Jose Meza — OGILVIE

Jose MezaOF Great Lakes Loons

Age 23 R/R 6'2" / 160 lbs A+ HR +15%K -3% Svc 0.0 · Ctrl thru 2035 peak 0.8 · 2029

wRC+†90

AVG/OBP/SLG.190 / .292 / .294

PA320

HR6

SB12

K%32

BB%10

MLB%0%

Scouting · now → future

HIT

35→35

PWR

30→30

SPD

55→55

EYE

56↗60

trajectory · 10 yrs

0.8peak

2029

Contract

ESTIMATED · service-time + WAR-based estimator (needs verification)

Status MiLB

MLB Debut 2030

Service 0 yrs (est)

Team Control Through 2035

First FA Year 2036

Year	Status	Salary	Proj WAR	Value	Surplus
2026	MiLB	still in minors · estimated debut 2030
2027	MiLB	still in minors · estimated debut 2030
2028	MiLB	still in minors · estimated debut 2030
2029	MiLB	still in minors · estimated debut 2030
2030	Pre-Arb	$0.9M	+0.8	$4M	+$3M
2031	Pre-Arb	$0.9M	+0.7	$3M	+$2M
2032	Pre-Arb	$0.9M	+0.5	$2M	+$1M
2033	Arb-1	$0.4M	+0.3	$1M	+$1M
2034	Arb-2	$5M	—	—	—
2035	Arb-3	$0.6M	—	—	—
Totals		$2M		$9M	+$8M

Career Projections (MLE)

All seasons translated to park-neutral MLB-equivalent (MLE) and combined per year. 2026 = actual YTD + projected ROS + total. 2027+ via Advanced Marcel + aging curve.

Year	Age	Lvl	Team	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
2021	18	ROK	DSL LAD Bautista	200	173	30	5	2	2	13	73	10	8	6	.173	.270	.260	.531	.087	.277	36	6	.229	33	-15	-1	+0	-1	+6	-1.2
2022	19	ROK	DSL LAD Mega	218	197	40	5	1	5	12	62	5	9	3	.203	.266	.315	.581	.112	.261	28	6	.251	50	-12	+1	+0	-1	+6	-0.7
2023	20	ROK	ACL Dodgers	235	194	44	5	1	3	35	73	4	3	4	.227	.356	.309	.666	.082	.342	31	15	.303	90	-3	-1	+0	-1	+7	0.2
2024	21	A	2 teams	365	318	62	13	3	8	31	127	11	3	3	.195	.289	.330	.619	.135	.287	35	8	.270	65	-15	-1	+0	-2	+10	-0.7
2025	22	A	Rancho Cucamonga Quakes	531	450	77	12	2	7	58	170	16	30	10	.171	.288	.253	.542	.082	.250	32	11	.250	50	-30	+2	+0	-3	+15	-1.7
▸ 2026 Season (actual · ROS · total)
2026	23	A+	Great Lakes Loons	64	53	15	1	0	3	8	23	2	5	1	.283	.397	.472	.869	.189	.429	36	12	.359	133	+2	+1	+0	-0	+2	0.4
ROS	23	MLB	LAD	256	220	44	7	1	5	28	84	6	7	2	.200	.307	.309	.616	.109	.297	33	11	.288	79	-6	+1	+0	-2	+7	0.0
TOT	23	MLB	LAD	320	273	59	8	1	8	36	107	8	12	3	.216	.325	.341	.666	.125	.319	33	11	.302	90	-4	+1	+0	-2	+9	0.5
▸ 2027+ Projections — Projections at projected PA · Park-neutral MLB equivalent · Marcel + aging curve
2027	24	MLB 10%	LAD	518	445	94	15	2	10	57	153	11	14	4	.211	.316	.321	.637	.110	.293	30	11	.295	84	-9	+1	+0	-3	+15	0.4
2028	25	MLB 25%	LAD	548	472	101	16	2	12	60	158	11	15	4	.214	.317	.333	.649	.119	.293	29	11	.301	88	-7	+1	+0	-3	+16	0.6
2029	26	MLB 43%	LAD	564	485	106	17	3	13	62	161	12	15	4	.219	.322	.346	.668	.128	.294	29	11	.303	90	-6	+1	+0	-3	+16	0.8
2030	27	MLB 58%	LAD	570	490	108	18	3	13	63	162	12	15	4	.220	.324	.349	.673	.129	.294	28	11	.304	91	-6	+1	+0	-3	+16	0.8
2031	28	MLB 68%	LAD	569	489	106	17	2	13	64	163	11	13	4	.217	.321	.339	.660	.123	.294	29	11	.303	90	-6	+1	-1	-3	+16	0.7
2032	29	MLB 77%	LAD	561	482	103	17	2	12	63	162	11	12	3	.214	.318	.332	.650	.118	.293	29	11	.301	88	-7	+1	-1	-3	+16	0.5
2033	30	MLB 84%	LAD	552	474	100	16	2	12	62	162	11	11	3	.211	.316	.329	.645	.118	.291	29	11	.298	86	-9	+1	-2	-3	+16	0.3
2034	31	MLB 91%	LAD	539	462	97	16	2	11	61	160	11	9	2	.210	.316	.325	.641	.115	.290	30	11	.294	83	-10	+1	-3	-3	+15	-0.0
2035	32	MLB	LAD	521	448	92	15	2	10	58	157	10	7	1	.205	.310	.315	.625	.109	.288	30	11	.289	80	-12	+1	-4	-3	+15	-0.3
2036	33	MLB	LAD	492	425	85	13	2	9	55	152	9	5	1	.200	.305	.304	.608	.104	.286	31	11	.284	76	-14	+1	-4	-3	+14	-0.6
2037	34	MLB	LAD	445	385	77	12	2	8	49	141	8	4	0	.200	.303	.304	.607	.104	.285	32	11	.278	71	-15	+1	-5	-3	+13	-0.9
▸ Career Totals (MLE all levels + projections)
Career	—			7684	6609	1366	219	34	153	831	2320	169	180	58	.207	.311	.320	.631	.113	.290	30	11	.288	79	-183	+13	-20	-46	+218	-1.9
/ 162G	—			650	559	116	19	3	13	70	196	14	15	5	.208	.311	.322	.633	.114	.291	30	11	.288	79	-15	+1	-2	-4	+18	-0.2

Historical Stats — Raw (Per Stint)

Actual MLB + MiLB stats as they happened, one row per stint at each level. Use this to see what really got recorded; the table above shows MLB-equivalent translations + projections.

Year	Age	Lvl	Team	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
2021	18	ROK	DSL LAD Bautista	200	163	42	7	3	3	23	48	10	8	6	.258	.383	.393	.775	.135	.339	24	12	.349	125	+6	-1	—	—	+6	1.1
2022	19	ROK	DSL LAD Mega	218	187	50	8	2	6	22	41	5	9	3	.267	.360	.428	.788	.160	.306	19	10	.348	124	+6	+1	—	—	+6	1.3
2023	20	ROK	ACL Dodgers	235	174	50	7	1	4	55	51	4	3	4	.287	.468	.408	.876	.121	.380	22	23	.403	166	+18	-1	—	—	+7	2.4
2024	21	A	Rancho Cucamonga Quakes	256	216	47	10	3	7	27	63	9	2	2	.218	.329	.389	.718	.171	.267	25	11	.321	104	+1	-0	—	—	+7	0.8
2024	21	ROK	ACL Dodgers	109	87	32	7	1	3	19	27	2	1	1	.368	.491	.575	1.065	.207	.500	25	17	.464	213	+14	-0	—	—	+3	1.7
2025	22	A	Rancho Cucamonga Quakes	531	428	95	17	4	8	80	125	16	30	10	.222	.365	.336	.701	.114	.288	24	15	.324	106	+4	+2	—	—	+15	2.1

Career Path Comps

What happened to players with a similar stat profile at this age · Y-axis: wRC+ (100 = league avg, 150 = elite, 80 = below) · Bands: P10–P90 outer, P25–P75 inner · Gold: OGILVIE projection

		Scouting				At Match Age									Next 3 Yrs									Peak 3 Yrs
Comp	Age	HIT	PWR	SPD	EYE	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR	AVG/OBP/SLG	ISO	BABIP	HR	SB	K%	BB%	wRC+	WAR
Jose Meza (OGILVIE)	23	35	30	55	60	.190/.292/.294	.104	.279	6	6	32%	10%	68	-0.3	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Kyler Fedko 2023 · A	23	44→42	42→45	51→55	48	.212/.285/.319	.107	.280	9	10	26%	8%	69	-0.4	.204/.289/.323	.119	.267	12	14	26%	10%	72	-0.2	.192/.284/.327	.135	.243	17	21	27%	10%	69	-0.2
✕Dylan Neuse 2023 · A	24	37	39→40	49→50	44	.183/.260/.264	.081	.271	8	14	33%	9%	45	-2.1	.205/.286/.296	.091	.292	7	13	29%	9%	63	-0.8	.211/.301/.305	.094	.293	5	13	27%	10%	72	-0.1
✕Connor Kokx 2023 · A	23	43	41→40	48→60	49	.207/.293/.305	.098	.273	9	14	26%	9%	69	-0.4	.206/.291/.286	.080	.292	6	24	29%	8%	64	-0.8	.196/.287/.257	.061	.289	3	34	30%	8%	52	-0.7
✕Andy Paz 2016 · A	23	39	37→40	48→45	43	.187/.248/.258	.071	.267	5	5	31%	8%	37	-2.6	.224/.274/.290	.066	.298	4	7	24%	7%	55	-1.4	.235/.280/.283	.048	.303	3	7	22%	6%	52	-0.5
✕Taylor Krick 2011 · A	23	43	39→30	48→40	49	.206/.285/.289	.084	.266	7	7	24%	9%	62	-0.9	.224/.308/.287	.063	.307	4	5	26%	8%	71	-0.3	.233/.323/.283	.051	.326	2	2	26%	8%	73	-0.0
✕Alerick Soularie 2022 · A	23	44	42→45	54→60	45	.209/.287/.315	.106	.311	10	14	34%	9%	69	-0.4	.196/.278/.313	.117	.292	12	21	34%	9%	66	-0.6	.179/.268/.293	.114	.262	13	29	34%	9%	54	-0.6
✕Jake Thomas 2016 · A	23	44	39→30	49→50	47	.211/.289/.295	.084	.283	7	8	26%	8%	65	-0.7	.207/.290/.274	.067	.301	3	8	29%	10%	59	-1.1	.202/.298/.259	.057	.302	1	8	29%	11%	56	-0.6
✕Stuart Fairchild 2019 · A	23	45→43	40→50	53→55	41	.214/.276/.312	.098	.301	7	14	30%	6%	63	-0.8	.215/.282/.341	.126	.287	11	10	28%	7%	74	-0.1	.220/.305/.384	.164	.292	18	16	29%	8%	91	+0.6
✕Luis Santana 2022 · AA	23	40	42→45	47→50	40	.200/.259/.300	.100	.270	10	10	29%	6%	53	-1.5	.216/.274/.342	.126	.277	14	9	26%	6%	70	-0.3	.207/.266/.330	.122	.258	15	7	25%	5%	61	-0.6
✕Blake Rambusch 2023 · A	23	47	37→30	52→60	42	.219/.287/.296	.076	.318	4	13	30%	7%	63	-0.8	.226/.311/.291	.065	.315	3	28	26%	9%	72	-0.2	.233/.319/.287	.055	.311	2	37	23%	9%	71	-0.1
✕Justin Farmer 2023 · A	24	39→33	44→40	54→55	51	.190/.295/.305	.115	.292	12	19	36%	12%	71	-0.3	.186/.265/.277	.091	.307	8	19	38%	9%	50	-1.7	.159/.224/.221	.061	.275	5	22	41%	7%	15	-2.3
✕Landon Lassiter 2016 · A	23	49→48	40	51→50	47	.230/.301/.324	.094	.311	6	8	26%	8%	76	+0.1	.217/.277/.288	.071	.327	4	9	32%	6%	57	-1.2	.210/.268/.271	.062	.324	3	9	34%	5%	46	-1.1
✕Alexander Campos 2022 · A	22	43	42→40	46→45	48	.209/.289/.317	.108	.281	9	6	28%	9%	70	-0.3	.221/.304/.311	.090	.312	8	6	29%	9%	75	-0.0	.236/.329/.308	.072	.330	7	6	28%	11%	83	+0.2
✕Corey Joyce 2022 · AA	23	41	39→45	53→50	39	.193/.262/.274	.080	.300	7	13	36%	7%	48	-1.9	.208/.307/.328	.120	.294	12	12	31%	10%	82	+0.5	.212/.315/.318	.105	.301	9	14	29%	10%	80	+0.2
✕Mike Martin 2016 · A	23	41	35→30	52→55	42	.188/.277/.248	.060	.302	3	12	35%	8%	49	-1.8	.222/.280/.282	.060	.288	3	17	22%	6%	56	-1.3	.244/.292/.297	.053	.299	2	23	18%	5%	62	-0.3
✕Dwanya Williams-Sutton 2021 · A	24	43	42→45	50	47	.200/.318/.299	.099	.300	9	10	32%	10%	81	+0.4	.205/.313/.328	.123	.324	13	13	37%	10%	85	+0.7	.163/.299/.288	.124	.266	15	18	39%	11%	69	-0.2
✕Nolan Fontana 2014 · A	23	43	40	52→50	58	.204/.316/.298	.095	.286	7	14	28%	14%	78	+0.2	.203/.301/.290	.087	.301	4	9	30%	12%	69	-0.4	.184/.288/.306	.123	.253	10	12	29%	12%	68	-0.2
✕Evan Chambers 2012 · A	23	37→35	43→40	47→50	47	.187/.274/.296	.109	.264	11	17	32%	9%	59	-1.1	.182/.270/.264	.082	.295	7	14	37%	10%	49	-1.8	.146/.247/.189	.043	.262	2	14	40%	11%	20	-2.1
✕Kier Meredith 2022 · A	22	43	37→30	51→60	45	.202/.271/.271	.070	.286	4	11	29%	8%	50	-1.7	.211/.307/.281	.070	.293	4	26	27%	8%	69	-0.4	.229/.340/.278	.049	.305	3	35	22%	10%	80	+0.1
✕Dominic De La Osa 2009 · A	23	44→36	41→40	47→45	43	.207/.286/.303	.096	.304	9	8	32%	8%	66	-0.6	.191/.252/.252	.061	.269	4	7	28%	6%	37	-2.6	.170/.226/.205	.035	.236	1	6	27%	5%	11	-3.0

Contract

Career Projections (MLE) Show advanced cols

Historical Stats — Raw (Per Stint)

Career Path Comps

Career Projections (MLE)