Tanner Mally

OF San Jose Giants

Age 22 R/R A HR -15%H -3% Svc 0.0 · Ctrl thru 2037

Overview

APROSPECTMiLBFA 2038

OGV OFP35OGV rank#2044ETA2029MLB%0%

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2026	21	ASF	107	.383/.528/.494	0	8	158	—	.356/.472/.460
2027PROJMLE	22	MLBSF	217	.221/.304/.321	3	7	79	0.0
2028PROJ34% MLB	23	MLBSF	41	.220/.303/.323	1	1	82	-0.0
2029PROJ46% MLB	24	MLBSF	90	.222/.305/.330	1	3	85	-0.0

Next 3 projected WAR -0.0Projected 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 2026 A

wOBA99

wRC+98

Chase%95

Speed94

BB%91

K%89

Career WAR0.0 WAR median (P10 -0.1 / P90 0.0, comp-based) · star 1% · didn't stick 96%

CompsManuel Hilario (age 22) · BJ Boyd (age 22) · Rico Noel (age 22)

2027 FULL-SEASON PROJECTIONpark-neutral Marcel · 67 proj PA

wRC+†79

AVG/OBP/SLG.219 / .302 / .318

PA67

HR1

K%26

BB%9

ISO100

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	Arb-1	$0.2M	—	—	—
2036	Ext	beyond projection horizon · no WAR estimate
2037	Ext	beyond projection horizon · no WAR estimate
Totals		$0.0M		$-0.4M	+$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.

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)
2026	21	A	San Jose Giants	—	107	81	31	3	3	0	20	16	5	8	1	383	528	494	1.022	111	470	15	19	457	158	—	—	—	—	—	—
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2026	21	A MLE	San Jose Giants	—	107	87	31	3	3	0	14	22	5	8	1	356	472	460	931	103	449	21	13	401	158	—	—	—	—	—	—
2026	21	A MLE	San Jose Giants	—	107	87	31	3	3	0	14	22	5	8	1	356	472	460	931	103	470	21	13	401	158	—	—	—	—	—	—
2026	21	A RAW	San Jose Giants	—	107	81	31	3	3	0	20	16	5	8	1	383	528	494	1.022	111	470	15	19	457	158	—	—	—	—	—	—
2026	21	A MLE	San Jose Giants	—	107	87	31	3	3	0	14	22	5	8	1	356	472	460	931	103	449	21	13	401	158	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2026	21	A MLE	San Jose Giants	—	107	87	31	3	3	0	14	22	5	8	1	356	472	460	931	103	449	21	13	401	158	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	22	MLB MLE	SF	0%	217	192	43	6	3	3	19	55	4	7	1	221	304	321	625	100	292	25	9	285	79	-5	+1	+0	-1	+5	0.0
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	23	MLB MLE	SF	34%	120	106	23	3	1	2	11	30	2	4	1	224	307	330	637	106	293	25	9	290	82	-3	+0	+0	-1	+3	-0.0
2029	24	MLB MLE	SF	46%	194	172	38	5	2	3	17	49	3	7	1	226	309	338	647	112	294	25	9	293	85	-4	+1	+0	-1	+4	0.1
2030	25	MLB MLE	SF	52%	258	229	51	7	3	4	23	64	4	9	2	228	310	344	654	116	294	25	9	296	87	-4	+1	+0	-2	+6	0.1
2031	26	MLB MLE	SF	54%	309	274	63	9	3	6	27	77	5	11	2	229	310	348	659	119	294	25	9	298	88	-4	+1	+0	-2	+7	0.2
2032	27	MLB MLE	SF	54%	334	296	67	10	3	6	30	83	5	12	2	229	310	349	659	120	293	25	9	298	88	-5	+2	+0	-2	+7	0.2
2033	28	MLB MLE	SF	54%	346	307	70	10	4	6	31	87	5	12	2	227	308	344	652	117	292	25	9	295	86	-6	+2	-1	-2	+7	0.0
2034	29	MLB MLE	SF	53%	344	306	68	10	3	6	30	87	5	12	2	224	305	337	642	113	291	25	9	291	83	-7	+2	-1	-2	+7	-0.1
2035	30	MLB MLE	SF	52%	337	300	66	10	3	5	29	86	5	11	2	221	301	329	631	108	289	26	9	287	80	-8	+1	-2	-2	+7	-0.3
2028	23	MLB CTX	SF	34%	41	36	8	1	0	1	4	10	1	1	0	220	303	323	626	103	289	24	10	285	82	-1	+0	+0	-0	+1	-0.0
2029	24	MLB CTX	SF	46%	90	80	18	3	1	1	8	23	2	3	1	222	305	330	635	107	290	26	9	289	85	-2	+0	+0	-1	+2	-0.0
2030	25	MLB CTX	SF	52%	133	118	26	4	1	2	12	33	2	5	1	224	306	335	642	111	290	25	9	291	87	-2	+1	+0	-1	+3	0.1
2031	26	MLB CTX	SF	54%	168	148	34	5	2	3	15	42	3	6	1	225	307	340	646	115	290	25	9	293	88	-2	+1	+0	-1	+4	0.1
2032	27	MLB CTX	SF	54%	181	160	36	5	2	3	16	45	3	7	1	225	306	340	646	115	289	25	9	293	88	-3	+1	+0	-1	+4	0.1
2033	28	MLB CTX	SF	54%	188	166	38	6	2	3	17	47	3	7	1	223	304	335	640	112	288	25	9	290	86	-3	+1	-0	-1	+4	0.0
2034	29	MLB CTX	SF	53%	183	162	36	5	2	3	16	46	3	6	1	220	302	329	631	109	287	25	9	287	83	-4	+1	-1	-1	+4	-0.1
2035	30	MLB CTX	SF	52%	177	157	35	5	2	3	15	45	3	6	1	217	298	321	619	104	285	25	8	282	80	-4	+1	-1	-1	+4	-0.2
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1161	1027	231	34	12	19	103	291	20	41	7	225	308	337	645	112	293	25	9	289	88	-21	+5	-2	-7	+25	0.1
/ 162G	—	A+C		—	650	575	130	19	7	11	58	163	11	23	4	226	309	341	650	115	295	25	9	289	88	-12	+3	-1	-4	+14	0.0
Career	—	MLE		—	2416	2137	491	69	26	39	218	602	40	88	15	230	313	341	654	111	299	25	9	298	88	-42	+10	-4	-14	+50	0.0
/ 162G	—	MLE		—	650	575	132	19	7	10	59	162	11	24	4	230	314	339	653	110	300	25	9	298	88	-12	+3	-1	-4	+14	0.0

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.

2026 ASan Jose Giants · 107 PAwOBA .451 → .401 (−51)K% 15.0% → 20.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.95%	÷1.000	×1.360	—	—	20.34%
BB% (unintentional) /PA	18.69%	÷1.000	×0.690	—	—	12.90%
HBP% /PA	4.67%	—	×0.780	—	—	3.64%
Singles /BIP	38.46%	÷1.000	×0.920	×1.050	—	37.15%
Doubles /BIP	4.62%	÷1.000	×0.830	×1.050	—	4.02%
Triples /BIP	4.62%	÷1.000	×0.910	×1.050	—	4.41%
Home runs /BIP	0.00%	÷1.000	×0.800	—	—	0.00%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 22 vs a level average of 22.0 → +0.0% on BB/HBP/hit rates (0.0 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .451 → .401; ISO .111 → .107; BABIP .470 → .449. 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
#1Willie Harrislast yr '11 career totals — 10 seasons, 2,743 PA	4.1	2,743	39	106	241	331	351	-11	-2	5.3	6.2
/162	664	9	26	-2.7	-0.5	1.3	pk7: 7
#2Michael Saunderslast yr '17 career totals — 8 seasons, 2,711 PA	4.1	2,711	81	55	233	305	399	-12	-13	4.1	4.5
/162	664	20	13	-2.9	-3.2	1.0	pk7: 5
#3Jake Cavelast yr '24 career totals — 7 seasons, 1,564 PA	2.4	1,564	45	13	236	292	400	-11	-6	1.3	1.3
/162	664	19	6	-4.5	-2.5	0.6	pk7: 1
#4James Mouton'94-'01 * career totals — 8 seasons, 1,795 PA	2.7	1,795	18	109	235*	314*	329*	-11	-0	0.7	1.0
/162	664	7	40	-4.2	-0.1	0.3	pk7: 1
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 5 projected seasons	1.8	1,227	21	43	227	305	342	-7	+0	1.0	1.0
/162	664	11	23	-4.0	+0.0	0.5	pk7: 1
#5Jimmy Stewart'63-'73 * career totals — 8 seasons, 1,602 PA	2.4	1,602	8	38	235*	309*	329*	-1	+2	-0.2	0.5
/162	664	3	16	-0.4	+0.7	-0.1	pk7: 1
#6Jolbert Cabrera'98-'08 * career totals — 6 seasons, 1,531 PA	2.3	1,531	18	38	247*	295*	354*	-3	-9	-1.1	-0.7
/162	664	8	16	-1.4	-4.0	-0.5	pk7: -0

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
Tanner Mally (OGILVIE)	22	—	—	—	—	219/302/318	99	292	1	2	26%	9%	79	-0.0	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Manuel Hilario 2014 · A	22	40	38→40	56→55	49	191/271/276	85	240	4	18	21%	8%	57	-1.8	201/255/276	75	276	5	10	27%	6%	51	-2.3	163/211/203	40	218	4	10	26%	4%	15	-0.9
✕BJ Boyd 2015 · A	22	42→52	38→45	48→50	43	204/257/282	78	272	5	10	26%	6%	53	-2.1	243/287/328	85	307	6	11	22%	5%	74	-0.5	259/306/366	107	315	10	10	20%	5%	87	+0.2
✕Rico Noel 2011 · A	22	42→52	40→30	54→65	44	204/276/295	91	269	7	12	26%	7%	63	-1.3	218/282/277	59	305	2	40	27%	7%	61	-1.5	228/293/276	47	319	1	49	26%	7%	62	-0.9
✕Joe Mahoney 2010 · A	23	44→54	41→50	56→50	36	214/250/317	103	299	8	13	31%	4%	58	-1.7	246/284/379	133	320	13	10	26%	4%	85	+0.2	255/293/393	138	321	13	8	24%	4%	91	+0.5
✕John Nogowski 2015 · A	22	45→53	38→45	50→45	43	214/263/300	86	276	4	6	23%	6%	59	-1.7	238/294/320	82	277	6	3	15%	6%	75	-0.5	253/330/362	109	274	12	2	12%	9%	97	+0.7
✕Marty Herum 2014 · A	22	45→54	36→45	50→45	43	210/268/278	68	301	3	7	30%	7%	56	-1.9	237/265/322	85	296	6	4	21%	3%	64	-1.3	259/291/356	98	314	8	4	20%	4%	80	-0.1
✕Henry Wrigley 2009 · A	22	44→48	42→50	52→45	40	215/246/322	107	258	9	7	21%	4%	57	-1.8	218/245/344	126	261	12	6	21%	3%	62	-1.4	232/263/388	157	269	17	3	21%	4%	77	-0.2
✕Rafael Vera 2010 · A	22	43	39→45	51→40	41	209/261/294	85	286	7	11	29%	6%	57	-1.8	200/249/284	84	295	4	4	32%	5%	50	-2.3	188/238/271	83	278	4	2	32%	5%	41	-1.4
✕Danny Mars 2016 · A	22	47→57	35→40	54→55	44	220/264/277	57	272	3	18	20%	5%	53	-2.1	245/286/321	76	328	4	18	25%	5%	72	-0.7	250/292/323	74	335	4	18	25%	5%	72	-0.6
✕Francisco Lugo 2014 · AAA	22	44→54	41→30	53→45	40	214/240/313	99	255	8	11	20%	3%	53	-2.1	236/266/310	74	283	5	10	18%	4%	61	-1.5	287/316/348	62	336	5	9	16%	3%	85	+0.2
✕Denis Phipps 2008 · A	23	41→51	40→50	47→50	39	204/253/287	83	272	8	11	28%	5%	53	-2.1	205/249/302	97	279	8	10	28%	5%	54	-2.0	228/278/359	131	309	13	11	29%	6%	76	-0.3
✕Dalton Kelly 2016 · A	21	43→53	36→45	58→50	44	189/277/263	74	328	2	14	39%	10%	57	-1.8	237/307/343	106	333	6	12	28%	8%	86	+0.3	238/320/344	106	328	8	13	27%	9%	88	+0.4
✕Jeffrey Baez 2014 · A	20	43→46	38→50	58→60	40	204/257/283	78	279	5	27	28%	5%	53	-2.1	208/250/311	103	289	9	22	30%	5%	57	-1.8	195/248/328	132	258	16	15	30%	6%	59	-0.9
✕Justin Ellison 2017 · A	22	42→43	40→50	52→50	40	205/244/307	103	270	6	11	26%	5%	53	-2.1	197/234/310	113	298	10	10	36%	4%	50	-2.3	174/202/298	125	276	13	8	41%	3%	33	-1.8
✕Enmanuel Valdez 2021 · A	22	40→47	42→55	46→45	40	199/252/305	106	273	10	9	30%	6%	57	-1.8	221/280/394	173	275	22	8	27%	7%	88	+0.5	223/287/404	181	270	24	8	27%	8%	92	+0.5
✕Romy Gonzalez 2019 · A	22	38→44	46→50	53→55	33	191/240/322	131	304	14	10	41%	5%	56	-1.9	200/255/314	114	307	11	12	36%	6%	60	-1.6	270/316/424	155	341	15	16	25%	5%	108	+0.9
✕Jeff Flagg 2009 · A	23	38	43→45	54→40	43	191/257/306	115	265	11	10	31%	8%	59	-1.6	191/244/295	104	317	10	6	40%	6%	51	-2.2	171/221/272	101	290	11	3	43%	6%	35	-1.1
✕Franklin Labour 2019 · A	21	39	38→45	49→50	45	189/262/271	82	256	4	11	26%	7%	53	-2.1	189/262/284	95	312	7	8	38%	8%	56	-1.9	153/243/273	121	268	13	9	44%	10%	46	-1.1
✕Avain Rachal 2015 · A	21	41	38→40	53→50	42	196/262/272	77	283	5	11	31%	7%	53	-2.1	192/267/268	76	303	6	10	36%	8%	55	-2.0	181/262/247	66	288	5	10	36%	8%	45	-1.4
✕Granden Goetzman 2016 · A	23	43→45	40→45	49→55	36	208/247/300	92	290	7	13	30%	4%	53	-2.1	214/249/313	99	289	7	21	27%	4%	57	-1.8	212/242/319	107	281	7	25	27%	4%	52	-0.9

Tanner Mally

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps