Nick Madrigal

3B Durham Bulls

Age 30 R/R 5'7" / 175 lbs AAA HR -6%H +4% Svc 2.161 · Ctrl thru 2030 ILForty Man (40M)

Overview

AAA40M40-MANPre-ArbFA 2031

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2023	26	AAACHC	70	.424/.514/.678	1	0	189	—	.377/.457/.607
2023	26	MLBCHC	294	.263/.311/.352	2	10	85	1.2
2024	27	MLBCHC	94	.221/.280/.256	0	1	53	-0.6
2026	29	AAATB	379	.299/.392/.389	4	11	95	—	.275/.346/.347
2026	29	MLBTB	63	.278/.371/.315	0	1	99	0.3
2027PROJ45% MLB	30	MLBTB	94	.259/.331/.335	1	2	90	0.2
2028PROJ53% MLB	31	MLBTB	135	.254/.326/.323	1	2	86	0.0

Last 3 seasons WAR 0.8 · Next 2 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2030Service2.161First FA2031

2027Pre-Arb$0.8M2028Arb-1$0.4M2029Ext$2M2030Ext$2M

Skills · percentileStatcast 2023-2026

Speed37

xwOBA36

Avg EV13

Barrel%10

Bat Spd10

CompsMichael Spidale (age 30) · Norris Hopper (age 30) · Heber Gomez (age 30)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 9390vR 81

AVG/OBP/SLG.259 / .331 / .335

PA94

HR1

K%12

BB%7

BABIP288

ISO71

MLB%45%

Value2027 · WAR

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

Tools · Splits · ContractCON 76 / POW 32

Toolsscout grade · Statcast %ile · 2023-2026

CONTACT76

xAVG

650.264

POWER32

Barrel%

100.5

Avg EV

1386.2

Bat Spd

1065.4

EYE55

scouting grade only

SPEED49

Sprint

3726.9

Platoon Splits2024 · 94 PA

vs LHP

vs RHP

Edge

—

wRC+

◀◀L

wOBA

284

228

◀◀L

ISO

069

018

◀◀L

BABIP

280

235

◀◀L

12.1%

9.8%

▶▶R

BB%

6.1%

3.3%

◀◀L

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

Status Pre-Arb

MLB Debut 2020

Service 2.161 yrs.days

Team Control Through 2030

First FA Year 2031

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Pre-Arb	$0.8M	+0.2	$0.9M	+$0.1M
2028	Arb-1	$0.4M	—	—	—
2029	Ext	beyond projection horizon · no WAR estimate
2030	Ext	beyond projection horizon · no WAR estimate
Totals		$0.8M		$0.9M	+$0.1M

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)
2018	21	A+	Winston-Salem Dash	—	107	98	30	4	0	0	5	5	3	6	3	306	358	347	705	41	319	5	5	321	93	—	—	—	—	—	—
2018	21	A	Kannapolis Intimidators	—	49	44	15	3	0	0	1	0	1	2	2	341	370	409	779	68	319	0	2	329	108	—	—	—	—	—	—
2018	21	ROK	ACL White Sox	—	17	13	2	0	0	0	1	0	3	0	1	154	353	154	507		154	0	6	277	26	—	—	—	—	—	—
2019	22	AAA	Charlotte Knights	—	134	118	39	6	1	1	13	5	1	4	3	331	402	424	825	93	336	4	10	368	99	—	—	—	—	—	—
2019	22	AA	Birmingham Barons	—	180	164	56	11	2	1	14	5	2	14	6	341	400	451	851	110	348	3	8	382	109	—	—	—	—	—	—
2019	22	A+	Winston-Salem Dash	—	218	191	52	10	2	2	17	6	6	17	4	272	350	377	727	105	269	3	8	325	70	—	—	—	—	—	—
2020	23	MLB	Chicago White Sox	—	109	103	35	3	0	0	4	7	2	2	1	340	376	369	745	29	365	6	4	340	116	+2	+0	+2	+0	+2	0.7
2021	24	MLB	Chicago White Sox	—	215	200	61	10	4	2	11	17	3	1	2	305	350	425	775	120	324	8	5	344	119	+5	-1	+1	+1	+5	1.1
2022	25	MLB	Chicago Cubs	—	228	209	52	7	0	0	14	27	3	3	1	249	305	282	588	33	286	12	6	271	68	-9	+0	+2	+1	+5	-0.1
2022	25	AAA	Iowa Cubs	—	43	39	12	1	0	0	3	5	1	0	0	308	372	333	705	26	353	12	7	327	85	—	—	—	—	—	—
2023	26	MLB	Chicago Cubs	—	294	270	71	16	1	2	10	24	9	10	2	263	311	352	663	89	283	8	3	296	85	-5	+1	+8	+1	+6	1.2
2023	26	AAA	Iowa Cubs	—	70	59	25	6	3	1	8	7	3	0	0	424	514	678	1.192	254	471	10	11	514	189	—	—	—	—	—	—
2024	27	MLB	Chicago Cubs	—	94	86	19	3	0	0	4	10	3	1	0	221	280	256	535	35	250	11	4	248	53	-5	+0	-3	+0	+2	-0.6
2026	29	MLB	Tampa Bay Rays	—	63	54	15	2	0	0	7	3	1	1	1	278	371	315	686	37	288	5	11	317	99	-0	-0	+0	+2	+1	0.3
2026	29	AAA	Durham Bulls	—	379	321	96	13	2	4	40	32	9	11	3	299	392	389	781	90	315	8	11	349	95	—	—	—	—	—	—
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1003	922	253	41	5	4	50	88	21	18	7	274	326	343	669	68	299	9	5	302	91	-11	+1	+9	+5	+22	2.6
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2018	21	A+ MLE	Winston-Salem Dash	—	107	100	31	4	0	0	3	8	3	6	3	310	349	350	699	40	325	7	3	306	93	—	—	—	—	—	—
2018	21	A MLE	Kannapolis Intimidators	—	49	44	15	3	0	0	1	0	1	2	2	341	370	409	779	68	293	0	2	328	108	—	—	—	—	—	—
2018	21	ROK MLE	ACL White Sox	—	17	13	2	0	0	0	1	0	3	0	1	154	353	154	507		124	0	6	207	26	—	—	—	—	—	—
2019	22	AAA MLE	Charlotte Knights	—	134	122	36	6	1	1	9	6	1	4	3	295	348	385	734	90	297	4	7	315	99	—	—	—	—	—	—
2019	22	AA MLE	Birmingham Barons	—	180	167	52	10	2	1	11	6	2	14	6	311	361	413	774	102	314	3	6	330	109	—	—	—	—	—	—
2019	22	A+ MLE	Winston-Salem Dash	—	218	198	47	8	1	2	10	9	6	17	4	237	294	318	613	81	234	4	5	272	70	—	—	—	—	—	—
2020	23	MLB MLE	Chicago White Sox	—	109	103	35	3	0	0	4	7	2	2	1	340	376	369	745	29	365	6	4	340	116	+2	+0	+2	+0	+2	0.7
2021	24	MLB MLE	Chicago White Sox	—	215	200	61	10	4	2	11	17	3	1	2	305	350	425	775	120	324	8	5	343	119	+5	-1	+1	+1	+5	1.1
2022	25	MLB MLE	Chicago Cubs	—	228	209	52	7	0	0	14	27	3	3	1	249	305	282	588	33	286	12	6	269	68	-9	+0	+2	+1	+5	-0.1
2022	25	AAA MLE	Iowa Cubs	—	43	40	12	1	0	0	2	6	1	0	0	300	349	325	674	25	342	14	5	294	85	—	—	—	—	—	—
2023	26	MLB MLE	Chicago Cubs	—	294	270	71	16	1	2	10	24	9	10	2	263	311	352	663	89	283	8	3	293	85	-5	+1	+8	+1	+6	1.2
2023	26	AAA MLE	Iowa Cubs	—	70	61	23	5	3	1	6	8	3	0	0	377	457	607	1.064	230	428	11	9	447	189	—	—	—	—	—	—
2024	27	MLB MLE	Chicago Cubs	—	94	86	19	3	0	0	4	10	3	1	0	221	280	256	535	35	250	11	4	247	53	-5	+0	-3	+0	+2	-0.6
2026	29	MLB MLE	Tampa Bay Rays	—	63	54	15	2	0	0	7	3	1	1	1	278	371	315	686	37	288	5	11	314	99	-0	-0	+0	+2	+1	0.3
2026	29	AAA MLE	Durham Bulls	—	379	334	92	11	2	3	27	38	9	11	3	275	346	347	693	72	289	10	7	308	95	—	—	—	—	—	—
2018	21	A+ MLE	3 teams	—	173	158	48	7	0	0	4	8	7	8	6	304	349	348	697	44	312	5	2	303	91	—	—	—	—	—	—
2018	21	A+ RAW	3 teams	—	173	155	47	7	0	0	7	5	7	8	6	303	361	348	709	45	305	3	4	319	91	—	—	—	—	—	—
2019	22	A+ MLE	3 teams	—	532	488	135	24	4	3	29	21	9	35	13	277	329	361	690	84	282	4	5	302	91	—	—	—	—	—	—
2019	22	A+ RAW	3 teams	—	532	473	147	27	5	4	44	16	9	35	13	311	380	414	795	104	313	3	8	355	91	—	—	—	—	—	—
2020	23	MLB MLE	Chicago White Sox	—	109	103	35	3	0	0	4	7	2	2	1	340	376	369	745	29	365	6	4	340	116	+2	+0	+2	+0	+2	0.7
2020	23	MLB RAW	Chicago White Sox	—	109	103	35	3	0	0	4	7	2	2	1	340	376	369	745	29	365	6	4	340	116	+2	+0	+2	+0	+2	0.7
2021	24	MLB MLE	Chicago White Sox	—	215	200	61	10	4	2	11	17	3	1	2	305	350	425	775	120	324	8	5	343	119	+5	-1	+1	+1	+5	1.1
2021	24	MLB RAW	Chicago White Sox	—	215	200	61	10	4	2	11	17	3	1	2	305	350	425	775	120	324	8	5	344	119	+5	-1	+1	+1	+5	1.1
2022	25	MLB MLE	2 teams	—	271	249	64	8	0	0	16	33	4	3	1	257	312	289	601	32	296	12	6	273	71	-9	+0	+2	+1	+6	-0.1
2022	25	MLB RAW	2 teams	—	271	248	64	8	0	0	17	32	4	3	1	258	316	290	606	32	296	12	6	280	71	-9	+0	+2	+1	+6	-0.1
2023	26	MLB MLE	2 teams	—	364	331	94	21	4	3	16	32	12	10	2	284	340	399	739	115	307	9	4	323	105	+2	+1	+8	+1	+8	2.1
2023	26	MLB RAW	2 teams	—	364	329	96	22	4	3	18	31	12	10	2	292	351	410	761	119	315	9	5	338	105	+2	+1	+8	+1	+8	2.1
2024	27	MLB MLE	Chicago Cubs	—	94	86	19	3	0	0	4	10	3	1	0	221	280	256	535	35	250	11	4	247	53	-5	+0	-3	+0	+2	-0.6
2024	27	MLB RAW	Chicago Cubs	—	94	86	19	3	0	0	4	10	3	1	0	221	280	256	535	35	250	11	4	248	53	-5	+0	-3	+0	+2	-0.6
2026	29	AAA MLE	2 teams	—	442	388	107	13	2	3	34	41	10	12	4	276	350	343	692	67	295	9	8	309	95	—	—	—	—	—	—
2026	29	AAA RAW	2 teams	—	442	375	111	15	2	4	47	35	10	12	4	296	389	379	768	83	311	8	11	345	95	—	—	—	—	—	—
2018	21	A+ MLE	3 teams	—	173	160	48	7	0	0	4	8	7	8	6	300	349	344	693	44	296	5	2	303	91	—	—	—	—	—	—
2019	22	A+ MLE	3 teams	—	532	489	135	24	4	3	29	21	9	35	13	276	329	360	689	84	277	4	5	302	91	—	—	—	—	—	—
2020	23	MLB MLE	Chicago White Sox	—	109	102	35	3	0	0	4	7	2	2	1	343	376	373	749	29	365	6	4	340	116	—	—	—	—	—	—
2021	24	MLB MLE	Chicago White Sox	—	215	199	61	10	4	2	11	17	3	1	2	307	350	427	778	121	324	8	5	343	119	—	—	—	—	—	—
2022	25	MLB MLE	2 teams	—	271	249	64	8	0	0	16	33	4	3	1	257	312	289	601	32	295	12	6	273	71	—	—	—	—	—	—
2023	26	MLB MLE	2 teams	—	364	333	94	21	4	3	16	32	12	10	2	282	340	396	736	114	311	9	4	323	105	—	—	—	—	—	—
2024	27	MLB MLE	Chicago Cubs	—	94	87	19	3	0	0	4	10	3	1	0	218	280	253	532	34	250	11	4	247	53	—	—	—	—	—	—
2026	29	AAA MLE	2 teams	—	442	395	107	13	2	3	34	41	10	12	4	271	350	337	686	66	289	9	8	309	95	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	27	MLB MLE	Chicago Cubs	—	94	87	19	3	0	0	4	10	3	1	0	218	280	253	532	34	250	11	4	247	53	—	—	—	—	—	—
2026	29	AAA MLE	2 teams	—	442	395	107	13	2	3	34	41	10	12	4	271	350	337	686	66	289	9	8	309	95	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	30	MLB MLE	TB	45%	380	340	87	13	2	3	29	45	8	8	2	255	328	333	661	77	284	12	8	302	90	-4	+1	+1	+1	+8	0.5
▸ 2027 — real world (MLB)
2027	30	MLB CTX	TB	45%	94	85	21	3	0	1	7	11	2	2	1	259	331	335	666	76	288	12	7	304	90	-1	+0	+0	+0	+2	0.2
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	31	MLB MLE	TB	53%	254	228	57	8	1	2	19	31	5	3	1	250	323	321	644	71	280	12	7	295	86	-4	+0	-2	+1	+6	0.0
2028	31	MLB CTX	TB	53%	135	121	31	4	1	1	10	17	3	2	1	254	326	323	649	70	284	13	7	297	86	-2	+0	-1	+0	+3	0.0
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1138	1043	284	45	6	5	60	105	24	20	8	272	327	341	668	69	298	9	5	302	93	-13	+1	+8	+6	+25	2.6
/ 162G	—	A+C		—	650	596	162	26	3	3	34	60	14	11	5	272	327	341	667	69	297	9	5	302	93	-8	+0	+4	+3	+14	1.5
Career	—	MLE		—	2662	2417	668	104	16	15	153	225	59	80	31	276	335	351	686	75	297	8	6	305	93	-18	+2	+7	+7	+32	3.1
/ 162G	—	MLE		—	650	590	163	25	4	4	37	55	14	20	8	276	333	353	686	76	297	8	6	305	93	-8	+1	+3	+3	+14	1.4

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 AAADurham Bulls · 379 PAwOBA .347 → .308 (−39)K% 8.4% → 10.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	8.44%	÷1.000	×1.200	—	—	10.13%
BB% (unintentional) /PA	10.55%	÷1.000	×0.690	—	×0.982	7.15%
HBP% /PA	2.37%	—	×0.940	—	×0.982	2.19%
Singles /BIP	26.64%	÷1.000	×0.930	×1.021	×0.982	24.84%
Doubles /BIP	4.50%	÷1.000	×0.830	×1.021	×0.982	3.74%
Triples /BIP	0.69%	÷1.000	×0.970	×1.021	×0.982	0.67%
Home runs /BIP	1.38%	÷1.000	×0.700	—	×0.982	0.95%

Factors used: league row International League. Park (home-season share, regressed): no park row on file (treated as neutral) — applied at half strength. Age: age 29 vs a level average of 27.2 → −1.8% on BB/HBP/hit rates (1.8 yr older than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.021.

Result: wOBA .347 → .308; ISO .090 → .076; BABIP .315 → .289. 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 AAAIowa Cubs · 70 PAwOBA .501 → .447 (−54)K% 10.0% → 11.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	10.00%	÷1.019	×1.200	—	—	11.78%
BB% (unintentional) /PA	11.43%	÷1.014	×0.690	—	×1.018	7.91%
HBP% /PA	4.29%	—	×0.940	—	×1.018	4.10%
Singles /BIP	28.85%	÷1.025	×0.930	—	×1.018	26.66%
Doubles /BIP	11.54%	÷1.049	×0.830	—	×1.018	9.29%
Triples /BIP	5.77%	÷0.916	×0.970	—	×1.018	6.22%
Home runs /BIP	1.92%	÷0.965	×0.700	—	×1.018	1.42%

Factors used: league row International League. Park (home-season share, regressed): HR 93, 1B 105, 2B 110, 3B 83, K 104, BB 103 — 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).

Result: wOBA .501 → .447; ISO .254 → .235; BABIP .471 → .428. 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).

2022 AAAIowa Cubs · 43 PAwOBA .319 → .294 (−25)K% 11.6% → 14.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	11.63%	÷0.988	×1.200	—	—	14.12%
BB% (unintentional) /PA	6.98%	÷1.048	×0.690	—	×1.033	4.75%
HBP% /PA	2.33%	—	×0.940	—	×1.033	2.26%
Singles /BIP	32.35%	÷0.985	×0.930	—	×1.033	31.55%
Doubles /BIP	2.94%	÷0.967	×0.830	—	×1.033	2.61%
Triples /BIP	0.00%	÷1.118	×0.970	—	×1.033	0.00%
Home runs /BIP	0.00%	÷0.996	×0.700	—	×1.033	0.00%

Factors used: league row International League. Park (home-season share, regressed): HR 99, 1B 97, 2B 93, 3B 124, K 98, BB 110 — 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).

Result: wOBA .319 → .294; ISO .026 → .023; BABIP .353 → .342. 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).

2019 A+Winston-Salem Dash · 218 PAwOBA .318 → .272 (−46)K% 2.8% → 4.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	2.75%	÷0.972	×1.420	—	—	4.02%
BB% (unintentional) /PA	6.88%	÷0.997	×0.630	—	×1.012	4.40%
HBP% /PA	2.75%	—	×0.860	—	×1.012	2.40%
Singles /BIP	20.54%	÷0.991	×0.940	×0.950	×1.012	18.73%
Doubles /BIP	5.41%	÷1.054	×0.830	×0.950	×1.012	4.09%
Triples /BIP	1.08%	÷1.150	×0.800	×0.950	×1.012	0.72%
Home runs /BIP	1.08%	÷1.113	×0.900	—	×1.012	0.89%

Factors used: league row CAR. Park (home-season share, regressed): HR 122, 1B 98, 2B 111, 3B 130, K 94, BB 99 — 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 ×0.950.

Result: wOBA .318 → .272; ISO .105 → .083; BABIP .269 → .234. 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).

2019 AABirmingham Barons · 180 PAwOBA .372 → .330 (−42)K% 2.8% → 3.4%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	2.78%	÷1.007	×1.220	—	—	3.36%
BB% (unintentional) /PA	7.78%	÷0.929	×0.680	—	×1.042	5.93%
HBP% /PA	1.11%	—	×0.840	—	×1.042	0.97%
Singles /BIP	26.42%	÷0.998	×0.920	×0.950	×1.042	24.10%
Doubles /BIP	6.92%	÷1.009	×0.910	×0.950	×1.042	6.17%
Triples /BIP	1.26%	÷1.150	×0.880	×0.950	×1.042	0.95%
Home runs /BIP	0.63%	÷0.993	×0.800	—	×1.042	0.53%

Factors used: league row SL. Park (home-season share, regressed): HR 99, 1B 100, 2B 102, 3B 130, K 101, BB 86 — applied at half strength. Age: age 22 vs a level average of 24.8 → +4.2% on BB/HBP/hit rates (2.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .372 → .330; ISO .110 → .095; BABIP .348 → .314. 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).

2019 AAACharlotte Knights · 134 PAwOBA .361 → .315 (−46)K% 3.7% → 4.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	3.73%	÷0.962	×1.200	—	—	4.66%
BB% (unintentional) /PA	9.70%	÷1.077	×0.690	—	×1.050	6.53%
HBP% /PA	0.75%	—	×0.940	—	×1.050	0.74%
Singles /BIP	27.43%	÷1.061	×0.930	×0.950	×1.050	23.99%
Doubles /BIP	5.31%	÷0.921	×0.830	×0.950	×1.050	4.77%
Triples /BIP	0.88%	÷0.900	×0.970	×0.950	×1.050	0.95%
Home runs /BIP	0.88%	÷1.212	×0.840	—	×1.050	0.64%

Factors used: league row IL. Park (home-season share, regressed): HR 142, 1B 112, 2B 84, 3B 80, K 92, BB 115 — applied at half strength. Age: age 22 vs a level average of 27.2 → +5.0% on BB/HBP/hit rates (5.2 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×0.950.

Result: wOBA .361 → .315; ISO .093 → .086; BABIP .336 → .297. 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).

2018 A+Winston-Salem Dash · 107 PAwOBA .316 → .306 (−10)K% 4.7% → 7.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	4.67%	÷0.928	×1.420	—	—	7.15%
BB% (unintentional) /PA	4.67%	÷1.049	×0.630	—	×1.027	2.88%
HBP% /PA	2.80%	—	×0.860	—	×1.027	2.48%
Singles /BIP	27.96%	÷0.984	×0.940	×1.047	×1.027	28.72%
Doubles /BIP	4.30%	÷0.939	×0.830	×1.047	×1.027	4.09%
Triples /BIP	0.00%	÷1.150	×0.800	×1.047	×1.027	0.00%
Home runs /BIP	0.00%	÷1.014	×0.900	—	×1.027	0.00%

Factors used: league row CAR. Park (home-season share, regressed): HR 103, 1B 97, 2B 88, 3B 130, K 86, BB 110 — applied at half strength. Age: age 21 vs a level average of 22.8 → +2.7% on BB/HBP/hit rates (1.8 yr younger than average). Ground-ball/fly-ball mix moves 1B/2B/3B by ×1.047.

Result: wOBA .316 → .306; ISO .041 → .039; BABIP .319 → .325. 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).

2018 AKannapolis Intimidators · 49 PAwOBA .341 → .328 (−13)K% 0.0% → 0.0%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	0.00%	÷0.986	×1.340	—	—	0.00%
BB% (unintentional) /PA	2.04%	÷0.966	×0.660	—	×1.015	1.41%
HBP% /PA	2.04%	—	×0.770	—	×1.015	1.59%
Singles /BIP	27.27%	÷1.026	×0.920	×1.020	×1.015	25.32%
Doubles /BIP	6.82%	÷0.986	×0.820	×1.020	×1.015	5.87%
Triples /BIP	0.00%	÷1.100	×0.940	×1.020	×1.015	0.00%
Home runs /BIP	0.00%	÷1.034	×0.820	—	×1.015	0.00%

Factors used: league row SAL. Park (home-season share, regressed): HR 107, 1B 105, 2B 97, 3B 120, K 97, BB 93 — 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 ×1.020.

Result: wOBA .341 → .328; ISO .068 → .063; BABIP .319 → .293. 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
#1Brock Holtlast yr '21 career totals — 10 seasons, 2,661 PA	4.0	2,661	25	41	262	332	362	+5	+3	7.4	7.8
/162	664	6	10	+1.2	+0.6	1.9	pk7: 8
#2Jeff Frye'92-'01 * career totals — 8 seasons, 2,451 PA	3.7	2,451	16	56	277*	341*	375*	+6	+13	7.4	7.6
/162	664	4	15	+1.5	+3.5	2.0	pk7: 8
#3Craig Grebeck'90-'01 * career totals — 11 seasons, 2,298 PA	3.5	2,298	19	4	252*	330*	358*	+13	+14	6.5	7.4
/162	664	5	1	+3.8	+4.2	1.9	pk7: 8
#4Eric Sogardlast yr '21 career totals — 10 seasons, 2,484 PA	3.7	2,484	26	46	246	312	337	+10	+22	6.0	6.6
/162	664	7	12	+2.7	+5.9	1.6	pk7: 7
#5Ehire Adrianzalast yr '22 career totals — 9 seasons, 1,519 PA	2.3	1,519	20	19	240	310	353	+10	+13	4.0	4.4
/162	664	9	8	+4.2	+5.5	1.8	pk7: 5
#6Bobby Valentine'71-'79 * career totals — 9 seasons, 1,909 PA	2.9	1,909	12	27	258*	316*	358*	+2	+1	3.1	3.8
/162	664	4	9	+0.6	+0.2	1.1	pk7: 4
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 7 projected seasons	1.9	1,231	6	22	271	323	341	+4	+14	2.4	2.5
/162	664	3	12	+2.4	+7.8	1.3	pk7: 3

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
Nick Madrigal (OGILVIE)	30	76	32	49	55	253/326/330	76	284	2	5	12%	8%	90	+0.2	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Michael Spidale 2012 · AAA	30	61→66	36→40	51→55	45	288/324/356	68	327	4	19	13%	4%	94	+0.9	260/291/331	71	293	5	14	13%	3%	76	-0.4	265/278/302	37	290	1	13	9%	1%	59	-0.8
✕Norris Hopper 2009 · MLB	30	60→69	35→30	53→55	50	284/326/345	62	314	3	21	11%	6%	92	+0.7	250/291/300	50	277	2	21	10%	5%	68	-1.0	254/292/286	32	280	0	24	9%	5%	62	-1.1
✕Heber Gomez 2008 · AAA	30	56→61	38→40	44→50	52	263/315/339	76	280	5	10	9%	6%	87	+0.4	250/301/327	77	274	5	12	10%	6%	79	-0.2	248/297/315	67	270	4	13	10%	5%	73	-0.5
✕Ryan Theriot 2011 · MLB	31	59→65	36→40	49→55	53	276/332/342	65	310	4	18	12%	7%	94	+0.9	264/313/333	69	296	3	13	11%	6%	84	+0.2	266/314/325	59	297	1	12	10%	6%	84	+0.1
✕Roman Gonzalez 2007 · AAA	30	59→63	36→30	40→50	48	277/325/343	66	310	2	9	11%	5%	91	+0.7	246/284/307	61	284	2	12	13%	4%	68	-1.0	234/268/280	46	274	0	11	14%	3%	52	-1.1
✕Aaron Miles 2007 · MLB	30	56→65	38→40	48→45	49	262/302/346	84	284	5	7	10%	5%	84	+0.1	269/308/346	77	296	5	5	10%	5%	86	+0.3	270/306/333	63	299	3	5	10%	5%	82	-0.0
✕Will Rhymes 2013 · MLB	30	54→61	39→40	48→55	52	253/308/341	88	279	6	11	12%	7%	85	+0.2	231/286/317	86	255	6	8	12%	7%	72	-0.7	228/283/306	78	251	4	6	11%	7%	66	-0.9
✕Kevin Newman 2024 · MLB	30	52→62	40→45	52→50	49	246/296/342	96	276	6	13	13%	6%	81	-0.1	238/281/331	93	287	7	11	19%	5%	72	-0.7	228/265/303	75	282	5	9	20%	5%	60	-0.6
●Oswaldo Morejon 2010 · AAA	31	55→65	39→40	48→50	51	262/321/351	88	286	6	14	11%	6%	93	+0.8	256/317/341	85	278	6	11	10%	6%	89	+0.5	284/328/376	92	304	6	7	8%	4%	98	+0.8
●Jamey Carroll 2005 · MLB	31	56→63	38→40	51→50	58	261/336/353	92	293	4	11	12%	10%	99	+1.2	259/337/339	80	298	4	10	13%	10%	96	+1.0	283/359/342	59	336	1	12	14%	10%	106	+1.3
✕Ramon Rios 2017 · AAA	29	63→68	38→40	47→50	45	300/324/377	77	321	5	7	9%	3%	99	+1.2	267/297/350	83	289	7	4	10%	3%	82	+0.0	263/291/335	72	282	6	1	9%	2%	74	-0.4
✕Irving Falu 2013 · MLB	30	58→62	39→40	53→55	47	275/310/364	88	309	5	20	13%	5%	91	+0.6	244/284/318	74	275	4	18	12%	5%	70	-0.8	251/289/337	86	274	7	13	11%	5%	74	-0.4
✕Abel Martinez 2007 · AAA	30	62	40→45	46→50	47	297/337/389	92	319	7	14	10%	4%	107	+1.8	262/303/348	86	285	7	15	10%	4%	84	+0.2	272/314/373	101	284	9	17	8%	4%	90	+0.4
✕David Eckstein 2006 · MLB	31	58→64	39→40	48→55	55	276/342/363	87	290	6	12	7%	8%	103	+1.5	283/344/361	78	300	5	9	7%	6%	103	+1.6	282/344/347	66	300	3	8	7%	6%	101	+1.1
●Carlos Alberto Gastelum 2011 · AAA	31	55→65	38→40	48→55	49	262/307/341	79	285	5	16	11%	5%	84	+0.2	273/318/361	88	310	7	11	14%	5%	93	+0.8	286/328/368	83	326	6	8	14%	5%	94	+0.7
✕Manny Mayorson 2012 · AAA	29	57→63	36→40	51→55	49	267/305/335	68	288	3	20	9%	5%	82	-0.0	242/280/320	78	268	6	12	12%	5%	70	-0.9	226/238/257	32	244	0	0	7%	2%	34	-1.0
✕Alejandro Ahumada 2008 · AAA	29	58→68	39→40	50→55	46	279/320/370	91	323	6	19	16%	5%	96	+1.0	250/292/326	76	307	5	18	19%	4%	75	-0.5	255/303/316	61	320	4	10	20%	5%	73	-0.3
✕Sergio Gastelum 2009 · AAA	30	58→63	39→40	46→50	50	276/342/365	89	307	6	8	12%	6%	103	+1.6	261/324/339	78	289	4	10	11%	6%	91	+0.7	259/322/327	68	287	3	9	10%	6%	85	+0.2
✕Amadeo Zazueta 2017 · AAA	31	56→61	37→40	49→50	45	263/296/339	76	292	5	14	12%	4%	79	-0.2	266/297/365	99	292	8	11	12%	4%	86	+0.3	271/302/371	100	293	8	8	10%	3%	87	+0.3
✕Angel Sanchez 2013 · MLB	29	54→60	37→40	48→45	51	257/308/325	68	284	5	8	12%	7%	81	-0.1	221/269/300	79	255	7	6	16%	6%	61	-1.5	187/228/242	55	223	4	0	17%	4%	31	-1.1

Nick Madrigal

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps