Mac Williamson

PosLF

B/TR/R

Born1990-07-15

Age (2027)37

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2019

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2017	27	AAASF	382	.244/.302/.423	14	4	55	—	.193/.241/.330
2017	27	MLBSF	73	.235/.288/.397	3	1	92	-0.7
2018	28	AAASF	215	.269/.376/.533	13	1	107	—	.232/.315/.432
2018	28	MLBSF	105	.213/.295/.383	4	1	98	-0.0
2019	29	AAASEA	106	.367/.452/.722	9	1	141	—	.284/.346/.516
2019	29	MLBSEA	144	.156/.250/.258	4	2	49	-0.9
2020	30	No MLB games played
2021	31	No MLB games played
2022	32	No MLB games played
2023	33	No MLB games played
2024	34	No MLB games played
2025	35	No MLB games played
2026	36	No MLB games played

Last 3 seasons WAR -1.6

Contract

No contract data on file.

Skills · percentileStatcast 2017-2019

Speed58

Barrel%26

xwOBA18

Avg EV10

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)
2012	22	ROK	AZL Giants	—	19	17	3	0	0	2	2	5	0	0	0	176	263	529	793	353	100	26	11	340	34	—	—	—	—	—	—
2013	23	A+	San Jose Giants	—	599	520	152	31	2	25	51	132	21	10	1	292	377	504	881	212	345	22	9	386	105	—	—	—	—	—	—
2014	24	A+	San Jose Giants	—	100	85	27	7	0	3	13	14	2	6	1	318	420	506	926	188	353	14	13	412	124	—	—	—	—	—	—
2015	25	MLB	San Francisco Giants	—	34	32	7	0	1	0	0	8	1	0	0	219	242	281	524	62	280	24	0	229	43	-2	+0	-2	-7	+1	-1.0
2015	25	AAA	Sacramento River Cats	—	227	189	47	12	0	8	26	55	11	1	0	249	372	439	811	190	307	24	11	364	92	—	—	—	—	—	—
2015	25	AA	Richmond Flying Squirrels	—	290	259	76	16	2	5	25	53	5	3	1	293	367	429	795	135	351	18	9	356	101	—	—	—	—	—	—
2015	25	INTL	Scottsdale Scorpions	—	86	73	27	3	0	2	11	11	0	1	2	370	452	493	946	123	403	13	13	414	166	—	—	—	—	—	—
2016	26	MLB	San Francisco Giants	—	127	112	25	3	0	6	13	35	2	0	1	223	315	411	726	187	268	28	10	326	113	+2	-0	+1	-4	+3	0.2
2016	26	AAA	Sacramento River Cats	—	226	208	56	14	0	11	12	53	3	2	1	269	318	495	814	226	306	23	5	352	76	—	—	—	—	—	—
2017	27	MLB	San Francisco Giants	—	73	68	16	2	0	3	5	25	0	1	1	235	288	397	685	162	325	34	7	305	92	-1	-0	-3	-5	+2	-0.7
2017	27	AAA	Sacramento River Cats	—	382	352	86	21	0	14	25	100	4	4	1	244	302	423	725	179	301	26	7	321	55	—	—	—	—	—	—
2018	28	MLB	San Francisco Giants	—	105	94	20	4	0	4	11	27	0	1	1	213	295	383	678	170	254	26	10	305	98	-0	-0	+1	-3	+2	-0.0
2018	28	AAA	Sacramento River Cats	—	215	182	49	7	1	13	23	44	8	1	0	269	376	533	909	264	283	20	11	395	107	—	—	—	—	—	—
2019	29	MLB	Seattle Mariners	—	144	128	20	1	0	4	14	44	2	2	1	156	250	258	508	102	200	31	10	239	49	-9	+0	-1	-3	+3	-0.9
2019	29	AAA	Tacoma Rainiers	—	106	90	33	5	0	9	13	27	1	1	0	367	452	722	1.174	356	429	25	12	492	141	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	483	434	88	10	1	17	43	139	5	4	4	203	282	348	630	145	254	29	9	285	79	-12	-1	-2	-22	+10	-2.7
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2012	22	ROK MLE	AZL Giants	—	19	18	2	0	0	1	1	7	0	0	0	111	158	278	436	167	76	37	5	218	34	—	—	—	—	—	—
2013	23	A+ MLE	San Jose Giants	—	599	541	134	26	2	25	32	179	21	10	1	248	315	442	757	194	310	30	5	324	105	—	—	—	—	—	—
2014	24	A+ MLE	San Jose Giants	—	100	89	25	6	0	3	9	18	2	6	1	281	360	449	809	169	328	18	9	352	124	—	—	—	—	—	—
2015	25	MLB MLE	San Francisco Giants	—	34	32	7	0	1	0	0	8	1	0	0	219	242	281	524	62	280	24	0	232	43	-2	+0	-2	-7	+1	-1.0
2015	25	AAA MLE	Sacramento River Cats	—	227	196	41	10	0	6	19	68	11	1	0	209	314	352	666	143	284	30	8	304	92	—	—	—	—	—	—
2015	25	AA MLE	Richmond Flying Squirrels	—	290	269	74	14	2	5	15	64	5	3	1	275	325	398	723	123	344	22	5	317	101	—	—	—	—	—	—
2016	26	MLB MLE	San Francisco Giants	—	127	112	25	3	0	6	13	35	2	0	1	223	315	411	726	187	268	28	10	335	113	+2	-0	+1	-4	+3	0.2
2016	26	AAA MLE	Sacramento River Cats	—	226	214	48	11	0	8	6	66	3	2	1	224	256	388	643	164	275	29	3	280	76	—	—	—	—	—	—
2017	27	MLB MLE	San Francisco Giants	—	73	69	16	2	0	3	4	25	0	1	1	232	274	391	665	159	325	34	5	304	92	-1	-0	-3	-5	+2	-0.7
2017	27	AAA MLE	Sacramento River Cats	—	382	358	69	16	0	11	19	130	4	4	1	193	241	330	571	137	265	34	5	250	55	—	—	—	—	—	—
2018	28	MLB MLE	San Francisco Giants	—	105	94	20	4	0	4	11	27	0	1	1	213	295	383	678	170	254	26	10	312	98	-0	-0	+1	-3	+2	-0.0
2018	28	AAA MLE	Sacramento River Cats	—	215	190	44	6	1	10	15	53	8	1	0	232	315	432	746	200	262	25	7	327	107	—	—	—	—	—	—
2019	29	MLB MLE	Seattle Mariners	—	144	128	20	1	0	4	14	44	2	2	1	156	250	258	508	102	200	31	10	241	49	-9	+0	-1	-3	+3	-0.9
2019	29	AAA MLE	Tacoma Rainiers	—	106	95	27	4	0	6	8	35	1	1	0	284	346	516	862	232	360	33	8	376	141	—	—	—	—	—	—
2012	22	ROK MLE	AZL Giants	—	19	18	2	0	0	1	1	7	0	0	0	111	158	278	436	167	100	37	5	218	34	—	—	—	—	—	—
2012	22	ROK RAW	AZL Giants	—	19	17	3	0	0	2	2	5	0	0	0	176	263	529	793	353	100	26	11	340	34	—	—	—	—	—	—
2013	23	A+ MLE	San Jose Giants	—	599	541	134	26	2	25	32	179	21	10	1	248	315	442	757	194	319	30	5	324	105	—	—	—	—	—	—
2013	23	A+ RAW	San Jose Giants	—	599	520	152	31	2	25	51	132	21	10	1	292	377	504	881	212	345	22	9	386	105	—	—	—	—	—	—
2014	24	A+ MLE	San Jose Giants	—	100	89	25	6	0	3	9	18	2	6	1	281	360	449	809	169	324	18	9	352	124	—	—	—	—	—	—
2014	24	A+ RAW	San Jose Giants	—	100	85	27	7	0	3	13	14	2	6	1	318	420	506	926	188	353	14	13	412	124	—	—	—	—	—	—
2015	25	AA MLE	4 teams	—	637	576	142	28	3	13	39	162	17	5	3	247	313	373	687	127	318	25	6	306	93	—	—	—	—	—	—
2015	25	AA RAW	4 teams	—	637	553	157	31	3	15	62	127	17	5	3	284	373	432	806	148	341	20	10	360	93	—	—	—	—	—	—
2016	26	AAA MLE	2 teams	—	353	326	73	14	0	14	19	101	5	2	2	224	277	396	673	172	276	29	5	300	89	—	—	—	—	—	—
2016	26	AAA RAW	2 teams	—	353	320	81	17	0	17	25	88	5	2	2	253	317	466	783	213	294	25	7	342	89	—	—	—	—	—	—
2017	27	AAA MLE	2 teams	—	455	427	85	18	0	14	23	155	4	5	2	199	247	340	586	141	274	34	5	259	61	—	—	—	—	—	—
2017	27	AAA RAW	2 teams	—	455	420	102	23	0	17	30	125	4	5	2	243	300	419	719	176	305	27	7	318	61	—	—	—	—	—	—
2018	28	AAA MLE	2 teams	—	320	284	65	10	1	15	26	80	8	2	1	229	311	430	741	201	262	25	8	322	104	—	—	—	—	—	—
2018	28	AAA RAW	2 teams	—	320	276	69	11	1	17	34	71	8	2	1	250	349	482	831	232	274	22	11	365	104	—	—	—	—	—	—
2019	29	MLB MLE	2 teams	—	250	223	48	5	0	11	22	79	3	3	1	215	294	386	680	170	274	32	9	298	88	-4	+0	-1	-3	+5	-0.1
2019	29	MLB RAW	2 teams	—	250	218	53	6	0	13	27	71	3	3	1	243	335	450	784	206	294	28	11	346	88	-4	+0	-1	-3	+5	-0.1
2012	22	ROK MLE	AZL Giants	—	19	18	2	0	0	1	1	7	0	0	0	111	158	278	436	167	76	37	5	218	34	—	—	—	—	—	—
2013	23	A+ MLE	San Jose Giants	—	599	541	134	26	2	25	32	179	21	10	1	248	315	442	757	194	310	30	5	324	105	—	—	—	—	—	—
2014	24	A+ MLE	San Jose Giants	—	100	89	25	6	0	3	9	18	2	6	1	281	360	449	809	169	328	18	9	352	124	—	—	—	—	—	—
2015	25	AA MLE	4 teams	—	637	576	142	28	3	13	39	162	17	5	3	247	313	373	687	127	315	25	6	306	93	—	—	—	—	—	—
2016	26	AAA MLE	2 teams	—	353	326	73	14	0	14	19	101	5	2	2	224	277	396	673	172	272	29	5	300	89	—	—	—	—	—	—
2017	27	AAA MLE	2 teams	—	455	425	85	18	0	14	23	155	4	5	2	200	247	341	588	141	275	34	5	259	61	—	—	—	—	—	—
2018	28	AAA MLE	2 teams	—	320	283	65	10	1	15	26	80	8	2	1	230	311	431	742	201	259	25	8	322	104	—	—	—	—	—	—
2019	29	MLB MLE	2 teams	—	250	223	48	5	0	11	22	79	3	3	1	215	294	386	680	170	268	32	9	298	88	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	483	434	88	10	1	17	43	139	5	4	4	203	282	348	630	145	254	29	9	285	92	-12	-1	-2	-22	+10	-2.7
/ 162G	—	A+C		—	650	584	118	13	1	23	58	187	7	5	5	202	282	346	628	144	253	29	9	285	92	-16	-1	-3	-29	+14	-3.6
Career	—	MLE		—	2733	2484	574	107	6	96	171	781	60	33	11	231	297	395	691	164	294	29	6	304	92	-12	-1	-2	-22	+10	-2.7
/ 162G	—	MLE		—	650	591	136	25	1	23	41	186	14	8	3	230	296	393	688	162	293	29	6	304	92	-16	-1	-3	-29	+14	-3.6

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 AAATacoma Rainiers · 106 PAwOBA .485 → .376 (−109)K% 25.5% → 33.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.47%	÷0.990	×1.290	—	—	33.19%
BB% (unintentional) /PA	11.32%	÷1.039	×0.710	—	×0.982	7.59%
HBP% /PA	0.94%	—	×0.980	—	×0.982	0.91%
Singles /BIP	30.16%	÷0.985	×0.900	—	×0.982	27.06%
Doubles /BIP	7.94%	÷0.998	×0.810	—	×0.982	6.32%
Triples /BIP	0.00%	÷1.073	×0.810	—	×0.982	0.00%
Home runs /BIP	14.29%	÷1.049	×0.780	—	×0.982	10.43%

Factors used: league row PCL. Park (home-season share, regressed): HR 110, 1B 97, 2B 100, 3B 115, K 98, BB 108 — 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).

Result: wOBA .485 → .376; ISO .356 → .258; BABIP .429 → .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).

2018 AAASacramento River Cats · 215 PAwOBA .386 → .327 (−59)K% 20.5% → 24.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	20.47%	÷1.071	×1.290	—	—	24.64%
BB% (unintentional) /PA	10.23%	÷1.043	×0.710	—	×0.992	6.91%
HBP% /PA	3.72%	—	×0.980	—	×0.992	3.62%
Singles /BIP	20.29%	÷0.963	×0.900	×1.035	×0.992	19.47%
Doubles /BIP	5.07%	÷0.910	×0.810	×1.035	×0.992	4.64%
Triples /BIP	0.72%	÷1.150	×0.810	×1.035	×0.992	0.52%
Home runs /BIP	9.42%	÷0.998	×0.780	—	×0.992	7.31%

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 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 ×1.035.

Result: wOBA .386 → .327; ISO .264 → .211; BABIP .283 → .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).

2017 AAASacramento River Cats · 382 PAwOBA .313 → .250 (−63)K% 26.2% → 34.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	26.18%	÷0.989	×1.290	—	—	34.14%
BB% (unintentional) /PA	6.54%	÷0.952	×0.710	—	×1.003	4.90%
HBP% /PA	1.05%	—	×0.980	—	×1.003	1.03%
Singles /BIP	20.24%	÷1.027	×0.900	×1.024	×1.003	18.22%
Doubles /BIP	8.33%	÷0.975	×0.810	×1.024	×1.003	7.11%
Triples /BIP	0.00%	÷0.920	×0.810	×1.024	×1.003	0.00%
Home runs /BIP	5.56%	÷0.889	×0.780	—	×1.003	4.89%

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 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 ×1.024.

Result: wOBA .313 → .250; ISO .179 → .142; BABIP .301 → .265. 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 · 226 PAwOBA .341 → .280 (−61)K% 23.5% → 29.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.45%	÷1.034	×1.290	—	—	29.26%
BB% (unintentional) /PA	3.98%	÷1.052	×0.710	—	×1.018	2.74%
HBP% /PA	1.33%	—	×0.980	—	×1.018	1.32%
Singles /BIP	20.00%	÷0.960	×0.900	×0.995	×1.018	18.99%
Doubles /BIP	9.03%	÷0.973	×0.810	×0.995	×1.018	7.62%
Triples /BIP	0.00%	÷0.900	×0.810	×0.995	×1.018	0.00%
Home runs /BIP	7.10%	÷1.082	×0.780	—	×1.018	5.21%

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 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 ×0.995.

Result: wOBA .341 → .280; ISO .226 → .168; BABIP .306 → .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).

2015 AARichmond Flying Squirrels · 290 PAwOBA .343 → .317 (−26)K% 18.3% → 22.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	18.28%	÷1.003	×1.220	—	—	22.24%
BB% (unintentional) /PA	7.24%	÷0.992	×0.700	—	×0.998	5.10%
HBP% /PA	1.72%	—	×0.850	—	×0.998	1.46%
Singles /BIP	25.73%	÷1.013	×0.970	×1.050	×0.998	25.81%
Doubles /BIP	7.77%	÷0.998	×0.860	×1.050	×0.998	7.01%
Triples /BIP	0.97%	÷1.025	×0.950	×1.050	×0.998	0.94%
Home runs /BIP	2.43%	÷0.860	×0.780	—	×0.998	2.20%

Factors used: league row EL. Park (home-season share, regressed): HR 72, 1B 103, 2B 100, 3B 105, K 101, BB 98 — 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 ×1.050.

Result: wOBA .343 → .317; ISO .135 → .123; BABIP .351 → .344. 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 · 227 PAwOBA .355 → .304 (−51)K% 24.2% → 29.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.23%	÷1.047	×1.290	—	—	29.85%
BB% (unintentional) /PA	11.01%	÷0.960	×0.710	—	×1.033	8.42%
HBP% /PA	4.85%	—	×0.980	—	×1.033	4.91%
Singles /BIP	20.15%	÷1.003	×0.900	×1.049	×1.033	19.59%
Doubles /BIP	8.96%	÷1.024	×0.810	×1.049	×1.033	7.68%
Triples /BIP	0.00%	÷0.925	×0.810	×1.049	×1.033	0.00%
Home runs /BIP	5.97%	÷0.982	×0.780	—	×1.033	4.90%

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 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 ×1.049.

Result: wOBA .355 → .304; ISO .190 → .153; BABIP .307 → .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).

2014 A+San Jose Giants · 100 PAwOBA .401 → .352 (−49)K% 14.0% → 18.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.00%	÷1.026	×1.340	—	—	18.29%
BB% (unintentional) /PA	13.00%	÷1.009	×0.710	—	×0.988	9.04%
HBP% /PA	2.00%	—	×0.790	—	×0.988	1.56%
Singles /BIP	23.94%	÷0.996	×0.910	×1.049	×0.988	22.66%
Doubles /BIP	9.86%	÷0.953	×0.820	×1.049	×0.988	8.79%
Triples /BIP	0.00%	÷0.979	×0.760	×1.049	×0.988	0.00%
Home runs /BIP	4.23%	÷0.931	×0.920	—	×0.988	4.12%

Factors used: league row CAL. Park (home-season share, regressed): HR 86, 1B 99, 2B 91, 3B 96, K 105, BB 102 — 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 ×1.049.

Result: wOBA .401 → .352; ISO .188 → .177; BABIP .353 → .328. 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 A+San Jose Giants · 599 PAwOBA .377 → .324 (−53)K% 22.0% → 29.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.04%	÷0.992	×1.340	—	—	29.78%
BB% (unintentional) /PA	8.01%	÷1.063	×0.710	—	×0.998	5.34%
HBP% /PA	3.51%	—	×0.790	—	×0.998	2.76%
Singles /BIP	24.23%	÷1.032	×0.910	×1.023	×0.998	21.82%
Doubles /BIP	7.99%	÷0.953	×0.820	×1.023	×0.998	7.02%
Triples /BIP	0.52%	÷0.900	×0.760	×1.023	×0.998	0.44%
Home runs /BIP	6.44%	÷0.866	×0.920	—	×0.998	6.83%

Factors used: league row CAL. Park (home-season share, regressed): HR 73, 1B 106, 2B 91, 3B 80, K 98, BB 113 — 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 ×1.023.

Result: wOBA .377 → .324; ISO .212 → .203; BABIP .345 → .310. 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 — .