Nick Swisher

PosRF

B/TS/L

Born1980-11-25

Age (2027)46

No MLB projection yet. Career stats below are historical.

Overview

AAALAST SEASON 2016

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2014	33	MLBCleveland Ind…	401	.208/.280/.331	8	0	71	-2.4
2015	34	AAA/AA/ACLE	74	.311/.432/.426	1	0	95	—	.278/.365/.386
2015	34	MLBATL	260	.196/.315/.320	6	0	83	-0.6
2016	35	AAAScranton/Wilk…	236	.255/.299/.377	7	0	43	—	.200/.231/.284
2017	36	No MLB games played
2018	37	No MLB games played
2019	38	No MLB games played
2020	39	No MLB games played
2021	40	No MLB games played
2022	41	No MLB games played
2023	42	No MLB games played
2024	43	No MLB games played
2025	44	No MLB games played
2026	45	No MLB games played

Last 3 seasons WAR -3.1

Contract

No contract data on file.

Skills · percentileStatcast 2015

xwOBA27

Avg EV20

Barrel%10

Speed10

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)
2004	23	MLB	Oakland Athletics	—	71	60	15	4	0	2	8	11	2	0	0	250	357	417	774	167	271	15	11	345	120	+2	+0	-4	-1	+2	-0.1
2005	24	MLB	Oakland Athletics	—	522	462	109	32	1	21	55	110	4	0	1	236	322	446	768	210	265	21	11	340	114	+8	-0	-0	-6	+11	1.4
2005	24	AAA	Sacramento River Cats	—	26	23	9	3	0	0	2	7	1	0	1	391	462	522	983	130	562	27	8	439	121	—	—	—	—	—	—
2006	25	MLB	Oakland Athletics	—	672	556	141	24	2	35	97	152	11	1	2	254	375	493	868	239	283	23	14	380	138	+30	-1	-6	-12	+15	2.7
2007	26	MLB	Oakland Athletics	—	659	539	141	36	1	22	100	131	10	3	2	262	387	455	841	193	301	20	15	372	129	+23	-0	-5	+2	+14	3.5
2008	27	MLB	Chicago White Sox	—	588	497	109	21	1	24	82	135	4	3	3	219	334	410	745	191	249	23	14	333	107	+5	-1	-3	+2	+13	1.6
2009	28	MLB	New York Yankees	—	607	498	124	35	1	29	97	126	3	0	0	249	375	498	873	249	272	21	16	379	141	+30	+0	-9	-7	+13	2.8
2010	29	MLB	New York Yankees	—	635	566	163	33	3	29	58	139	6	1	2	288	360	511	871	223	335	22	9	380	140	+30	-1	-3	-7	+14	3.4
2011	30	MLB	New York Yankees	—	635	526	137	30	0	23	95	125	5	2	2	260	379	449	827	188	295	20	15	366	127	+20	-0	+0	-7	+14	2.8
2012	31	MLB	New York Yankees	—	624	537	146	36	0	24	77	141	4	2	3	272	367	473	840	201	324	23	12	370	132	+24	-1	-6	-7	+14	2.4
2013	32	MLB	Cleveland Indians	—	634	549	135	27	2	22	77	138	4	1	0	246	343	423	765	177	288	22	12	341	115	+11	+0	-8	-11	+14	0.6
2014	33	MLB	Cleveland Indians	—	401	360	75	20	0	8	36	111	0	0	0	208	280	331	611	122	273	28	9	274	71	-14	+0	-12	-7	+9	-2.4
2015	34	MLB	Atlanta Braves	—	260	219	43	9	0	6	35	54	3	0	0	196	315	320	635	123	228	21	13	292	83	-5	+0	-3	-4	+6	-0.6
2015	34	AAA	Columbus Clippers	—	37	32	12	2	0	1	5	8	0	0	0	375	459	531	991	156	478	22	14	440	113	—	—	—	—	—	—
2015	34	AA	Akron RubberDucks	—	17	15	1	0	0	0	1	4	1	0	0	67	176	67	243		91	24	6	138	-39	—	—	—	—	—	—
2015	34	A	Lake County Captains	—	20	14	6	2	0	0	5	2	1	0	0	429	600	571	1.171	143	500	10	25	521	174	—	—	—	—	—	—
2016	35	AAA	Scranton/Wilkes-Barre RailRiders	—	236	220	56	6	0	7	14	54	0	0	0	255	299	377	676	123	304	23	6	301	43	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	6308	5369	1338	307	11	245	817	1373	56	13	15	249	354	447	802	198	287	22	13	354	126	+194	-3	-56	-63	+137	21.2
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2004	23	MLB MLE	Oakland Athletics	—	71	60	15	4	0	2	8	11	2	0	0	250	357	417	774	167	271	15	11	345	120	+2	+0	-4	-1	+2	-0.1
2005	24	MLB MLE	Oakland Athletics	—	522	465	109	32	1	21	52	110	4	0	1	234	317	443	760	209	265	21	10	336	114	+8	-0	-0	-6	+11	1.4
2005	24	AAA MLE	Sacramento River Cats	—	26	24	7	2	0	0	1	9	1	0	1	292	346	375	721	83	506	35	4	347	121	—	—	—	—	—	—
2006	25	MLB MLE	Oakland Athletics	—	672	563	141	24	2	35	90	152	11	1	2	250	364	487	851	236	283	23	13	372	138	+30	-1	-6	-12	+15	2.7
2007	26	MLB MLE	Oakland Athletics	—	659	551	141	36	1	22	88	131	10	3	2	256	368	445	813	189	301	20	13	359	129	+23	-0	-5	+2	+14	3.5
2008	27	MLB MLE	Chicago White Sox	—	588	503	109	21	1	24	76	135	4	3	3	217	324	406	730	189	249	23	13	326	107	+5	-1	-3	+2	+13	1.6
2009	28	MLB MLE	New York Yankees	—	607	500	124	35	1	29	95	126	3	0	0	248	371	496	867	248	272	21	16	377	141	+30	+0	-9	-7	+13	2.8
2010	29	MLB MLE	New York Yankees	—	635	566	161	33	3	27	58	139	6	1	2	284	357	496	854	212	335	22	9	374	140	+30	-1	-3	-7	+14	3.4
2011	30	MLB MLE	New York Yankees	—	635	532	136	30	0	22	89	125	5	2	2	256	367	436	804	180	295	20	14	355	127	+20	-0	+0	-7	+14	2.8
2012	31	MLB MLE	New York Yankees	—	624	539	144	36	0	22	75	141	4	2	3	267	361	456	817	189	324	23	12	363	132	+24	-1	-6	-7	+14	2.4
2013	32	MLB MLE	Cleveland Indians	—	634	552	135	27	2	22	74	138	4	1	0	245	338	420	758	176	288	22	12	338	115	+11	+0	-8	-11	+14	0.6
2014	33	MLB MLE	Cleveland Indians	—	401	360	75	20	0	8	36	111	0	0	0	208	280	331	611	122	273	28	9	273	71	-14	+0	-12	-7	+9	-2.4
2015	34	MLB MLE	Atlanta Braves	—	260	220	43	9	0	6	34	54	3	0	0	195	311	318	629	123	228	21	13	291	83	-5	+0	-3	-4	+6	-0.6
2015	34	AAA MLE	Columbus Clippers	—	37	34	11	2	0	1	3	10	0	0	0	324	378	471	849	147	401	27	8	335	113	—	—	—	—	—	—
2015	34	AA MLE	Akron RubberDucks	—	17	15	1	0	0	0	1	5	1	0	0	67	176	67	243		85	29	6	110	-39	—	—	—	—	—	—
2015	34	A MLE	Lake County Captains	—	20	16	6	2	0	0	3	3	1	0	0	375	500	500	1.000	125	430	15	15	426	174	—	—	—	—	—	—
2016	35	AAA MLE	Scranton/Wilkes-Barre RailRiders	—	236	225	45	4	0	5	9	65	0	0	0	200	231	284	515	84	255	28	4	232	43	—	—	—	—	—	—
2004	23	MLB MLE	Oakland Athletics	—	71	60	15	4	0	2	8	11	2	0	0	250	357	417	774	167	271	15	11	345	120	+2	+0	-4	-1	+2	-0.1
2004	23	MLB RAW	Oakland Athletics	—	71	60	15	4	0	2	8	11	2	0	0	250	357	417	774	167	271	15	11	345	120	+2	+0	-4	-1	+2	-0.1
2005	24	MLB MLE	2 teams	—	548	489	116	34	1	21	53	119	5	0	2	237	318	440	758	202	271	22	10	337	114	+9	-1	-0	-6	+12	1.5
2005	24	MLB RAW	2 teams	—	548	485	118	35	1	21	57	117	5	0	2	243	329	449	779	206	279	21	10	345	114	+9	-1	-0	-6	+12	1.5
2006	25	MLB MLE	Oakland Athletics	—	672	563	141	24	2	35	90	152	11	1	2	250	364	487	851	236	277	23	13	372	138	+30	-1	-6	-12	+15	2.7
2006	25	MLB RAW	Oakland Athletics	—	672	556	141	24	2	35	97	152	11	1	2	254	375	493	868	239	283	23	14	380	138	+30	-1	-6	-12	+15	2.7
2007	26	MLB MLE	Oakland Athletics	—	659	551	141	36	1	22	88	131	10	3	2	256	368	445	813	189	292	20	13	359	129	+23	-0	-5	+2	+14	3.5
2007	26	MLB RAW	Oakland Athletics	—	659	539	141	36	1	22	100	131	10	3	2	262	387	455	841	193	301	20	15	372	129	+23	-0	-5	+2	+14	3.5
2008	27	MLB MLE	Chicago White Sox	—	588	503	109	21	1	24	76	135	4	3	3	217	324	406	730	189	244	23	13	326	107	+5	-1	-3	+2	+13	1.6
2008	27	MLB RAW	Chicago White Sox	—	588	497	109	21	1	24	82	135	4	3	3	219	334	410	745	191	249	23	14	333	107	+5	-1	-3	+2	+13	1.6
2009	28	MLB MLE	New York Yankees	—	607	500	124	35	1	29	95	126	3	0	0	248	371	496	867	248	271	21	16	377	141	+30	+0	-9	-7	+13	2.8
2009	28	MLB RAW	New York Yankees	—	607	498	124	35	1	29	97	126	3	0	0	249	375	498	873	249	272	21	16	379	141	+30	+0	-9	-7	+13	2.8
2010	29	MLB MLE	New York Yankees	—	635	566	161	33	3	27	58	139	6	1	2	284	357	496	854	212	333	22	9	374	140	+30	-1	-3	-7	+14	3.4
2010	29	MLB RAW	New York Yankees	—	635	566	163	33	3	29	58	139	6	1	2	288	360	511	871	223	335	22	9	380	140	+30	-1	-3	-7	+14	3.4
2011	30	MLB MLE	New York Yankees	—	635	532	136	30	0	22	89	125	5	2	2	256	367	436	804	180	290	20	14	355	127	+20	-0	+0	-7	+14	2.8
2011	30	MLB RAW	New York Yankees	—	635	526	137	30	0	23	95	125	5	2	2	260	379	449	827	188	295	20	15	366	127	+20	-0	+0	-7	+14	2.8
2012	31	MLB MLE	New York Yankees	—	624	539	144	36	0	22	75	141	4	2	3	267	361	456	817	189	320	23	12	363	132	+24	-1	-6	-7	+14	2.4
2012	31	MLB RAW	New York Yankees	—	624	537	146	36	0	24	77	141	4	2	3	272	367	473	840	201	324	23	12	370	132	+24	-1	-6	-7	+14	2.4
2013	32	MLB MLE	Cleveland Indians	—	634	552	135	27	2	22	74	138	4	1	0	245	338	420	758	176	285	22	12	338	115	+11	+0	-8	-11	+14	0.6
2013	32	MLB RAW	Cleveland Indians	—	634	549	135	27	2	22	77	138	4	1	0	246	343	423	765	177	288	22	12	341	115	+11	+0	-8	-11	+14	0.6
2014	33	MLB MLE	Cleveland Indians	—	401	360	75	20	0	8	36	111	0	0	0	208	280	331	611	122	273	28	9	273	71	-14	+0	-12	-7	+9	-2.4
2014	33	MLB RAW	Cleveland Indians	—	401	360	75	20	0	8	36	111	0	0	0	208	280	331	611	122	273	28	9	274	71	-14	+0	-12	-7	+9	-2.4
2015	34	MLB MLE	4 teams	—	334	285	60	12	0	7	41	71	5	0	0	211	320	326	647	116	252	21	12	295	86	-6	+0	-3	-4	+7	-0.5
2015	34	MLB RAW	4 teams	—	334	280	62	13	0	7	46	68	5	0	0	221	341	343	684	121	264	20	14	314	86	-6	+0	-3	-4	+7	-0.5
2016	35	AAA MLE	Scranton/Wilkes-Barre RailRiders	—	236	225	45	4	0	5	9	65	0	0	0	200	231	284	515	84	255	28	4	232	43	—	—	—	—	—	—
2016	35	AAA RAW	Scranton/Wilkes-Barre RailRiders	—	236	220	56	6	0	7	14	54	0	0	0	255	299	377	676	123	304	23	6	301	43	—	—	—	—	—	—
2004	23	MLB MLE	Oakland Athletics	—	71	61	15	4	0	2	8	11	2	0	0	246	357	410	767	164	271	15	11	345	120	—	—	—	—	—	—
2005	24	MLB MLE	2 teams	—	548	485	116	34	1	21	53	119	5	0	2	239	318	443	761	204	276	22	10	337	114	—	—	—	—	—	—
2006	25	MLB MLE	Oakland Athletics	—	672	566	141	24	2	35	90	152	11	1	2	249	364	484	849	235	283	23	13	372	138	—	—	—	—	—	—
2007	26	MLB MLE	Oakland Athletics	—	659	556	141	36	1	22	88	131	10	3	2	254	368	441	809	187	301	20	13	359	129	—	—	—	—	—	—
2008	27	MLB MLE	Chicago White Sox	—	588	503	109	21	1	24	76	135	4	3	3	217	324	406	730	189	249	23	13	326	107	—	—	—	—	—	—
2009	28	MLB MLE	New York Yankees	—	607	504	124	35	1	29	95	126	3	0	0	246	371	492	863	246	272	21	16	377	141	—	—	—	—	—	—
2010	29	MLB MLE	New York Yankees	—	635	566	161	33	3	27	58	139	6	1	2	284	357	496	854	212	335	22	9	374	140	—	—	—	—	—	—
2011	30	MLB MLE	New York Yankees	—	635	536	136	30	0	22	89	125	5	2	2	254	367	433	800	179	295	20	14	355	127	—	—	—	—	—	—
2012	31	MLB MLE	New York Yankees	—	624	540	144	36	0	22	75	141	4	2	3	267	361	456	816	189	324	23	12	363	132	—	—	—	—	—	—
2013	32	MLB MLE	Cleveland Indians	—	634	551	135	27	2	22	74	138	4	1	0	245	338	421	759	176	288	22	12	338	115	—	—	—	—	—	—
2014	33	MLB MLE	Cleveland Indians	—	401	362	75	20	0	8	36	111	0	0	0	207	280	329	609	122	273	28	9	273	71	—	—	—	—	—	—
2015	34	MLB MLE	4 teams	—	334	285	60	12	0	7	41	71	5	0	0	211	320	326	647	116	252	21	12	295	86	—	—	—	—	—	—
2016	35	AAA MLE	Scranton/Wilkes-Barre RailRiders	—	236	225	45	4	0	5	9	65	0	0	0	200	231	284	515	84	255	28	4	232	43	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	6308	5369	1338	307	11	245	817	1373	56	13	15	249	354	447	802	198	287	22	13	354	119	+194	-3	-56	-63	+137	21.2
/ 162G	—	A+C		—	650	553	138	32	1	25	84	141	6	1	2	250	354	447	801	197	288	22	13	354	119	+20	-0	-6	-6	+14	2.2
Career	—	MLE		—	6644	5725	1402	316	11	246	792	1464	59	13	16	245	343	433	775	188	284	22	12	344	119	+194	-3	-56	-63	+137	21.2
/ 162G	—	MLE		—	650	560	137	31	1	24	77	143	6	1	2	245	342	432	774	188	284	22	12	344	119	+20	-0	-6	-6	+14	2.2

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.

2016 AAAScranton/Wilkes-Barre RailRiders · 236 PAwOBA .295 → .232 (−63)K% 22.9% → 27.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.88%	÷0.994	×1.200	—	—	27.62%
BB% (unintentional) /PA	5.93%	÷0.977	×0.690	—	×0.940	3.94%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	25.90%	÷0.991	×0.930	×0.979	×0.940	22.39%
Doubles /BIP	3.61%	÷1.059	×0.830	×0.979	×0.940	2.61%
Triples /BIP	0.00%	÷0.900	×0.970	×0.979	×0.940	0.00%
Home runs /BIP	4.22%	÷0.996	×0.840	—	×0.940	3.34%

Factors used: league row IL. Park (home-season share, regressed): HR 99, 1B 98, 2B 112, 3B 80, K 99, BB 95 — applied at half strength. Age: age 36 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (8.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.979.

Result: wOBA .295 → .232; ISO .123 → .093; BABIP .304 → .255. 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 AAAColumbus Clippers · 37 PAwOBA .429 → .335 (−94)K% 21.6% → 25.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	21.62%	÷1.009	×1.200	—	—	25.70%
BB% (unintentional) /PA	13.51%	÷1.068	×0.690	—	×0.940	8.21%
HBP% /PA	0.00%	—	×0.940	—	×0.940	0.00%
Singles /BIP	37.50%	÷1.005	×0.930	—	×0.940	32.63%
Doubles /BIP	8.33%	÷1.019	×0.830	—	×0.940	6.38%
Triples /BIP	0.00%	÷0.900	×0.970	—	×0.940	0.00%
Home runs /BIP	4.17%	÷1.217	×0.840	—	×0.940	2.70%

Factors used: league row IL. Park (home-season share, regressed): HR 143, 1B 101, 2B 104, 3B 80, K 102, BB 114 — applied at half strength. Age: age 35 vs a level average of 27.2 → −6.0% on BB/HBP/hit rates (7.8 yr older than average).

Result: wOBA .429 → .335; ISO .156 → .111; BABIP .478 → .401. 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 ALake County Captains · 20 PAwOBA .509 → .426 (−83)K% 10.0% → 13.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.00%	÷1.014	×1.360	—	—	13.41%
BB% (unintentional) /PA	25.00%	÷0.995	×0.690	—	×0.940	16.29%
HBP% /PA	5.00%	—	×0.780	—	×0.940	3.67%
Singles /BIP	33.33%	÷1.007	×0.920	—	×0.940	28.62%
Doubles /BIP	16.67%	÷0.902	×0.830	—	×0.940	14.41%
Triples /BIP	0.00%	÷1.103	×0.910	—	×0.940	0.00%
Home runs /BIP	0.00%	÷1.169	×0.800	—	×0.940	0.00%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 134, 1B 101, 2B 80, 3B 121, K 103, BB 99 — applied at half strength. Age: age 35 vs a level average of 22.0 → −6.0% on BB/HBP/hit rates (13.0 yr older than average).

Result: wOBA .509 → .426; ISO .143 → .137; BABIP .500 → .430. 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 AAAkron RubberDucks · 17 PAwOBA .135 → .110 (−25)K% 23.5% → 28.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.53%	÷1.019	×1.220	—	—	28.18%
BB% (unintentional) /PA	5.88%	÷0.934	×0.700	—	×0.940	4.14%
HBP% /PA	5.88%	—	×0.850	—	×0.940	4.70%
Singles /BIP	9.09%	÷0.971	×0.970	—	×0.940	8.53%
Doubles /BIP	0.00%	÷1.053	×0.860	—	×0.940	0.00%
Triples /BIP	0.00%	÷1.150	×0.950	—	×0.940	0.00%
Home runs /BIP	0.00%	÷0.842	×0.780	—	×0.940	0.00%

Factors used: league row EL. Park (home-season share, regressed): HR 68, 1B 94, 2B 111, 3B 130, K 104, BB 87 — applied at half strength. Age: age 35 vs a level average of 24.8 → −6.0% on BB/HBP/hit rates (10.2 yr older than average).

Result: wOBA .135 → .110; ISO .000 → .000; BABIP .091 → .085. 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).

2005 AAASacramento River Cats · 26 PAwOBA .428 → .347 (−81)K% 26.9% → 34.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	26.92%	÷1.000	×1.290	—	—	34.73%
BB% (unintentional) /PA	7.69%	÷1.000	×0.710	—	×1.033	5.64%
HBP% /PA	3.85%	—	×0.980	—	×1.033	3.89%
Singles /BIP	37.50%	÷1.000	×0.900	—	×1.033	34.86%
Doubles /BIP	18.75%	÷1.000	×0.810	—	×1.033	15.69%
Triples /BIP	0.00%	÷1.000	×0.810	—	×1.033	0.00%
Home runs /BIP	0.00%	÷1.000	×0.780	—	×1.033	0.00%

Factors used: league row PCL. Park (home-season share, regressed): no park row on file (treated as neutral) — 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).

Result: wOBA .428 → .347; ISO .130 → .099; BABIP .562 → .506. 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).