Chris Stratton

P Los Angeles Dodgers

Age 36 RHP 6'2" / 205 lbs MLB

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2023	32	MLBTEX	64	0	83	3.92	3.31	24.0%	7.4%	1.2
2024	33	MLBKC	57	0	58	5.55	4.35	17.1%	12.5%	0.1
2025	34	MLBLAD	15	0	21	7.71	4.91	21.2%	9.6%	-0.1
2026	35	No MLB games played
2027PROJ	36	MLBLAD	167	0	175	4.66	4.62	21.0%	9.9%	-0.1
2028PROJ	37	MLBLAD	109	0	114	4.96	5.10	19.9%	10.6%	-0.6
2029PROJ	38	MLBLAD	77	0	81	5.32	5.55	18.7%	11.3%	-0.8

Last 3 seasons WAR 1.3 · Next 3 projected WAR -1.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

K%43

xwOBA vs22

FB Velo10

CompsKeiichi Yabu (age 36) · Pete Walker (age 36) · Francisco Del Rosario (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 121113vR 105

ERA4.66

FIP4.62

K%21.0%

BB%9.9%

K-BB%11.1%

Proj IP175

Value2027 · WAR

Runs saved vs league avg · 175 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1091.2

Whiff%

p6327.3%

p4321.2%

xwOBA vs

p220.345

Contract

No contract data on file. Pre-MLB players and minor-league free agents do not yet have salary projections — entries are added at first 40-man roster placement.

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)
2015	24	MIX MLE	2 levels	26	26	148	627	111	61	9	100%	5.7	15.1%	10.4%	4.8%	1.7%	3.95	4.09	—
2015	24	MIX RAW	2 levels	26	26	148	627	111	61	9	100%	5.7	17.7%	9.7%	8.0%	1.4%	3.95	3.63	—
2016	25	MIX MLE	2 levels	28	20	136	569	109	43	7	71%	4.8	17.2%	7.6%	9.6%	1.2%	3.85	3.24	+3.6
2016	25	MIX RAW	2 levels	28	20	136	569	109	43	7	71%	4.8	19.2%	7.6%	11.6%	1.2%	3.85	3.11	+3.8
2017	26	MIX MLE	2 levels	28	25	138	608	122	50	15	89%	4.9	18.7%	8.4%	10.3%	2.9%	4.50	4.19	+2.0
2017	26	MIX RAW	2 levels	28	25	138	608	122	50	15	89%	4.9	20.1%	8.2%	11.8%	2.5%	4.50	3.83	+2.6
2018	27	MIX MLE	2 levels	32	30	169	728	136	61	22	94%	5.3	18.3%	8.4%	9.9%	3.3%	4.79	4.42	+1.9
2018	27	MIX RAW	2 levels	32	30	169	728	136	61	22	94%	5.3	18.7%	8.4%	10.3%	3.0%	4.79	4.27	+2.3
2019	28	MIX MLE	2 levels	37	5	80	364	73	33	14	14%	2.2	20.1%	9.3%	10.8%	4.1%	5.51	4.83	-0.3
2019	28	MIX RAW	2 levels	37	5	80	364	73	33	14	14%	2.2	20.1%	9.1%	11.0%	3.8%	5.51	4.79	-0.2
2020	29	MLB MLE	Pittsburgh Pirates	27	0	30	131	39	13	3	0%	1.1	29.8%	9.9%	19.8%	2.5%	3.90	3.20	+0.5
2020	29	MLB RAW	Pittsburgh Pirates	27	0	30	131	39	13	3	0%	1.1	29.8%	9.9%	19.8%	2.3%	3.90	3.10	+0.5
2021	30	MLB MLE	Pittsburgh Pirates	68	0	79	337	86	31	9	0%	1.2	25.5%	9.2%	16.3%	2.9%	3.63	3.69	+0.8
2021	30	MLB RAW	Pittsburgh Pirates	68	0	79	337	86	31	9	0%	1.2	25.5%	9.2%	16.3%	2.7%	3.63	3.58	+0.9
2022	31	MLB MLE	St. Louis Cardinals	60	1	63	279	60	23	4	2%	1.1	21.5%	8.2%	13.3%	1.5%	4.26	3.17	+1.0
2022	31	MLB RAW	St. Louis Cardinals	60	1	63	279	60	23	4	2%	1.1	21.5%	8.2%	13.3%	1.4%	4.26	3.12	+1.1
2023	32	MLB MLE	Texas Rangers	64	0	83	338	81	25	8	0%	1.3	24.0%	7.4%	16.6%	2.2%	3.92	3.22	+1.3
2023	32	MLB RAW	Texas Rangers	64	0	83	338	81	25	8	0%	1.3	24.0%	7.4%	16.6%	2.4%	3.92	3.31	+1.2
2024	33	MLB MLE	Kansas City Royals	57	0	58	257	44	32	5	0%	1.0	17.1%	12.5%	4.7%	2.1%	5.55	4.39	+0.1
2024	33	MLB MLE	Kansas City Royals	57	0	58	257	44	32	5	0%	1.0	17.1%	12.5%	4.7%	2.1%	5.55	4.39	+0.1
2024	33	MLB RAW	Kansas City Royals	57	0	58	257	44	32	5	0%	1.0	17.1%	12.5%	4.7%	1.9%	5.55	4.35	+0.1
2025	34	MLB MLE	Los Angeles Dodgers	15	0	21	104	22	10	4	0%	1.4	21.2%	9.6%	11.5%	3.7%	7.71	4.56	-0.0
2025	34	MLB MLE	Los Angeles Dodgers	15	0	21	104	22	10	4	0%	1.4	21.2%	9.6%	11.5%	3.7%	7.71	4.56	-0.0
2025	34	MLB RAW	Los Angeles Dodgers	15	0	21	104	22	10	4	0%	1.4	21.2%	9.6%	11.5%	3.8%	7.71	4.91	-0.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			374	42	624	2714	570	252	71	11%	1.7	21.0%	9.3%	11.7%	2.6%	4.64	3.96	+4.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	LAD	167	0	175	739	155	73	22	0%	1.0	21.0%	9.9%	11.1%	3.0%	4.66	4.62	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	LAD	109	0	114	483	96	51	17	0%	1.0	19.9%	10.6%	9.3%	3.4%	4.88	5.02	-0.6
2029	38	MLB MLE	LAD	77	0	81	343	64	39	13	0%	1.1	18.7%	11.3%	7.5%	3.9%	5.23	5.46	-0.8
2028	37	MLB CTX	LAD	109	0	114	481	96	51	17	0%	1.0	19.9%	10.6%	9.3%	3.6%	4.96	5.10	-0.6
2029	38	MLB CTX	LAD	77	0	81	342	64	39	14	0%	1.1	18.7%	11.3%	7.5%	4.1%	5.32	5.55	-0.8
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		560	42	820	3540	730	342	102	8%	1.5	20.6%	9.7%	11.0%	2.9%	4.75	4.16	+3.7
/ 200 IP	—	RAW		137	10	200	863	178	83	25	7%	1.5	20.6%	9.7%	11.0%	2.9%	4.75	4.16	+0.9
Career	—	MLE		442	107	1005	4342	845	389	108	24%	2.3	19.5%	9.0%	10.5%	2.5%	4.44	3.96	+2.8
/ 200 IP	—	MLE		88	21	200	864	168	77	21	24%	2.3	19.5%	9.0%	10.5%	2.5%	4.44	3.96	+0.7

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.

2019 AAAIndianapolis Indians · 4 IPFIP 4.79 → 5.45 (+0.66)K% 20.0% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.880	17.60%
BB% (unintentional) /BF	5.00%	—	×1.050	5.25%
HR / BF /BF	5.00%	÷0.953	×1.100	5.77%

Factors used: pitcher AAA level factors — K ×0.88, BB ×1.05, 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 4.79 → 5.45, 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).

2018 AAASacramento River Cats · 24 IPFIP 3.71 → 4.16 (+0.45)K% 23.3% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.30%	—	×0.880	20.50%
BB% (unintentional) /BF	7.77%	—	×1.050	8.16%
HR / BF /BF	2.91%	÷0.998	×1.100	3.21%

Result: FIP 3.71 → 4.16, 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).

2017 AAASacramento River Cats · 79 IPFIP 3.75 → 4.36 (+0.61)K% 20.2% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.17%	—	×0.880	17.75%
BB% (unintentional) /BF	6.25%	—	×1.050	6.56%
HR / BF /BF	2.84%	÷0.889	×1.100	3.51%

Result: FIP 3.75 → 4.36, 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).

2016 AAASacramento River Cats · 126 IPFIP 2.99 → 3.24 (+0.25)K% 19.6% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.58%	—	×0.880	17.23%
BB% (unintentional) /BF	7.22%	—	×1.050	7.59%
HR / BF /BF	1.14%	÷1.082	×1.100	1.16%

Result: FIP 2.99 → 3.24, 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).

2015 AAASacramento River Cats · 98 IPFIP 3.65 → 3.98 (+0.33)K% 17.3% → 15.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.35%	—	×0.880	15.27%
BB% (unintentional) /BF	9.64%	—	×1.050	10.12%
HR / BF /BF	1.45%	÷0.982	×1.100	1.62%

Result: FIP 3.65 → 3.98, 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).

2015 AARichmond Flying Squirrels · 50 IPFIP 3.58 → 4.31 (+0.73)K% 18.4% → 14.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.40%	—	×0.810	14.90%
BB% (unintentional) /BF	9.91%	—	×1.100	10.90%
HR / BF /BF	1.42%	÷0.860	×1.200	1.97%

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 72).

Result: FIP 3.58 → 4.31, 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).

Career Path Comps

What happened to similar pitchers from this age · Y-axis: ERA+ (100 = league avg, 150 = elite, 80 = below) · Bands: P10–P90 outer, P25–P75 inner · Gold: OGILVIE projection

			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
Chris Stratton (OGILVIE)	—	—	—	—	—	3.0%	—	5.20	19.4%	8.6%	196	+0.58	—	—	—
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	49
Pete Walker R 2005 · 4.79 FIP	36	SU	4.79	12.0%	9.2%	2.8%	.261	4.45	19.6%	11.6%	30	-0.34	4.45	327	46
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	44
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	44
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	44
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	43
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	39
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	38
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	38
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	38
Masato Yoshii R 2001 · 4.64 FIP	36	SW	4.64	12.8%	4.9%	3.6%	.281	4.15	13.4%	8.0%	131	-0.49	4.15	757	37
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	34
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	34
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	33
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	33
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	30
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	30
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	29
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	29
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	29

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 historical, 1901-2023), also matched on career-to-date at the same age. * Historical (Lahman) careers: rates era-adjusted to 2001-24 league levels; WAR is an OGILVIE proxy.

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	5.0	83	20.7%	9.9%	1.12	4.72
Dave Hamilton ('72-'80, 2 WAR*)	3.5	78	21.3%*	10.2%*	1.13*	4.58*
Dustin McGowan ('17)	3.3	65	19.2%	9.9%	1.08	4.50
Jay Witasick ('07)	3.7	73	19.6%	11.0%	1.19	4.64
Bobby Ayala ('99)	2.9	72	21.3%	9.7%	1.11	4.78
Jim Ray ('65-'74, 2 WAR*)	3.1	88	20.5%*	10.4%*	1.14*	4.38*
Ted Power ('81-'93, 8 WAR*)	5.8	97	18.4%*	8.9%*	1.05*	4.38*