Kris Bryant

RF Colorado Rockies

Age 35 R/R 6'5" / 240 lbs MLB HR +30%H +12% Svc 11.994 · Ctrl thru 2028 ILInjured 60-Day (D60)

Overview

MLBIL-6040-MANExtFA 2028

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR	MLE AVG/OBP/SLG
2023	31	MLBCOL	335	.233/.313/.367	10	0	88	-0.8
2024	32	AAACOL	33	.290/.333/.516	2	0	63	—	.219/.242/.344
2024	32	MLBCOL	155	.218/.325/.301	2	0	80	-0.7
2025	33	MLBCOL	41	.154/.195/.205	0	0	9	-0.4
2026	34	No MLB games played
2027PROJ	35	MLBCOL	72	.243/.314/.378	2	1	86	-0.1
2028PROJ	36	MLBCOL	80	.230/.299/.349	1	0	75	-0.8

Last 3 seasons WAR -1.9 · Next 2 projected WAR -0.9Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2028Service11.994First FA2028NTC

2027Ext$26M2028Ext$26M

Skills · percentileStatcast 2023-2025

xwOBA48

Bat Spd48

Barrel%31

Speed13

Avg EV10

CompsShawn Wooten (age 35) · Issmael Salas (age 35) · Pedro Diaz (age 35)

2027 FULL-SEASON PROJECTIONstats below = full-season projection

showingPROJ

wRC+†vL 8686vR 83

AVG/OBP/SLG.243 / .314 / .378

PA72

HR2

K%26

BB%7

BABIP316

ISO136

MLB%100%

Value2027 · WAR

━ projected WAR━ paid WAR ($/8M) surplus deficit-$52M ctrl surplus

Tools · Splits · ContractCON 35 / POW 40

Toolsscout grade · Statcast %ile · 2023-2025

CONTACT35

xAVG

200.227

POWER40

Barrel%

315.8

Avg EV

1085.9

Bat Spd

4871.2

EYE45

scouting grade only

SPEED40

Sprint

1325.7

Platoon Splits2024-2025 · 183 PA

vs LHP

vs RHP

Edge

143

—

wRC+

-8

▶▶R

wOBA

156

309

▶▶R

ISO

026

092

▶▶R

BABIP

227

340

▶▶R

40.0%

28.7%

▶▶R

BB%

2.5%

9.1%

▶▶R

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

Status FA

MLB Debut 2015

Service 11.994 yrs.days

Team Control Through 2028

First FA Year 2028

NTC

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext	$26M	—	—	—
2028	Ext	$26M	—	—	—
Totals		$52M		$-4M	-$53M

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)
2013	21	A+	Daytona Cubs	—	62	57	19	5	1	5	3	17	2	1	0	333	387	719	1.106	386	400	27	5	472	123	—	—	—	—	—	—
2013	21	INTL	Mesa Solar Sox	—	92	77	28	8	1	6	14	23	0	3	0	364	462	727	1.189	364	449	25	15	499	224	—	—	—	—	—	—
2014	22	AAA	Iowa Cubs	—	297	244	72	14	1	21	43	85	9	7	2	295	419	619	1.038	324	367	29	14	446	116	—	—	—	—	—	—
2014	22	AA	Tennessee Smokies	—	297	248	88	20	0	22	43	77	5	8	2	355	459	702	1.161	347	440	26	14	494	155	—	—	—	—	—	—
2015	23	MLB	Chicago Cubs	—	650	559	154	31	5	26	77	199	9	13	4	275	372	488	860	213	378	31	12	378	141	+32	+1	+4	+2	+14	5.4
2015	23	AAA	Iowa Cubs	—	33	28	9	1	0	3	2	9	1	2	0	321	387	679	1.066	357	333	27	6	430	161	—	—	—	—	—	—
2016	24	MLB	Chicago Cubs	—	699	603	176	35	3	39	75	154	18	8	5	292	386	554	940	262	332	22	11	409	161	+50	-0	+7	+2	+15	7.6
2017	25	MLB	Chicago Cubs	—	665	549	162	38	4	29	95	128	15	7	5	295	413	537	950	242	334	19	14	414	163	+50	-1	+3	+2	+14	7.0
2018	26	MLB	Chicago Cubs	—	457	389	106	28	3	13	48	107	17	2	4	272	377	460	837	188	342	23	11	372	130	+17	-1	-1	+2	+10	2.6
2019	27	MLB	Chicago Cubs	—	634	543	153	35	1	31	74	145	15	4	0	282	383	521	904	239	331	23	12	397	155	+41	+1	-3	+2	+14	5.7
2020	28	MLB	Chicago Cubs	—	147	131	27	5	1	4	12	40	4	0	0	206	293	351	644	145	264	27	8	293	84	-3	+0	-2	+1	+3	-0.1
2021	29	MLB	San Francisco Giants	—	586	513	136	32	2	25	62	135	9	10	2	265	354	481	836	216	313	23	11	368	138	+27	+1	-8	+2	+13	3.5
2022	30	MLB	Colorado Rockies	—	181	160	49	12	0	5	17	27	2	0	0	306	380	475	855	169	338	15	9	376	132	+7	+0	-3	-2	+4	0.5
2022	30	AAA	Albuquerque Isotopes	—	22	20	3	2	0	0	2	8	0	0	0	150	227	250	477	100	250	36	9	221	-8	—	—	—	—	—	—
2023	31	MLB	Colorado Rockies	—	335	300	70	10	0	10	29	68	6	0	0	233	313	367	680	133	270	20	9	309	88	-5	+0	-7	-4	+7	-0.8
2024	32	MLB	Colorado Rockies	—	155	133	29	5	0	2	13	48	8	0	0	218	325	301	625	83	321	31	8	293	80	-4	+0	-4	-2	+3	-0.7
2024	32	AAA	Albuquerque Isotopes	—	33	31	9	1	0	2	2	8	0	0	0	290	333	516	849	226	333	24	6	372	63	—	—	—	—	—	—
2025	33	MLB	Colorado Rockies	—	41	39	6	2	0	0	2	13	0	0	0	154	195	205	400	51	231	32	5	185	9	-4	+0	+0	-0	+1	-0.4
▸ Career Totals (raw MLB only — historical actual)
MLB Career	—			—	4550	3919	1068	233	19	184	504	1064	103	44	20	273	370	483	853	210	328	23	11	376	141	+221	+1	-14	+6	+99	31.8
▸ Career — adjusted (every ACTUAL line translated to park/league-neutral MLB equivalents)
2013	21	A+ MLE	Daytona Cubs	—	62	58	15	4	1	3	2	23	2	1	0	259	306	517	824	259	353	37	3	350	123	—	—	—	—	—	—
2014	22	AAA MLE	Iowa Cubs	—	297	264	60	10	1	18	23	110	9	7	2	227	311	477	788	250	301	37	8	340	116	—	—	—	—	—	—
2014	22	AA MLE	Tennessee Smokies	—	297	262	76	15	0	18	29	96	5	8	2	290	372	553	925	263	387	32	10	397	155	—	—	—	—	—	—
2015	23	MLB MLE	Chicago Cubs	—	650	559	153	30	4	27	77	199	9	13	4	274	371	487	857	213	378	31	12	377	141	+32	+1	+4	+2	+14	5.4
2015	23	AAA MLE	Iowa Cubs	—	33	29	9	1	0	3	1	11	1	2	0	310	355	655	1.010	345	304	33	3	406	161	—	—	—	—	—	—
2016	24	MLB MLE	Chicago Cubs	—	699	608	177	34	3	41	70	154	18	8	5	291	381	559	940	268	332	22	10	406	161	+50	-0	+7	+2	+15	7.6
2017	25	MLB MLE	Chicago Cubs	—	665	554	161	37	3	30	90	128	15	7	5	291	404	531	934	240	334	19	14	408	163	+50	-1	+3	+2	+14	7.0
2018	26	MLB MLE	Chicago Cubs	—	457	395	106	27	3	14	42	107	17	2	4	268	363	458	822	190	342	23	9	361	130	+17	-1	-1	+2	+10	2.6
2019	27	MLB MLE	Chicago Cubs	—	634	544	153	34	1	32	73	145	15	4	0	281	381	524	905	243	331	23	12	397	155	+41	+1	-3	+2	+14	5.7
2020	28	MLB MLE	Chicago Cubs	—	147	131	27	5	1	4	12	40	4	0	0	206	293	351	644	145	264	27	8	292	84	-3	+0	-2	+1	+3	-0.1
2021	29	MLB MLE	San Francisco Giants	—	586	517	139	32	2	27	58	135	9	10	2	269	353	495	848	226	313	23	10	372	138	+27	+1	-8	+2	+13	3.5
2022	30	MLB MLE	Colorado Rockies	—	181	160	47	12	0	4	17	27	2	0	0	294	369	444	812	150	338	15	9	363	132	+7	+0	-3	-2	+4	0.5
2022	30	AAA MLE	Albuquerque Isotopes	—	22	21	2	1	0	0	1	10	0	0	0	95	136	143	279	48	201	45	5	156	-8	—	—	—	—	—	—
2023	31	MLB MLE	Colorado Rockies	—	335	300	67	10	0	9	29	68	6	0	0	223	304	347	651	123	270	20	9	298	88	-5	+0	-7	-4	+7	-0.8
2024	32	MLB MLE	Colorado Rockies	—	155	133	28	5	0	2	13	48	8	0	0	211	318	293	611	83	321	31	8	286	80	-4	+0	-4	-2	+3	-0.7
2024	32	AAA MLE	Albuquerque Isotopes	—	33	32	7	1	0	1	1	10	0	0	0	219	242	344	586	125	283	30	3	262	63	—	—	—	—	—	—
2025	33	MLB MLE	Colorado Rockies	—	41	39	6	2	0	0	2	13	0	0	0	154	195	205	400	51	231	32	5	181	9	-4	+0	+0	-0	+1	-0.4
2013	21	INTL MLE	2 teams	—	154	145	37	10	2	9	6	56	2	4	0	255	294	538	832	283	346	36	4	350	123	—	—	—	—	—	—
2013	21	INTL RAW	2 teams	—	154	134	47	13	2	11	17	40	2	4	0	351	431	724	1.155	373	429	26	11	488	123	—	—	—	—	—	—
2014	22	AAA MLE	2 teams	—	594	526	136	25	1	36	52	206	14	15	4	259	341	515	856	257	350	35	9	368	136	—	—	—	—	—	—
2014	22	AAA RAW	2 teams	—	594	492	160	34	1	43	86	162	14	15	4	325	439	661	1.100	335	405	27	14	470	136	—	—	—	—	—	—
2015	23	MLB MLE	2 teams	—	683	588	162	31	4	30	78	210	10	15	4	276	370	495	865	219	372	31	11	379	142	+34	+1	+4	+2	+15	5.8
2015	23	MLB RAW	2 teams	—	683	587	163	32	5	29	79	208	10	15	4	278	373	497	870	220	375	30	12	380	142	+34	+1	+4	+2	+15	5.8
2016	24	MLB MLE	Chicago Cubs	—	699	608	177	34	3	41	70	154	18	8	5	291	381	559	940	268	327	22	10	406	161	+50	-0	+7	+2	+15	7.6
2016	24	MLB RAW	Chicago Cubs	—	699	603	176	35	3	39	75	154	18	8	5	292	386	554	940	262	332	22	11	409	161	+50	-0	+7	+2	+15	7.6
2017	25	MLB MLE	Chicago Cubs	—	665	554	161	37	3	30	90	128	15	7	5	291	404	531	934	240	326	19	14	408	163	+50	-1	+3	+2	+14	7.0
2017	25	MLB RAW	Chicago Cubs	—	665	549	162	38	4	29	95	128	15	7	5	295	413	537	950	242	334	19	14	414	163	+50	-1	+3	+2	+14	7.0
2018	26	MLB MLE	Chicago Cubs	—	457	395	106	27	3	14	42	107	17	2	4	268	363	458	822	190	332	23	9	361	130	+17	-1	-1	+2	+10	2.6
2018	26	MLB RAW	Chicago Cubs	—	457	389	106	28	3	13	48	107	17	2	4	272	377	460	837	188	342	23	11	372	130	+17	-1	-1	+2	+10	2.6
2019	27	MLB MLE	Chicago Cubs	—	634	544	153	34	1	32	73	145	15	4	0	281	381	524	905	243	328	23	12	397	155	+41	+1	-3	+2	+14	5.7
2019	27	MLB RAW	Chicago Cubs	—	634	543	153	35	1	31	74	145	15	4	0	282	383	521	904	239	331	23	12	397	155	+41	+1	-3	+2	+14	5.7
2020	28	MLB MLE	Chicago Cubs	—	147	131	27	5	1	4	12	40	4	0	0	206	293	351	644	145	264	27	8	292	84	-3	+0	-2	+1	+3	-0.1
2020	28	MLB RAW	Chicago Cubs	—	147	131	27	5	1	4	12	40	4	0	0	206	293	351	644	145	264	27	8	293	84	-3	+0	-2	+1	+3	-0.1
2021	29	MLB MLE	San Francisco Giants	—	586	517	139	32	2	27	58	135	9	10	2	269	353	495	848	226	314	23	10	372	138	+27	+1	-8	+2	+13	3.5
2021	29	MLB RAW	San Francisco Giants	—	586	513	136	32	2	25	62	135	9	10	2	265	354	481	836	216	313	23	11	368	138	+27	+1	-8	+2	+13	3.5
2022	30	MLB MLE	2 teams	—	203	181	49	13	0	4	18	37	2	0	0	271	343	409	752	138	317	18	9	341	117	+4	+0	-3	-2	+4	0.3
2022	30	MLB RAW	2 teams	—	203	180	52	14	0	5	19	35	2	0	0	289	363	450	813	161	331	17	9	359	117	+4	+0	-3	-2	+4	0.3
2023	31	MLB MLE	Colorado Rockies	—	335	300	67	10	0	9	29	68	6	0	0	223	304	347	651	123	260	20	9	298	88	-5	+0	-7	-4	+7	-0.8
2023	31	MLB RAW	Colorado Rockies	—	335	300	70	10	0	10	29	68	6	0	0	233	313	367	680	133	270	20	9	309	88	-5	+0	-7	-4	+7	-0.8
2024	32	MLB MLE	2 teams	—	188	165	35	6	0	3	14	58	8	0	0	212	305	303	608	91	305	31	7	282	77	-5	+0	-4	-2	+4	-0.7
2024	32	MLB RAW	2 teams	—	188	164	38	6	0	4	15	56	8	0	0	232	326	341	668	110	324	30	8	307	77	-5	+0	-4	-2	+4	-0.7
2025	33	MLB MLE	Colorado Rockies	—	41	39	6	2	0	0	2	13	0	0	0	154	195	205	400	51	231	32	5	181	9	-4	+0	+0	-0	+1	-0.4
2025	33	MLB RAW	Colorado Rockies	—	41	39	6	2	0	0	2	13	0	0	0	154	195	205	400	51	231	32	5	185	9	-4	+0	+0	-0	+1	-0.4
2013	21	INTL MLE	2 teams	—	154	145	37	10	2	9	6	56	2	4	0	255	294	538	832	283	353	36	4	350	123	—	—	—	—	—	—
2014	22	AAA MLE	2 teams	—	594	523	136	25	1	36	52	206	14	15	4	260	341	518	859	258	344	35	9	368	136	—	—	—	—	—	—
2015	23	MLB MLE	2 teams	—	683	590	162	31	4	30	78	210	10	15	4	275	370	493	863	219	374	31	11	379	142	—	—	—	—	—	—
2016	24	MLB MLE	Chicago Cubs	—	699	606	177	34	3	41	70	154	18	8	5	292	381	561	942	269	332	22	10	406	161	—	—	—	—	—	—
2017	25	MLB MLE	Chicago Cubs	—	665	555	161	37	3	30	90	128	15	7	5	290	404	530	933	240	334	19	14	408	163	—	—	—	—	—	—
2018	26	MLB MLE	Chicago Cubs	—	457	395	106	27	3	14	42	107	17	2	4	268	363	458	822	190	342	23	9	361	130	—	—	—	—	—	—
2019	27	MLB MLE	Chicago Cubs	—	634	541	153	34	1	32	73	145	15	4	0	283	381	527	908	244	331	23	12	397	155	—	—	—	—	—	—
2020	28	MLB MLE	Chicago Cubs	—	147	130	27	5	1	4	12	40	4	0	0	208	293	354	646	146	264	27	8	292	84	—	—	—	—	—	—
2021	29	MLB MLE	San Francisco Giants	—	586	514	139	32	2	27	58	135	9	10	2	270	353	498	851	228	313	23	10	372	138	—	—	—	—	—	—
2022	30	MLB MLE	2 teams	—	203	181	49	13	0	4	18	37	2	0	0	271	343	409	752	138	324	18	9	341	117	—	—	—	—	—	—
2023	31	MLB MLE	Colorado Rockies	—	335	297	67	10	0	9	29	68	6	0	0	226	304	350	655	125	270	20	9	298	88	—	—	—	—	—	—
2024	32	MLB MLE	2 teams	—	188	164	35	6	0	3	14	58	8	0	0	213	305	305	610	91	315	31	7	282	77	—	—	—	—	—	—
2025	33	MLB MLE	Colorado Rockies	—	41	39	6	2	0	0	2	13	0	0	0	154	195	205	400	51	231	32	5	181	9	—	—	—	—	—	—
▸ Model inputs — neutral MLE (past 3 seasons)
2024	32	MLB MLE	2 teams	—	188	164	35	6	0	3	14	58	8	0	0	213	305	305	610	91	315	31	7	282	77	—	—	—	—	—	—
2025	33	MLB MLE	Colorado Rockies	—	41	39	6	2	0	0	2	13	0	0	0	154	195	205	400	51	231	32	5	181	9	—	—	—	—	—	—
▸ 2027 season
▸ 2027 — full MLE projection (neutral park, full availability)
2027	35	MLB MLE	COL	100%	143	128	30	6	1	3	11	37	3	2	1	231	303	352	655	121	302	26	8	295	86	-2	+0	-1	-2	+3	-0.2
▸ 2027 — real world (MLB)
2027	35	MLB CTX	COL	100%	72	66	15	3	0	2	5	19	1	1	0	243	314	378	691	135	316	26	7	310	86	-1	+0	-0	-1	+2	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (PA × MLB%)
2028	36	MLB MLE	COL	100%	80	73	15	3	0	1	6	22	1	0	0	219	289	326	615	107	291	28	8	279	75	-2	+0	-7	-1	+2	-0.8
2028	36	MLB CTX	COL	100%	80	73	15	3	0	1	6	22	1	0	0	230	299	349	648	120	304	28	8	292	75	-2	+0	-7	-1	+2	-0.8
▸ Career Totals
▸ Career totals — MLB only (actual + projected)
Career	—	A+C		—	4630	3992	1083	236	19	185	510	1086	104	44	20	271	368	479	847	208	327	23	11	375	135	+218	+1	-21	+5	+100	30.9
/ 162G	—	A+C		—	650	560	152	33	3	26	72	152	15	6	3	271	369	480	850	209	327	23	11	375	135	+31	+0	-3	+1	+14	4.3
Career	—	MLE		—	5538	4832	1284	272	20	241	555	1398	122	66	24	266	356	480	836	214	324	25	10	368	135	+217	+1	-21	+4	+102	30.9
/ 162G	—	MLE		—	650	567	150	32	2	28	65	164	14	8	3	265	354	476	830	212	323	25	10	368	135	+30	+0	-3	+1	+14	4.3

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.

2024 AAAAlbuquerque Isotopes · 33 PAwOBA .361 → .262 (−99)K% 24.2% → 29.7%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	24.24%	÷0.979	×1.200	—	—	29.71%
BB% (unintentional) /PA	6.06%	÷1.018	×0.690	—	×0.952	3.91%
HBP% /PA	0.00%	—	×0.940	—	×0.952	0.00%
Singles /BIP	26.09%	÷0.990	×0.930	—	×0.952	23.34%
Doubles /BIP	4.35%	÷0.988	×0.830	—	×0.952	3.48%
Triples /BIP	0.00%	÷1.150	×0.970	—	×0.952	0.00%
Home runs /BIP	8.70%	÷1.096	×0.700	—	×0.952	5.29%

Factors used: league row International League. Park (home-season share, regressed): HR 119, 1B 98, 2B 98, 3B 130, K 96, BB 104 — applied at half strength. Age: age 32 vs a level average of 27.2 → −4.8% on BB/HBP/hit rates (4.8 yr older than average).

Result: wOBA .361 → .262; ISO .226 → .137; BABIP .333 → .283. 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 AAAAlbuquerque Isotopes · 22 PAwOBA .216 → .156 (−60)K% 36.4% → 43.8%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	36.36%	÷0.995	×1.200	—	—	43.84%
BB% (unintentional) /PA	9.09%	÷0.945	×0.690	—	×0.972	6.45%
HBP% /PA	0.00%	—	×0.940	—	×0.972	0.00%
Singles /BIP	8.33%	÷1.047	×0.930	—	×0.972	7.20%
Doubles /BIP	16.67%	÷1.038	×0.830	—	×0.972	12.95%
Triples /BIP	0.00%	÷1.150	×0.970	—	×0.972	0.00%
Home runs /BIP	0.00%	÷1.061	×0.700	—	×0.972	0.00%

Factors used: league row International League. Park (home-season share, regressed): HR 112, 1B 109, 2B 108, 3B 130, K 99, BB 89 — 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 .216 → .156; ISO .100 → .071; BABIP .250 → .201. 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 AAAIowa Cubs · 33 PAwOBA .444 → .406 (−38)K% 27.3% → 33.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.27%	÷1.051	×1.290	—	—	33.47%
BB% (unintentional) /PA	6.06%	÷0.996	×0.710	—	×1.050	4.53%
HBP% /PA	3.03%	—	×0.980	—	×1.050	3.12%
Singles /BIP	26.32%	÷1.008	×0.900	—	×1.050	24.67%
Doubles /BIP	5.26%	÷0.984	×0.810	—	×1.050	4.55%
Triples /BIP	0.00%	÷0.948	×0.810	—	×1.050	0.00%
Home runs /BIP	15.79%	÷0.916	×0.780	—	×1.050	14.12%

Factors used: league row PCL. Park (home-season share, regressed): HR 83, 1B 102, 2B 97, 3B 90, K 110, BB 99 — 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).

Result: wOBA .444 → .406; ISO .357 → .325; BABIP .333 → .304. 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 AAAIowa Cubs · 297 PAwOBA .432 → .340 (−92)K% 28.6% → 37.1%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	28.62%	÷0.996	×1.290	—	—	37.06%
BB% (unintentional) /PA	13.13%	÷1.249	×0.710	—	×1.050	7.84%
HBP% /PA	3.03%	—	×0.980	—	×1.050	3.12%
Singles /BIP	22.64%	÷1.021	×0.900	×0.950	×1.050	19.91%
Doubles /BIP	8.81%	÷1.111	×0.810	×0.950	×1.050	6.40%
Triples /BIP	0.63%	÷1.150	×0.810	×0.950	×1.050	0.44%
Home runs /BIP	13.21%	÷0.929	×0.780	—	×1.050	11.65%

Factors used: league row PCL. Park (home-season share, regressed): HR 86, 1B 104, 2B 122, 3B 130, K 99, BB 150 — 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 .432 → .340; ISO .324 → .267; BABIP .367 → .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).

2014 AATennessee Smokies · 297 PAwOBA .479 → .397 (−82)K% 25.9% → 32.3%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	25.93%	÷0.980	×1.220	—	—	32.27%
BB% (unintentional) /PA	13.13%	÷0.948	×0.680	—	×1.042	9.81%
HBP% /PA	1.68%	—	×0.840	—	×1.042	1.47%
Singles /BIP	26.90%	÷0.958	×0.920	×0.950	×1.042	25.57%
Doubles /BIP	11.70%	÷1.140	×0.910	×0.950	×1.042	9.24%
Triples /BIP	0.00%	÷1.065	×0.880	×0.950	×1.042	0.00%
Home runs /BIP	12.87%	÷1.009	×0.800	—	×1.042	10.63%

Factors used: league row SL. Park (home-season share, regressed): HR 102, 1B 92, 2B 128, 3B 113, K 96, BB 90 — 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 .479 → .397; ISO .347 → .278; BABIP .440 → .387. 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).

2013 A+Daytona Cubs · 62 PAwOBA .458 → .350 (−108)K% 27.4% → 36.5%

Component	Raw	Park ÷	Level factor ×	GB/FB ×	Age ×	= MLE
K% /PA	27.42%	÷1.066	×1.420	—	—	36.54%
BB% (unintentional) /PA	4.84%	÷1.032	×0.750	—	×1.027	3.61%
HBP% /PA	3.23%	—	×0.930	—	×1.027	3.08%
Singles /BIP	20.00%	÷1.015	×0.920	—	×1.027	18.62%
Doubles /BIP	12.50%	÷1.132	×0.960	—	×1.027	10.89%
Triples /BIP	2.50%	÷0.900	×0.810	—	×1.027	2.31%
Home runs /BIP	12.50%	÷1.144	×0.880	—	×1.027	9.87%

Factors used: league row FSL. Park (home-season share, regressed): HR 129, 1B 103, 2B 126, 3B 80, K 113, BB 106 — 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).

Result: wOBA .458 → .350; ISO .386 → .279; BABIP .400 → .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).

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
#1J. D. Drew'98-'11 * career totals — 13 seasons, 6,153 PA	9.3	6,153	242	87	268*	371*	478*	-54	+1	37.3	32.7
/162	664	26	9	-5.8	+0.1	4.0	pk7: 28
#2Travis Hafnerlast yr '13 career totals — 12 seasons, 4,782 PA	7.2	4,782	213	11	273	376	498	-61	-7	32.9	31.7
/162	664	30	2	-8.4	-1.0	4.6	pk7: 31
#3Jayson Werthlast yr '17 career totals — 15 seasons, 6,366 PA	9.6	6,366	229	132	267	360	455	-68	-36	32.1	29.8
/162	664	24	14	-7.1	-3.7	3.3	pk7: 27
OGOGILVIE CAREER PROJECTION career totals — every cell rolls up across 12 projected seasons	7.0	4,669	187	45	274	367	484	-51	-22	29.5	29.7
/162	664	27	6	-7.3	-3.1	4.2	pk7: 30
#4Kevin Youkilislast yr '13 career totals — 10 seasons, 4,436 PA	6.7	4,436	150	26	281	382	478	-47	-1	30.3	28.9
/162	664	22	4	-7.1	-0.1	4.5	pk7: 28
#5Sixto Lezcano'74-'85 * career totals — 12 seasons, 4,814 PA	7.2	4,814	148	37	267*	359*	476*	-51	-9	24.0	23.1
/162	664	20	5	-7.1	-1.3	3.3	pk7: 22
#6Steve Kemp'77-'88 * career totals — 9 seasons, 4,714 PA	7.1	4,714	130	39	273*	366*	456*	-56	-12	20.9	20.4
/162	664	18	5	-7.9	-1.7	2.9	pk7: 20

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
Kris Bryant (OGILVIE)	35	35	40	40	45	229/301/349	120	302	1	1	26%	7%	86	+0.0	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—
✕Shawn Wooten 2007 · MLB	35	42	43→45	47→30	41	210/254/317	107	250	12	3	22%	4%	60	-1.6	201/252/285	84	249	8	5	22%	6%	52	-2.2	175/230/219	43	218	4	0	20%	5%	27	-1.2
✕Issmael Salas 2017 · AAA	35	48	42→45	42→50	44	236/275/342	107	266	10	7	16%	4%	73	-0.6	233/279/343	110	256	11	6	14%	5%	75	-0.5	231/277/333	102	249	11	3	12%	5%	71	-0.5
✕Pedro Diaz 2014 · AAA	35	44→47	42→45	44→45	37	220/250/325	105	282	9	6	26%	3%	59	-1.6	219/246/344	125	261	15	4	23%	3%	63	-1.4	215/239/340	125	249	17	0	21%	2%	57	-0.9
✕Abraham Valencia 2014 · AAA	34	44→50	47→45	43→45	41	227/283/359	132	269	16	4	23%	5%	82	-0.0	232/294/334	102	282	10	7	21%	5%	79	-0.2	237/301/296	59	285	5	3	18%	5%	70	-0.4
✕Jolbert Cabrera 2008 · AAA	35	45→48	43→45	41→50	42	220/265/333	113	253	10	6	18%	4%	68	-1.0	239/282/363	124	289	10	11	21%	4%	81	-0.1	253/295/377	124	306	10	12	20%	4%	87	+0.2
✕Brian Burgamy 2016 · AAA	35	44→47	49→50	41→50	48	226/293/372	145	276	17	5	24%	8%	88	+0.4	226/294/342	116	303	11	9	27%	8%	81	-0.1	225/298/331	106	305	9	10	27%	9%	80	-0.1
✕Vinny Rottino 2016 · AAA	36	44→54	42→45	49→55	45	218/276/317	99	276	10	5	24%	7%	68	-1.0	209/276/303	94	272	9	6	25%	7%	65	-1.2	173/252/229	57	227	6	0	25%	8%	38	-1.4
✕Emmanuel Valdez 2013 · AAA	34	43→51	48→50	44→40	44	219/286/353	134	288	16	4	29%	8%	81	-0.1	211/288/362	151	294	18	5	33%	9%	84	+0.2	198/283/349	151	275	19	4	33%	9%	80	-0.1
✕Jantzen Witte 2024 · AAA	34	42→52	47→45	44→45	42	217/269/353	136	280	15	8	28%	6%	74	-0.5	214/273/354	140	279	16	8	28%	7%	76	-0.4	187/251/323	136	227	18	0	26%	8%	59	-0.5
✕Ryan Roberts 2015 · MLB	34	44→52	44→50	41→55	46	219/271/332	113	265	11	7	22%	7%	70	-0.9	224/276/353	129	256	14	4	18%	6%	76	-0.4	221/271/348	127	243	14	0	16%	6%	72	-0.6
✕Shamar Almeida 2007 · AAA	35	45	43→45	46→45	55	219/315/325	107	285	11	8	26%	12%	86	+0.3	234/316/337	103	275	9	5	17%	10%	88	+0.4	249/330/346	97	284	9	2	15%	10%	94	+0.5
✕Edgard Clemente 2010 · AAA	34	46	50	39→40	49	236/302/386	150	279	18	6	22%	8%	95	+0.9	246/305/399	153	301	17	8	23%	7%	99	+1.2	225/287/381	156	284	20	9	28%	6%	84	+0.0
✕Doug Bernier 2015 · MLB	35	47→52	39→40	45	44	226/278/314	89	286	6	7	23%	6%	68	-1.0	221/274/303	82	283	7	6	23%	6%	64	-1.3	211/266/280	69	273	7	4	24%	6%	53	-1.3
✕Matt McBride 2019 · MLB	34	38→48	51→55	42→40	45	204/261/364	161	235	18	4	23%	6%	74	-0.5	214/266/385	171	257	22	7	26%	6%	81	-0.1	174/211/355	181	185	29	0	27%	3%	51	-0.6
✕Ildelfonso Lara 2009 · AAA	34	48→51	42→45	50→45	47	233/291/341	108	288	9	6	22%	7%	79	-0.2	229/288/340	111	296	11	8	25%	7%	78	-0.3	227/285/319	92	295	10	5	25%	6%	70	-0.4
✕Mike Rivera 2012 · MLB	35	39	43→55	44→45	43	195/264/303	108	264	10	6	29%	7%	61	-1.5	211/270/317	106	285	11	7	29%	6%	66	-1.1	194/245/256	62	262	7	0	28%	4%	40	-0.8
✕Jonathan Aceves 2013 · AAA	35	43	47→50	42→30	42	220/269/354	135	276	15	6	26%	6%	75	-0.5	211/264/333	122	256	14	4	23%	6%	68	-1.0	228/295/332	104	273	11	1	20%	7%	78	-0.2
✕Chad Mottola 2007 · MLB	35	40→41	49→50	47→45	39	211/248/355	144	254	17	7	25%	4%	67	-1.1	220/262/368	148	262	17	9	23%	5%	75	-0.5	213/253/365	152	243	18	7	21%	4%	69	-0.7
✕Valentino Pascucci 2013 · AAA	34	39→42	51→55	39→45	46	201/283/356	154	288	19	2	36%	10%	81	-0.1	211/298/364	153	309	20	5	35%	10%	88	+0.5	184/291/334	150	259	23	0	36%	11%	78	-0.1
✕Jason Wood 2007 · AAA	37	46	45	44→40	44	230/280/349	119	279	13	4	23%	6%	77	-0.3	225/273/322	97	282	10	5	23%	6%	68	-1.0	218/265/289	71	274	7	3	22%	5%	56	-0.8

Kris Bryant

Career Stats + Projections

How the minor-league lines were translated

Career Comps

Career Path Comps