Matt Chapman

3B San Francisco Giants

Age 34 R/R 6'1" / 220 lbs MLB HR -15%H -3% Svc 9.634 · Ctrl thru 2027 ILInjured 60-Day (D60)

Overview

MLBIL-6040-MANExtFA 2028

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2024	31	MLBSF	647	.247/.329/.463	27	15	125	4.9
2025	32	MLBSF	535	.231/.343/.430	21	9	123	3.4
2026	33	MLBSF	352	.235/.327/.368	7	0	100	0.9
2027PROJ	34	MLBSF	160	.236/.331/.402	5	2	112	0.8
2028PROJ	35	MLBSF	250	.224/.318/.377	7	1	102	0.3

Last 3 seasons WAR 9.2 · Next 2 projected WAR 1.1Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service9.634First FA2028Options4NTC

2027Ext$20M

Skills · percentileStatcast 2024-2026

Bat Spd89

Avg EV88

xwOBA67

Speed66

Barrel%64

CompsChris Young (age 34) · Fernando Tatis (age 34) · Nick Hundley (age 34)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 113112vR 108

AVG/OBP/SLG.236 / .331 / .402

PA160

HR5

K%23

BB%11

BABIP284

ISO173

MLB%100%

Value2027 · WAR

trajectory · projected WAR by year

Tools · Splits · Contract

ToolsStatcast %ile · 2024-2026

CONTACT

xAVG

310.236

POWER

Barrel%

6410.3

Avg EV

8891.7

Bat Spd

8975.4

SPEED

Sprint

6628.1

Platoon Splits2024-2026 · 1295 PA

vs LHP

vs RHP

Edge

325

970

—

wRC+

112

118

▶R

wOBA

334

343

—

ISO

165

209

▶▶R

BABIP

297

287

◀L

20.9%

24.4%

◀L

BB%

12.0%

10.4%

◀L

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

Status FA

MLB Debut 2017

Service 9.634 yrs.days

Team Control Through 2027

First FA Year 2028

NTCOPT

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$20M	+0.8	$3M	-$17M
Totals		$20M		$3M	-$17M

Options · projected decision
2031	MUTUAL OPT	$20.0M / $0.0M buyout	DECLINE
2032	PLAYER OPT	$17.0M / $2.0M buyout	EXERCISE
2033	PLAYER OPT	$18.0M / $2.0M buyout	EXERCISE
2034	MUTUAL OPT	$20.0M / $2.0M buyout	DECLINE

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)
2014	21	A	Beloit Snappers	—	202	190	45	8	3	5	7	46	5	2	1	237	282	389	672	153	288	23	3	299	36	—	—	—	—	—	—
2014	21	ROK	AZL Athletics	—	15	14	6	1	1	0	1	1	0	0	0	429	467	643	1.110	214	462	7	7	481	122	—	—	—	—	—	—
2015	22	A+	Stockton Ports	—	352	304	76	21	3	23	39	79	5	4	1	250	345	566	911	316	257	22	11	389	67	—	—	—	—	—	—
2016	23	AAA	Nashville Sounds	—	85	76	15	1	1	7	9	26	0	0	0	197	282	513	796	316	186	31	11	343	70	—	—	—	—	—	—
2016	23	AA	Midland RockHounds	—	504	438	107	26	4	29	59	147	3	7	4	244	338	521	859	276	293	29	12	371	68	—	—	—	—	—	—
2017	24	MLB	Oakland Athletics	—	326	290	68	23	2	14	32	92	2	0	3	234	315	472	787	238	290	28	10	343	123	+9	-1	+7	+2	+7	2.4
2017	24	AAA	Nashville Sounds	—	204	175	45	6	2	16	25	63	1	5	4	257	353	589	942	331	293	31	12	399	105	—	—	—	—	—	—
2018	25	MLB	Oakland Athletics	—	616	547	152	42	6	24	58	146	9	1	2	278	357	508	865	230	338	24	9	378	146	+34	-1	+7	+2	+13	5.7
2019	26	MLB	Oakland Athletics	—	670	583	145	36	3	36	73	147	11	1	1	249	343	506	849	257	270	22	11	371	143	+35	-0	+11	+2	+15	6.4
2020	27	MLB	Oakland Athletics	—	152	142	33	9	2	10	8	54	1	0	0	232	278	535	813	303	291	36	5	347	129	+5	+0	+1	+1	+3	1.1
2021	28	MLB	Oakland Athletics	—	622	529	111	15	3	27	80	202	4	3	2	210	318	403	721	193	272	32	13	320	108	+6	-0	+14	+2	+13	3.7
2022	29	MLB	Toronto Blue Jays	—	621	538	123	27	1	27	68	170	10	2	2	229	326	433	759	204	277	27	11	336	113	+9	-0	+3	+2	+13	2.8
2023	30	MLB	Toronto Blue Jays	—	581	509	122	39	2	17	62	165	8	4	2	240	332	424	756	185	319	28	11	337	113	+9	+0	+4	+2	+13	2.8
2024	31	MLB	San Francisco Giants	—	647	575	142	39	2	27	64	158	6	15	2	247	329	463	791	216	293	24	10	349	125	+20	+2	+10	+2	+14	4.9
2025	32	MLB	San Francisco Giants	—	535	454	105	23	2	21	71	126	6	9	4	231	343	430	772	198	270	24	13	344	123	+15	+0	+5	+2	+12	3.4
2026	33	MLB	San Francisco Giants	—	352	307	72	18	1	7	37	81	5	0	2	235	327	368	695	134	293	23	11	314	100	+0	-1	+1	+1	+8	0.9
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	5122	4474	1073	271	24	210	553	1341	62	35	20	240	332	452	784	212	292	26	11	345	120	+121	-1	+64	+19	+111	32.0
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2014	21	A MLE	Beloit Snappers	—	202	192	36	6	3	3	5	64	5	2	1	188	228	297	525	109	255	32	2	221	36	—	—	—	—	—	—
2014	21	ROK MLE	AZL Athletics	—	15	14	5	1	1	0	1	1	0	0	0	357	400	571	971	214	358	7	7	349	122	—	—	—	—	—	—
2015	22	A+ MLE	Stockton Ports	—	352	315	56	16	2	12	28	99	5	4	1	178	256	356	611	178	206	28	8	267	67	—	—	—	—	—	—
2016	23	AAA MLE	Nashville Sounds	—	85	79	13	1	1	6	6	33	0	0	0	165	224	430	654	266	159	39	7	271	70	—	—	—	—	—	—
2016	23	AA MLE	Midland RockHounds	—	504	460	82	18	3	22	37	195	3	7	4	178	244	374	618	196	240	39	7	269	68	—	—	—	—	—	—
2017	24	MLB MLE	Oakland Athletics	—	326	290	68	22	2	15	32	92	2	0	3	234	315	479	794	245	290	28	10	350	123	+9	-1	+7	+2	+7	2.4
2017	24	AAA MLE	Nashville Sounds	—	204	183	38	4	2	14	17	81	1	5	4	208	279	481	759	273	256	40	8	324	105	—	—	—	—	—	—
2018	25	MLB MLE	Oakland Athletics	—	616	547	153	41	6	26	58	146	9	1	2	280	358	519	878	239	338	24	9	384	146	+34	-1	+7	+2	+13	5.7
2019	26	MLB MLE	Oakland Athletics	—	670	583	148	35	3	40	73	147	11	1	1	254	348	530	878	276	270	22	11	380	143	+35	-0	+11	+2	+15	6.4
2020	27	MLB MLE	Oakland Athletics	—	152	142	34	9	2	11	8	54	1	0	0	239	285	563	848	324	291	36	5	359	129	+5	+0	+1	+1	+3	1.1
2021	28	MLB MLE	Oakland Athletics	—	622	529	114	15	3	30	80	202	4	3	2	216	323	425	748	210	272	32	13	328	108	+6	-0	+14	+2	+13	3.7
2022	29	MLB MLE	Toronto Blue Jays	—	621	539	123	27	1	27	67	170	10	2	2	228	325	432	757	204	277	27	11	335	113	+9	-0	+3	+2	+13	2.8
2023	30	MLB MLE	Toronto Blue Jays	—	581	510	122	39	2	17	61	165	8	4	2	239	330	424	753	184	319	28	10	335	113	+9	+0	+4	+2	+13	2.8
2024	31	MLB MLE	San Francisco Giants	—	647	578	145	39	2	29	61	158	6	15	2	251	329	476	804	225	293	24	9	354	125	+20	+2	+10	+2	+14	4.9
2025	32	MLB MLE	San Francisco Giants	—	535	455	108	23	2	23	70	126	6	9	4	237	347	448	795	211	270	24	13	350	123	+15	+0	+5	+2	+12	3.4
2026	33	MLB MLE	San Francisco Giants	—	352	308	74	18	1	8	36	81	5	0	2	240	330	383	713	143	293	23	10	317	100	+0	-1	+1	+1	+8	0.9
2014	21	A MLE	2 teams	—	217	207	39	6	3	3	5	66	5	2	1	188	226	290	516	101	261	30	2	230	42	—	—	—	—	—	—
2014	21	A RAW	2 teams	—	217	204	51	9	4	5	8	47	5	2	1	250	295	407	702	157	303	22	4	311	42	—	—	—	—	—	—
2015	22	A+ MLE	Stockton Ports	—	352	315	56	16	2	12	28	99	5	4	1	178	256	356	611	178	212	28	8	267	67	—	—	—	—	—	—
2015	22	A+ RAW	Stockton Ports	—	352	304	76	21	3	23	39	79	5	4	1	250	345	566	911	316	257	22	11	389	67	—	—	—	—	—	—
2016	23	AA MLE	2 teams	—	589	539	95	19	4	28	43	228	3	7	4	176	241	382	623	206	233	39	7	269	68	—	—	—	—	—	—
2016	23	AA RAW	2 teams	—	589	514	122	27	5	36	68	173	3	7	4	237	330	519	849	282	278	29	12	367	68	—	—	—	—	—	—
2017	24	MLB MLE	2 teams	—	530	473	107	27	4	29	49	173	3	5	7	226	303	484	787	258	283	33	9	340	116	+10	-2	+7	+2	+11	3.0
2017	24	MLB RAW	2 teams	—	530	465	113	29	4	30	57	155	3	5	7	243	330	516	846	273	291	29	11	365	116	+10	-2	+7	+2	+11	3.0
2018	25	MLB MLE	Oakland Athletics	—	616	547	153	41	6	26	58	146	9	1	2	280	358	519	878	239	337	24	9	384	146	+34	-1	+7	+2	+13	5.7
2018	25	MLB RAW	Oakland Athletics	—	616	547	152	42	6	24	58	146	9	1	2	278	357	508	865	230	338	24	9	378	146	+34	-1	+7	+2	+13	5.7
2019	26	MLB MLE	Oakland Athletics	—	670	583	148	35	3	40	73	147	11	1	1	254	348	530	878	276	271	22	11	380	143	+35	-0	+11	+2	+15	6.4
2019	26	MLB RAW	Oakland Athletics	—	670	583	145	36	3	36	73	147	11	1	1	249	343	506	849	257	270	22	11	371	143	+35	-0	+11	+2	+15	6.4
2020	27	MLB MLE	Oakland Athletics	—	152	142	34	9	2	11	8	54	1	0	0	239	285	563	848	324	295	36	5	359	129	+5	+0	+1	+1	+3	1.1
2020	27	MLB RAW	Oakland Athletics	—	152	142	33	9	2	10	8	54	1	0	0	232	278	535	813	303	291	36	5	347	129	+5	+0	+1	+1	+3	1.1
2021	28	MLB MLE	Oakland Athletics	—	622	529	114	15	3	30	80	202	4	3	2	216	323	425	748	210	275	32	13	328	108	+6	-0	+14	+2	+13	3.7
2021	28	MLB RAW	Oakland Athletics	—	622	529	111	15	3	27	80	202	4	3	2	210	318	403	721	193	272	32	13	320	108	+6	-0	+14	+2	+13	3.7
2022	29	MLB MLE	Toronto Blue Jays	—	621	539	123	27	1	27	67	170	10	2	2	228	325	432	757	204	277	27	11	335	113	+9	-0	+3	+2	+13	2.8
2022	29	MLB RAW	Toronto Blue Jays	—	621	538	123	27	1	27	68	170	10	2	2	229	326	433	759	204	277	27	11	336	113	+9	-0	+3	+2	+13	2.8
2023	30	MLB MLE	Toronto Blue Jays	—	581	510	122	39	2	17	61	165	8	4	2	239	330	424	753	184	318	28	10	335	113	+9	+0	+4	+2	+13	2.8
2023	30	MLB RAW	Toronto Blue Jays	—	581	509	122	39	2	17	62	165	8	4	2	240	332	424	756	185	319	28	11	337	113	+9	+0	+4	+2	+13	2.8
2024	31	MLB MLE	San Francisco Giants	—	647	578	145	39	2	29	61	158	6	15	2	251	329	476	804	225	295	24	9	354	125	+20	+2	+10	+2	+14	4.9
2024	31	MLB RAW	San Francisco Giants	—	647	575	142	39	2	27	64	158	6	15	2	247	329	463	791	216	293	24	10	349	125	+20	+2	+10	+2	+14	4.9
2025	32	MLB MLE	San Francisco Giants	—	535	455	108	23	2	23	70	126	6	9	4	237	347	448	795	211	274	24	13	350	123	+15	+0	+5	+2	+12	3.4
2025	32	MLB RAW	San Francisco Giants	—	535	454	105	23	2	21	71	126	6	9	4	231	343	430	772	198	270	24	13	344	123	+15	+0	+5	+2	+12	3.4
2026	33	MLB MLE	San Francisco Giants	—	352	308	74	18	1	8	36	81	5	0	2	240	330	383	713	143	297	23	10	317	100	+0	-1	+1	+1	+8	0.9
2026	33	MLB RAW	San Francisco Giants	—	352	307	72	18	1	7	37	81	5	0	2	235	327	368	695	134	293	23	11	314	100	+0	-1	+1	+1	+8	0.9
2014	21	A MLE	2 teams	—	217	205	39	6	3	3	5	66	5	2	1	190	226	293	518	102	262	30	2	230	42	—	—	—	—	—	—
2015	22	A+ MLE	Stockton Ports	—	352	316	56	16	2	12	28	99	5	4	1	177	256	354	610	177	206	28	8	267	67	—	—	—	—	—	—
2016	23	AA MLE	2 teams	—	589	538	95	19	4	28	43	228	3	7	4	177	241	383	624	206	228	39	7	269	68	—	—	—	—	—	—
2017	24	MLB MLE	2 teams	—	530	473	107	27	4	29	49	173	3	5	7	226	303	484	787	258	277	33	9	340	116	—	—	—	—	—	—
2018	25	MLB MLE	Oakland Athletics	—	616	544	153	41	6	26	58	146	9	1	2	281	358	522	880	241	338	24	9	384	146	—	—	—	—	—	—
2019	26	MLB MLE	Oakland Athletics	—	670	581	148	35	3	40	73	147	11	1	1	255	348	532	880	277	270	22	11	380	143	—	—	—	—	—	—
2020	27	MLB MLE	Oakland Athletics	—	152	142	34	9	2	11	8	54	1	0	0	239	285	563	848	324	291	36	5	359	129	—	—	—	—	—	—
2021	28	MLB MLE	Oakland Athletics	—	622	533	114	15	3	30	80	202	4	3	2	214	323	422	745	208	272	32	13	328	108	—	—	—	—	—	—
2022	29	MLB MLE	Toronto Blue Jays	—	621	539	123	27	1	27	67	170	10	2	2	228	325	432	757	204	277	27	11	335	113	—	—	—	—	—	—
2023	30	MLB MLE	Toronto Blue Jays	—	581	507	122	39	2	17	61	165	8	4	2	241	330	426	756	185	319	28	10	335	113	—	—	—	—	—	—
2024	31	MLB MLE	San Francisco Giants	—	647	575	145	39	2	29	61	158	6	15	2	252	329	478	807	226	293	24	9	354	125	—	—	—	—	—	—
2025	32	MLB MLE	San Francisco Giants	—	535	454	108	23	2	23	70	126	6	9	4	238	347	449	796	211	270	24	13	350	123	—	—	—	—	—	—
2026	33	MLB MLE	San Francisco Giants	—	352	308	74	18	1	8	36	81	5	0	2	240	330	383	713	143	293	23	10	317	100	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	31	MLB MLE	San Francisco Giants	—	647	575	145	39	2	29	61	158	6	15	2	252	329	478	807	226	293	24	9	354	125	—	—	—	—	—	—
2025	32	MLB MLE	San Francisco Giants	—	535	454	108	23	2	23	70	126	6	9	4	238	347	449	796	211	270	24	13	350	123	—	—	—	—	—	—
2026	33	MLB MLE	San Francisco Giants	—	352	308	74	18	1	8	36	81	5	0	2	240	330	383	713	143	293	23	10	317	100	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	34	MLB MLE	SF	100%	382	332	79	18	1	12	42	88	5	5	1	241	335	415	750	174	288	23	11	334	112	+6	+1	+2	+1	+8	0.8
▸ 2027 — real world (MLB)
2027	34	MLB CTX	SF	100%	160	139	33	7	1	5	18	37	2	2	0	236	331	402	733	166	284	23	11	328	112	+2	+0	+1	+1	+3	0.8
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	35	MLB MLE	SF	100%	250	218	51	11	1	8	27	60	3	1	0	229	322	389	711	160	277	24	11	318	102	+0	+0	-4	+1	+5	0.3
2028	35	MLB CTX	SF	100%	250	218	49	11	1	7	27	60	3	1	0	224	318	377	694	153	273	24	11	312	102	+0	+0	-4	+1	+5	0.3
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	5372	4692	1122	282	25	217	580	1401	65	36	20	239	331	449	780	210	291	26	11	344	112	+122	-1	+59	+20	+116	32.3
/ 162G	—	A+C		—	650	568	135	34	3	26	70	170	8	4	2	238	330	445	775	208	290	26	11	344	112	+15	-0	+7	+2	+14	3.9
Career	—	MLE		—	6894	6082	1402	332	37	296	684	1912	81	56	30	231	316	443	760	213	282	28	10	333	112	+124	-0	+60	+20	+120	33.1
/ 162G	—	MLE		—	650	573	131	31	3	28	64	180	8	5	3	229	314	440	754	211	279	28	10	333	112	+15	-0	+7	+2	+14	3.9

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.

2017 AAANashville Sounds · 204 PAwOBA .392 → .324 (−68)K% 30.9% → 39.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	30.88%	÷1.003	×1.290	—	—	39.73%
BB% (unintentional) /PA	11.76%	÷1.029	×0.710	—	×1.048	8.50%
HBP% /PA	0.49%	—	×0.980	—	×1.048	0.50%
Singles /BIP	18.75%	÷0.984	×0.900	×0.950	×1.048	17.08%
Doubles /BIP	5.36%	÷1.021	×0.810	×0.950	×1.048	4.23%
Triples /BIP	1.79%	÷0.900	×0.810	×0.950	×1.048	1.60%
Home runs /BIP	14.29%	÷0.868	×0.780	—	×1.048	13.46%

Factors used: league row PCL. Park (home-season share, regressed): HR 74, 1B 97, 2B 104, 3B 80, K 101, BB 106 — applied at half strength. Age: age 24 vs a level average of 27.2 → +4.8% on BB/HBP/hit rates (3.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .392 → .324; ISO .331 → .286; BABIP .293 → .256. 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).

2016 AAMidland RockHounds · 504 PAwOBA .360 → .269 (−91)K% 29.2% → 38.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	29.17%	÷0.920	×1.220	—	—	38.69%
BB% (unintentional) /PA	10.71%	÷1.062	×0.700	—	×1.027	7.25%
HBP% /PA	0.60%	—	×0.870	—	×1.027	0.53%
Singles /BIP	16.49%	÷1.022	×0.920	×0.950	×1.027	14.48%
Doubles /BIP	8.93%	÷1.075	×0.840	×0.950	×1.027	6.81%
Triples /BIP	1.37%	÷1.150	×0.940	×0.950	×1.027	1.10%
Home runs /BIP	9.97%	÷0.999	×0.800	—	×1.027	8.20%

Factors used: league row TL. Park (home-season share, regressed): HR 100, 1B 104, 2B 115, 3B 130, K 84, BB 112 — applied at half strength. Age: age 23 vs a level average of 24.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .360 → .269; ISO .276 → .207; BABIP .293 → .240. 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).

2016 AAANashville Sounds · 85 PAwOBA .329 → .271 (−58)K% 30.6% → 39.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	30.59%	÷1.004	×1.290	—	—	39.29%
BB% (unintentional) /PA	9.41%	÷0.979	×0.710	—	×1.050	7.17%
HBP% /PA	0.00%	—	×0.980	—	×1.050	0.00%
Singles /BIP	12.00%	÷1.035	×0.900	×0.950	×1.050	10.41%
Doubles /BIP	2.00%	÷0.898	×0.810	×0.950	×1.050	1.80%
Triples /BIP	2.00%	÷1.015	×0.810	×0.950	×1.050	1.59%
Home runs /BIP	14.00%	÷0.856	×0.780	—	×1.050	13.39%

Factors used: league row PCL. Park (home-season share, regressed): HR 71, 1B 107, 2B 80, 3B 103, K 101, BB 96 — applied at half strength. Age: age 23 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (4.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .329 → .271; ISO .316 → .270; BABIP .186 → .159. 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).

2015 A+Stockton Ports · 352 PAwOBA .382 → .267 (−116)K% 22.4% → 28.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.44%	÷1.072	×1.340	—	—	28.04%
BB% (unintentional) /PA	11.08%	÷1.000	×0.710	—	×1.012	7.96%
HBP% /PA	1.42%	—	×0.790	—	×1.012	1.14%
Singles /BIP	12.89%	÷0.959	×0.910	×0.951	×1.012	11.77%
Doubles /BIP	9.33%	÷1.047	×0.820	×0.951	×1.012	7.04%
Triples /BIP	1.33%	÷0.926	×0.760	×0.951	×1.012	1.05%
Home runs /BIP	10.22%	÷1.715	×0.920	—	×1.012	5.55%

Factors used: league row CAL. Park (home-season share, regressed): HR 243, 1B 92, 2B 109, 3B 85, K 114, BB 100 — applied at half strength. Age: age 22 vs a level average of 22.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.951.

Result: wOBA .382 → .267; ISO .316 → .188; BABIP .257 → .206. 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).

2014 ABeloit Snappers · 202 PAwOBA .290 → .221 (−69)K% 22.8% → 31.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	22.77%	÷0.974	×1.360	—	—	31.80%
BB% (unintentional) /PA	3.47%	÷1.003	×0.690	—	×1.015	2.42%
HBP% /PA	2.48%	—	×0.780	—	×1.015	1.96%
Singles /BIP	20.14%	÷0.987	×0.920	×0.977	×1.015	18.61%
Doubles /BIP	5.56%	÷1.060	×0.830	×0.977	×1.015	4.31%
Triples /BIP	2.08%	÷0.942	×0.910	×0.977	×1.015	2.00%
Home runs /BIP	3.47%	÷1.155	×0.800	—	×1.015	2.44%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 131, 1B 97, 2B 112, 3B 88, K 95, BB 101 — 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). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.977.

Result: wOBA .290 → .221; ISO .153 → .106; BABIP .288 → .255. 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).

2014 ROKAZL Athletics · 15 PAwOBA .468 → .349 (−119)K% 6.7% → 9.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	6.67%	÷1.000	×1.440	—	—	9.60%
BB% (unintentional) /PA	6.67%	÷1.000	×0.620	—	×0.985	4.07%
HBP% /PA	0.00%	—	×0.720	—	×0.985	0.00%
Singles /BIP	30.77%	÷1.000	×0.820	—	×0.985	24.85%
Doubles /BIP	7.69%	÷1.000	×0.720	—	×0.985	5.46%
Triples /BIP	7.69%	÷1.000	×0.720	—	×0.985	5.46%
Home runs /BIP	0.00%	÷1.000	×0.780	—	×0.985	0.00%

Factors used: level-wide ROK 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 21 vs a level average of 19.5 → −1.5% on BB/HBP/hit rates (1.5 yr older than average).

Result: wOBA .468 → .349; ISO .214 → .151; BABIP .462 → .358. 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
#1Kyle Seagerlast yr '21 career totals — 11 seasons, 6,204 PA	9.3	6,204	242	55	251	321	442	+22	+55	35.4	32.4
/162	664	26	6	+2.4	+5.8	3.8	pk7: 29
#2Roy Smalley'75-'87 * career totals — 13 seasons, 6,594 PA	9.9	6,594	163	27	253*	344*	418*	+20	+50	33.1	30.5
/162	664	16	3	+2.0	+5.1	3.3	pk7: 28
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 12 projected seasons	8.4	5,550	223	38	241	329	451	+20	+86	29.3	26.9
/162	664	27	5	+2.4	+10.2	3.5	pk7: 25
#3Doug Rader'67-'77 * career totals — 11 seasons, 5,832 PA	8.8	5,832	155	37	249*	320*	439*	+12	+51	29.6	26.6
/162	664	18	4	+1.4	+5.8	3.4	pk7: 24
#4Todd Frazierlast yr '20 career totals — 10 seasons, 4,909 PA	7.4	4,909	218	73	242	319	448	+18	+35	27.6	26.2
/162	664	29	10	+2.4	+4.7	3.7	pk7: 25
#5Bill Melton'68-'77 * career totals — 10 seasons, 4,557 PA	6.9	4,557	160	23	256*	338*	465*	-3	+48	26.5	26.2
/162	664	23	3	-0.4	+7.0	3.9	pk7: 26
#6Jonathan Schooplast yr '23 career totals — 10 seasons, 4,601 PA	6.9	4,601	173	15	253	293	428	+2	+60	19.4	18.8
/162	664	25	2	+0.3	+8.6	2.8	pk7: 18

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
Matt Chapman (OGILVIE)	34	—	—	—	—	239/332/412	173	288	6	2	23%	11%	112	+0.7	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Chris Young 2018 · MLB	34	48	52→60	46→55	52	243/320/416	173	282	19	8	21%	9%	107	+1.9	228/306/401	173	275	21	10	24%	9%	99	+1.3	165/250/354	189	182	28	9	29%	9%	70	-0.2
✕Fernando Tatis 2009 · MLB	34	47→52	52→55	47→45	48	241/305/410	169	278	20	7	21%	8%	101	+1.4	250/310/410	160	287	17	7	19%	7%	103	+1.5	273/327/425	151	304	13	6	14%	5%	113	+1.4
✕Nick Hundley 2018 · MLB	34	50→45	51→55	42→40	42	251/291/417	166	302	17	4	23%	5%	97	+1.1	232/279/394	162	302	19	5	29%	6%	87	+0.4	219/266/377	158	287	19	3	30%	6%	80	-0.1
✕José Cruz Jr. 2007 · MLB	33	47→50	52→65	47→50	61	240/339/421	181	278	19	8	21%	13%	116	+2.5	227/313/360	133	276	12	11	21%	11%	93	+0.8	225/299/351	126	277	11	14	22%	9%	86	+0.2
✕John Buck 2014 · MLB	34	42→52	51→60	43→30	48	221/293/376	155	261	21	3	24%	8%	89	+0.5	232/296/358	126	288	15	5	24%	7%	85	+0.2	229/290/300	70	298	6	0	24%	7%	68	-0.4
✕Dexter Fowler 2020 · MLB	34	45→55	53→55	45→55	57	231/330/403	172	275	20	9	24%	12%	109	+2.0	243/327/399	156	303	19	10	25%	10%	107	+1.8	236/319/400	165	293	26	6	28%	10%	106	+0.3
●Robinson Chirinos 2017 · MLB	33	43→46	56→60	40→50	49	229/308/427	198	263	24	3	23%	8%	107	+1.8	236/339/446	210	295	26	4	28%	10%	122	+3.0	229/342/435	206	294	26	2	29%	11%	120	+1.8
✕Curt Casali 2023 · MLB	34	44	49→50	48→30	52	219/317/366	147	293	17	4	29%	11%	96	+1.0	223/315/328	105	288	10	4	24%	10%	86	+0.3	214/312/278	65	277	4	0	22%	10%	74	-0.2
✕Rickie Weeks Jr. 2017 · MLB	34	47→57	53→55	50→55	50	240/324/415	175	297	21	10	26%	9%	109	+2.0	235/321/397	162	339	18	10	33%	10%	104	+1.6	205/311/338	133	377	13	5	43%	11%	88	+0.1
✕Stephen Vogt 2020 · MLB	35	47→57	56→60	47→40	49	246/306/449	203	282	22	6	22%	8%	110	+2.1	216/287/381	165	257	19	5	23%	9%	88	+0.4	179/259/328	149	204	20	1	24%	9%	66	-0.4
●Casey Blake 2008 · MLB	34	52→54	53→60	43→45	50	263/330/440	176	300	20	6	19%	8%	117	+2.6	265/332/440	175	312	19	4	21%	8%	118	+2.6	261/330/435	173	311	19	2	22%	8%	117	+2.5
✕Jason Kipnis 2021 · MLB	34	46→56	50→55	49→55	52	234/308/394	160	270	18	8	21%	9%	99	+1.2	234/309/390	156	295	18	7	26%	8%	98	+1.2	215/293/362	147	274	17	2	27%	8%	83	+0.0
✕Yan Gomes 2021 · MLB	34	48→53	53→55	50→40	46	247/308/419	172	293	20	5	23%	7%	104	+1.6	249/294/408	159	282	20	4	19%	5%	97	+1.1	249/292/399	150	282	19	2	19%	4%	94	+0.6
✕Zack Cozart 2019 · MLB	33	50→56	53→60	52→50	55	256/333/443	186	279	19	4	16%	9%	118	+2.7	219/290/349	130	248	13	6	17%	8%	82	-0.0	121/178/142	21	144	0	0	15%	5%	-8	-1.2
✕Michael McKenry 2017 · MLB	32	45→47	48→55	44→40	54	221/316/371	150	289	15	7	27%	11%	97	+1.1	201/287/318	117	283	11	6	30%	10%	74	-0.6	159/252/243	84	230	7	0	31%	10%	42	-1.3
✕Tom Wilson 2006 · MLB	35	50	50	48→30	49	248/315/411	163	306	17	4	24%	9%	105	+1.7	232/291/378	146	300	15	6	27%	7%	88	+0.5	221/277/354	134	291	14	2	28%	7%	74	-0.3
✕Ramon Castro 2010 · MLB	34	45	58→55	46→40	49	244/306/444	200	273	27	4	22%	8%	109	+2.0	247/311/445	198	288	27	6	24%	8%	112	+2.2	252/309/461	209	269	36	4	22%	8%	115	+0.6
✕Sean Rodríguez 2019 · MLB	34	40→50	53→60	47→50	48	210/300/379	170	280	22	6	32%	10%	93	+0.8	224/314/391	167	309	20	8	32%	10%	101	+1.4	208/313/386	178	292	22	3	34%	11%	99	+0.4
●Chris Iannetta 2017 · MLB	34	43→53	47→60	44→45	55	218/314/354	137	273	15	3	25%	12%	92	+0.7	228/324/405	177	285	21	2	26%	11%	107	+1.9	221/324/409	187	276	23	0	27%	12%	110	+1.0
✕Jorge Cantu 2016 · AAA	34	51→58	55	48→40	47	264/320/449	185	285	23	6	18%	6%	116	+2.5	226/282/349	123	273	13	6	22%	6%	78	-0.3	201/259/306	105	251	12	3	24%	6%	59	-0.9

Matt Chapman

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps