Devon Travis

Pos2B

B/TR/R

Born1991-02-21

Age (2027)36

No MLB projection yet. Career stats below are historical.

Overview

MLBLAST SEASON 2018

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2016	25	AAA/A+TOR	38	.306/.324/.417	0	0	84	—	.308/.326/.421
2016	25	MLBTOR	432	.300/.333/.454	11	4	120	2.5
2017	26	MLBTOR	197	.259/.294/.438	5	4	101	0.5
2018	27	AAATOR	64	.210/.234/.274	1	1	16	—	.190/.203/.254
2018	27	MLBTOR	378	.232/.275/.381	11	3	82	-1.1
2019	28	No MLB games played
2020	29	No MLB games played
2021	30	No MLB games played
2022	31	No MLB games played
2023	32	No MLB games played
2024	33	No MLB games played
2025	34	No MLB games played
2026	35	No MLB games played

Last 3 seasons WAR 1.9

Contract

No contract data on file.

Skills · percentileStatcast 2016-2018

xwOBA49

Speed37

Avg EV31

Barrel%25

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)
2013	22	A+	Lakeland Flying Tigers	—	237	214	75	11	2	10	18	32	2	8	1	350	406	561	967	210	371	14	8	419	124	—	—	—	—	—	—
2013	22	A	West Michigan Whitecaps	—	339	290	102	17	2	6	35	32	8	14	3	352	435	486	922	134	375	9	10	406	125	—	—	—	—	—	—
2013	22	INTL	Mesa Solar Sox	—	80	72	17	3	0	2	7	14	0	3	0	236	304	361	665	125	263	18	9	297	87	—	—	—	—	—	—
2014	23	AA	Erie SeaWolves	—	441	396	118	20	7	10	37	60	2	16	5	298	361	460	821	162	327	14	8	359	101	—	—	—	—	—	—
2015	24	MLB	Toronto Blue Jays	—	239	217	66	18	0	8	18	43	2	3	1	304	361	498	859	194	347	18	8	377	141	+12	+0	+2	+1	+5	2.0
2015	24	AAA	Buffalo Bisons	—	38	32	7	1	0	0	6	9	0	1	0	219	342	250	592	31	304	24	16	287	52	—	—	—	—	—	—
2016	25	MLB	Toronto Blue Jays	—	432	410	123	28	1	11	20	87	0	4	1	300	333	454	786	154	358	20	5	346	120	+10	+0	+3	+2	+9	2.5
2016	25	AAA	Buffalo Bisons	—	22	22	6	2	0	0	0	2	0	0	0	273	273	364	636	91	300	9	0	281	56	—	—	—	—	—	—
2016	25	A+	Dunedin Blue Jays	—	16	14	5	2	0	0	1	1	0	0	0	357	400	500	900	143	357	6	6	374	122	—	—	—	—	—	—
2017	26	MLB	Toronto Blue Jays	—	197	185	48	18	0	5	7	38	2	4	2	259	294	438	732	178	299	19	4	317	101	+0	+0	-0	+1	+4	0.5
2018	27	MLB	Toronto Blue Jays	—	378	357	83	14	3	11	16	64	5	3	2	232	275	381	656	148	255	17	4	292	82	-8	-0	-12	+2	+8	-1.1
2018	27	AAA	Buffalo Bisons	—	64	62	13	1	0	1	2	7	0	1	0	210	234	274	509	65	222	11	3	230	16	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1246	1169	320	78	4	35	61	232	9	14	6	274	315	437	752	163	315	19	5	331	110	+15	+0	-8	+5	+27	4.0
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2013	22	A+ MLE	Lakeland Flying Tigers	—	237	219	65	11	1	8	13	45	2	8	1	297	342	466	808	169	334	19	5	351	124	—	—	—	—	—	—
2013	22	A MLE	West Michigan Whitecaps	—	339	302	95	16	2	5	23	44	8	14	3	315	378	430	809	116	340	13	7	353	125	—	—	—	—	—	—
2014	23	AA MLE	Erie SeaWolves	—	441	407	112	19	6	8	26	72	2	16	5	275	322	410	732	135	309	16	6	318	101	—	—	—	—	—	—
2015	24	MLB MLE	Toronto Blue Jays	—	239	218	66	18	0	8	18	43	2	3	1	303	361	495	857	193	347	18	8	376	141	+12	+0	+2	+1	+5	2.0
2015	24	AAA MLE	Buffalo Bisons	—	38	34	7	1	0	0	4	11	0	1	0	206	289	235	525	29	299	29	11	245	52	—	—	—	—	—	—
2016	25	MLB MLE	Toronto Blue Jays	—	432	410	123	28	1	11	20	87	0	4	1	300	333	454	786	154	358	20	5	345	120	+10	+0	+3	+2	+9	2.5
2016	25	AAA MLE	Buffalo Bisons	—	22	22	6	2	0	0	0	3	0	0	0	273	273	364	636	91	284	14	0	251	56	—	—	—	—	—	—
2016	25	A+ MLE	Dunedin Blue Jays	—	16	14	5	2	0	0	1	1	0	0	0	357	400	500	900	143	312	6	6	349	122	—	—	—	—	—	—
2017	26	MLB MLE	Toronto Blue Jays	—	197	185	48	18	0	5	7	38	2	4	2	259	294	438	732	178	299	19	4	317	101	+0	+0	-0	+1	+4	0.5
2018	27	MLB MLE	Toronto Blue Jays	—	378	358	83	14	3	11	15	64	5	3	2	232	272	380	652	148	255	17	4	289	82	-8	-0	-12	+2	+8	-1.1
2018	27	AAA MLE	Buffalo Bisons	—	64	63	12	1	0	1	1	8	0	1	0	190	203	254	457	63	202	12	2	192	16	—	—	—	—	—	—
2013	22	A MLE	3 teams	—	656	595	182	30	4	15	41	101	10	25	4	306	361	445	806	139	343	15	6	352	124	—	—	—	—	—	—
2013	22	A RAW	3 teams	—	656	576	194	31	4	18	60	78	10	25	4	337	409	498	907	161	361	12	9	397	124	—	—	—	—	—	—
2014	23	AA MLE	Erie SeaWolves	—	441	407	112	19	6	8	26	72	2	16	5	275	322	410	732	135	314	16	6	318	101	—	—	—	—	—	—
2014	23	AA RAW	Erie SeaWolves	—	441	396	118	20	7	10	37	60	2	16	5	298	361	460	821	162	327	14	8	359	101	—	—	—	—	—	—
2015	24	MLB MLE	2 teams	—	277	252	73	19	0	8	22	54	2	4	1	290	351	460	812	171	340	19	8	358	129	+9	+0	+2	+1	+6	1.9
2015	24	MLB RAW	2 teams	—	277	249	73	19	0	8	24	52	2	4	1	293	359	466	825	173	342	19	9	364	129	+9	+0	+2	+1	+6	1.9
2016	25	MLB MLE	3 teams	—	470	446	133	32	1	11	21	91	0	4	1	298	330	448	778	150	353	19	4	341	117	+10	+0	+3	+2	+10	2.5
2016	25	MLB RAW	3 teams	—	470	446	134	32	1	11	21	90	0	4	1	300	332	451	783	150	354	19	4	344	117	+10	+0	+3	+2	+10	2.5
2017	26	MLB MLE	Toronto Blue Jays	—	197	185	48	18	0	5	7	38	2	4	2	259	294	438	732	178	299	19	4	317	101	+0	+0	-0	+1	+4	0.5
2017	26	MLB RAW	Toronto Blue Jays	—	197	185	48	18	0	5	7	38	2	4	2	259	294	438	732	178	299	19	4	317	101	+0	+0	-0	+1	+4	0.5
2018	27	MLB MLE	2 teams	—	442	421	95	15	3	12	16	72	5	4	2	226	262	361	623	135	246	16	4	275	72	-15	+0	-12	+2	+10	-1.6
2018	27	MLB RAW	2 teams	—	442	419	96	15	3	12	18	71	5	4	2	229	269	365	634	136	250	16	4	283	72	-15	+0	-12	+2	+10	-1.6
2013	22	A MLE	3 teams	—	656	600	182	30	4	15	41	101	10	25	4	303	361	442	802	138	338	15	6	352	124	—	—	—	—	—	—
2014	23	AA MLE	Erie SeaWolves	—	441	410	112	19	6	8	26	72	2	16	5	273	322	407	729	134	309	16	6	318	101	—	—	—	—	—	—
2015	24	MLB MLE	2 teams	—	277	251	73	19	0	8	22	54	2	4	1	291	351	462	814	171	341	19	8	358	129	—	—	—	—	—	—
2016	25	MLB MLE	3 teams	—	470	446	133	32	1	11	21	91	0	4	1	298	330	448	778	150	353	19	4	341	117	—	—	—	—	—	—
2017	26	MLB MLE	Toronto Blue Jays	—	197	186	48	18	0	5	7	38	2	4	2	258	294	435	729	177	299	19	4	317	101	—	—	—	—	—	—
2018	27	MLB MLE	2 teams	—	442	418	95	15	3	12	16	72	5	4	2	227	262	364	626	136	248	16	4	275	72	—	—	—	—	—	—
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1246	1169	320	78	4	35	61	232	9	14	6	274	315	437	752	163	315	19	5	331	108	+15	+0	-8	+5	+27	4.0
/ 162G	—	A+C		—	650	610	167	41	2	18	32	121	5	7	3	274	315	436	751	162	315	19	5	331	108	+8	+0	-4	+3	+14	2.1
Career	—	MLE		—	2483	2306	643	133	14	59	133	428	21	57	15	279	324	425	749	147	318	17	5	328	108	+15	+0	-8	+5	+27	4.0
/ 162G	—	MLE		—	650	604	168	35	4	15	35	112	5	15	4	278	323	424	747	146	318	17	5	328	108	+8	+0	-4	+3	+14	2.1

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.

2018 AAABuffalo Bisons · 64 PAwOBA .224 → .192 (−32)K% 10.9% → 13.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.94%	÷0.993	×1.200	—	—	13.22%
BB% (unintentional) /PA	3.12%	÷0.954	×0.690	—	×1.003	2.27%
HBP% /PA	0.00%	—	×0.940	—	×1.003	0.00%
Singles /BIP	20.00%	÷0.970	×0.930	×0.957	×1.003	18.41%
Doubles /BIP	1.82%	÷0.987	×0.830	×0.957	×1.003	1.47%
Triples /BIP	0.00%	÷1.150	×0.970	×0.957	×1.003	0.00%
Home runs /BIP	1.82%	÷1.080	×0.840	—	×1.003	1.42%

Factors used: league row IL. Park (home-season share, regressed): HR 116, 1B 94, 2B 97, 3B 130, K 99, BB 91 — 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 ×0.957.

Result: wOBA .224 → .192; ISO .065 → .050; BABIP .222 → .202. 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 AAABuffalo Bisons · 22 PAwOBA .273 → .251 (−22)K% 9.1% → 11.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.09%	÷0.937	×1.200	—	—	11.64%
BB% (unintentional) /PA	0.00%	÷1.034	×0.690	—	×1.033	0.00%
HBP% /PA	0.00%	—	×0.940	—	×1.033	0.00%
Singles /BIP	20.00%	÷1.008	×0.930	—	×1.033	19.06%
Doubles /BIP	10.00%	÷0.917	×0.830	—	×1.033	9.35%
Triples /BIP	0.00%	÷1.150	×0.970	—	×1.033	0.00%
Home runs /BIP	0.00%	÷1.164	×0.840	—	×1.033	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 133, 1B 102, 2B 83, 3B 130, K 87, BB 107 — 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 .273 → .251; ISO .091 → .083; BABIP .300 → .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+Dunedin Blue Jays · 16 PAwOBA .388 → .349 (−39)K% 6.2% → 9.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	6.25%	÷0.955	×1.420	—	—	9.30%
BB% (unintentional) /PA	6.25%	÷1.038	×0.750	—	×0.978	4.42%
HBP% /PA	0.00%	—	×0.930	—	×0.978	0.00%
Singles /BIP	23.08%	÷1.042	×0.920	—	×0.978	19.92%
Doubles /BIP	15.38%	÷1.064	×0.960	—	×0.978	13.57%
Triples /BIP	0.00%	÷1.150	×0.810	—	×0.978	0.00%
Home runs /BIP	0.00%	÷0.912	×0.880	—	×0.978	0.00%

Factors used: league row FSL. Park (home-season share, regressed): HR 82, 1B 108, 2B 113, 3B 130, K 91, BB 108 — applied at half strength. Age: age 25 vs a level average of 22.8 → −2.2% on BB/HBP/hit rates (2.2 yr older than average).

Result: wOBA .388 → .349; ISO .143 → .134; BABIP .357 → .312. 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 AAABuffalo Bisons · 38 PAwOBA .281 → .245 (−36)K% 23.7% → 28.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.68%	÷1.008	×1.200	—	—	28.20%
BB% (unintentional) /PA	15.79%	÷1.040	×0.690	—	×1.048	10.98%
HBP% /PA	0.00%	—	×0.940	—	×1.048	0.00%
Singles /BIP	26.09%	÷0.981	×0.930	—	×1.048	25.91%
Doubles /BIP	4.35%	÷0.944	×0.830	—	×1.048	4.01%
Triples /BIP	0.00%	÷0.900	×0.970	—	×1.048	0.00%
Home runs /BIP	0.00%	÷0.966	×0.840	—	×1.048	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 93, 1B 96, 2B 89, 3B 80, K 102, BB 108 — 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 .281 → .245; ISO .031 → .029; BABIP .304 → .299. 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 AAErie SeaWolves · 441 PAwOBA .352 → .318 (−34)K% 13.6% → 16.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.61%	÷1.020	×1.220	—	—	16.28%
BB% (unintentional) /PA	8.16%	÷0.981	×0.700	—	×1.027	5.98%
HBP% /PA	0.45%	—	×0.850	—	×1.027	0.40%
Singles /BIP	24.11%	÷1.042	×0.970	×1.004	×1.027	23.14%
Doubles /BIP	5.95%	÷0.945	×0.860	×1.004	×1.027	5.58%
Triples /BIP	2.08%	÷1.150	×0.950	×1.004	×1.027	1.77%
Home runs /BIP	2.98%	÷0.975	×0.780	—	×1.027	2.45%

Factors used: league row EL. Park (home-season share, regressed): HR 95, 1B 108, 2B 89, 3B 130, K 104, BB 96 — applied at half strength. Age: age 23 vs a level average of 24.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.004.

Result: wOBA .352 → .318; ISO .162 → .142; BABIP .327 → .309. 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 AWest Michigan Whitecaps · 339 PAwOBA .398 → .353 (−45)K% 9.4% → 13.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	9.44%	÷0.990	×1.360	—	—	12.97%
BB% (unintentional) /PA	9.73%	÷0.977	×0.690	—	—	6.87%
HBP% /PA	2.36%	—	×0.780	—	—	1.84%
Singles /BIP	29.84%	÷1.048	×0.920	×1.031	—	26.99%
Doubles /BIP	6.59%	÷0.936	×0.830	×1.031	—	6.02%
Triples /BIP	0.78%	÷0.900	×0.910	×1.031	—	0.81%
Home runs /BIP	2.33%	÷0.931	×0.800	—	—	2.00%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 86, 1B 110, 2B 87, 3B 80, K 98, BB 95 — applied at half strength. Age: age 22 vs a level average of 22.0 → +0.0% on BB/HBP/hit rates (0.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.031.

Result: wOBA .398 → .353; ISO .134 → .125; BABIP .375 → .340. 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+Lakeland Flying Tigers · 237 PAwOBA .412 → .351 (−61)K% 13.5% → 18.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.50%	÷1.018	×1.420	—	—	18.84%
BB% (unintentional) /PA	7.59%	÷1.045	×0.750	—	×1.012	5.52%
HBP% /PA	0.84%	—	×0.930	—	×1.012	0.79%
Singles /BIP	28.57%	÷1.011	×0.920	×0.975	×1.012	25.65%
Doubles /BIP	6.04%	÷0.944	×0.960	×0.975	×1.012	6.06%
Triples /BIP	1.10%	÷1.150	×0.810	×0.975	×1.012	0.76%
Home runs /BIP	5.49%	÷1.067	×0.880	—	×1.012	4.59%

Factors used: league row FSL. Park (home-season share, regressed): HR 113, 1B 102, 2B 89, 3B 130, K 104, BB 109 — applied at half strength. Age: age 22 vs a level average of 22.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.975.

Result: wOBA .412 → .351; ISO .210 → .177; BABIP .371 → .334. 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 — .