Lorenzo Meola — OGILVIE

Lorenzo MeolaSS San Jose Giants

Age 22 R/R 5'11" / 180 lbs A HR -15%H -3% Svc 0.0 · Ctrl thru 2036

2026 ROS PROJECTION547 act + 9 ros · stats below = ROS only

showingROS

wRC+†85

AVG/OBP/SLG.125 / .222 / .125

PA9

HR0

K%33

BB%11

BABIP289

ISO141

MLB%0%

Value2026 · WAR

━ projected WAR━ paid WAR ($/8M) surplus deficit+$64M ctrl surplus

Tools · ContractCON 45 / POW 42

Toolsscout grade

CONTACT45

scouting grade only

POWER42

scouting grade only

EYE49

scouting grade only

SPEED55

scouting grade only

DEFENSE60

scouting grade only

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

Status MiLB

MLB Debut 2031

Service 0 yrs (est)

Team Control Through 2036

First FA Year 2037

Year	Status	Salary	Proj WAR	Value	Surplus
2026	MiLB	still in minors · estimated debut 2031
2027	MiLB	still in minors · estimated debut 2031
2028	MiLB	still in minors · estimated debut 2031
2029	MiLB	still in minors · estimated debut 2031
2030	MiLB	still in minors · estimated debut 2031
2031	Pre-Arb	$0.9M	+1.4	$8M	+$7M
2032	Pre-Arb	$0.9M	+1.3	$7M	+$6M
2033	Pre-Arb	$1.0M	+1.2	$6M	+$5M
2034	Arb-1	$3M	+0.7	$2M	+$0.3M
2035	Arb-2	$4M	+0.5	$1M	+$0.0M (cut)
2036	Arb-3	$4M	+0.4	$0.7M	+$0.0M (cut)
Totals		$6M		$23M	+$19M

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. 2026 = actual YTD + projected ROS + total. 2027+ 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)
2025	21	A	San Jose Giants	—	70	66	18	4	0	3	4	17	0	3	1	273	314	470	784	197	326	24	6	345	119	+2	+0	—	—	+2	0.3
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2025	21	A MLE	San Jose Giants	—	70	67	14	3	0	2	3	24	0	3	1	209	243	343	586	134	287	34	4	243	51	-4	+0	—	—	+2	-0.2
2025	21	A MLE	San Jose Giants	—	70	67	14	3	0	2	3	24	0	3	1	209	243	343	586	134	293	34	4	243	51	—	—	—	—	—	—
2025	21	A RAW	San Jose Giants	—	70	66	18	4	0	3	4	17	0	3	1	273	314	470	784	197	326	24	6	345	51	—	—	—	—	—	—
2025	21	A MLE	San Jose Giants	—	70	67	14	3	0	2	3	24	0	3	1	209	243	343	586	134	287	34	4	243	51	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons + 2026 so far)
2025	21	A MLE	San Jose Giants	—	70	67	14	3	0	2	3	24	0	3	1	209	243	343	586	134	287	34	4	243	51	—	—	—	—	—	—
YTD	22	A MLE	SF	—	547	491	106	31	4	13	37	178	13	17	5	216	288	375	663	159	304	33	7	283	78	-15	+1	+0	+6	+12	0.5
▸ 2026 season
YTD	22	A RAW	SF	—	547	476	128	40	5	15	52	133	13	17	5	269	357	468	825	200	338	24	10	362	78	—	—	—	—	—	—
ROS	22	A MLE	SF	0%	9	8	1	0	0	0	1	3	0	0	0	125	222	125	347		295	33	11	294	85	-0	+0	+0	+0	+0	0.0
TOT	22	A MLE	SF	—	556	499	107	31	4	13	38	181	13	17	5	214	287	371	658	156	302	33	7	283	78	-15	+1	+0	+6	+12	0.5
▸ Career projection (2027+) — MLB context, MLB playing time (PA × MLB%)
2027	23	MLB MLE	SF	43%	336	304	68	17	2	9	23	96	6	7	2	224	291	382	673	158	292	29	7	296	86	-5	+1	+3	+4	+7	0.9
2028	24	MLB MLE	SF	58%	609	551	125	31	4	16	43	172	10	12	4	227	295	385	679	158	294	28	7	299	89	-8	+1	+5	+7	+13	1.8
2029	25	MLB MLE	SF	65%	627	567	129	31	4	17	45	175	10	13	4	228	296	386	682	159	294	28	7	302	91	-7	+1	+5	+7	+14	2.0
2030	26	MLB MLE	SF	68%	638	577	131	32	4	17	46	177	10	14	4	227	295	385	680	158	293	28	7	303	92	-6	+1	+5	+7	+14	2.1
2031	27	MLB MLE	SF	68%	637	575	131	32	4	17	47	177	10	14	4	228	297	386	684	158	293	28	7	304	92	-6	+1	+5	+7	+14	2.1
2032	28	MLB MLE	SF	68%	629	567	128	31	3	17	47	175	10	13	4	226	296	381	677	155	293	28	7	303	91	-7	+1	+4	+7	+14	1.9
2033	29	MLB MLE	SF	66%	619	557	124	30	3	16	47	174	10	13	4	223	295	373	668	151	292	28	8	301	90	-7	+1	+3	+7	+13	1.7
2034	30	MLB MLE	SF	66%	439	396	87	21	2	11	33	124	7	9	2	220	291	366	657	146	291	28	8	298	88	-6	+1	+1	+5	+10	1.1
2035	31	MLB MLE	SF	65%	425	383	84	20	2	11	32	122	7	8	2	219	291	368	660	149	289	29	8	295	86	-7	+1	+1	+5	+9	0.8
2036	32	MLB MLE	SF	70%	407	367	79	19	2	10	31	118	6	7	2	215	287	360	647	144	286	29	8	289	82	-9	+1	-0	+4	+9	0.5
2037	33	MLB MLE	SF	74%	377	340	72	17	2	9	28	111	6	6	2	212	283	353	636	141	281	29	7	282	77	-10	+0	-1	+4	+8	0.1
2027	23	MLB CTX	SF	43%	145	132	28	7	1	3	10	42	2	3	1	212	278	348	626	136	286	29	7	277	86	-2	+0	+1	+2	+3	0.4
2028	24	MLB CTX	SF	58%	353	319	70	17	2	9	25	100	6	7	2	219	289	370	658	150	287	28	7	280	89	-5	+1	+3	+4	+8	1.0
2029	25	MLB CTX	SF	65%	406	368	81	20	2	10	29	113	6	8	2	220	288	367	655	147	287	28	7	283	91	-4	+1	+3	+4	+9	1.3
2030	26	MLB CTX	SF	68%	434	392	87	21	3	11	32	120	7	9	3	222	292	375	667	153	287	28	7	284	92	-4	+1	+3	+5	+9	1.4
2031	27	MLB CTX	SF	68%	433	391	86	21	2	11	32	120	7	9	3	220	291	368	659	148	287	28	7	284	92	-4	+1	+3	+5	+9	1.4
2032	28	MLB CTX	SF	68%	428	386	85	21	2	11	32	119	7	9	3	220	292	370	662	150	287	28	7	284	91	-5	+1	+3	+5	+9	1.3
2033	29	MLB CTX	SF	66%	411	371	81	20	2	10	31	115	6	8	2	218	289	364	653	146	286	28	8	282	90	-5	+1	+2	+4	+9	1.2
2034	30	MLB CTX	SF	66%	288	260	57	14	2	7	22	82	4	6	2	219	290	369	659	150	285	28	8	280	88	-4	+0	+1	+3	+6	0.7
2035	31	MLB CTX	SF	65%	276	249	53	13	1	6	21	79	4	5	1	213	285	345	630	133	283	29	8	276	86	-5	+1	+0	+3	+6	0.5
2036	32	MLB CTX	SF	70%	286	259	53	13	1	6	21	83	4	5	1	205	275	332	607	127	280	29	7	271	82	-6	+1	-0	+3	+6	0.4
2037	33	MLB CTX	SF	74%	279	252	51	12	1	6	21	82	4	5	1	202	274	329	604	127	276	29	8	264	77	-8	+1	-1	+3	+6	0.1
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	3739	3379	732	179	19	90	276	1055	57	74	21	217	287	361	648	144	285	28	7	280	88	-52	+6	+19	+40	+81	9.6
/ 162G	—	A+C		—	650	587	127	31	3	16	48	183	10	13	4	216	287	361	648	145	284	28	7	280	88	-9	+1	+3	+7	+14	1.7
Career	—	MLE		—	5822	5259	1173	284	32	152	426	1648	92	119	35	223	293	376	669	153	293	28	7	298	88	-80	+10	+30	+62	+125	14.9
/ 162G	—	MLE		—	650	587	132	32	4	17	48	184	10	13	4	225	295	380	674	155	296	28	7	298	88	-9	+1	+3	+7	+14	1.7

PLAYER · career totals + /162 games	162s	PA	HR	SB	AVG	OBP	SLG	POS	FLD	WAR	JAWS
#1Brandon Ingelast yr '13 career totals — 13 seasons, 5,617 PA	8.5	5,617	152	45	233	301	384	+26	+73	19.8	20.5
/162	664	18	5	+3.1	+8.6	2.3	pk7: 21
#2Freddy Galvislast yr '21 career totals — 10 seasons, 4,238 PA	6.4	4,238	109	57	246	292	387	+42	+68	18.0	17.6
/162	664	17	9	+6.6	+10.6	2.8	pk7: 17
#3John Bucklast yr '14 career totals — 11 seasons, 4,009 PA	6.0	4,009	134	6	234	301	398	+72	+7	16.9	15.7
/162	664	22	1	+11.9	+1.2	2.8	pk7: 14
#4Zack Cozartlast yr '19 career totals — 8 seasons, 3,350 PA	5.0	3,350	85	21	246	300	398	+33	+38	14.2	14.8
/162	664	17	4	+6.6	+7.6	2.8	pk7: 15
#5Bobby Crosbylast yr '10 career totals — 7 seasons, 2,832 PA	4.3	2,832	62	34	238	305	374	+28	+33	11.6	11.6
/162	664	15	8	+6.7	+7.7	2.7	pk7: 12
#6Danny Espinosalast yr '17 career totals — 8 seasons, 3,267 PA	4.9	3,267	98	64	221	297	378	+16	+36	11.0	11.4
/162	664	20	13	+3.2	+7.3	2.2	pk7: 12
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 8 projected seasons	5.9	3,897	102	78	224	291	378	+42	+32	9.9	9.0
/162	664	17	13	+7.1	+5.5	1.7	pk7: 8

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
Lorenzo Meola (OGILVIE)	22	45	42	55	49	218/286/367	149	295	4	4	30%	7%	85	+0.1	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕José Caballero 2019 · A	22	48→46	43→45	47→70	45	232/293/349	117	280	10	9	21%	6%	82	-0.0	219/293/329	110	283	9	26	25%	8%	78	-0.3	225/318/339	113	300	10	63	26%	9%	88	+0.3
✕Agustín Ramírez 2023 · A	21	43→46	45→55	51→55	44	214/275/343	129	286	12	11	29%	7%	74	-0.5	230/293/385	155	277	18	13	23%	8%	91	+0.6	230/293/400	170	266	20	16	22%	7%	92	+0.7
✕Juan Mora 2022 · A	22	46	44→45	55	46	225/303/346	121	300	11	14	27%	8%	86	+0.3	225/283/333	108	304	9	9	28%	5%	75	-0.5	222/278/328	106	295	10	8	27%	5%	70	-0.4
✕Blaze Alexander 2021 · A	22	45→48	42→45	51→50	42	215/281/322	107	301	9	11	30%	7%	72	-0.7	206/277/331	125	321	14	12	37%	7%	73	-0.6	239/319/362	123	339	12	15	30%	8%	93	+0.6
✕Seth Mejias-Brean 2013 · A	22	46→53	47→45	53→45	45	232/286/369	137	279	14	8	22%	6%	84	+0.1	238/300/344	106	300	9	5	22%	8%	83	+0.1	237/305/338	101	301	8	5	22%	8%	80	-0.1
✕Leo Jiménez 2023 · A	22	46→49	40→45	48→45	49	216/322/312	96	283	8	9	24%	9%	86	+0.3	226/314/335	109	285	10	7	23%	7%	88	+0.4	228/319/335	107	286	10	5	22%	7%	87	+0.2
✕Dom Keegan 2023 · A	22	48→51	46→45	50→40	42	233/294/368	135	326	14	8	31%	7%	87	+0.4	230/295/357	127	313	12	3	28%	8%	85	+0.2	225/291/352	127	306	12	1	29%	7%	82	+0.0
✕Chris Nelson 2008 · A	22	46→56	47→50	53→50	42	228/277/370	142	282	13	13	24%	6%	81	-0.1	229/282/356	127	285	12	12	23%	6%	80	-0.2	257/301/390	133	311	13	7	21%	5%	91	+0.5
✕Jack Marder 2012 · A	22	48	44→45	49→50	41	233/290/350	117	288	11	12	23%	4%	82	-0.0	238/296/353	115	290	10	13	21%	5%	84	+0.2	236/297/344	109	286	10	12	20%	5%	81	-0.0
✕Drew Gilbert 2023 · A	22	48	47→50	53→45	48	239/297/376	137	271	15	16	18%	7%	90	+0.6	224/293/368	144	273	16	9	23%	7%	87	+0.4	210/294/357	148	242	16	5	20%	8%	82	+0.0
✕Nick Loftin 2022 · A	23	47→52	45	53→50	48	229/294/357	128	272	11	11	20%	7%	84	+0.2	233/296/353	120	273	13	14	19%	7%	84	+0.2	228/318/350	122	261	10	9	16%	10%	91	+0.5
●Jonathan India 2019 · A	22	42→52	45→50	51→50	44	205/288/330	125	297	12	10	33%	9%	76	-0.4	221/310/345	124	301	12	10	29%	9%	88	+0.5	247/345/400	152	300	17	11	22%	10%	111	+1.9
✕Janelfry Zorrilla 2013 · A	22	45	48→45	49→50	45	226/287/370	144	271	15	11	22%	6%	85	+0.2	222/268/315	93	305	9	6	29%	5%	65	-1.2	222/262/301	79	303	9	3	29%	4%	57	-1.0
✕Jeferson Morales 2022 · A	23	41→48	47→45	48→50	48	209/292/350	141	261	14	9	25%	8%	83	+0.1	204/281/333	129	269	13	9	28%	7%	75	-0.5	221/295/347	125	275	11	8	22%	7%	80	-0.1
✕Phillip Ervin 2014 · A	22	47→51	48→50	54→55	46	237/301/379	142	295	16	14	25%	8%	93	+0.8	207/275/323	116	264	10	20	24%	7%	70	-0.9	235/299/371	136	298	13	21	24%	7%	86	+0.3
✕Nick Senzel 2017 · A	22	48→57	45→50	51→50	46	230/295/364	133	302	10	16	26%	8%	86	+0.3	251/305/391	140	330	12	14	26%	7%	96	+1.0	256/310/402	146	332	13	16	26%	7%	97	+0.7
✕Steve Tolleson 2006 · A	22	42→52	44→45	50	51	209/292/329	120	248	12	8	20%	9%	78	-0.3	237/309/339	102	303	8	12	22%	9%	86	+0.3	252/311/367	115	300	10	12	18%	7%	89	+0.4
✕Ryan Bliss 2022 · A	22	43→49	47→45	51→60	39	215/265/348	133	294	15	16	31%	6%	72	-0.7	207/268/330	123	280	12	32	29%	7%	69	-0.9	221/289/349	127	292	11	41	26%	7%	78	-0.2
✕Paul Vasquez 2008 · A	23	48	43→45	52→30	39	232/271/346	114	307	10	10	28%	5%	72	-0.7	232/270/336	104	293	8	5	23%	3%	70	-0.8	235/268/339	105	293	8	2	22%	2%	67	-0.4
✕Brent Morel 2009 · A	22	51→57	43→50	54→50	45	251/298/360	109	298	11	12	20%	6%	86	+0.3	251/287/380	129	292	13	12	18%	5%	86	+0.3	253/289/383	129	290	13	11	17%	4%	85	+0.2

Career Stats + Projections

Career Comps

Career Path Comps