Maddox Latta — OGILVIE

Maddox Latta2B Vancouver Canadians

Age 23 R/R 5'11" / 193 lbs A+ Svc 0.0 · Ctrl thru 2035

2026 ROS PROJECTION443 act + 9 ros · stats below = ROS only

showingROS

wRC+†72

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

PA9

HR0

K%22

BB%11

BABIP274

ISO108

MLB%0%

Value2026 · WAR

━ projected WAR━ paid WAR ($/8M) surplus deficit+$1M ctrl surplus

Tools · ContractCON 42 / POW 42

Toolsscout grade

CONTACT42

scouting grade only

POWER42

scouting grade only

EYE57

scouting grade only

SPEED57

scouting grade only

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	+0.2	$0.6M	+$0.0M
2031	Pre-Arb	$0.9M	+0.1	$0.3M	+$0.0M (cut)
2032	Pre-Arb	$0.9M	—	—	—
2033	Arb-1	$0.4M	—	—	—
2034	Ext	beyond projection horizon · no WAR estimate
2035	Ext	beyond projection horizon · no WAR estimate
Totals		$0.9M		$0.6M	+$0.0M

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	22	A	Dunedin Blue Jays	—	100	90	24	4	1	0	9	25	1	9	2	267	340	333	673	67	369	25	9	310	96	-1	+1	—	—	+2	0.3
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2025	22	A MLE	Dunedin Blue Jays	—	100	92	20	3	1	0	7	37	1	9	2	217	280	272	552	54	354	37	7	244	51	-6	+1	—	—	+2	-0.3
2025	22	A MLE	Dunedin Blue Jays	—	100	92	20	3	1	0	7	37	1	9	2	217	280	272	552	54	364	37	7	244	51	—	—	—	—	—	—
2025	22	A RAW	Dunedin Blue Jays	—	100	90	24	4	1	0	9	25	1	9	2	267	340	333	673	67	369	25	9	310	51	—	—	—	—	—	—
2025	22	A MLE	Dunedin Blue Jays	—	100	92	20	3	1	0	7	37	1	9	2	217	280	272	552	54	354	37	7	244	51	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons + 2026 so far)
2025	22	A MLE	Dunedin Blue Jays	—	100	92	20	3	1	0	7	37	1	9	2	217	280	272	552	54	354	37	7	244	51	—	—	—	—	—	—
YTD	23	A+ MLE	TOR	—	443	391	73	13	0	7	40	119	9	12	5	187	277	274	551	87	246	27	9	249	55	-24	+0	+0	+2	+10	-1.3
▸ 2026 season
YTD	23	A+ RAW	TOR	—	443	367	89	17	0	9	64	85	9	12	5	243	368	362	731	120	290	19	14	336	55	—	—	—	—	—	—
ROS	23	A+ MLE	TOR	0%	9	8	1	0	0	0	1	2	0	0	0	125	222	125	347		273	22	11	275	72	-0	+0	+0	+0	+0	-0.0
TOT	23	A+ MLE	TOR	—	452	399	74	13	0	7	41	121	9	12	5	185	276	271	547	85	245	27	9	250	55	-24	+0	+0	+2	+10	-1.3
▸ Career projection (2027+) — MLB context, MLB playing time (PA × MLB%)
2027	24	MLB MLE	TOR	38%	334	296	64	12	1	6	30	84	5	6	2	216	299	324	623	108	276	25	9	283	78	-9	+0	+0	+1	+7	-0.0
2028	25	MLB MLE	TOR	55%	386	343	75	14	1	8	35	96	5	7	3	219	300	335	636	117	277	25	9	288	81	-9	+0	+0	+1	+8	0.1
2029	26	MLB MLE	TOR	64%	418	371	81	15	1	9	38	103	6	8	3	218	301	337	638	119	278	25	9	291	83	-9	+0	+0	+1	+9	0.2
2030	27	MLB MLE	TOR	66%	426	378	83	16	1	9	39	105	6	8	3	220	303	339	641	119	278	25	9	291	83	-8	+0	+0	+2	+9	0.3
2031	28	MLB MLE	TOR	67%	430	381	84	16	1	9	40	107	6	8	3	220	304	339	643	118	278	25	9	290	83	-9	+0	-1	+2	+9	0.2
2032	29	MLB MLE	TOR	66%	423	375	82	15	1	9	39	106	6	7	3	219	302	336	638	117	277	25	9	289	81	-9	+0	-1	+2	+9	0.0
2033	30	MLB MLE	TOR	65%	416	369	79	15	1	8	39	105	5	6	2	214	298	325	623	111	275	25	9	286	79	-10	+0	-2	+1	+9	-0.1
2027	24	MLB CTX	TOR	38%	127	113	24	5	0	2	11	32	2	2	1	212	294	310	603	97	277	25	9	274	78	-3	+0	+0	+0	+3	-0.0
2028	25	MLB CTX	TOR	55%	214	190	42	8	1	4	19	53	3	4	1	221	302	337	639	116	279	25	9	279	81	-5	+0	+0	+1	+5	0.1
2029	26	MLB CTX	TOR	64%	266	236	53	10	1	6	24	66	4	5	2	225	307	352	659	127	279	25	9	281	83	-5	+0	+0	+1	+6	0.1
2030	27	MLB CTX	TOR	66%	280	248	55	10	1	6	26	69	4	5	2	222	306	343	648	121	279	25	9	282	83	-6	+0	+0	+1	+6	0.2
2031	28	MLB CTX	TOR	67%	287	255	56	10	1	6	26	71	4	5	2	220	302	337	639	118	279	25	9	281	83	-6	+0	-0	+1	+6	0.1
2032	29	MLB CTX	TOR	66%	278	246	54	10	1	6	26	70	4	5	2	220	304	341	646	122	278	25	9	280	81	-6	+0	-1	+1	+6	0.0
2033	30	MLB CTX	TOR	65%	271	240	52	10	1	5	25	69	4	4	1	217	301	329	630	112	277	25	9	277	79	-7	+0	-1	+1	+6	-0.1
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1723	1528	336	63	6	35	157	430	25	30	11	220	303	338	641	118	281	25	9	280	80	-38	+2	-3	+6	+37	0.5
/ 162G	—	A+C		—	650	576	127	24	2	13	59	162	9	11	4	220	302	337	639	116	282	25	9	280	80	-14	+1	-1	+2	+14	0.2
Career	—	MLE		—	2942	2613	569	106	8	58	268	745	40	59	21	218	300	331	631	113	280	25	9	287	80	-63	+2	-4	+10	+62	0.7
/ 162G	—	MLE		—	650	577	126	23	2	13	59	165	9	13	5	218	301	333	634	114	281	25	9	287	80	-14	+1	-1	+2	+14	0.2

PLAYER · career totals + /162 games	162s	PA	HR	SB	AVG	OBP	SLG	POS	FLD	WAR	JAWS
#1Gordon Beckhamlast yr '19 career totals — 10 seasons, 3,764 PA	5.7	3,764	80	34	237	301	368	+13	+19	8.2	8.4
/162	664	14	6	+2.4	+3.4	1.4	pk7: 9
#2Yolmer Sánchezlast yr '19 career totals — 6 seasons, 2,438 PA	3.7	2,438	31	30	244	299	357	+9	+26	5.4	5.4
/162	664	8	8	+2.4	+7.2	1.5	pk7: 5
#3Adam Engellast yr '22 career totals — 6 seasons, 1,540 PA	2.3	1,540	30	47	225	280	350	+4	+33	4.1	4.1
/162	664	13	20	+1.8	+14.4	1.8	pk7: 4
#4Dustin Ackleylast yr '16 career totals — 6 seasons, 2,347 PA	3.5	2,347	46	31	241	304	367	-5	+7	3.8	3.8
/162	664	13	9	-1.3	+1.9	1.1	pk7: 4
#5Guillermo Heredialast yr '22 career totals — 6 seasons, 1,530 PA	2.3	1,530	25	6	231	311	345	+3	+17	3.5	3.5
/162	664	11	3	+1.2	+7.6	1.5	pk7: 3
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 4 projected seasons	2.6	1,724	35	31	219	299	336	+6	+4	1.7	1.7
/162	664	14	12	+2.4	+1.5	0.7	pk7: 2
#6Nick Senzellast yr '24 career totals — 6 seasons, 1,633 PA	2.5	1,633	40	33	232	299	363	+6	-14	0.9	0.9
/162	664	16	13	+2.4	-5.8	0.4	pk7: 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
Maddox Latta (OGILVIE)	23	37→42	37→42	52→57	57	207/291/303	96	273	3	4	26%	9%	72	-0.2	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕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
✕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
✕Zack Short 2018 · A	23	42→39	45→50	48→45	53	208/299/335	127	266	12	11	25%	10%	82	+0.0	198/288/331	133	281	13	8	31%	10%	77	-0.3	175/276/313	138	241	16	6	32%	11%	66	-0.7
✕Corey Toups 2016 · A	23	49→50	41→45	56→50	46	235/295/344	109	306	7	17	25%	7%	81	-0.1	216/276/329	113	308	9	16	31%	6%	71	-0.8	204/268/315	111	299	9	16	33%	6%	62	-1.0
✕Sean Jamieson 2013 · A	24	41→45	43→45	53→50	48	203/279/317	114	256	10	15	24%	8%	70	-0.8	227/288/340	113	300	9	10	26%	6%	78	-0.3	236/295/344	109	315	9	9	26%	6%	78	-0.2
✕Alexander Campos 2022 · A	22	42	42→40	46→45	48	206/291/311	105	278	9	6	28%	9%	73	-0.6	220/302/308	88	316	7	6	30%	9%	76	-0.4	234/327/306	73	330	7	6	28%	11%	84	+0.1
✕Daniel Lyons 2009 · A	24	48	41→45	50	49	226/308/329	103	289	7	13	23%	8%	84	+0.1	200/286/282	82	269	5	14	25%	9%	65	-1.2	168/264/228	61	231	3	15	26%	10%	44	-1.2
✕Taylor Featherston 2013 · A	23	47	44→50	51→50	44	227/293/352	125	306	11	11	28%	7%	83	+0.1	223/270/362	139	292	14	12	28%	5%	76	-0.4	216/259/355	139	278	14	12	27%	4%	68	-0.7
✕Marco Hernandez 2021 · A	23	46	39→40	49→45	47	220/285/308	87	282	6	8	23%	7%	70	-0.9	203/293/301	98	281	9	4	28%	9%	72	-0.7	159/264/242	83	213	9	0	28%	10%	47	-0.7
✕Alex Isola 2021 · A	23	46→45	45→50	49→40	44	227/279/349	122	287	12	8	25%	6%	76	-0.4	217/284/360	143	275	18	4	27%	8%	82	-0.0	214/284/354	140	266	19	2	26%	8%	78	-0.2
✕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
✕Taylor Krick 2011 · A	23	43	39→30	48→40	50	207/292/288	80	272	6	7	25%	9%	68	-1.0	227/312/290	63	312	3	5	26%	8%	76	-0.4	234/324/284	50	326	2	2	25%	8%	77	-0.1
✕Miguel Amaya 2022 · AA	23	43→41	43→50	50→40	50	212/304/325	114	273	10	7	25%	9%	82	-0.0	224/309/361	137	297	15	5	28%	8%	92	+0.7	229/300/373	144	272	16	1	22%	7%	90	+0.2
✕Hoy Park 2019 · A	23	46→49	39→45	55	51	217/298/301	85	285	6	17	25%	9%	74	-0.6	228/306/324	96	300	7	16	24%	9%	81	-0.1	215/302/326	111	288	12	18	27%	10%	79	-0.2
✕Kyler Fedko 2023 · A	23	43→42	42→50	51→55	47	210/284/319	109	280	9	10	27%	8%	72	-0.7	203/288/320	117	266	12	14	26%	10%	74	-0.5	197/276/341	144	245	18	22	27%	9%	73	-0.4
✕Nolan Fontana 2014 · A	23	45→43	40	52→50	59	208/323/305	98	291	7	14	28%	14%	85	+0.2	207/302/294	87	304	4	9	29%	12%	74	-0.6	184/288/307	123	253	11	12	29%	12%	71	-0.3
✕Chris Denove 2006 · A	23	44	40	46→40	46	211/286/303	92	280	7	5	26%	8%	70	-0.9	200/259/270	70	271	4	4	26%	6%	51	-2.2	245/303/334	88	297	6	3	18%	6%	79	-0.1
✕Chase d'Arnaud 2010 · A	23	49→50	42→45	55→60	48	235/310/354	119	288	8	19	20%	7%	90	+0.6	222/281/326	104	280	6	27	22%	6%	72	-0.7	230/293/350	120	293	11	26	24%	7%	80	-0.1
✕Jackson Brennan 2007 · A	24	46→42	42→45	46→50	47	219/308/327	108	299	9	10	28%	9%	84	+0.1	209/301/306	97	289	8	10	28%	9%	77	-0.3	195/297/282	87	272	7	10	28%	9%	68	-0.5
✕Cody Cipriano 2009 · A	24	40→48	45	55→45	46	199/274/329	130	265	11	12	28%	8%	71	-0.8	222/292/331	109	312	8	8	29%	8%	78	-0.3	229/303/334	105	320	7	7	28%	8%	81	-0.1

Career Stats + Projections

Career Comps

Career Path Comps