Clayton Kershaw

PosP

B/TL/L

Born1988-03-19

Age (2027)39

No MLB projection yet. Career stats below are historical.

Overview

MLBSP

Pitching

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	35	MLBLAD	24	24	132	2.46	3.81	26.2%	7.6%	2.5
2024	36	MIX3 levels	10	10	40	4.28	3.02	21.1%	5.8%	1.2	K 18.0% · BB 6.0% · FIP 3.13
2025	37	MIX4 levels	28	27	134	3.23	3.57	18.6%	7.4%	3.0	K 18.1% · BB 7.6% · FIP 3.44
2026	38	No MLB games played
2027PROJ	39	MLBLAD	11	11	69	4.08	4.36	16.8%	8.0%	0.8
2028PROJ	40	MLBLAD	4	4	27	4.58	4.86	15.7%	8.7%	0.2

Last 3 seasons WAR 6.7 · Next 2 projected WAR 1.0Projected rows: MLB playing time only, park and league context applied.

Hitting

Year	Age	Lvl / Team	PA	AVG/OBP/SLG	HR	SB	wRC+	WAR
2018	30	MLBLAD	57	.239/.340/.283	0	0	76	-0.0
2019	31	MLBLAD	65	.102/.120/.122	0	0	-54	-1.1
2021	33	MLBLAD	39	.222/.243/.222	0	0	26	-0.3
2022	34	No MLB games played
2023	35	No MLB games played
2024	36	No MLB games played
2025	37	No MLB games played
2026	38	No MLB games played

Last 3 seasons WAR -1.4

Contract

No contract data on file.

CompsMauricio Lara (age 38) · Juan Antonio Pena (age 37) · Francisco Campos (age 42)

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 WAR & MLE are computed

Stats are park-neutral MLB equivalents. The "TEAM" column shows current org affiliation only — we do NOT project at that team's park.
Rates (K%, BB%, HR-BF) are park-stripped; MiLB stat lines are translated to their MLB-equivalent via per-level MLE factors.
WAR is computed from projected runs prevented vs a replacement pitcher (the marginal arm a team would call up if this pitcher were unavailable) — calibrated to the standard WAR scale.

Year	Age	Lvl	Team	G	GS	IP	BF	K	BB	HR	GS%	IP/G	K%	BB%	K-BB%	HR%	ERA	FIP	WAR
▸ Career — actual stats, one row per year (as recorded, no adjustments)
▸ Career — adjusted (each season translated to a park/league-neutral MLB-equivalent line; levels combined per year)
▸ Model inputs — neutral MLE (past 3 seasons + 2027 so far)
2008	20	MLB MLE	Los Angeles Dodgers	22	21	108	470	100	49	11	95%	4.9	21.3%	10.4%	10.9%	2.3%	4.26	3.91	+1.9
2008	20	MLB RAW	Los Angeles Dodgers	22	21	108	470	100	49	11	95%	4.9	21.3%	10.4%	10.9%	2.3%	4.26	3.94	+1.9
2009	21	MLB MLE	Los Angeles Dodgers	31	30	171	701	185	87	7	97%	5.5	26.4%	12.4%	14.0%	1.0%	2.79	2.99	+5.2
2009	21	MLB RAW	Los Angeles Dodgers	31	30	171	701	185	87	7	97%	5.5	26.4%	12.4%	14.0%	1.0%	2.79	2.99	+5.2
2010	22	MLB MLE	Los Angeles Dodgers	32	32	204	848	212	72	13	100%	6.4	25.0%	8.5%	16.5%	1.5%	2.91	2.87	+6.6
2010	22	MLB RAW	Los Angeles Dodgers	32	32	204	848	212	72	13	100%	6.4	25.0%	8.5%	16.5%	1.5%	2.91	2.91	+6.5
2011	23	MLB MLE	Los Angeles Dodgers	33	33	233	912	248	52	15	100%	7.1	27.2%	5.7%	21.5%	1.6%	2.28	2.39	+9.3
2011	23	MLB RAW	Los Angeles Dodgers	33	33	233	912	248	52	15	100%	7.1	27.2%	5.7%	21.5%	1.6%	2.28	2.48	+9.0
2012	24	MLB MLE	Los Angeles Dodgers	33	33	228	901	229	58	16	100%	6.9	25.4%	6.4%	19.0%	1.7%	2.53	2.70	+8.0
2012	24	MLB RAW	Los Angeles Dodgers	33	33	228	901	229	58	16	100%	6.9	25.4%	6.4%	19.0%	1.8%	2.53	2.77	+7.7
2013	25	MLB MLE	Los Angeles Dodgers	33	33	236	908	232	50	11	100%	7.2	25.6%	5.5%	20.0%	1.2%	1.83	2.28	+9.9
2013	25	MLB RAW	Los Angeles Dodgers	33	33	236	908	232	50	11	100%	7.2	25.6%	5.5%	20.0%	1.2%	1.83	2.38	+9.5
2014	26	MLB MLE	Los Angeles Dodgers	27	27	198	749	239	31	9	100%	7.3	31.9%	4.1%	27.8%	1.2%	1.77	1.56	+10.7
2014	26	MLB RAW	Los Angeles Dodgers	27	27	198	749	239	31	9	100%	7.3	31.9%	4.1%	27.8%	1.2%	1.77	1.75	+10.2
2015	27	MLB MLE	Los Angeles Dodgers	33	33	233	890	301	41	15	100%	7.1	33.8%	4.6%	29.2%	1.6%	2.13	1.71	+12.0
2015	27	MLB RAW	Los Angeles Dodgers	33	33	233	890	301	41	15	100%	7.1	33.8%	4.6%	29.2%	1.7%	2.13	1.88	+11.3
2016	28	MIX MLE	2 levels	22	22	152	553	177	10	8	100%	6.9	31.6%	1.8%	29.8%	1.4%	1.66	1.44	+8.4
2016	28	MIX RAW	2 levels	22	22	152	553	177	10	8	100%	6.9	32.0%	1.8%	30.2%	1.4%	1.66	1.65	+7.9
2017	29	MIX MLE	2 levels	28	28	180	695	210	30	24	100%	6.4	29.7%	4.4%	25.3%	3.2%	2.30	2.93	+5.6
2017	29	MIX RAW	2 levels	28	28	180	695	210	30	24	100%	6.4	30.2%	4.3%	25.9%	3.5%	2.30	3.00	+5.5
2018	30	MLB MLE	Los Angeles Dodgers	26	26	161	650	155	29	17	100%	6.2	23.8%	4.5%	19.4%	2.5%	2.73	3.03	+4.8
2018	30	MLB RAW	Los Angeles Dodgers	26	26	161	650	155	29	17	100%	6.2	23.8%	4.5%	19.4%	2.6%	2.73	3.09	+4.7
2019	31	MIX MLE	3 levels	31	30	189	745	201	43	31	97%	6.1	26.8%	5.8%	21.0%	3.8%	3.05	3.66	+4.0
2019	31	MIX RAW	3 levels	31	30	189	745	201	43	31	97%	6.1	27.0%	5.8%	21.2%	4.2%	3.05	3.79	+3.7
2020	32	MLB MLE	Los Angeles Dodgers	10	10	58	221	62	8	8	100%	5.8	28.1%	3.6%	24.4%	3.5%	2.16	3.10	+1.7
2020	32	MLB RAW	Los Angeles Dodgers	10	10	58	221	62	8	8	100%	5.8	28.1%	3.6%	24.4%	3.6%	2.16	3.17	+1.6
2021	33	MIX MLE	2 levels	23	23	125	501	147	21	16	100%	5.4	29.5%	4.3%	25.2%	2.9%	3.61	2.77	+4.1
2021	33	MIX RAW	2 levels	23	23	125	501	147	21	16	100%	5.4	29.3%	4.2%	25.1%	3.2%	3.61	2.92	+3.9
2022	34	MIX MLE	2 levels	23	23	130	509	144	24	11	100%	5.7	27.8%	4.7%	23.1%	1.9%	2.28	2.41	+5.0
2022	34	MIX RAW	2 levels	23	23	130	509	144	24	11	100%	5.7	28.3%	4.7%	23.6%	2.2%	2.28	2.54	+4.8
2023	35	MLB MLE	Los Angeles Dodgers	24	24	132	523	137	40	19	100%	5.5	26.2%	7.6%	18.5%	3.5%	2.46	3.77	+2.6
2023	35	MLB RAW	Los Angeles Dodgers	24	24	132	523	137	40	19	100%	5.5	26.2%	7.6%	18.5%	3.6%	2.46	3.81	+2.5
2024	36	MIX MLE	3 levels	10	10	40	171	36	10	3	100%	4.0	18.0%	6.0%	12.0%	1.4%	4.28	3.13	+1.1
2024	36	MIX MLE	3 levels	10	10	40	171	36	10	3	100%	4.0	18.0%	6.0%	12.0%	1.4%	4.28	3.13	+1.1
2024	36	MIX RAW	3 levels	10	10	40	171	36	10	3	100%	4.0	21.1%	5.8%	15.2%	1.8%	4.28	3.02	+1.2
2025	37	MIX MLE	4 levels	28	27	134	539	100	40	11	96%	4.8	18.1%	7.6%	10.6%	1.7%	3.23	3.44	+3.2
2025	37	MIX MLE	4 levels	28	27	134	539	100	40	11	96%	4.8	18.1%	7.6%	10.6%	1.7%	3.23	3.44	+3.2
2025	37	MIX RAW	4 levels	28	27	134	539	100	40	11	96%	4.8	18.6%	7.4%	11.1%	2.0%	3.23	3.57	+3.0
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			455	451	2855	11279	3052	685	235	99%	6.3	27.1%	6.1%	21.0%	2.1%	2.53	2.75	+97.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	39	MLB	LAD	11	11	69	291	49	23	7	100%	6.3	16.8%	8.0%	8.8%	2.3%	4.08	4.36	+0.8
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	40	MLB MLE	LAD	4	4	27	113	18	10	3	100%	6.7	15.7%	8.7%	7.0%	2.8%	4.51	4.79	+0.2
2028	40	MLB CTX	LAD	4	4	27	114	18	10	3	100%	6.8	15.7%	8.7%	7.0%	2.9%	4.58	4.86	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		459	455	2882	11392	3070	695	238	99%	6.3	26.9%	6.1%	20.8%	2.1%	2.55	2.75	+98.2
/ 200 IP	—	RAW		32	32	200	791	213	48	17	99%	6.3	26.9%	6.1%	20.8%	2.1%	2.55	2.75	+6.8
Career	—	MLE		469	465	2912	11486	3099	697	231	99%	6.2	27.0%	6.1%	20.9%	2.0%	2.54	2.69	+97.0
/ 200 IP	—	MLE		32	32	200	789	213	48	16	99%	6.2	27.0%	6.1%	20.9%	2.0%	2.54	2.69	+6.7

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 AAAOklahoma City Comets · 12 IPFIP 6.24 → 6.95 (+0.71)K% 18.6% → 16.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.60%	—	×0.860	16.00%
BB% (unintentional) /BF	6.98%	—	×0.900	6.28%
HR / BF /BF	6.98%	÷0.956	×1.100	8.03%

Factors used: pitcher AAA level factors (Triple-A 2023+ robo-zone set: walks improve on promotion) — K ×0.86, BB ×0.90, HR ×1.10. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 91).

Result: FIP 6.24 → 6.95, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2024 AAAOklahoma City Baseball Club · 7 IPFIP 3.41 → 4.13 (+0.72)K% 25.9% → 22.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.93%	—	×0.860	22.30%
BB% (unintentional) /BF	3.70%	—	×0.900	3.33%
HR / BF /BF	3.70%	÷0.896	×1.100	4.55%

Result: FIP 3.41 → 4.13, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

2019 AATulsa Drillers · 6 IPFIP 5.79 → 7.82 (+2.03)K% 27.3% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.27%	—	×0.810	22.09%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	9.09%	÷0.909	×1.200	11.99%

Factors used: pitcher AA level factors — K ×0.81, BB ×1.10, HR ×1.20. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 82).

Result: FIP 5.79 → 7.82, computed from the rates in the table with the same formula before and after. The MLE line is not regressed to the mean — regression is applied when seasons are blended into a projection (how).

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	14.8	148	26.7%	6.4%	0.81	2.59
Pedro Martínez ('09)	14.1	166	27.7%	6.7%	0.76	2.93
Curt Schilling ('07)	16.2	180	23.6%	5.3%	0.95	3.42
Johan Santana ('12)	10.1	169	24.1%	6.9%	0.98	3.20
Jose Rijo ('84-'02, 33 WAR*)	9.4	145	25.9%*	8.4%*	0.93*	3.56*
Kevin Brown ('86-'05, 68 WAR*)	16.3	181	21.5%*	6.3%*	0.67*	3.34*
Bret Saberhagen ('84-'01, 57 WAR*)	12.8	171	21.4%*	4.4%*	0.95*	3.37*

Clayton Kershaw

Career Stats + Projections

How the minor-league lines were translated

Career Path Comps

Career Comps

			Profile at Match Age					Next 3 Yrs				Career
Comp	Age	Role	FIP	K%	BB%	HR/BF	BABIP	FIP	K%	K-BB%	IP	ΔFIP	Low FIP	IP	Match
Clayton Kershaw (OGILVIE)	—	—	—	—	—	2.3%	—	4.79	15.7%	7.0%	27	+0.43	—	—	—
Mauricio Lara L 2017 · 3.70 FIP	38	SP	3.70	12.0%	6.3%	1.5%	.301	4.96	12.3%	5.5%	111	+1.26	3.62	444	68
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	54
Francisco Campos R 2015 · 4.77 FIP	42	SP	4.77	11.5%	7.9%	3.0%	.303	4.08	12.2%	7.0%	197	-0.69	4.06	332	34
Walter Silva R 2016 · 4.22 FIP	39	SP	4.22	10.3%	5.5%	2.4%	.294	4.35	10.0%	1.8%	235	+0.13	3.67	453	33
Pablo Ortega R 2016 · 4.01 FIP	39	SP	4.01	8.9%	4.8%	1.9%	.262	4.49	13.3%	7.1%	148	+0.48	4.01	375	31
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	28
Lorenzo Barcelo R 2016 · 3.48 FIP	38	SP	3.48	12.8%	1.1%	2.4%	.329	—	—	—	—	—	3.14	259	19
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	11