Xorge Carrillo

PosC

B/TR/R

Born1989-04-12

Age (2027)38

No MLB projection yet. Career stats below are historical.

Overview

INTLLAST SEASON 2023

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2021	32	INTLAguilas de Me…	182	.204/.267/.241	1	0	43	—
2022	33	INTLAguilas de Me…	216	.208/.269/.276	1	1	51	—
2023	34	INTLAguilas de Me…	152	.215/.282/.269	1	1	50	—

Contract

No contract data on file.

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. Catcher framing: only 2027 Statcast framing is modeled (Baseball Savant server-caches the leaderboard to the current season); prior-year framing is unmodeled — projections for established framers may run 1-3 WAR low.

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	23	A	Savannah Sand Gnats	—	41	39	6	0	0	1	2	8	0	0	0	154	195	231	426	77	167	20	5	195	-10	—	—	—	—	—	—
2013	24	AA	Binghamton Mets	—	121	108	32	6	0	0	8	21	2	0	0	296	356	352	708	56	360	17	7	316	99	—	—	—	—	—	—
2013	24	INTL	Aguilas de Mexicali	—	203	179	51	10	0	3	12	35	6	1	0	285	350	391	741	106	336	17	6	326	107	—	—	—	—	—	—
2014	25	AAA	Las Vegas 51s	—	20	16	5	2	0	0	1	1	2	0	0	312	421	438	859	125	312	5	5	373	124	—	—	—	—	—	—
2014	25	AA	Binghamton Mets	—	215	191	54	7	0	2	11	32	10	0	0	283	354	351	705	68	327	15	5	320	83	—	—	—	—	—	—
2014	25	INTL	Aguilas de Mexicali	—	234	205	56	19	1	9	20	37	5	0	0	273	352	507	859	234	290	16	9	371	137	—	—	—	—	—	—
2015	26	AA	Binghamton Mets	—	366	325	79	15	1	10	27	50	11	1	2	243	322	388	710	145	258	14	7	319	60	—	—	—	—	—	—
2015	26	INTL	Aguilas de Mexicali	—	189	173	47	7	0	3	13	28	1	0	2	272	326	364	690	92	306	15	7	310	96	—	—	—	—	—	—
2016	27	AAA	Las Vegas 51s	—	16	15	5	0	0	0	0	1	0	0	0	333	333	333	667		357	6	0	283	63	—	—	—	—	—	—
2016	27	AA	Binghamton Mets	—	311	275	74	14	0	4	29	48	5	0	1	269	350	364	713	95	311	15	9	324	65	—	—	—	—	—	—
2016	27	INTL	Mexico	—	226	189	54	11	1	3	22	30	9	0	0	286	386	402	788	116	319	13	10	351	123	—	—	—	—	—	—
2017	28	AAA	Las Vegas 51s	—	272	252	68	12	0	9	17	62	2	0	0	270	321	425	746	155	324	23	6	331	44	—	—	—	—	—	—
2017	28	INTL	Aguilas de Mexicali	—	222	199	53	6	0	3	15	42	5	1	0	266	333	342	675	75	318	19	7	306	93	—	—	—	—	—	—
2018	29	INTL	Aguilas de Mexicali	—	204	169	48	8	0	2	23	32	10	0	0	284	401	367	768	83	336	16	11	354	126	—	—	—	—	—	—
2019	30	AAA	Toros de Tijuana	—	242	212	65	12	1	6	20	33	7	1	2	307	385	458	842	151	339	14	8	373	87	—	—	—	—	—	—
2019	30	INTL	Aguilas de Mexicali	—	194	168	40	6	0	1	17	31	4	2	1	238	323	292	614	54	283	16	9	281	76	—	—	—	—	—	—
2020	31	INTL	Aguilas de Mexicali	—	187	163	48	11	0	1	19	30	4	0	1	294	382	380	762	86	353	16	10	347	121	—	—	—	—	—	—
2021	32	INTL	Aguilas de Mexicali	—	182	162	33	1	1	1	10	24	4	0	0	204	267	241	508	37	232	13	5	231	43	—	—	—	—	—	—
2022	33	INTL	Aguilas de Mexicali	—	216	192	40	10	0	1	10	32	6	1	0	208	269	276	545	68	242	15	5	243	51	—	—	—	—	—	—
2023	34	INTL	Aguilas de Mexicali	—	152	130	28	4	0	1	4	11	8	1	1	215	282	269	551	54	221	7	3	241	50	—	—	—	—	—	—
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2012	23	A MLE	Savannah Sand Gnats	—	41	40	5	0	0	1	1	10	0	0	0	125	146	200	346	75	151	24	2	153	-10	—	—	—	—	—	—
2013	24	AA MLE	Binghamton Mets	—	121	110	35	6	0	0	6	25	2	0	0	318	364	373	737	55	379	21	5	315	99	—	—	—	—	—	—
2014	25	AAA MLE	Las Vegas 51s	—	20	16	5	2	0	0	1	1	2	0	0	312	421	438	859	125	276	5	5	351	124	—	—	—	—	—	—
2014	25	AA MLE	Binghamton Mets	—	215	194	54	6	0	2	8	39	10	0	0	278	340	340	680	62	321	18	4	291	83	—	—	—	—	—	—
2015	26	AA MLE	Binghamton Mets	—	366	333	69	12	1	7	19	63	11	1	2	207	273	312	585	105	229	17	5	257	60	—	—	—	—	—	—
2016	27	AAA MLE	Las Vegas 51s	—	16	15	5	0	0	0	0	1	0	0	0	333	333	333	667		324	6	0	262	63	—	—	—	—	—	—
2016	27	AA MLE	Binghamton Mets	—	311	284	66	11	0	3	20	55	5	0	1	232	294	303	597	70	273	18	6	265	65	—	—	—	—	—	—
2017	28	AAA MLE	Las Vegas 51s	—	272	258	51	9	0	5	11	84	2	0	0	198	236	291	527	93	268	31	4	233	44	—	—	—	—	—	—
2019	30	AAA MLE	Toros de Tijuana	—	242	219	56	9	1	4	13	36	7	1	2	256	318	361	679	105	282	15	5	297	87	—	—	—	—	—	—
2012	23	A MLE	Savannah Sand Gnats	—	41	40	5	0	0	1	1	10	0	0	0	125	146	200	346	75	138	24	2	153	-10	—	—	—	—	—	—
2012	23	A RAW	Savannah Sand Gnats	—	41	39	6	0	0	1	2	8	0	0	0	154	195	231	426	77	167	20	5	195	-10	—	—	—	—	—	—
2013	24	INTL MLE	2 teams	—	324	292	92	15	0	0	15	66	8	1	0	315	365	366	732	51	400	20	5	315	99	—	—	—	—	—	—
2013	24	INTL RAW	2 teams	—	324	287	83	16	0	3	20	56	8	1	0	289	352	376	729	87	345	17	6	323	99	—	—	—	—	—	—
2014	25	INTL MLE	3 teams	—	469	428	115	15	0	3	16	80	17	0	0	269	321	325	646	56	319	17	3	296	87	—	—	—	—	—	—
2014	25	INTL RAW	3 teams	—	469	412	115	28	1	11	32	70	17	0	0	279	356	432	788	153	309	15	7	348	87	—	—	—	—	—	—
2015	26	AA MLE	2 teams	—	555	509	105	18	2	11	29	95	12	1	4	206	265	314	580	108	231	17	5	257	60	—	—	—	—	—	—
2015	26	AA RAW	2 teams	—	555	498	126	22	1	13	40	78	12	1	4	253	324	380	703	127	275	14	7	315	60	—	—	—	—	—	—
2016	27	AA MLE	3 teams	—	553	496	120	19	0	5	34	95	14	0	1	242	309	310	619	69	286	17	6	265	65	—	—	—	—	—	—
2016	27	AA RAW	3 teams	—	553	479	133	25	1	7	51	79	14	0	1	278	364	378	742	100	316	14	9	334	65	—	—	—	—	—	—
2017	28	AAA MLE	2 teams	—	494	463	92	15	0	10	20	153	7	1	0	199	243	296	539	97	270	31	4	233	44	—	—	—	—	—	—
2017	28	AAA RAW	2 teams	—	494	451	121	18	0	12	32	104	7	1	0	268	327	388	715	120	322	21	6	320	44	—	—	—	—	—	—
2018	29	INTL MLE	Aguilas de Mexicali	—	204	169	48	8	0	2	23	32	10	0	0	284	401	367	768	83	336	16	11	—	—	—	—	—	—	—	—
2018	29	INTL RAW	Aguilas de Mexicali	—	204	169	48	8	0	2	23	32	10	0	0	284	401	367	768	83	336	16	11	354	126	—	—	—	—	—	—
2019	30	AAA MLE	2 teams	—	436	393	100	16	2	7	24	64	11	3	3	254	315	359	674	104	286	15	6	297	87	—	—	—	—	—	—
2019	30	AAA RAW	2 teams	—	436	380	105	18	1	7	37	64	11	3	3	276	357	384	742	108	314	15	8	332	87	—	—	—	—	—	—
2020	31	INTL MLE	Aguilas de Mexicali	—	187	163	48	11	0	1	19	30	4	0	1	294	382	380	762	86	353	16	10	—	—	—	—	—	—	—	—
2020	31	INTL RAW	Aguilas de Mexicali	—	187	163	48	11	0	1	19	30	4	0	1	294	382	380	762	86	353	16	10	347	121	—	—	—	—	—	—
2021	32	INTL MLE	Aguilas de Mexicali	—	182	162	33	1	1	1	10	24	4	0	0	204	267	241	508	37	232	13	5	—	—	—	—	—	—	—	—
2021	32	INTL RAW	Aguilas de Mexicali	—	182	162	33	1	1	1	10	24	4	0	0	204	267	241	508	37	232	13	5	231	43	—	—	—	—	—	—
2022	33	INTL MLE	Aguilas de Mexicali	—	216	192	40	10	0	1	10	32	6	1	0	208	269	276	545	68	242	15	5	—	—	—	—	—	—	—	—
2022	33	INTL RAW	Aguilas de Mexicali	—	216	192	40	10	0	1	10	32	6	1	0	208	269	276	545	68	242	15	5	243	51	—	—	—	—	—	—
2023	34	INTL MLE	Aguilas de Mexicali	—	152	130	28	4	0	1	4	11	8	1	1	215	282	269	551	54	221	7	3	—	—	—	—	—	—	—	—
2023	34	INTL RAW	Aguilas de Mexicali	—	152	130	28	4	0	1	4	11	8	1	1	215	282	269	551	54	221	7	3	241	50	—	—	—	—	—	—
2012	23	A MLE	Savannah Sand Gnats	—	41	40	5	0	0	1	1	10	0	0	0	125	146	200	346	75	151	24	2	153	-10	—	—	—	—	—	—
2013	24	INTL MLE	2 teams	—	324	298	92	15	0	0	15	66	8	1	0	309	365	359	724	50	379	20	5	315	99	—	—	—	—	—	—
2014	25	INTL MLE	3 teams	—	469	433	115	15	0	3	16	80	17	0	0	266	321	321	642	55	317	17	3	296	87	—	—	—	—	—	—
2015	26	AA MLE	2 teams	—	555	509	105	18	2	11	29	95	12	1	4	206	265	314	580	108	229	17	5	257	60	—	—	—	—	—	—
2016	27	AA MLE	3 teams	—	553	500	120	19	0	5	34	95	14	0	1	240	309	308	617	68	275	17	6	265	65	—	—	—	—	—	—
2017	28	AAA MLE	2 teams	—	494	464	92	15	0	10	20	153	7	1	0	198	243	295	538	97	268	31	4	233	44	—	—	—	—	—	—
2019	30	AAA MLE	2 teams	—	436	398	100	16	2	7	24	64	11	3	3	251	315	354	670	103	282	15	6	297	87	—	—	—	—	—	—
▸ Career Totals
Career	—	MLE		—	3813	3437	826	132	5	43	205	692	101	8	10	240	302	319	622	79	286	18	5	205	25	-341	-2	+0	+68	+83	-19.7
/ 162G	—	MLE		—	650	586	141	23	1	7	35	118	17	1	2	241	303	319	622	78	287	18	5	205	25	-58	-1	+0	+12	+14	-3.4

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 AAAToros de Tijuana · 242 PAwOBA .365 → .297 (−68)K% 13.6% → 14.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.64%	÷1.111	×1.200	—	—	14.72%
BB% (unintentional) /PA	8.26%	÷1.007	×0.690	—	×0.972	5.51%
HBP% /PA	2.89%	—	×0.940	—	×0.972	2.64%
Singles /BIP	25.70%	÷0.989	×0.930	×0.950	×0.972	22.31%
Doubles /BIP	6.70%	÷1.051	×0.830	×0.950	×0.972	4.89%
Triples /BIP	0.56%	÷0.993	×0.920	×0.950	×0.972	0.48%
Home runs /BIP	3.35%	÷1.023	×0.700	—	×0.972	2.23%

Factors used: level-wide AAA factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 105, 1B 98, 2B 110, 3B 99, K 122, BB 101 — 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 .365 → .297; ISO .151 → .110; BABIP .339 → .282. 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 AAALas Vegas 51s · 272 PAwOBA .322 → .233 (−88)K% 22.8% → 31.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.79%	÷0.948	×1.290	—	—	31.01%
BB% (unintentional) /PA	5.88%	÷1.015	×0.710	—	×0.992	4.08%
HBP% /PA	0.74%	—	×0.980	—	×0.992	0.71%
Singles /BIP	24.74%	÷0.994	×0.900	×0.958	×0.992	21.28%
Doubles /BIP	6.32%	÷0.998	×0.810	×0.958	×0.992	4.87%
Triples /BIP	0.00%	÷1.111	×0.810	×0.958	×0.992	0.00%
Home runs /BIP	4.74%	÷1.209	×0.780	—	×0.992	3.03%

Factors used: league row PCL. Park (home-season share, regressed): HR 142, 1B 99, 2B 100, 3B 122, K 90, BB 103 — 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 ×0.958.

Result: wOBA .322 → .233; ISO .155 → .097; BABIP .324 → .268. 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 AABinghamton Mets · 311 PAwOBA .318 → .265 (−53)K% 15.4% → 17.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	15.43%	÷1.062	×1.220	—	—	17.73%
BB% (unintentional) /PA	9.32%	÷0.996	×0.700	—	×0.978	6.41%
HBP% /PA	1.61%	—	×0.850	—	×0.978	1.34%
Singles /BIP	24.67%	÷1.034	×0.970	×0.989	×0.978	22.38%
Doubles /BIP	6.17%	÷1.070	×0.860	×0.989	×0.978	4.79%
Triples /BIP	0.00%	÷0.956	×0.950	×0.989	×0.978	0.00%
Home runs /BIP	1.76%	÷1.085	×0.780	—	×0.978	1.24%

Factors used: league row EL. Park (home-season share, regressed): HR 117, 1B 107, 2B 114, 3B 91, K 112, BB 99 — applied at half strength. Age: age 27 vs a level average of 24.8 → −2.2% on BB/HBP/hit rates (2.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.989.

Result: wOBA .318 → .265; ISO .095 → .072; BABIP .311 → .273. 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 AAALas Vegas 51s · 16 PAwOBA .275 → .262 (−13)K% 6.2% → 8.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	6.25%	÷0.971	×1.290	—	—	8.30%
BB% (unintentional) /PA	0.00%	÷0.962	×0.710	—	×1.003	0.00%
HBP% /PA	0.00%	—	×0.980	—	×1.003	0.00%
Singles /BIP	35.71%	÷0.994	×0.900	—	×1.003	32.43%
Doubles /BIP	0.00%	÷1.128	×0.810	—	×1.003	0.00%
Triples /BIP	0.00%	÷0.980	×0.810	—	×1.003	0.00%
Home runs /BIP	0.00%	÷1.088	×0.780	—	×1.003	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 118, 1B 99, 2B 126, 3B 96, K 94, BB 92 — 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).

Result: wOBA .275 → .262; ISO .000 → .000; BABIP .357 → .324. 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 AABinghamton Mets · 366 PAwOBA .312 → .257 (−55)K% 13.7% → 17.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	13.66%	÷0.974	×1.220	—	—	17.11%
BB% (unintentional) /PA	7.38%	÷0.988	×0.700	—	×0.988	5.16%
HBP% /PA	3.01%	—	×0.850	—	×0.988	2.52%
Singles /BIP	19.27%	÷1.030	×0.970	×0.985	×0.988	17.68%
Doubles /BIP	5.45%	÷1.034	×0.860	×0.985	×0.988	4.41%
Triples /BIP	0.36%	÷0.900	×0.950	×0.985	×0.988	0.37%
Home runs /BIP	3.64%	÷1.088	×0.780	—	×0.988	2.58%

Factors used: league row EL. Park (home-season share, regressed): HR 118, 1B 106, 2B 107, 3B 80, K 95, BB 98 — applied at half strength. Age: age 26 vs a level average of 24.8 → −1.2% on BB/HBP/hit rates (1.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.985.

Result: wOBA .312 → .257; ISO .145 → .109; BABIP .258 → .229. 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 AABinghamton Mets · 215 PAwOBA .315 → .291 (−24)K% 14.9% → 18.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.88%	÷1.011	×1.220	—	—	17.97%
BB% (unintentional) /PA	5.12%	÷1.032	×0.700	—	×0.998	3.46%
HBP% /PA	4.65%	—	×0.850	—	×0.998	3.95%
Singles /BIP	28.30%	÷0.990	×0.970	×1.030	×0.998	28.51%
Doubles /BIP	4.40%	÷1.050	×0.860	×1.030	×0.998	3.71%
Triples /BIP	0.00%	÷1.036	×0.950	×1.030	×0.998	0.00%
Home runs /BIP	1.26%	÷1.009	×0.780	—	×0.998	0.97%

Factors used: league row EL. Park (home-season share, regressed): HR 102, 1B 98, 2B 110, 3B 107, K 102, BB 106 — 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 ×1.030.

Result: wOBA .315 → .291; ISO .068 → .056; BABIP .327 → .321. 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 AAALas Vegas 51s · 20 PAwOBA .382 → .351 (−32)K% 5.0% → 6.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	5.00%	÷0.945	×1.290	—	—	6.82%
BB% (unintentional) /PA	5.00%	÷1.060	×0.710	—	×1.033	3.46%
HBP% /PA	10.00%	—	×0.980	—	×1.033	10.12%
Singles /BIP	20.00%	÷0.991	×0.900	—	×1.033	18.76%
Doubles /BIP	13.33%	÷1.057	×0.810	—	×1.033	10.56%
Triples /BIP	0.00%	÷0.900	×0.810	—	×1.033	0.00%
Home runs /BIP	0.00%	÷0.925	×0.780	—	×1.033	0.00%

Factors used: league row PCL. Park (home-season share, regressed): HR 85, 1B 98, 2B 111, 3B 80, K 89, BB 112 — 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 .382 → .351; ISO .125 → .105; BABIP .312 → .276. 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 AABinghamton Mets · 121 PAwOBA .314 → .315 (+1)K% 17.4% → 20.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.36%	÷1.034	×1.220	—	—	20.49%
BB% (unintentional) /PA	6.61%	÷1.019	×0.700	—	×1.012	4.60%
HBP% /PA	1.65%	—	×0.850	—	×1.012	1.42%
Singles /BIP	29.89%	÷0.949	×0.970	×1.047	×1.012	32.38%
Doubles /BIP	6.90%	÷0.991	×0.860	×1.047	×1.012	6.34%
Triples /BIP	0.00%	÷0.900	×0.950	×1.047	×1.012	0.00%
Home runs /BIP	0.00%	÷1.012	×0.780	—	×1.012	0.00%

Factors used: league row EL. Park (home-season share, regressed): HR 102, 1B 90, 2B 98, 3B 80, K 107, BB 104 — 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 ×1.047.

Result: wOBA .314 → .315; ISO .056 → .052; BABIP .360 → .379. 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 — .