgers · 272 PAwOBA .342 → .253 (−89)K% 33.8% → 40.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	33.82%	÷1.003	×1.200	—	—	40.46%
BB% (unintentional) /PA	15.44%	÷0.976	×0.690	—	×0.972	10.61%
HBP% /PA	0.37%	—	×0.940	—	×0.972	0.34%
Singles /BIP	25.93%	÷1.021	×0.930	×0.950	×0.972	21.81%
Doubles /BIP	8.15%	÷0.983	×0.830	×0.950	×0.972	6.35%
Triples /BIP	0.00%	÷1.150	×0.970	×0.950	×0.972	0.00%
Home runs /BIP	6.67%	÷1.006	×0.700	—	×0.972	4.51%

Factors used: league row International League. Park (home-season share, regressed): HR 101, 1B 104, 2B 97, 3B 130, K 101, BB 95 — 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 .342 → .253; ISO .167 → .116; BABIP .359 → .290. 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).

2022 AAASalt Lake Bees · 140 PAwOBA .272 → .211 (−61)K% 32.1% → 39.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	32.14%	÷0.989	×1.200	—	—	39.01%
BB% (unintentional) /PA	12.14%	÷1.034	×0.690	—	×0.982	7.96%
HBP% /PA	0.71%	—	×0.940	—	×0.982	0.66%
Singles /BIP	18.42%	÷0.993	×0.930	×1.035	×0.982	17.54%
Doubles /BIP	5.26%	÷0.956	×0.830	×1.035	×0.982	4.64%
Triples /BIP	0.00%	÷1.150	×0.970	×1.035	×0.982	0.00%
Home runs /BIP	5.26%	÷0.978	×0.700	—	×0.982	3.70%

Factors used: league row International League. Park (home-season share, regressed): HR 96, 1B 99, 2B 91, 3B 130, K 98, BB 107 — 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 ×1.035.

Result: wOBA .272 → .211; ISO .132 → .095; 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).

2021 AAASacramento River Cats · 70 PAwOBA .334 → .277 (−56)K% 31.4% → 36.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	31.43%	÷1.034	×1.200	—	—	36.47%
BB% (unintentional) /PA	11.43%	÷0.956	×0.690	—	×0.992	8.18%
HBP% /PA	0.00%	—	×0.940	—	×0.992	0.00%
Singles /BIP	33.33%	÷1.049	×0.930	—	×0.992	29.31%
Doubles /BIP	5.13%	÷0.916	×0.830	—	×0.992	4.61%
Triples /BIP	2.56%	÷0.900	×0.920	—	×0.992	2.60%
Home runs /BIP	2.56%	÷0.995	×0.700	—	×0.992	1.79%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 99, 1B 110, 2B 83, 3B 80, K 107, BB 91 — 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).

Result: wOBA .334 → .277; ISO .115 → .096; BABIP .421 → .372. 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 AAASacramento River Cats · 102 PAwOBA .432 → .352 (−79)K% 20.6% → 27.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.59%	÷0.971	×1.290	—	—	27.34%
BB% (unintentional) /PA	17.65%	÷0.980	×0.710	—	×1.018	13.01%
HBP% /PA	0.98%	—	×0.980	—	×1.018	0.98%
Singles /BIP	29.03%	÷0.982	×0.900	—	×1.018	27.08%
Doubles /BIP	9.68%	÷0.981	×0.810	—	×1.018	8.14%
Triples /BIP	1.61%	÷1.008	×0.810	—	×1.018	1.32%
Home runs /BIP	4.84%	÷1.027	×0.780	—	×1.018	3.74%

Factors used: league row PCL. Park (home-season share, regressed): HR 105, 1B 96, 2B 96, 3B 102, K 94, BB 96 — 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 .432 → .352; ISO .205 → .159; BABIP .424 → .380. 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 AAASacramento River Cats · 356 PAwOBA .336 → .274 (−62)K% 28.9% → 34.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	28.93%	÷1.071	×1.290	—	—	34.84%
BB% (unintentional) /PA	10.39%	÷1.043	×0.710	—	×1.033	7.31%
HBP% /PA	0.28%	—	×0.980	—	×1.033	0.28%
Singles /BIP	23.94%	÷0.963	×0.900	×0.952	×1.033	22.01%
Doubles /BIP	12.68%	÷0.910	×0.810	×0.952	×1.033	11.09%
Triples /BIP	1.88%	÷1.150	×0.810	×0.952	×1.033	1.30%
Home runs /BIP	1.88%	÷0.998	×0.780	—	×1.033	1.52%

Factors used: league row PCL. Park (home-season share, regressed): HR 100, 1B 93, 2B 82, 3B 130, K 114, BB 109 — 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 ×0.952.

Result: wOBA .336 → .274; ISO .149 → .118; BABIP .392 → .349. 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 A+San Jose Giants · 133 PAwOBA .360 → .271 (−89)K% 31.6% → 41.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	31.58%	÷1.018	×1.340	—	—	41.56%
BB% (unintentional) /PA	12.78%	÷0.968	×0.710	—	×0.988	9.26%
HBP% /PA	0.00%	—	×0.790	—	×0.988	0.00%
Singles /BIP	21.92%	÷0.990	×0.910	×0.973	×0.988	19.37%
Doubles /BIP	15.07%	÷1.008	×0.820	×0.973	×0.988	11.78%
Triples /BIP	0.00%	÷0.900	×0.760	×0.973	×0.988	0.00%
Home runs /BIP	5.48%	÷1.010	×0.920	—	×0.988	4.93%

Factors used: league row CAL. Park (home-season share, regressed): HR 102, 1B 98, 2B 102, 3B 80, K 104, BB 94 — 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 ×0.973.

Result: wOBA .360 → .271; ISO .200 → .151; BABIP .386 → .322. 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 · 54 PAwOBA .346 → .290 (−56)K% 22.2% → 29.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.22%	÷0.989	×1.290	—	—	28.98%
BB% (unintentional) /PA	14.81%	÷0.952	×0.710	—	×1.048	11.58%
HBP% /PA	0.00%	—	×0.980	—	×1.048	0.00%
Singles /BIP	26.47%	÷1.027	×0.900	—	×1.048	24.31%
Doubles /BIP	2.94%	÷0.975	×0.810	—	×1.048	2.56%
Triples /BIP	0.00%	÷0.920	×0.810	—	×1.048	0.00%
Home runs /BIP	5.88%	÷0.889	×0.780	—	×1.048	5.41%

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 24 vs a level average of 27.2 → +4.8% on BB/HBP/hit rates (3.2 yr younger than average).

Result: wOBA .346 → .290; ISO .152 → .131; BABIP .312 → .284. 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 A+San Jose Giants · 311 PAwOBA .369 → .306 (−63)K% 21.2% → 28.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.22%	÷0.998	×1.340	—	—	28.50%
BB% (unintentional) /PA	13.83%	÷0.992	×0.710	—	×0.998	9.87%
HBP% /PA	0.32%	—	×0.790	—	×0.998	0.25%
Singles /BIP	25.25%	÷1.008	×0.910	×1.025	×0.998	23.32%
Doubles /BIP	6.06%	÷0.951	×0.820	×1.025	×0.998	5.34%
Triples /BIP	2.02%	÷0.900	×0.760	×1.025	×0.998	1.75%
Home runs /BIP	4.55%	÷1.045	×0.920	—	×0.998	3.99%

Factors used: league row CAL. Park (home-season share, regressed): HR 109, 1B 102, 2B 90, 3B 80, K 100, BB 98 — 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 ×1.025.

Result: wOBA .369 → .306; ISO .178 → .150; BABIP .346 → .313. 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 — .