Nomar Diaz

C San Jose Giants

Age 23 R/R 6'1" / 180 lbs A HR -15%H -3% Svc 0.0 · Ctrl thru 2037

Overview

APROSPECTMiLBFA 2038

OGV OFP35OGV rank#1822ETA2029MLB%0%

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2023	19	ROKACL Giants Bl…	57	.216/.298/.314	1	0	9	—	.151/.211/.245
2024	20	ROKSF	32	.250/.419/.458	1	1	72	—	.192/.323/.385
2025	21	ASF	52	.143/.308/.310	2	0	14	—	.111/.231/.200
2027PROJMLE	23	MLBSF	162	.201/.279/.310	3	2	68	0.0
2028PROJ30% MLB	24	MLBSF	26	.201/.279/.309	0	0	71	0.0
2029PROJ44% MLB	25	MLBSF	68	.203/.281/.315	1	1	73	0.1

Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2037Service0.0First FA2038

2027MiLB2028MiLB2029MiLB2030MiLB2031MiLB2032Pre-Arb$0.9M2033Pre-Arb$0.9M2034Pre-Arb$1.0M+3

Skills · percentileMiLB Statcast 2025 A

BB%94

ISO90

Chase%85

Z-Con%65

Whiff%47

wOBA36

CompsBryan Holaday (age 23) · Andrew Susac (age 23) · Shane Muntz (age 23)

2027 FULL-SEASON PROJECTIONpark-neutral Marcel · 38 proj PA

wRC+†68

AVG/OBP/SLG.199 / .277 / .307

PA38

HR1

K%32

BB%8

ISO108

MLB%0%

Value2027 · WAR

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

Tools · Contract

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

Status MiLB

MLB Debut 2032

Service 0 yrs (est)

Team Control Through 2037

First FA Year 2038

Year	Status	Salary	Proj WAR	Value	Surplus
2027	MiLB	still in minors · estimated debut 2032
2028	MiLB	still in minors · estimated debut 2032
2029	MiLB	still in minors · estimated debut 2032
2030	MiLB	still in minors · estimated debut 2032
2031	MiLB	still in minors · estimated debut 2032
2032	Pre-Arb	$0.9M	+0.1	$0.3M	+$0.0M (cut)
2033	Pre-Arb	$0.9M	—	—	—
2034	Pre-Arb	$1.0M	—	—	—
2035	Ext	beyond projection horizon · no WAR estimate
2036	Ext	beyond projection horizon · no WAR estimate
2037	Ext	beyond projection horizon · no WAR estimate
Totals		$0.0M		$0.0M	+$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. 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)
2023	19	ROK	ACL Giants Black	—	57	51	11	0	1	1	5	16	1	0	0	216	298	314	612	98	294	28	9	282	9	—	—	—	—	—	—
2024	20	ROK	ACL Giants	—	32	24	6	2	0	1	4	8	3	1	1	250	419	458	878	208	312	25	12	386	72	—	—	—	—	—	—
2025	21	A	San Jose Giants	—	52	42	6	1	0	2	10	14	0	0	0	143	308	310	617	167	154	27	19	291	14	—	—	—	—	—	—
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2023	19	ROK MLE	ACL Giants Black	—	57	53	8	0	1	1	3	23	1	0	0	151	211	245	456	94	227	40	5	182	9	—	—	—	—	—	—
2024	20	ROK MLE	ACL Giants	—	32	26	5	2	0	1	2	12	3	1	1	192	323	385	707	192	244	38	6	275	72	—	—	—	—	—	—
2025	21	A MLE	San Jose Giants	—	52	45	5	1	0	1	7	20	0	0	0	111	231	200	431	89	131	38	13	189	14	—	—	—	—	—	—
2023	19	ROK MLE	ACL Giants Black	—	57	53	8	0	1	1	3	23	1	0	0	151	211	245	456	94	241	40	5	182	9	—	—	—	—	—	—
2023	19	ROK RAW	ACL Giants Black	—	57	51	11	0	1	1	5	16	1	0	0	216	298	314	612	98	294	28	9	282	9	—	—	—	—	—	—
2024	20	ROK MLE	ACL Giants	—	32	26	5	2	0	1	2	12	3	1	1	192	323	385	707	192	286	38	6	275	72	—	—	—	—	—	—
2024	20	ROK RAW	ACL Giants	—	32	24	6	2	0	1	4	8	3	1	1	250	419	458	878	208	312	25	12	386	72	—	—	—	—	—	—
2025	21	A MLE	San Jose Giants	—	52	45	5	1	0	1	7	20	0	0	0	111	231	200	431	89	167	38	13	189	14	—	—	—	—	—	—
2025	21	A RAW	San Jose Giants	—	52	42	6	1	0	2	10	14	0	0	0	143	308	310	617	167	154	27	19	291	14	—	—	—	—	—	—
2023	19	ROK MLE	ACL Giants Black	—	57	53	8	0	1	1	3	23	1	0	0	151	211	245	456	94	227	40	5	182	9	—	—	—	—	—	—
2024	20	ROK MLE	ACL Giants	—	32	27	5	2	0	1	2	12	3	1	1	185	323	370	693	185	244	38	6	275	72	—	—	—	—	—	—
2025	21	A MLE	San Jose Giants	—	52	45	5	1	0	1	7	20	0	0	0	111	231	200	431	89	131	38	13	189	14	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	20	ROK MLE	ACL Giants	—	32	27	5	2	0	1	2	12	3	1	1	185	323	370	693	185	244	38	6	275	72	—	—	—	—	—	—
2025	21	A MLE	San Jose Giants	—	52	45	5	1	0	1	7	20	0	0	0	111	231	200	431	89	131	38	13	189	14	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	23	MLB MLE	SF	0%	162	146	30	6	1	3	13	52	2	2	1	201	279	310	588	108	289	32	8	268	68	-6	+0	+0	+3	+4	0.0
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	24	MLB MLE	SF	30%	87	79	16	3	0	2	7	28	1	1	0	205	282	318	599	113	290	32	8	272	71	-3	+0	+0	+2	+2	0.1
2029	25	MLB MLE	SF	44%	153	137	28	5	1	3	13	48	2	2	1	207	284	324	608	117	290	31	8	276	73	-5	+0	+0	+3	+3	0.1
2030	26	MLB MLE	SF	51%	212	189	40	8	1	4	18	65	3	2	1	209	286	329	615	120	290	31	8	278	75	-6	+0	+0	+4	+5	0.2
2031	27	MLB MLE	SF	52%	252	226	47	9	1	5	21	77	3	3	1	210	287	330	617	120	290	31	8	279	75	-7	+0	+0	+4	+5	0.3
2032	28	MLB MLE	SF	53%	281	252	53	10	1	6	23	85	4	2	1	210	286	329	616	119	289	30	8	279	75	-8	+0	-1	+5	+6	0.2
2033	29	MLB MLE	SF	53%	290	260	54	10	1	6	24	89	4	1	1	207	283	323	606	116	287	31	8	275	72	-10	-0	-1	+5	+6	0.0
2034	30	MLB MLE	SF	52%	294	264	53	10	1	5	24	91	4	0	1	204	280	315	595	111	285	31	8	270	69	-11	-0	-2	+5	+6	-0.2
2028	24	MLB CTX	SF	30%	26	24	4	1	0	0	2	8	0	0	0	201	279	309	588	108	286	31	8	268	71	-1	+0	+0	+0	+1	0.0
2029	25	MLB CTX	SF	44%	68	61	11	2	0	1	6	21	1	1	0	203	281	315	596	112	286	31	9	271	73	-2	+0	+0	+1	+1	0.1
2030	26	MLB CTX	SF	51%	108	96	20	4	1	2	9	33	2	1	0	205	283	320	603	115	286	31	8	274	75	-3	+0	+0	+2	+2	0.1
2031	27	MLB CTX	SF	52%	132	118	24	5	1	2	11	40	2	1	0	206	283	321	605	115	286	30	8	274	75	-4	+0	+0	+2	+3	0.2
2032	28	MLB CTX	SF	53%	150	135	28	5	1	3	12	45	2	1	0	206	283	320	603	114	285	30	8	274	75	-4	+0	-0	+3	+3	0.1
2033	29	MLB CTX	SF	53%	152	136	28	5	1	3	13	46	2	1	0	204	280	314	595	111	283	30	9	270	72	-5	+0	-1	+3	+3	0.0
2034	30	MLB CTX	SF	52%	153	137	28	5	1	3	13	47	2	0	0	201	277	307	583	106	281	31	8	266	69	-6	+0	-1	+3	+3	-0.1
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	789	707	143	27	5	14	66	240	11	5	0	202	281	314	595	112	282	30	8	271	69	-25	+1	-2	+14	+17	0.5
/ 162G	—	A+C		—	650	582	118	22	4	12	54	198	9	4	0	203	280	316	596	113	282	30	8	271	69	-21	+1	-2	+12	+14	0.4
Career	—	MLE		—	1748	1565	316	59	7	35	145	550	26	12	7	202	281	316	596	114	285	31	8	270	69	-52	-0	-4	+29	+35	0.7
/ 162G	—	MLE		—	650	582	118	22	3	13	54	205	10	4	3	203	282	318	600	115	286	32	8	270	69	-21	-0	-2	+12	+14	0.3

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.

2025 ASan Jose Giants · 52 PAwOBA .284 → .189 (−95)K% 26.9% → 38.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	26.92%	÷0.962	×1.360	—	—	38.08%
BB% (unintentional) /PA	19.23%	÷1.055	×0.690	—	×1.015	12.77%
HBP% /PA	0.00%	—	×0.780	—	×1.015	0.00%
Singles /BIP	10.71%	÷1.007	×0.920	—	×1.015	9.93%
Doubles /BIP	3.57%	÷1.138	×0.830	—	×1.015	2.64%
Triples /BIP	0.00%	÷0.902	×0.910	—	×1.015	0.00%
Home runs /BIP	7.14%	÷1.358	×0.800	—	×1.015	4.27%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 172, 1B 101, 2B 128, 3B 80, K 92, BB 111 — applied at half strength. Age: age 21 vs a level average of 22.0 → +1.5% on BB/HBP/hit rates (1.0 yr younger than average).

Result: wOBA .284 → .189; ISO .167 → .094; BABIP .154 → .131. 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).

2024 ROKACL Giants · 32 PAwOBA .389 → .275 (−114)K% 25.0% → 36.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.00%	÷0.997	×1.440	—	—	36.11%
BB% (unintentional) /PA	12.50%	÷1.062	×0.620	—	×0.995	7.26%
HBP% /PA	9.38%	—	×0.720	—	×0.995	6.72%
Singles /BIP	18.75%	÷1.003	×0.820	—	×0.995	15.25%
Doubles /BIP	12.50%	÷0.927	×0.720	—	×0.995	9.66%
Triples /BIP	0.00%	÷0.900	×0.720	—	×0.995	0.00%
Home runs /BIP	6.25%	÷1.178	×0.780	—	×0.995	4.12%

Factors used: level-wide ROK factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 136, 1B 101, 2B 85, 3B 80, K 99, BB 112 — applied at half strength. Age: age 20 vs a level average of 19.5 → −0.5% on BB/HBP/hit rates (0.5 yr older than average).

Result: wOBA .389 → .275; ISO .208 → .147; BABIP .312 → .244. 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).

2023 ROKACL Giants Black · 57 PAwOBA .275 → .182 (−93)K% 28.1% → 40.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	28.07%	÷1.001	×1.440	—	—	40.38%
BB% (unintentional) /PA	8.77%	÷0.979	×0.620	—	×1.008	5.60%
HBP% /PA	1.75%	—	×0.720	—	×1.008	1.27%
Singles /BIP	25.71%	÷1.053	×0.820	—	×1.008	20.18%
Doubles /BIP	0.00%	÷1.017	×0.720	—	×1.008	0.00%
Triples /BIP	2.86%	÷1.000	×0.720	—	×1.008	2.07%
Home runs /BIP	2.86%	÷1.031	×0.780	—	×1.008	2.18%

Factors used: level-wide ROK factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 106, 1B 111, 2B 103, 3B 100, K 100, BB 96 — applied at half strength. Age: age 19 vs a level average of 19.5 → +0.8% on BB/HBP/hit rates (0.5 yr younger than average).

Result: wOBA .275 → .182; ISO .098 → .063; BABIP .294 → .227. 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).

PLAYER · career totals + /162 games	162s	PA	HR	SB	AVG	OBP	SLG	POS	FLD	WAR	JAWS
#1Curt Casalilast yr '24 career totals — 10 seasons, 1,566 PA	2.4	1,566	47	2	217	311	367	+28	+19	7.8	7.9
/162	664	20	1	+11.9	+7.9	3.3	pk7: 8
#2Sandy Leónlast yr '22 career totals — 9 seasons, 1,648 PA	2.5	1,648	31	1	209	276	313	+29	+56	6.4	6.7
/162	664	12	0	+11.9	+22.4	2.6	pk7: 7
#3Marc Hill'73-'86 * career totals — 10 seasons, 2,047 PA	3.1	2,047	34	1	218*	292*	337*	+35	-0	2.6	3.3
/162	664	11	0	+11.4	-0.0	0.8	pk7: 4
#4Mike Difelice'96-'08 * career totals — 7 seasons, 1,714 PA	2.6	1,714	28	3	226*	277*	345*	+30	+0	0.6	1.5
/162	664	11	1	+11.7	+0.0	0.2	pk7: 2
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 4 projected seasons	1.2	826	16	6	208	282	325	+15	+16	1.0	1.0
/162	664	13	5	+11.9	+12.9	0.8	pk7: 1
#5Chad Moeller'00-'10 * career totals — 10 seasons, 1,539 PA	2.3	1,539	29	2	218*	277*	341*	+27	-0	-0.2	1.0
/162	664	13	1	+11.6	-0.1	-0.1	pk7: 2
#6Steve Swisher'74-'82 * career totals — 7 seasons, 1,577 PA	2.4	1,577	20	4	211*	275*	330*	+28	+0	-0.1	0.6
/162	664	8	2	+11.9	+0.0	-0.0	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
Nomar Diaz (OGILVIE)	23	—	—	—	—	199/277/307	107	289	1	0	32%	8%	68	-0.1	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Bryan Holaday 2011 · A	23	42	42→45	47→40	42	201/282/303	102	295	9	5	33%	8%	69	-0.9	214/267/303	89	277	7	6	24%	6%	61	-1.5	214/275/334	120	247	13	2	19%	6%	74	-0.2
✕Andrew Susac 2013 · A	23	42→39	43→50	45→30	45	206/281/318	112	290	11	4	31%	9%	71	-0.8	222/298/364	142	296	16	3	29%	9%	88	+0.4	223/303/379	156	290	18	1	28%	9%	91	+0.3
✕Shane Muntz 2022 · A	23	41	43→45	49→45	42	198/279/311	113	290	11	6	33%	8%	70	-0.9	193/270/298	105	287	10	5	34%	7%	63	-1.4	171/253/273	102	254	11	4	35%	7%	50	-0.8
✕Darrien Miller 2024 · A	23	39→42	44→45	48→45	47	194/289/310	116	258	12	6	28%	9%	74	-0.6	192/313/294	102	262	8	5	27%	11%	80	-0.2	179/320/285	107	245	9	4	27%	12%	79	-0.1
✕Jason Lopez 2021 · A	23	41	43→50	46→45	38	204/251/318	114	286	10	7	32%	5%	59	-1.6	198/261/298	100	298	9	10	34%	8%	58	-1.7	169/237/256	87	252	9	10	35%	8%	38	-0.8
✕Abrahan Gutierrez 2023 · A	23	46→41	42→45	45→40	45	221/291/328	107	304	9	4	29%	8%	77	-0.3	215/272/303	88	278	7	5	24%	6%	63	-1.3	225/261/287	62	286	4	2	22%	4%	51	-0.5
✕Nick Hutchins 2019 · A	23	42	45→50	47→40	38	209/265/339	129	291	12	3	31%	5%	70	-0.9	205/246/304	99	290	8	5	31%	4%	54	-2.0	191/219/276	85	269	7	4	31%	2%	34	-1.7
✕Kevin Padlo 2019 · A	23	36→42	44→55	45→50	44	183/264/301	119	275	11	7	36%	9%	61	-1.5	207/286/371	164	296	18	9	34%	9%	86	+0.3	196/269/361	165	267	20	12	33%	8%	75	-0.3
✕Pedro Pagés 2022 · A	23	43→44	43→50	42→40	42	211/278/315	104	301	11	4	32%	7%	69	-0.9	213/278/334	121	292	14	4	30%	8%	74	-0.6	224/283/361	136	280	17	2	26%	7%	81	-0.0
✕Francisco Acuna 2023 · A	23	40→47	41→45	47→50	42	197/272/302	105	277	8	11	30%	7%	65	-1.2	213/283/307	94	282	7	13	25%	7%	70	-0.9	198/282/292	94	261	8	17	25%	8%	64	-0.7
✕Connor Pavolony 2023 · A	23	38	42→45	48→40	46	182/278/286	104	289	10	6	37%	10%	63	-1.3	173/251/252	79	287	6	6	38%	8%	45	-2.7	138/214/196	58	229	4	2	38%	8%	16	-2.0
✕Jeferson Morales 2023 · A	24	38→47	46→45	49→50	43	195/275/324	129	258	13	9	29%	7%	71	-0.8	216/290/343	127	275	12	9	25%	7%	80	-0.1	222/297/349	127	274	11	8	22%	7%	81	-0.0
✕Kody Huff 2024 · A	23	45→42	39→45	48→45	44	210/281/297	86	301	6	5	30%	8%	65	-1.2	212/281/318	106	292	10	7	29%	8%	71	-0.8	208/282/330	122	275	12	8	27%	8%	71	-0.5
✕Pablo Aliendo 2024 · AA	23	42	46→50	49→45	39	210/276/342	132	302	14	6	34%	6%	75	-0.5	196/262/340	144	304	17	5	39%	6%	70	-0.9	171/240/309	138	271	18	1	42%	5%	52	-1.0
✕Jimmy Glowenke 2022 · A	23	42→38	46→45	43→45	40	207/277/338	131	296	13	5	33%	6%	74	-0.5	200/273/321	121	279	13	5	31%	7%	70	-0.9	187/266/295	108	253	12	5	29%	7%	58	-1.0
✕Marcus Greene Jr. 2018 · A	23	39	47→50	42→40	43	203/264/334	131	264	15	4	29%	7%	69	-0.9	213/268/317	104	301	10	5	31%	6%	65	-1.2	197/247/259	63	294	6	0	34%	6%	40	-0.9
✕Carson Blair 2014 · A	24	38	44→50	45→30	41	189/257/310	121	293	11	6	37%	8%	60	-1.6	204/277/337	133	332	13	3	39%	9%	74	-0.5	194/271/327	133	315	13	1	40%	9%	67	-0.5
✕Joe DeCarlo 2017 · A	23	38	45→50	47→30	44	191/278/314	124	278	13	2	34%	9%	70	-0.8	195/271/326	131	277	15	3	33%	8%	70	-0.9	176/253/298	122	249	15	0	34%	7%	54	-0.9
✕Alex De Goti 2018 · A	23	41→48	41→45	44→50	44	200/272/294	94	288	9	7	32%	8%	62	-1.4	226/280/344	118	285	12	7	24%	6%	76	-0.4	220/276/336	116	280	10	6	24%	6%	70	-0.7
✕Jhoneiker Betancourt 2023 · A	23	43	40→50	50→40	42	205/278/298	94	281	7	6	28%	6%	65	-1.2	202/274/329	127	271	14	4	30%	7%	72	-0.7	175/259/316	141	226	20	0	32%	7%	67	-0.5

Nomar Diaz

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps