Jake Rogers

C Chicago White Sox

Age 32 R/R 5'11" / 201 lbs MLB HR +4% Svc 6.233 · Ctrl thru 2027

Overview

MLB40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2024	29	MLBDET	337	.197/.257/.352	10	1	72	2.6
2025	30	MLBDET	142	.187/.281/.333	3	0	74	0.5
2026	31	MLBCWS	171	.161/.281/.287	4	1	62	-0.0
2027PROJ	32	MLBCWS	144	.201/.292/.357	4	1	83	0.5
2028PROJ82% MLB	33	MLBCWS	94	.193/.283/.340	3	0	75	-0.1

Last 3 seasons WAR 3.0 · Next 2 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service6.233First FA2028

2027Ext / FA-deal$4M

Skills · percentileStatcast 2024-2026

Barrel%60

Avg EV46

Bat Spd45

xwOBA30

Speed27

CompsAnthony Recker (age 33) · Dustin Garneau (age 33) · Chad Wallach (age 32)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 8683vR 73

AVG/OBP/SLG.201 / .292 / .357

PA144

HR4

K%26

BB%10

BABIP249

ISO150

MLB%100%

Value2027 · WAR

trajectory · projected WAR by year

Tools · Splits · ContractCON 40 / POW 50

Toolsscout grade · Statcast %ile · 2024-2026

CONTACT40

xAVG

100.199

POWER50

Barrel%

609.6

Avg EV

4688.8

Bat Spd

4570.8

EYE55

scouting grade only

SPEED45

Sprint

2726.4

Platoon Splits2024-2026 · 504 PA

vs LHP

vs RHP

Edge

129

375

—

wRC+

▶▶R

wOBA

245

269

▶R

ISO

104

162

▶▶R

BABIP

217

249

▶R

28.7%

28.8%

—

BB%

11.6%

5.9%

◀◀L

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

Status FA

MLB Debut 2019

Service 6.233 yrs.days

Team Control Through 2027

First FA Year 2028

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$4M	+0.5	$2M	-$2M
Totals		$4M		$2M	-$2M

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)
2016	21	A	Quad Cities River Bandits	—	82	72	15	3	1	1	8	25	2	1	0	208	305	319	624	111	304	30	10	288	24	—	—	—	—	—	—
2017	22	A+	Lakeland Flying Tigers	—	375	320	84	18	3	12	45	74	4	13	8	263	360	450	810	188	300	20	12	356	68	—	—	—	—	—	—
2017	22	A	Quad Cities River Bandits	—	116	102	26	7	1	6	9	28	4	1	0	255	339	520	859	265	290	24	8	372	72	—	—	—	—	—	—
2018	23	AA	Erie SeaWolves	—	408	352	77	15	1	17	41	112	6	7	1	219	311	412	723	193	261	27	10	316	57	—	—	—	—	—	—
2018	23	INTL	Mesa Solar Sox	—	52	48	8	5	0	0	3	11	1	0	0	167	231	271	502	104	216	21	6	230	42	—	—	—	—	—	—
2019	24	MLB	Detroit Tigers	—	128	112	14	3	0	4	13	51	1	0	0	125	222	259	481	134	175	40	10	221	39	-9	+0	+1	+8	+3	0.2
2019	24	AAA	Toledo Mud Hens	—	191	166	37	10	1	9	18	53	6	0	0	223	321	458	779	235	269	28	9	343	84	—	—	—	—	—	—
2019	24	AA	Erie SeaWolves	—	112	86	26	3	1	5	19	26	3	0	0	302	444	535	979	233	356	23	17	417	126	—	—	—	—	—	—
2021	26	MLB	Detroit Tigers	—	127	113	27	5	3	6	11	46	0	1	0	239	306	496	802	257	344	36	9	340	121	+3	+0	+2	+2	+3	1.0
2023	28	MLB	Detroit Tigers	—	365	331	73	11	0	21	28	118	3	1	1	221	287	444	731	224	268	32	8	320	108	+3	-0	+15	+7	+8	3.3
2024	29	MLB	Detroit Tigers	—	337	310	61	16	1	10	22	99	3	1	0	197	257	352	608	155	251	29	7	271	72	-11	+0	+23	+6	+7	2.6
2025	30	MLB	Detroit Tigers	—	142	123	23	5	2	3	15	39	1	0	0	187	281	333	614	146	241	27	11	274	74	-4	+0	+3	+3	+3	0.5
2025	30	AAA	Toledo Mud Hens	—	18	15	5	3	0	0	3	3	0	0	0	333	444	533	978	200	417	17	17	431	127	—	—	—	—	—	—
2026	31	MLB	Chicago White Sox	—	171	143	23	4	1	4	23	51	1	1	0	161	281	287	568	126	209	30	13	260	62	-8	+0	+1	+3	+4	-0.0
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	1270	1132	221	44	7	48	112	404	9	4	1	195	273	374	647	178	251	32	9	286	80	-31	+0	+44	+28	+28	7.1
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2016	21	A MLE	Quad Cities River Bandits	—	82	75	12	2	1	1	5	35	2	1	0	160	232	253	485	93	262	43	6	204	24	—	—	—	—	—	—
2017	22	A+ MLE	Lakeland Flying Tigers	—	375	338	70	18	2	6	27	106	4	13	8	207	274	325	599	118	269	28	7	268	68	—	—	—	—	—	—
2017	22	A MLE	Quad Cities River Bandits	—	116	105	21	6	1	4	6	36	4	1	0	200	270	390	660	190	243	31	5	274	72	—	—	—	—	—	—
2018	23	AA MLE	Erie SeaWolves	—	408	364	67	13	1	12	29	137	6	7	1	184	256	324	580	140	236	34	7	253	57	—	—	—	—	—	—
2019	24	MLB MLE	Detroit Tigers	—	128	112	14	3	0	4	13	51	1	0	0	125	222	259	481	134	175	40	10	225	39	-9	+0	+1	+8	+3	0.2
2019	24	AAA MLE	Toledo Mud Hens	—	191	171	33	8	1	9	13	60	6	0	0	193	274	409	683	216	238	31	7	293	84	—	—	—	—	—	—
2019	24	AA MLE	Erie SeaWolves	—	112	92	25	3	1	3	13	32	3	0	0	272	380	424	804	152	335	29	12	354	126	—	—	—	—	—	—
2021	26	MLB MLE	Detroit Tigers	—	127	113	27	5	3	6	11	46	0	1	0	239	306	496	802	257	344	36	9	347	121	+3	+0	+2	+2	+3	1.0
2023	28	MLB MLE	Detroit Tigers	—	365	331	75	11	0	22	28	118	3	1	1	227	293	459	752	233	268	32	8	327	108	+3	-0	+15	+7	+8	3.3
2024	29	MLB MLE	Detroit Tigers	—	337	311	61	16	1	10	21	99	3	1	0	196	254	350	604	154	251	29	6	274	72	-11	+0	+23	+6	+7	2.6
2025	30	MLB MLE	Detroit Tigers	—	142	123	23	5	2	3	15	39	1	0	0	187	281	333	614	146	241	27	11	278	74	-4	+0	+3	+3	+3	0.5
2025	30	AAA MLE	Toledo Mud Hens	—	18	16	5	3	0	0	2	3	0	0	0	312	389	500	889	188	363	17	11	356	127	—	—	—	—	—	—
2026	31	MLB MLE	Chicago White Sox	—	171	143	23	4	1	4	23	51	1	1	0	161	281	287	568	126	209	30	13	259	62	-8	+0	+1	+3	+4	-0.0
2016	21	A MLE	Quad Cities River Bandits	—	82	75	12	2	1	1	5	35	2	1	0	160	232	253	485	93	282	43	6	204	24	—	—	—	—	—	—
2016	21	A RAW	Quad Cities River Bandits	—	82	72	15	3	1	1	8	25	2	1	0	208	305	319	624	111	304	30	10	288	24	—	—	—	—	—	—
2017	22	A+ MLE	2 teams	—	491	442	90	23	3	10	34	142	8	14	8	204	273	337	610	133	269	29	7	269	68	—	—	—	—	—	—
2017	22	A+ RAW	2 teams	—	491	422	110	25	4	18	54	102	8	14	8	261	355	467	822	206	298	21	11	360	68	—	—	—	—	—	—
2018	23	AA MLE	2 teams	—	460	411	75	14	1	14	33	154	7	7	1	182	255	324	579	141	244	33	7	253	57	—	—	—	—	—	—
2018	23	AA RAW	2 teams	—	460	400	85	20	1	17	44	123	7	7	1	212	302	395	697	183	255	27	10	307	57	—	—	—	—	—	—
2019	24	AAA MLE	3 teams	—	431	375	73	14	2	16	39	143	10	0	0	195	288	371	658	176	259	33	9	289	82	—	—	—	—	—	—
2019	24	AAA RAW	3 teams	—	431	364	77	16	2	18	50	130	10	0	0	212	323	415	738	203	268	30	12	326	82	—	—	—	—	—	—
2021	26	MLB MLE	Detroit Tigers	—	127	113	27	5	3	6	11	46	0	1	0	239	306	496	802	257	344	36	9	347	121	+3	+0	+2	+2	+3	1.0
2021	26	MLB RAW	Detroit Tigers	—	127	113	27	5	3	6	11	46	0	1	0	239	306	496	802	257	344	36	9	340	121	+3	+0	+2	+2	+3	1.0
2023	28	MLB MLE	Detroit Tigers	—	365	331	75	11	0	22	28	118	3	1	1	227	293	459	752	233	275	32	8	327	108	+3	-0	+15	+7	+8	3.3
2023	28	MLB RAW	Detroit Tigers	—	365	331	73	11	0	21	28	118	3	1	1	221	287	444	731	224	268	32	8	320	108	+3	-0	+15	+7	+8	3.3
2024	29	MLB MLE	Detroit Tigers	—	337	311	61	16	1	10	21	99	3	1	0	196	254	350	604	154	250	29	6	274	72	-11	+0	+23	+6	+7	2.6
2024	29	MLB RAW	Detroit Tigers	—	337	310	61	16	1	10	22	99	3	1	0	197	257	352	608	155	251	29	7	271	72	-11	+0	+23	+6	+7	2.6
2025	30	MLB MLE	2 teams	—	160	139	28	8	2	3	17	42	1	0	0	201	293	353	646	151	260	26	11	287	80	-4	+0	+3	+3	+3	0.6
2025	30	MLB RAW	2 teams	—	160	138	28	8	2	3	18	42	1	0	0	203	299	355	654	152	263	26	11	291	80	-4	+0	+3	+3	+3	0.6
2026	31	MLB MLE	Chicago White Sox	—	171	143	23	4	1	4	23	51	1	1	0	161	281	287	568	126	209	30	13	259	62	-8	+0	+1	+3	+4	-0.0
2026	31	MLB RAW	Chicago White Sox	—	171	143	23	4	1	4	23	51	1	1	0	161	281	287	568	126	209	30	13	260	62	-8	+0	+1	+3	+4	-0.0
2016	21	A MLE	Quad Cities River Bandits	—	82	75	12	2	1	1	5	35	2	1	0	160	232	253	485	93	262	43	6	204	24	—	—	—	—	—	—
2017	22	A+ MLE	2 teams	—	491	446	90	23	3	10	34	142	8	14	8	202	273	334	607	132	263	29	7	269	68	—	—	—	—	—	—
2018	23	AA MLE	2 teams	—	460	417	75	14	1	14	33	154	7	7	1	180	255	319	574	139	236	33	7	253	57	—	—	—	—	—	—
2019	24	AAA MLE	3 teams	—	431	379	73	14	2	16	39	143	10	0	0	193	288	367	654	174	245	33	9	289	82	—	—	—	—	—	—
2021	26	MLB MLE	Detroit Tigers	—	127	115	27	5	3	6	11	46	0	1	0	235	306	487	793	252	344	36	9	347	121	—	—	—	—	—	—
2023	28	MLB MLE	Detroit Tigers	—	365	331	75	11	0	22	28	118	3	1	1	227	293	459	752	233	268	32	8	327	108	—	—	—	—	—	—
2024	29	MLB MLE	Detroit Tigers	—	337	310	61	16	1	10	21	99	3	1	0	197	254	352	605	155	251	29	6	274	72	—	—	—	—	—	—
2025	30	MLB MLE	2 teams	—	160	141	28	8	2	3	17	42	1	0	0	199	293	348	641	149	255	26	11	287	80	—	—	—	—	—	—
2026	31	MLB MLE	Chicago White Sox	—	171	145	23	4	1	4	23	51	1	1	0	159	281	283	564	124	209	30	13	259	62	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	29	MLB MLE	Detroit Tigers	—	337	310	61	16	1	10	21	99	3	1	0	197	254	352	605	155	251	29	6	274	72	—	—	—	—	—	—
2025	30	MLB MLE	2 teams	—	160	141	28	8	2	3	17	42	1	0	0	199	293	348	641	149	255	26	11	287	80	—	—	—	—	—	—
2026	31	MLB MLE	Chicago White Sox	—	171	145	23	4	1	4	23	51	1	1	0	159	281	283	564	124	209	30	13	259	62	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	32	MLB MLE	CWS	100%	233	205	41	9	1	7	24	62	2	2	1	200	291	354	645	154	248	27	10	290	83	-5	+0	+3	+4	+5	0.5
▸ 2027 — real world (MLB)
2027	32	MLB CTX	CWS	100%	144	127	25	5	1	4	15	38	1	1	0	201	292	357	649	157	249	26	10	292	83	-3	+0	+2	+3	+3	0.5
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	33	MLB MLE	CWS	82%	115	101	20	4	1	3	12	31	1	0	0	192	282	337	619	145	241	27	10	280	75	-3	+0	-3	+2	+2	-0.1
2028	33	MLB CTX	CWS	82%	94	83	16	3	1	3	10	26	1	0	0	193	283	340	623	147	242	28	11	281	75	-3	+0	-2	+2	+2	-0.1
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	1364	1215	237	47	8	51	122	430	10	4	1	195	274	373	647	178	250	32	9	285	76	-34	+0	+42	+30	+30	7.0
/ 162G	—	A+C		—	650	579	112	22	4	24	58	205	5	2	0	193	273	370	642	176	249	32	9	285	76	-16	+0	+20	+14	+14	3.3
Career	—	MLE		—	2883	2568	509	106	16	93	238	899	37	27	10	198	276	361	636	162	259	31	8	281	76	-37	+1	+44	+33	+33	7.5
/ 162G	—	MLE		—	650	579	115	24	4	21	54	203	8	6	2	199	276	363	639	164	260	31	8	281	76	-16	+0	+19	+14	+14	3.2

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 AAAToledo Mud Hens · 18 PAwOBA .420 → .356 (−64)K% 16.7% → 18.6%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	16.67%	÷1.073	×1.200	—	—	18.64%
BB% (unintentional) /PA	16.67%	÷0.988	×0.690	—	×0.972	11.31%
HBP% /PA	0.00%	—	×0.940	—	×0.972	0.00%
Singles /BIP	16.67%	÷0.993	×0.930	—	×0.972	15.17%
Doubles /BIP	25.00%	÷0.953	×0.830	—	×0.972	21.16%
Triples /BIP	0.00%	÷0.905	×0.970	—	×0.972	0.00%
Home runs /BIP	0.00%	÷0.872	×0.700	—	×0.972	0.00%

Factors used: league row International League. Park (home-season share, regressed): HR 74, 1B 99, 2B 91, 3B 81, K 115, BB 98 — applied at half strength. Age: age 30 vs a level average of 27.2 → −2.8% on BB/HBP/hit rates (2.8 yr older than average).

Result: wOBA .420 → .356; ISO .200 → .178; BABIP .417 → .363. 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 AAAToledo Mud Hens · 191 PAwOBA .334 → .293 (−40)K% 27.7% → 31.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.75%	÷1.056	×1.200	—	—	31.54%
BB% (unintentional) /PA	9.42%	÷1.004	×0.690	—	×1.048	6.79%
HBP% /PA	3.14%	—	×0.940	—	×1.048	3.09%
Singles /BIP	15.04%	÷1.013	×0.930	×0.950	×1.048	13.75%
Doubles /BIP	8.85%	÷0.995	×0.830	×0.950	×1.048	7.35%
Triples /BIP	0.88%	÷1.096	×0.970	×0.950	×1.048	0.78%
Home runs /BIP	7.96%	÷0.888	×0.840	—	×1.048	7.89%

Factors used: league row IL. Park (home-season share, regressed): HR 78, 1B 103, 2B 99, 3B 119, K 111, BB 101 — 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 .334 → .293; ISO .235 → .220; BABIP .269 → .238. 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 AAErie SeaWolves · 112 PAwOBA .422 → .354 (−68)K% 23.2% → 28.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	23.21%	÷0.994	×1.220	—	—	28.48%
BB% (unintentional) /PA	16.96%	÷1.050	×0.700	—	×1.012	11.44%
HBP% /PA	2.68%	—	×0.850	—	×1.012	2.30%
Singles /BIP	28.33%	÷0.978	×0.970	—	×1.012	28.43%
Doubles /BIP	5.00%	÷1.077	×0.860	—	×1.012	4.04%
Triples /BIP	1.67%	÷1.127	×0.950	—	×1.012	1.42%
Home runs /BIP	8.33%	÷1.294	×0.780	—	×1.012	5.08%

Factors used: league row EL. Park (home-season share, regressed): HR 159, 1B 96, 2B 115, 3B 125, K 99, BB 110 — 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).

Result: wOBA .422 → .354; ISO .233 → .167; BABIP .356 → .335. 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 AAErie SeaWolves · 408 PAwOBA .313 → .253 (−60)K% 27.5% → 33.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.45%	÷0.999	×1.220	—	—	33.54%
BB% (unintentional) /PA	10.05%	÷1.007	×0.700	—	×1.027	7.18%
HBP% /PA	1.47%	—	×0.850	—	×1.027	1.28%
Singles /BIP	18.33%	÷1.016	×0.970	×0.958	×1.027	17.22%
Doubles /BIP	6.25%	÷0.974	×0.860	×0.958	×1.027	5.43%
Triples /BIP	0.42%	÷0.901	×0.950	×0.958	×1.027	0.43%
Home runs /BIP	7.08%	÷1.112	×0.780	—	×1.027	5.10%

Factors used: league row EL. Park (home-season share, regressed): HR 122, 1B 103, 2B 95, 3B 80, K 100, BB 101 — 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 ×0.958.

Result: wOBA .313 → .253; ISO .193 → .145; BABIP .261 → .236. 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 A+Lakeland Flying Tigers · 375 PAwOBA .349 → .268 (−81)K% 19.7% → 28.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	19.73%	÷0.995	×1.420	—	—	28.17%
BB% (unintentional) /PA	10.93%	÷1.138	×0.750	—	×1.012	7.29%
HBP% /PA	1.07%	—	×0.930	—	×1.012	1.00%
Singles /BIP	20.73%	÷1.012	×0.920	×0.967	×1.012	18.45%
Doubles /BIP	7.32%	÷0.918	×0.960	×0.967	×1.012	7.49%
Triples /BIP	1.22%	÷1.039	×0.810	×0.967	×1.012	0.93%
Home runs /BIP	4.88%	÷1.747	×0.880	—	×1.012	2.49%

Factors used: league row FSL. Park (home-season share, regressed): HR 249, 1B 102, 2B 84, 3B 108, K 99, BB 128 — 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 ×0.967.

Result: wOBA .349 → .268; ISO .188 → .125; BABIP .300 → .269. 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 AQuad Cities River Bandits · 116 PAwOBA .365 → .274 (−91)K% 24.1% → 31.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.14%	÷1.057	×1.360	—	—	31.07%
BB% (unintentional) /PA	7.76%	÷0.982	×0.690	—	—	5.45%
HBP% /PA	3.45%	—	×0.780	—	—	2.69%
Singles /BIP	16.22%	÷1.017	×0.920	×0.970	—	14.24%
Doubles /BIP	9.46%	÷0.956	×0.830	×0.970	—	7.96%
Triples /BIP	1.35%	÷1.150	×0.910	×0.970	—	1.04%
Home runs /BIP	8.11%	÷1.154	×0.800	—	—	5.62%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 131, 1B 103, 2B 91, 3B 130, K 111, BB 96 — 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 ×0.970.

Result: wOBA .365 → .274; ISO .265 → .186; BABIP .290 → .243. 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 AQuad Cities River Bandits · 82 PAwOBA .281 → .204 (−77)K% 30.5% → 42.2%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	30.49%	÷0.982	×1.360	—	—	42.21%
BB% (unintentional) /PA	9.76%	÷1.044	×0.690	—	×1.015	6.54%
HBP% /PA	2.44%	—	×0.780	—	×1.015	1.93%
Singles /BIP	21.28%	÷1.022	×0.920	×0.993	×1.015	19.29%
Doubles /BIP	6.38%	÷1.185	×0.830	×0.993	×1.015	4.50%
Triples /BIP	2.13%	÷1.004	×0.910	×0.993	×1.015	1.94%
Home runs /BIP	2.13%	÷0.911	×0.800	—	×1.015	1.90%

Factors used: level-wide A factors (no league-specific row for this league/era). Park (home-season share, regressed): HR 82, 1B 104, 2B 137, 3B 101, K 96, BB 109 — 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 ×0.993.

Result: wOBA .281 → .204; ISO .111 → .079; BABIP .304 → .262. 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
#1René Riveralast yr '21 career totals — 10 seasons, 1,602 PA	2.4	1,602	42	1	221	273	354	+29	+90	11.7	11.8
/162	664	17	0	+11.9	+37.2	4.9	pk7: 12
#2Jeff Mathislast yr '20 career totals — 15 seasons, 3,003 PA	4.5	3,003	53	12	194	253	300	+54	+136	11.2	10.8
/162	664	12	3	+11.9	+30.1	2.5	pk7: 10
#3Sandy 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
#4Charlie O'Brien'85-'00 * career totals — 12 seasons, 2,560 PA	3.9	2,560	56	1	214*	295*	358*	+46	+0	5.0	5.6
/162	664	15	0	+11.9	+0.0	1.3	pk7: 6
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 8 projected seasons	2.3	1,508	55	5	195	272	369	+27	+59	3.2	3.4
/162	664	24	2	+11.9	+26.0	1.4	pk7: 4
#5Marc 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
#6John Boccabella'63-'74 * career totals — 8 seasons, 1,595 PA	2.4	1,595	26	3	217*	267*	343*	+8	+6	-1.3	-0.1
/162	664	11	1	+3.5	+2.7	-0.5	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
Jake Rogers (OGILVIE)	32	40	50	45	55	198/288/351	153	248	4	1	27%	11%	83	+0.0	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Anthony Recker 2017 · MLB	33	40→48	49→50	48→45	47	208/286/351	143	274	17	5	30%	9%	81	-0.1	204/267/343	139	278	15	5	31%	7%	72	-0.7	182/239/318	136	247	15	1	32%	6%	53	-0.9
✕Dustin Garneau 2021 · MLB	33	40→38	49→50	45	46	208/290/361	153	270	17	5	29%	8%	85	+0.2	192/269/357	165	248	20	6	31%	7%	76	-0.4	145/226/306	160	173	21	1	31%	7%	48	-0.8
✕Chad Wallach 2024 · MLB	32	38→48	48→50	46→40	43	200/265/336	136	268	17	5	31%	7%	70	-0.9	201/264/329	128	278	15	5	32%	7%	67	-1.1	183/248/298	114	255	14	2	32%	6%	52	-0.9
✕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
✕Carlos Pérez 2024 · MLB	33	38→48	53→55	46→45	46	209/273/378	169	228	23	2	21%	7%	82	+0.0	209/273/380	171	232	21	3	21%	7%	82	+0.0	196/260/370	174	213	22	1	21%	7%	73	-0.4
✕Taylor Motter 2022 · MLB	32	42→49	52→50	47→55	51	220/299/386	166	266	19	4	25%	10%	93	+0.8	208/277/361	153	266	18	8	28%	9%	80	-0.2	185/254/326	141	236	18	8	29%	8%	61	-0.8
✕Elias Díaz 2023 · MLB	32	44→54	50	42→45	48	227/286/381	154	256	18	2	20%	7%	87	+0.4	245/298/378	133	297	14	2	22%	7%	91	+0.6	245/298/371	126	300	14	1	22%	6%	90	+0.4
✕Brian O'Keefe 2024 · MLB	31	39→42	50	41→40	43	205/263/360	156	263	18	4	29%	7%	74	-0.5	196/254/337	141	249	17	5	28%	7%	65	-1.2	174/231/302	128	216	17	3	28%	7%	46	-0.9
✕Miguel Ojeda 2007 · MLB	32	46→45	45	41→45	50	227/292/347	120	263	13	5	19%	8%	81	-0.1	233/294/353	120	273	12	7	19%	7%	83	+0.1	237/302/363	127	268	13	7	17%	8%	87	+0.2
●Robinson Chirinos 2016 · MLB	32	43→46	52→60	44→50	49	225/298/392	166	260	20	3	22%	8%	95	+0.9	234/328/442	208	282	26	4	26%	10%	118	+2.6	229/342/435	206	294	26	2	29%	11%	120	+1.8
✕Austin Wynns 2022 · MLB	31	44→51	45	48→40	46	221/280/340	119	261	14	6	22%	7%	76	-0.4	238/302/348	110	299	11	5	23%	7%	85	+0.2	237/301/332	94	305	9	2	24%	7%	80	-0.1
✕John Mayberry Jr. 2015 · MLB	31	44→48	51→55	44→50	48	227/295/393	167	269	18	5	23%	8%	93	+0.8	214/281/366	152	263	16	7	25%	8%	82	-0.0	161/216/284	123	202	11	4	26%	6%	38	-0.8
✕Corky Miller 2008 · MLB	32	41	50	48→40	48	210/293/357	147	228	18	7	18%	7%	86	+0.3	213/286/335	122	245	13	5	19%	7%	77	-0.3	211/280/323	112	244	12	0	18%	7%	71	-0.3
✕Eric Haase 2024 · MLB	31	44→42	50→65	48→40	42	227/277/378	151	289	19	6	28%	6%	83	+0.1	224/281/371	147	317	19	5	34%	7%	83	+0.1	213/275/368	155	309	22	1	37%	7%	80	-0.0
✕Cody Ransom 2009 · MLB	33	38→39	53→55	48→50	44	206/269/380	173	255	22	11	29%	7%	81	-0.1	212/273/380	168	268	20	10	28%	7%	82	+0.0	206/286/402	196	277	25	6	34%	9%	92	+0.4
✕Jett Bandy 2021 · MLB	31	39	49→50	44→45	38	208/269/350	142	268	18	6	30%	5%	74	-0.5	213/278/331	118	299	13	8	31%	6%	73	-0.6	163/234/229	66	238	8	0	33%	5%	34	-0.7
✕Jonathan Aceves 2010 · AAA	32	44	47→50	43→30	42	224/270/360	136	270	15	5	24%	5%	76	-0.4	214/260/338	124	269	14	5	25%	6%	68	-1.0	228/295/332	104	273	11	1	20%	7%	78	-0.2
✕Curt Casali 2022 · MLB	33	45	51→50	49→30	51	229/317/393	164	294	19	5	28%	10%	102	+1.5	218/314/337	119	290	12	4	27%	11%	88	+0.5	207/312/292	85	276	8	0	25%	11%	77	-0.1
✕Bryan Holaday 2020 · MLB	32	46	45	38→40	48	230/289/352	123	258	13	4	17%	6%	82	-0.0	222/283/363	141	267	15	7	23%	7%	82	-0.0	186/238/360	174	219	21	4	27%	5%	67	-0.3
✕Tim Federowicz 2019 · MLB	31	46→51	47→50	49→40	43	232/282/367	136	297	14	5	27%	6%	82	-0.0	205/258/332	127	273	15	6	29%	7%	65	-1.2	160/212/265	105	214	15	2	32%	6%	31	-1.5

Jake Rogers

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps