Yandy Díaz

1B Tampa Bay Rays

Age 35 R/R 6'0" / 235 lbs MLB HR -6%H +4% Svc 8.709 · Ctrl thru 2027

Overview

MLB40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2024	32	MLBTB	621	.281/.344/.414	14	0	114	0.6
2025	33	MLBTB	651	.300/.368/.482	25	3	130	2.0
2026	34	MLBTB	686	.293/.362/.446	22	3	124	2.3
2027PROJ	35	MLBTB	548	.284/.349/.444	18	3	123	1.7
2028PROJ	36	MLBTB	443	.271/.334/.416	14	2	112	0.1

Last 3 seasons WAR 4.9 · Next 2 projected WAR 1.8Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service8.709First FA2028

2027Ext / FA-deal$12M

Skills · percentileStatcast 2024-2026

Avg EV90

xwOBA83

Bat Spd65

Barrel%46

Speed22

Form.274/.335/.415 ▼ rolling true talent (60d window) · proj .281/.344/.446

CompsHunter Pence (age 35) · Jeff Conine (age 35) · Rondell White (age 34)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 126123vR 113

AVG/OBP/SLG.284 / .349 / .444

PA548

HR18

K%15

BB%8

BABIP305

ISO158

MLB%100%

Value2027 · WAR

trajectory · projected WAR by year

Tools · Recent Form · Splits · Contract

ToolsStatcast %ile · 2024-2026

CONTACT

xAVG

850.280

POWER

Barrel%

467.7

Avg EV

9092.0

Bat Spd

6572.9

SPEED

Sprint

2226.1

Recent Formrolling true talent ⓘ

K%15%·15% over 113 PA (60d) · proj 15%

BB%7%·7% over 113 PA (60d) · proj 8%

ISO.141▼.106 over 104 AB (60d) · proj .165

HR/BB3.5%▼2.3% over 88 BBE (60d) · proj 4.5%

BABIP.300·.279 over 86 BIP (60d) · proj .302

SLASH.274/.335/.415▼proj .281/.344/.446

Platoon Splits2024-2026 · 1388 PA

vs LHP

vs RHP

Edge

345

1043

—

wRC+

153

118

◀◀L

wOBA

394

343

◀L

ISO

231

132

◀◀L

BABIP

305

326

▶R

9.6%

16.1%

◀◀L

BB%

7.8%

8.1%

—

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

Status FA

MLB Debut 2017

Service 8.709 yrs.days

Team Control Through 2027

First FA Year 2028

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$12M	+1.6	$13M	+$0.3M
Totals		$12M		$13M	+$0.3M

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	22	A+	Carolina Mudcats	—	338	283	81	7	5	2	49	35	3	3	3	286	397	367	765	81	320	10	14	350	86	—	—	—	—	—	—
2015	23	AAA	Columbus Clippers	—	19	19	3	2	0	0	0	5	0	0	0	158	158	263	421	105	214	26	0	183	-15	—	—	—	—	—	—
2015	23	AA	Akron RubberDucks	—	564	476	150	13	5	7	78	65	3	8	7	315	415	408	822	92	350	12	14	371	125	—	—	—	—	—	—
2015	23	INTL	Scottsdale Scorpions	—	70	61	15	2	0	1	9	14	0	1	1	246	343	328	671	82	304	20	13	311	97	—	—	—	—	—	—
2016	24	AAA	Columbus Clippers	—	416	360	117	22	3	7	47	70	1	5	1	325	404	461	866	136	381	17	11	380	116	—	—	—	—	—	—
2016	24	AA	Akron RubberDucks	—	110	84	24	0	1	2	24	16	1	6	2	286	450	381	830	95	328	15	22	384	118	—	—	—	—	—	—
2016	24	INTL	Leones del Caracas	—	173	151	56	9	3	2	20	26	2	1	3	371	451	510	961	139	439	15	12	428	176	—	—	—	—	—	—
2017	25	MLB	Cleveland Indians	—	179	156	41	8	1	0	21	35	1	2	0	263	354	327	681	64	336	20	12	314	99	-0	+0	-1	+2	+4	0.5
2017	25	AAA	Columbus Clippers	—	374	309	108	17	1	5	60	56	1	1	2	350	457	460	916	110	412	15	16	410	139	—	—	—	—	—	—
2017	25	INTL	Leones del Caracas	—	45	30	10	2	1	1	11	1	3	0	0	333	545	567	1.112	233	310	2	24	479	211	—	—	—	—	—	—
2018	26	MLB	Cleveland Indians	—	120	109	34	5	2	1	11	19	0	0	0	312	375	422	797	110	371	16	9	358	127	+4	+0	-1	-10	+3	-0.4
2018	26	AAA	Columbus Clippers	—	426	348	102	24	0	3	70	75	2	2	3	293	414	388	802	95	360	18	16	364	109	—	—	—	—	—	—
2019	27	MLB	Tampa Bay Rays	—	347	307	82	20	1	14	35	61	1	2	2	267	344	476	820	208	288	18	10	358	128	+11	-0	-1	+1	+8	1.9
2020	28	MLB	Tampa Bay Rays	—	138	114	35	3	0	2	23	17	1	0	0	307	428	386	814	79	347	12	17	376	136	+6	+0	-3	+0	+3	0.7
2021	29	MLB	Tampa Bay Rays	—	541	465	119	20	1	13	69	85	3	1	1	256	356	387	743	131	286	16	13	336	110	+6	-0	-3	-10	+12	0.5
2022	30	MLB	Tampa Bay Rays	—	558	473	140	33	0	9	78	60	6	3	3	296	402	423	825	127	323	11	14	374	137	+25	-1	-5	+2	+12	3.4
2023	31	MLB	Tampa Bay Rays	—	600	525	173	35	0	22	65	94	8	0	1	330	411	522	933	192	367	16	11	412	165	+46	-0	-7	-11	+13	4.2
2024	32	MLB	Tampa Bay Rays	—	621	563	158	31	1	14	50	95	4	0	0	281	344	414	757	133	314	15	8	338	114	+11	+0	-7	-11	+13	0.6
2025	33	MLB	Tampa Bay Rays	—	651	583	175	29	1	25	57	92	6	3	1	300	368	482	850	182	318	14	9	374	130	+23	+0	-7	-12	+14	2.0
2026	34	MLB	Tampa Bay Rays	—	686	614	180	26	1	22	56	98	10	3	1	293	362	446	808	153	316	14	8	358	124	+20	+0	+0	-12	+15	2.3
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	4441	3909	1137	210	8	122	465	656	40	14	9	291	372	442	814	151	321	15	10	363	132	+168	-1	-35	-59	+96	17.3
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2014	22	A+ MLE	Carolina Mudcats	—	338	301	78	6	4	2	31	52	3	3	3	259	334	326	660	66	301	15	9	295	86	—	—	—	—	—	—
2015	23	AAA MLE	Columbus Clippers	—	19	19	3	2	0	0	0	6	0	0	0	158	158	263	421	105	191	32	0	146	-15	—	—	—	—	—	—
2015	23	AA MLE	Akron RubberDucks	—	564	495	160	12	5	7	59	78	3	8	7	323	399	410	809	87	362	14	10	353	125	—	—	—	—	—	—
2016	24	AAA MLE	Columbus Clippers	—	416	377	115	19	4	6	30	87	1	5	1	305	358	424	782	119	368	21	7	340	116	—	—	—	—	—	—
2016	24	AA MLE	Akron RubberDucks	—	110	90	25	0	1	2	18	20	1	6	2	278	404	367	770	89	326	18	16	342	118	—	—	—	—	—	—
2017	25	MLB MLE	Cleveland Indians	—	179	156	41	8	1	0	21	35	1	2	0	263	354	327	681	64	336	20	12	314	99	-0	+0	-1	+2	+4	0.5
2017	25	AAA MLE	Columbus Clippers	—	374	331	115	16	1	4	38	68	1	1	2	347	416	438	854	91	419	18	10	374	139	—	—	—	—	—	—
2018	26	MLB MLE	Cleveland Indians	—	120	110	34	5	2	1	10	19	0	0	0	309	367	418	785	109	371	16	8	355	127	+4	+0	-1	-10	+3	-0.4
2018	26	AAA MLE	Columbus Clippers	—	426	370	105	23	0	2	48	89	2	2	3	284	369	362	731	78	353	21	11	329	109	—	—	—	—	—	—
2019	27	MLB MLE	Tampa Bay Rays	—	347	308	81	20	1	14	34	61	1	2	2	263	338	471	809	208	288	18	10	357	128	+11	-0	-1	+1	+8	1.9
2020	28	MLB MLE	Tampa Bay Rays	—	138	115	34	3	0	2	22	17	1	0	0	296	413	374	787	78	347	12	16	369	136	+6	+0	-3	+0	+3	0.7
2021	29	MLB MLE	Tampa Bay Rays	—	541	469	117	20	1	13	65	85	3	1	1	249	345	380	724	130	286	16	12	330	110	+6	-0	-3	-10	+12	0.5
2022	30	MLB MLE	Tampa Bay Rays	—	558	475	139	34	0	9	76	60	6	3	3	293	397	421	818	128	323	11	14	371	137	+25	-1	-5	+2	+12	3.4
2023	31	MLB MLE	Tampa Bay Rays	—	600	525	173	36	0	23	65	94	8	0	1	330	411	530	941	200	367	16	11	412	165	+46	-0	-7	-11	+13	4.2
2024	32	MLB MLE	Tampa Bay Rays	—	621	563	157	32	1	14	50	95	4	0	0	279	342	414	756	135	314	15	8	337	114	+11	+0	-7	-11	+13	0.6
2025	33	MLB MLE	Tampa Bay Rays	—	651	587	170	27	1	24	53	92	6	3	1	290	354	462	816	172	318	14	8	361	130	+23	+0	-7	-12	+14	2.0
2026	34	MLB MLE	Tampa Bay Rays	—	686	620	179	27	1	23	50	98	10	3	1	289	351	447	798	158	316	14	7	352	124	+20	+0	+0	-12	+15	2.3
2014	22	A+ MLE	Carolina Mudcats	—	338	301	78	6	4	2	31	52	3	3	3	259	334	326	660	66	306	15	9	295	86	—	—	—	—	—	—
2014	22	A+ RAW	Carolina Mudcats	—	338	283	81	7	5	2	49	35	3	3	3	286	397	367	765	81	320	10	14	350	86	—	—	—	—	—	—
2015	23	AA MLE	3 teams	—	653	577	182	15	5	8	66	94	3	9	8	315	389	400	789	85	363	14	10	346	120	—	—	—	—	—	—
2015	23	AA RAW	3 teams	—	653	556	168	17	5	8	87	84	3	9	8	302	399	394	793	92	342	13	13	359	120	—	—	—	—	—	—
2016	24	AAA MLE	3 teams	—	699	622	187	26	6	10	64	142	4	12	6	301	370	410	780	109	371	20	9	340	117	—	—	—	—	—	—
2016	24	AAA RAW	3 teams	—	699	595	197	31	7	11	91	112	4	12	6	331	423	462	885	131	388	16	13	393	117	—	—	—	—	—	—
2017	25	AAA MLE	3 teams	—	598	523	169	25	3	4	64	111	5	3	2	323	402	405	807	82	400	19	11	355	126	—	—	—	—	—	—
2017	25	AAA RAW	3 teams	—	598	495	159	27	3	6	92	92	5	3	2	321	432	424	857	103	382	15	15	387	126	—	—	—	—	—	—
2018	26	AAA MLE	2 teams	—	546	480	138	28	2	3	58	108	2	2	3	287	367	373	740	85	361	20	11	335	113	—	—	—	—	—	—
2018	26	AAA RAW	2 teams	—	546	457	136	29	2	4	81	94	2	2	3	298	406	396	802	98	363	17	15	363	113	—	—	—	—	—	—
2019	27	MLB MLE	Tampa Bay Rays	—	347	308	81	20	1	14	34	61	1	2	2	263	338	471	809	208	283	18	10	357	128	+11	-0	-1	+1	+8	1.9
2019	27	MLB RAW	Tampa Bay Rays	—	347	307	82	20	1	14	35	61	1	2	2	267	344	476	820	208	288	18	10	358	128	+11	-0	-1	+1	+8	1.9
2020	28	MLB MLE	Tampa Bay Rays	—	138	115	34	3	0	2	22	17	1	0	0	296	413	374	787	78	333	12	16	369	136	+6	+0	-3	+0	+3	0.7
2020	28	MLB RAW	Tampa Bay Rays	—	138	114	35	3	0	2	23	17	1	0	0	307	428	386	814	79	347	12	17	376	136	+6	+0	-3	+0	+3	0.7
2021	29	MLB MLE	Tampa Bay Rays	—	541	469	117	20	1	13	65	85	3	1	1	249	345	380	724	130	277	16	12	330	110	+6	-0	-3	-10	+12	0.5
2021	29	MLB RAW	Tampa Bay Rays	—	541	465	119	20	1	13	69	85	3	1	1	256	356	387	743	131	286	16	13	336	110	+6	-0	-3	-10	+12	0.5
2022	30	MLB MLE	Tampa Bay Rays	—	558	475	139	34	0	9	76	60	6	3	3	293	397	421	818	128	319	11	14	371	137	+25	-1	-5	+2	+12	3.4
2022	30	MLB RAW	Tampa Bay Rays	—	558	473	140	33	0	9	78	60	6	3	3	296	402	423	825	127	323	11	14	374	137	+25	-1	-5	+2	+12	3.4
2023	31	MLB MLE	Tampa Bay Rays	—	600	525	173	36	0	23	65	94	8	0	1	330	411	530	941	200	366	16	11	412	165	+46	-0	-7	-11	+13	4.2
2023	31	MLB RAW	Tampa Bay Rays	—	600	525	173	35	0	22	65	94	8	0	1	330	411	522	933	192	367	16	11	412	165	+46	-0	-7	-11	+13	4.2
2024	32	MLB MLE	Tampa Bay Rays	—	621	563	157	32	1	14	50	95	4	0	0	279	342	414	756	135	312	15	8	337	114	+11	+0	-7	-11	+13	0.6
2024	32	MLB RAW	Tampa Bay Rays	—	621	563	158	31	1	14	50	95	4	0	0	281	344	414	757	133	314	15	8	338	114	+11	+0	-7	-11	+13	0.6
2025	33	MLB MLE	Tampa Bay Rays	—	651	587	170	27	1	24	53	92	6	3	1	290	354	462	816	172	307	14	8	361	130	+23	+0	-7	-12	+14	2.0
2025	33	MLB RAW	Tampa Bay Rays	—	651	583	175	29	1	25	57	92	6	3	1	300	368	482	850	182	318	14	9	374	130	+23	+0	-7	-12	+14	2.0
2026	34	MLB MLE	Tampa Bay Rays	—	686	620	179	27	1	23	50	98	10	3	1	289	351	447	798	158	309	14	7	352	124	+20	+0	+0	-12	+15	2.3
2026	34	MLB RAW	Tampa Bay Rays	—	686	614	180	26	1	22	56	98	10	3	1	293	362	446	808	153	316	14	8	358	124	+20	+0	+0	-12	+15	2.3
2014	22	A+ MLE	Carolina Mudcats	—	338	301	78	6	4	2	31	52	3	3	3	259	334	326	660	66	301	15	9	295	86	—	—	—	—	—	—
2015	23	AA MLE	3 teams	—	653	579	182	15	5	8	66	94	3	9	8	314	389	399	788	85	356	14	10	346	120	—	—	—	—	—	—
2016	24	AAA MLE	3 teams	—	699	626	187	26	6	10	64	142	4	12	6	299	370	407	777	109	359	20	9	340	117	—	—	—	—	—	—
2017	25	AAA MLE	3 teams	—	598	524	169	25	3	4	64	111	5	3	2	323	402	405	807	82	392	19	11	355	126	—	—	—	—	—	—
2018	26	AAA MLE	2 teams	—	546	481	138	28	2	3	58	108	2	2	3	287	367	372	739	85	357	20	11	335	113	—	—	—	—	—	—
2019	27	MLB MLE	Tampa Bay Rays	—	347	309	81	20	1	14	34	61	1	2	2	262	338	469	807	207	288	18	10	357	128	—	—	—	—	—	—
2020	28	MLB MLE	Tampa Bay Rays	—	138	114	34	3	0	2	22	17	1	0	0	298	413	377	790	79	347	12	16	369	136	—	—	—	—	—	—
2021	29	MLB MLE	Tampa Bay Rays	—	541	468	117	20	1	13	65	85	3	1	1	250	345	380	725	130	286	16	12	330	110	—	—	—	—	—	—
2022	30	MLB MLE	Tampa Bay Rays	—	558	471	139	34	0	9	76	60	6	3	3	295	397	425	821	130	323	11	14	371	137	—	—	—	—	—	—
2023	31	MLB MLE	Tampa Bay Rays	—	600	522	173	36	0	23	65	94	8	0	1	331	411	533	944	201	367	16	11	412	165	—	—	—	—	—	—
2024	32	MLB MLE	Tampa Bay Rays	—	621	562	157	32	1	14	50	95	4	0	0	279	342	415	757	135	314	15	8	337	114	—	—	—	—	—	—
2025	33	MLB MLE	Tampa Bay Rays	—	651	587	170	27	1	24	53	92	6	3	1	290	354	462	816	172	318	14	8	361	130	—	—	—	—	—	—
2026	34	MLB MLE	Tampa Bay Rays	—	686	621	179	27	1	23	50	98	10	3	1	288	351	446	798	158	316	14	7	352	124	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	32	MLB MLE	Tampa Bay Rays	—	621	562	157	32	1	14	50	95	4	0	0	279	342	415	757	135	314	15	8	337	114	—	—	—	—	—	—
2025	33	MLB MLE	Tampa Bay Rays	—	651	587	170	27	1	24	53	92	6	3	1	290	354	462	816	172	318	14	8	361	130	—	—	—	—	—	—
2026	34	MLB MLE	Tampa Bay Rays	—	686	621	179	27	1	23	50	98	10	3	1	288	351	446	798	158	316	14	7	352	124	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	35	MLB MLE	TB	100%	606	546	154	25	1	21	48	89	7	4	1	282	347	446	793	164	302	15	8	351	123	+17	+0	-1	-11	+13	1.6
▸ 2027 — real world (MLB)
2027	35	MLB CTX	TB	100%	548	494	139	22	1	18	43	81	6	3	1	284	349	444	793	161	305	15	8	351	123	+15	+0	-1	-10	+12	1.7
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	36	MLB MLE	TB	100%	443	401	108	17	1	14	34	69	5	2	1	269	333	418	750	149	292	16	8	333	112	+6	+0	-7	-8	+10	0.1
2028	36	MLB CTX	TB	100%	443	401	109	16	1	14	34	69	5	2	1	271	334	416	751	146	295	16	8	334	112	+6	+0	-7	-8	+10	0.1
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	4884	4310	1246	226	9	136	499	725	45	16	10	289	369	440	809	151	319	15	10	360	123	+174	-1	-42	-67	+106	17.3
/ 162G	—	A+C		—	650	574	165	30	1	18	66	96	6	2	1	287	367	437	804	150	317	15	10	360	123	+23	-0	-6	-9	+14	2.3
Career	—	MLE		—	7967	7060	2051	339	27	182	775	1259	67	46	33	291	366	424	790	133	330	16	10	350	123	+189	-1	-43	-77	+118	19.0
/ 162G	—	MLE		—	650	576	168	28	2	15	63	103	5	4	3	292	366	425	791	134	331	16	10	350	123	+23	-0	-5	-9	+14	2.3

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.

2018 AAAColumbus Clippers · 426 PAwOBA .358 → .329 (−28)K% 17.6% → 20.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	17.61%	÷1.017	×1.200	—	—	20.78%
BB% (unintentional) /PA	15.73%	÷0.972	×0.690	—	×1.003	11.20%
HBP% /PA	0.47%	—	×0.940	—	×1.003	0.44%
Singles /BIP	27.47%	÷0.974	×0.930	×1.050	×1.003	27.62%
Doubles /BIP	8.79%	÷0.958	×0.830	×1.050	×1.003	8.02%
Triples /BIP	0.00%	÷0.900	×0.970	×1.050	×1.003	0.00%
Home runs /BIP	1.10%	÷1.135	×0.840	—	×1.003	0.82%

Factors used: league row IL. Park (home-season share, regressed): HR 127, 1B 95, 2B 92, 3B 80, K 103, BB 94 — applied at half strength. Age: age 27 vs a level average of 27.2 → +0.3% on BB/HBP/hit rates (0.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .358 → .329; ISO .095 → .087; BABIP .360 → .353. 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).

2017 AAAColumbus Clippers · 374 PAwOBA .402 → .374 (−28)K% 15.0% → 18.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.97%	÷0.989	×1.200	—	—	18.17%
BB% (unintentional) /PA	15.78%	÷1.089	×0.690	—	×1.018	10.18%
HBP% /PA	0.27%	—	×0.940	—	×1.018	0.26%
Singles /BIP	33.60%	÷0.945	×0.930	×1.050	×1.018	35.33%
Doubles /BIP	6.72%	÷1.018	×0.830	×1.050	×1.018	5.86%
Triples /BIP	0.40%	÷0.900	×0.970	×1.050	×1.018	0.46%
Home runs /BIP	1.98%	÷1.231	×0.840	—	×1.018	1.37%

Factors used: league row IL. Park (home-season share, regressed): HR 146, 1B 89, 2B 104, 3B 80, K 98, BB 118 — applied at half strength. Age: age 26 vs a level average of 27.2 → +1.8% on BB/HBP/hit rates (1.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .402 → .374; ISO .110 → .094; BABIP .412 → .419. 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 AAAColumbus Clippers · 416 PAwOBA .375 → .340 (−35)K% 16.8% → 20.9%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.83%	÷0.965	×1.200	—	—	20.92%
BB% (unintentional) /PA	11.06%	÷1.077	×0.690	—	×1.033	7.32%
HBP% /PA	0.24%	—	×0.940	—	×1.033	0.23%
Singles /BIP	29.31%	÷1.018	×0.930	×1.050	×1.033	29.06%
Doubles /BIP	7.59%	÷1.043	×0.830	×1.050	×1.033	6.55%
Triples /BIP	1.03%	÷0.901	×0.970	×1.050	×1.033	1.21%
Home runs /BIP	2.41%	÷1.113	×0.840	—	×1.033	1.88%

Factors used: league row IL. Park (home-season share, regressed): HR 123, 1B 104, 2B 109, 3B 80, K 93, BB 115 — applied at half strength. Age: age 25 vs a level average of 27.2 → +3.3% on BB/HBP/hit rates (2.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .375 → .340; ISO .136 → .121; BABIP .381 → .368. 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 AAAkron RubberDucks · 110 PAwOBA .379 → .342 (−37)K% 14.5% → 17.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	14.55%	÷0.997	×1.220	—	—	17.80%
BB% (unintentional) /PA	21.82%	÷0.950	×0.700	—	×0.998	16.05%
HBP% /PA	0.91%	—	×0.850	—	×0.998	0.77%
Singles /BIP	30.88%	÷1.014	×0.970	×1.050	×0.998	30.97%
Doubles /BIP	0.00%	÷1.015	×0.860	×1.050	×0.998	0.00%
Triples /BIP	1.47%	÷1.150	×0.950	×1.050	×0.998	1.27%
Home runs /BIP	2.94%	÷0.949	×0.780	—	×0.998	2.41%

Factors used: league row EL. Park (home-season share, regressed): HR 90, 1B 103, 2B 103, 3B 130, K 99, BB 90 — applied at half strength. Age: age 25 vs a level average of 24.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.050.

Result: wOBA .379 → .342; ISO .095 → .084; BABIP .328 → .326. 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 AAAkron RubberDucks · 564 PAwOBA .364 → .353 (−11)K% 11.5% → 13.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.52%	÷1.019	×1.220	—	—	13.80%
BB% (unintentional) /PA	13.83%	÷0.934	×0.700	—	×1.012	10.49%
HBP% /PA	0.53%	—	×0.850	—	×1.012	0.46%
Singles /BIP	30.41%	÷0.971	×0.970	×1.046	×1.012	32.15%
Doubles /BIP	3.16%	÷1.053	×0.860	×1.046	×1.012	2.74%
Triples /BIP	1.22%	÷1.150	×0.950	×1.046	×1.012	1.06%
Home runs /BIP	1.70%	÷0.842	×0.780	—	×1.012	1.60%

Factors used: league row EL. Park (home-season share, regressed): HR 68, 1B 94, 2B 111, 3B 130, K 104, BB 87 — applied at half strength. Age: age 24 vs a level average of 24.8 → +1.2% on BB/HBP/hit rates (0.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.046.

Result: wOBA .364 → .353; ISO .092 → .086; BABIP .350 → .362. 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 AAAColumbus Clippers · 19 PAwOBA .177 → .146 (−31)K% 26.3% → 31.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	26.32%	÷1.009	×1.200	—	—	31.28%
BB% (unintentional) /PA	0.00%	÷1.068	×0.690	—	×1.048	0.00%
HBP% /PA	0.00%	—	×0.940	—	×1.048	0.00%
Singles /BIP	7.14%	÷1.005	×0.930	—	×1.048	6.93%
Doubles /BIP	14.29%	÷1.019	×0.830	—	×1.048	12.19%
Triples /BIP	0.00%	÷0.900	×0.970	—	×1.048	0.00%
Home runs /BIP	0.00%	÷1.217	×0.840	—	×1.048	0.00%

Factors used: league row IL. Park (home-season share, regressed): HR 143, 1B 101, 2B 104, 3B 80, K 102, BB 114 — 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).

Result: wOBA .177 → .146; ISO .105 → .084; BABIP .214 → .191. 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 A+Carolina Mudcats · 338 PAwOBA .342 → .295 (−47)K% 10.4% → 15.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.36%	÷0.961	×1.420	—	—	15.31%
BB% (unintentional) /PA	14.20%	÷0.966	×0.630	—	×0.998	9.24%
HBP% /PA	0.89%	—	×0.860	—	×0.998	0.76%
Singles /BIP	27.02%	÷1.011	×0.940	×1.040	×0.998	26.07%
Doubles /BIP	2.82%	÷0.983	×0.830	×1.040	×0.998	2.47%
Triples /BIP	2.02%	÷1.150	×0.800	×1.040	×0.998	1.46%
Home runs /BIP	0.81%	÷0.869	×0.900	—	×0.998	0.83%

Factors used: league row CAR. Park (home-season share, regressed): HR 74, 1B 102, 2B 97, 3B 130, K 92, BB 93 — applied at half strength. Age: age 23 vs a level average of 22.8 → −0.2% on BB/HBP/hit rates (0.2 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.040.

Result: wOBA .342 → .295; ISO .081 → .070; BABIP .320 → .301. 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).

Not traceable per stint: some stints lack the counting stats the component translation needs. Level-wide factors that apply instead — .

PLAYER · career totals + /162 games	162s	PA	HR	SB	AVG	OBP	SLG	POS	FLD	WAR	JAWS
#1John Kruk'86-'95 * career totals — 10 seasons, 4,603 PA	6.9	4,603	100	58	296*	394*	470*	-70	-31	24.6	23.2
/162	664	14	8	-10.1	-4.4	3.5	pk7: 22
#2Justin Morneaulast yr '16 career totals — 14 seasons, 6,392 PA	9.6	6,392	247	5	281	348	481	-112	-75	22.4	21.7
/162	664	26	1	-11.6	-7.8	2.3	pk7: 21
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 12 projected seasons	8.2	5,432	155	19	290	364	443	-100	-57	22.8	21.1
/162	664	19	2	-12.2	-7.0	2.8	pk7: 19
#3Brandon Beltlast yr '23 career totals — 13 seasons, 5,483 PA	8.3	5,483	194	47	261	357	460	-96	-62	19.4	18.2
/162	664	23	6	-11.7	-7.5	2.3	pk7: 17
#4Bruce Bochte'74-'86 * career totals — 12 seasons, 5,994 PA	9.0	5,994	100	43	279*	361*	422*	-94	-23	18.3	17.2
/162	664	11	5	-10.4	-2.6	2.0	pk7: 16
#5Billy Butlerlast yr '16 career totals — 10 seasons, 5,686 PA	8.6	5,686	147	5	290	354	441	-102	-46	17.1	16.9
/162	664	17	1	-11.9	-5.4	2.0	pk7: 17
#6Carlos May'68-'77 * career totals — 10 seasons, 4,723 PA	7.1	4,723	90	85	277*	360*	436*	-71	-30	10.9	11.8
/162	664	13	12	-10.0	-4.1	1.5	pk7: 13

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
Yandy Díaz (OGILVIE)	35	—	—	—	—	279/344/442	163	302	19	3	15%	8%	123	+2.8	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
●Hunter Pence 2018 · MLB	35	54→63	50→60	42→50	50	272/330/428	156	316	18	4	20%	8%	114	+2.3	254/305/409	155	306	19	8	23%	7%	100	+1.3	256/303/412	156	311	19	9	24%	6%	99	+0.7
●Jeff Conine 2001 · MLB	35	54→62	50→55	44→50	53	271/330/426	155	285	17	7	12%	8%	114	+2.3	282/338/434	152	303	17	9	13%	8%	118	+2.7	280/336/437	156	298	17	9	12%	7%	119	+2.6
✕Rondell White 2006 · MLB	34	56→62	53→60	42→40	48	286/336/467	180	304	21	3	14%	6%	125	+3.2	246/287/384	138	269	15	4	15%	4%	88	+0.5	226/260/342	116	248	13	1	15%	3%	68	-0.5
●Steve Pearce 2018 · MLB	35	48→58	54→60	38→50	51	248/318/428	181	275	22	3	20%	8%	110	+2.1	244/325/413	169	287	20	4	21%	9%	109	+2.0	237/331/406	169	278	20	0	22%	10%	111	+0.7
✕Sandy Madera 2016 · AAA	35	56→66	48→55	40→45	48	278/330/422	143	308	16	3	16%	6%	112	+2.2	270/317/400	130	293	15	5	13%	6%	103	+1.5	281/325/406	125	300	15	3	12%	5%	106	+1.2
✕Brian Jordan 2003 · MLB	36	55→56	54→55	45	47	280/327/463	184	301	22	5	16%	6%	122	+2.9	263/317/408	145	292	16	5	15%	7%	105	+1.7	256/311/372	117	287	12	4	15%	6%	96	+0.4
✕Jason Michaels 2011 · MLB	35	47→55	53→50	43→45	50	241/307/423	182	268	20	5	19%	8%	105	+1.7	217/273/345	128	253	13	6	20%	7%	74	-0.6	183/232/266	82	220	7	2	20%	6%	38	-0.8
●Yuli Gurriel 2018 · MLB	34	55→63	52→60	43→50	46	275/311/446	171	286	19	5	12%	4%	112	+2.2	279/319/458	179	289	21	5	12%	5%	118	+2.6	287/343/470	184	293	22	3	11%	7%	130	+2.8
●Raul Ibanez 2007 · MLB	35	55→60	55→60	42→45	52	281/337/471	190	300	23	5	16%	8%	126	+3.2	281/338/481	200	307	23	3	17%	8%	129	+3.4	280/340/489	209	306	25	2	17%	8%	133	+3.9
●Tino Martinez 2004 · MLB	36	52→53	53→55	42→40	54	265/329/436	172	274	21	3	13%	8%	116	+2.5	253/334/437	184	262	24	5	14%	10%	118	+2.6	247/335/440	193	249	27	3	14%	10%	120	+2.1
●Casey Blake 2009 · MLB	35	53→54	53→60	45	49	267/330/445	177	306	20	5	20%	7%	118	+2.7	259/330/425	166	312	18	3	22%	9%	114	+2.3	254/331/412	158	312	16	2	23%	9%	112	+1.7
✕Juan Rivera 2012 · MLB	34	52→61	49→60	42→40	52	260/317/408	147	276	17	5	14%	7%	105	+1.7	246/293/368	122	261	14	5	12%	5%	86	+0.3	242/281/351	109	252	13	2	10%	4%	76	-0.2
●Michel Abreu 2012 · AAA	33	52→62	52→55	43→30	49	264/321/431	167	295	21	2	18%	7%	112	+2.2	275/342/457	182	319	23	4	20%	9%	125	+3.2	293/364/485	192	339	25	0	20%	9%	141	+3.2
✕Ryan Raburn 2016 · MLB	35	49→58	54→60	47→50	50	251/322/437	186	296	21	3	23%	8%	114	+2.3	234/311/398	164	318	18	4	30%	9%	100	+1.3	216/298/370	154	306	18	1	33%	10%	91	+0.3
✕Jhonny Peralta 2017 · MLB	35	52→62	50→55	38→40	50	263/320/418	155	294	18	3	18%	7%	108	+1.9	239/293/372	133	285	15	6	21%	7%	87	+0.4	196/230/249	54	251	7	0	25%	4%	34	-0.6
●Willis Otáñez 2008 · AAA	35	57→62	52→55	45→40	49	289/339/450	160	316	20	3	16%	7%	122	+3.0	264/322/396	132	290	14	4	14%	7%	103	+1.6	285/345/434	149	308	18	0	13%	7%	122	+2.0
●Todd Zeile 2001 · MLB	35	52→55	54→50	41→30	59	264/345/456	192	277	21	5	14%	11%	126	+3.2	261/344/406	145	293	16	3	16%	11%	114	+2.4	256/342/389	133	288	15	1	16%	11%	110	+1.8
✕Mario Valenzuela 2013 · AAA	36	52→57	52→55	40	45	264/314/427	163	299	21	4	20%	6%	108	+1.9	238/285/352	114	308	13	5	26%	6%	80	-0.2	225/269/314	89	292	11	2	26%	5%	62	-0.5
✕Eric Karros 2003 · MLB	35	50→55	50→55	43→40	50	254/314/405	151	279	18	5	16%	8%	103	+1.6	257/311/404	147	278	17	5	14%	7%	101	+1.4	262/313/411	149	278	18	3	13%	7%	104	+0.7
✕Mark DeRosa 2010 · MLB	35	52→61	53→55	46→40	53	265/342/441	176	301	21	5	19%	10%	122	+2.9	241/314/361	120	286	13	6	20%	9%	93	+0.8	228/313/326	97	280	9	2	20%	10%	85	+0.1

Yandy Díaz

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps