Tarik Skubal

P Los Angeles Dodgers

Age 30 LHP 6'3" / 240 lbs MLB Svc 6.0 · Ctrl thru 2026 peak 5.2 · 2027 ILActive (A) · expected back 2026-07-01

Overview

MLBSPA back 2026-07-0140-MANFA 2027

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MLBDET	31	31	192	2.39	2.29	30.3%	4.6%	7.9
2025	28	MLBDET	31	31	195	2.21	2.34	32.2%	4.4%	7.9
2026	29	MIX2 levels	27	27	164	2.64	2.36	30.1%	4.2%	6.5	K 30.0% · BB 4.4% · FIP 2.31
2027PROJ	30	MLBLAD	25	25	148	2.74	2.72	31.6%	4.5%	5.1
2028PROJ	31	MLBLAD	23	23	140	2.92	3.01	30.8%	4.9%	4.3
2029PROJ	32	MLBLAD	22	22	130	3.14	3.27	30.0%	5.2%	3.6

Last 3 seasons WAR 22.3 · Next 3 projected WAR 12.9Projected rows: MLB playing time only, park and league context applied.

ContractCONFIRMED

Control thru2026Service6.663First FA2027

Skills · percentileStatcast 2026

K%90

xwOBA vs90

FB Velo82

Form2.25 ERA (proj 2.74) · K 26.4% (proj 31.6%) · BB 4.8% (proj 4.5%) ▼ last 60d · 32 IP through Oct 27

CompsErik Bedard (age 30) · Wei-Yin Chen (age 30) · Wandy Rodriguez (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 6573vR 81

ERA2.74

FIP2.72

K%31.6%

BB%4.5%

K-BB%27.1%

Proj IP148

Value2027 · WAR

Runs saved vs league avg · 148 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p8296.8

Whiff%

p9031.7%

p9030.0%

xwOBA vs

p900.266

Contract CONFIRMED · hand-curated extension

Status FA

MLB Debut 2020

Service 6.663 yrs.days

Team Control Through 2026

First FA Year 2027

Year	Status	Salary	Proj WAR	Value	Surplus
Totals		$0.0M		$0.0M	+$0.0M

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)
2018	21	MIX MLE	2 levels	5	1	10	39	16	2	0	20%	2.1	41.0%	5.1%	35.9%	0.0%	0.00	0.29	—
2018	21	MIX RAW	2 levels	5	1	10	39	16	2	0	20%	2.1	41.0%	5.1%	35.9%	0.0%	0.00	0.58	—
2019	22	MIX MLE	2 levels	24	24	123	490	179	37	7	100%	5.1	28.8%	8.5%	20.3%	1.7%	2.42	2.68	—
2019	22	MIX RAW	2 levels	24	24	123	490	179	37	7	100%	5.1	36.5%	7.6%	29.0%	1.4%	2.42	1.83	—
2020	23	MLB MLE	Detroit Tigers	8	7	32	134	37	11	9	88%	4.0	27.6%	8.2%	19.4%	7.0%	5.62	5.66	-0.1
2020	23	MLB RAW	Detroit Tigers	8	7	32	134	37	11	9	88%	4.0	27.6%	8.2%	19.4%	6.7%	5.62	5.47	-0.0
2021	24	MLB MLE	Detroit Tigers	31	29	149	634	164	47	35	94%	4.8	25.9%	7.4%	18.5%	5.8%	4.34	5.02	+0.6
2021	24	MLB RAW	Detroit Tigers	31	29	149	634	164	47	35	94%	4.8	25.9%	7.4%	18.5%	5.5%	4.34	4.89	+0.9
2022	25	MLB MLE	Detroit Tigers	21	21	118	477	117	32	9	100%	5.6	24.5%	6.7%	17.8%	2.0%	3.52	2.96	+3.6
2022	25	MLB RAW	Detroit Tigers	21	21	118	477	117	32	9	100%	5.6	24.5%	6.7%	17.8%	1.9%	3.52	2.92	+3.7
2023	26	MIX MLE	3 levels	20	20	95	366	122	17	5	100%	4.8	32.9%	4.5%	28.4%	1.3%	2.56	1.63	+4.7
2023	26	MIX RAW	3 levels	20	20	95	366	122	17	5	100%	4.8	33.3%	4.6%	28.7%	1.4%	2.56	1.75	+4.6
2024	27	MLB MLE	Detroit Tigers	31	31	192	753	228	35	15	100%	6.2	30.3%	4.6%	25.6%	2.1%	2.39	2.27	+7.9
2024	27	MLB MLE	Detroit Tigers	31	31	192	753	228	35	15	100%	6.2	30.3%	4.6%	25.6%	2.1%	2.39	2.27	+7.9
2024	27	MLB RAW	Detroit Tigers	31	31	192	753	228	35	15	100%	6.2	30.3%	4.6%	25.6%	2.0%	2.39	2.29	+7.9
2025	28	MLB MLE	Detroit Tigers	31	31	195	748	241	33	18	100%	6.3	32.2%	4.4%	27.8%	2.5%	2.21	2.32	+7.9
2025	28	MLB MLE	Detroit Tigers	31	31	195	748	241	33	18	100%	6.3	32.2%	4.4%	27.8%	2.5%	2.21	2.32	+7.9
2025	28	MLB RAW	Detroit Tigers	31	31	195	748	241	33	18	100%	6.3	32.2%	4.4%	27.8%	2.4%	2.21	2.34	+7.9
2026	29	MIX MLE	2 levels	27	27	164	637	192	27	14	100%	6.1	30.0%	4.4%	25.6%	2.2%	2.64	2.31	+6.6
2026	29	MIX MLE	2 levels	27	27	164	637	192	27	14	100%	6.1	30.0%	4.4%	25.6%	2.2%	2.64	2.31	+6.6
2026	29	MIX RAW	2 levels	27	27	164	637	192	27	14	100%	6.1	30.1%	4.2%	25.9%	2.2%	2.64	2.36	+6.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			163	160	925	3676	1075	199	104	98%	5.7	29.2%	5.4%	23.8%	2.8%	3.02	2.88	+29.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	LAD	25	25	148	625	197	28	15	100%	5.9	31.6%	4.5%	27.1%	2.4%	2.74	2.72	+5.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	LAD	23	23	140	589	182	29	16	100%	6.1	30.8%	4.9%	26.0%	2.7%	2.86	2.95	+4.3
2029	32	MLB MLE	LAD	22	22	130	547	164	29	16	100%	5.9	30.0%	5.2%	24.8%	2.9%	3.07	3.20	+3.5
2030	33	MLB MLE	LAD	20	20	118	500	146	29	16	100%	5.9	29.2%	5.7%	23.5%	3.2%	3.32	3.48	+2.8
2031	34	MLB MLE	LAD	17	17	105	442	125	27	15	100%	6.2	28.3%	6.2%	22.1%	3.5%	3.60	3.79	+2.0
2032	35	MLB MLE	LAD	15	15	87	368	100	25	14	100%	5.8	27.3%	6.8%	20.6%	3.8%	3.91	4.12	+1.3
2033	36	MLB MLE	LAD	11	11	65	276	73	20	12	100%	5.9	26.3%	7.3%	18.9%	4.2%	4.26	4.49	+0.7
2034	37	MLB MLE	LAD	7	7	42	178	45	14	8	100%	6.0	25.2%	8.0%	17.2%	4.6%	4.64	4.89	+0.2
2028	31	MLB CTX	LAD	23	23	140	591	182	29	16	100%	6.1	30.8%	4.9%	26.0%	2.8%	2.92	3.01	+4.3
2029	32	MLB CTX	LAD	22	22	130	549	165	29	17	100%	5.9	30.0%	5.2%	24.8%	3.0%	3.14	3.27	+3.6
2030	33	MLB CTX	LAD	20	20	118	498	145	28	17	100%	5.9	29.2%	5.7%	23.5%	3.3%	3.39	3.55	+2.8
2031	34	MLB CTX	LAD	18	18	105	443	125	27	16	100%	5.8	28.3%	6.2%	22.1%	3.6%	3.68	3.87	+2.0
2032	35	MLB CTX	LAD	14	14	87	367	100	25	15	100%	6.2	27.3%	6.8%	20.6%	4.0%	4.00	4.21	+1.3
2033	36	MLB CTX	LAD	11	11	65	274	72	20	12	100%	5.9	26.3%	7.3%	18.9%	4.4%	4.36	4.59	+0.7
2034	37	MLB CTX	LAD	7	7	42	177	45	14	9	100%	6.0	25.2%	8.0%	17.2%	4.8%	4.75	5.00	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		278	274	1612	6577	1910	372	205	99%	5.8	29.0%	5.7%	23.4%	3.1%	3.24	3.07	+47.0
/ 200 IP	—	RAW		34	34	200	816	237	46	25	99%	5.8	29.0%	5.7%	23.4%	3.1%	3.24	3.07	+5.8
Career	—	MLE		198	191	1078	4278	1256	246	117	96%	5.4	29.4%	5.7%	23.6%	2.7%	2.88	2.85	+43.9
/ 200 IP	—	MLE		37	35	200	794	233	46	22	96%	5.4	29.4%	5.7%	23.6%	2.7%	2.88	2.85	+5.4

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.

2023 AAAToledo Mud Hens · 10 IPFIP 2.64 → 3.28 (+0.64)K% 35.1% → 30.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.14%	—	×0.860	30.22%
BB% (unintentional) /BF	8.11%	—	×0.900	7.30%
HR / BF /BF	2.70%	÷0.901	×1.100	3.30%

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 80).

Result: FIP 2.64 → 3.28, 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 A+Lakeland Flying Tigers · 80 IPFIP 2.15 → 3.11 (+0.96)K% 30.3% → 23.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.31%	—	×0.770	23.34%
BB% (unintentional) /BF	5.94%	—	×1.150	6.83%
HR / BF /BF	1.56%	÷0.963	×1.250	2.03%

Factors used: pitcher A+ level factors — K ×0.77, BB ×1.15, HR ×1.25. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 93).

Result: FIP 2.15 → 3.11, 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 AAErie SeaWolves · 42 IPFIP 1.01 → 1.87 (+0.86)K% 48.2% → 39.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	48.24%	—	×0.810	39.07%
BB% (unintentional) /BF	10.59%	—	×1.100	11.65%
HR / BF /BF	1.18%	÷1.294	×1.200	1.09%

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 159).

Result: FIP 1.01 → 1.87, 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 AWest Michigan Whitecaps · 7 IPFIP 0.01 → 1.03 (+1.02)K% 42.3% → 31.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	42.31%	—	×0.740	31.31%
BB% (unintentional) /BF	3.85%	—	×1.180	4.54%
HR / BF /BF	0.00%	÷0.942	×1.300	0.00%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 88).

Result: FIP 0.01 → 1.03, 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
Tarik Skubal (OGILVIE)	—	—	—	—	—	2.4%	—	3.20	30.1%	24.8%	388	+0.48	—	—	—
Erik Bedard L 2009 · 3.42 FIP	30	SP	3.42	25.9%	9.8%	2.3%	.264	3.88	22.2%	12.9%	255	+0.46	2.96	1,317	47
Wei-Yin Chen L 2015 · 3.88 FIP	30	SP	3.88	19.3%	5.2%	3.2%	.288	4.31	19.3%	13.2%	290	+0.43	3.63	1,065	45
Wandy Rodriguez L 2009 · 3.40 FIP	30	SP	3.40	22.7%	6.8%	2.5%	.296	3.80	19.3%	12.2%	592	+0.40	3.37	1,572	39
Shota Imanaga L 2024 · 3.71 FIP	30	SP	3.71	25.1%	4.0%	4.0%	.262	4.76	22.8%	17.9%	319	+1.05	3.71	492	33
Jacob deGrom R 2018 · 1.69 FIP	30	SP	1.69	32.2%	5.1%	1.2%	.277	1.93	36.4%	31.3%	364	+0.24	0.74	1,692	32
Corey Kluber R 2016 · 3.10 FIP	30	SP	3.10	26.4%	6.5%	2.6%	.267	2.73	29.6%	25.0%	454	-0.37	2.16	1,636	22
Yu Darvish R 2017 · 3.59 FIP	30	SP	3.59	27.3%	7.4%	3.4%	.279	3.53	30.7%	23.5%	295	-0.06	1.97	1,798	22
Shohei Ohtani R 2023 · 3.58 FIP	29	SP	3.58	31.5%	10.4%	3.3%	.230	2.13	29.7%	23.1%	133	-1.45	1.76	613	21
Matt Tomshaw L 2019 · 3.09 FIP	30	SP-	3.09	21.6%	4.5%	2.3%	.297	8.67	18.4%	13.6%	53	+5.58	3.04	681	20
Juan Pablo Oramas L 2019 · 3.60 FIP	29	SP	3.60	21.2%	8.0%	2.3%	.337	—	—	—	—	—	3.46	440	19
Ryan Verdugo L 2017 · 3.36 FIP	30	SP-	3.36	22.7%	5.4%	2.7%	.333	—	—	—	—	—	3.36	153	17
Tanner Roark R 2017 · 3.88 FIP	30	SP	3.88	21.4%	7.6%	3.0%	.294	4.48	20.3%	13.2%	393	+0.60	2.40	1,194	16
Marco Estrada R 2013 · 3.81 FIP	30	SP	3.81	23.0%	5.7%	3.5%	.260	4.34	20.4%	12.6%	508	+0.53	3.28	1,244	16
Paul Oseguera L 2015 · 3.83 FIP	31	SP	3.83	17.6%	8.5%	2.1%	.297	5.62	5.6%	-6.9%	28	+1.79	3.83	162	15
Michael Pineda R 2019 · 3.79 FIP	30	SP	3.79	23.3%	4.5%	3.8%	.288	4.20	18.0%	13.4%	208	+0.41	2.00	1,034	15
Ryan Bollinger L 2018 · 3.56 FIP	27	SP	3.56	17.1%	7.5%	1.7%	.290	—	—	—	—	—	3.56	112	15
Tsuyoshi Wada L 2014 · 3.60 FIP	33	SP	3.60	19.7%	6.2%	2.5%	.290	3.66	16.8%	9.7%	119	+0.06	3.60	188	14
Jose De Paula L 2019 · 3.31 FIP	31	SP	3.31	19.0%	7.6%	1.6%	.394	—	—	—	—	—	3.31	118	13
Scott Diamond L 2016 · 3.46 FIP	30	SP	3.46	12.7%	4.8%	1.5%	.326	—	—	—	—	—	3.46	659	13
Jameson Taillon R 2022 · 3.74 FIP	30	SP	3.74	20.7%	4.4%	3.4%	.274	4.37	19.6%	14.2%	466	+0.63	3.12	1,401	13

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	8.8	117	29.3%	5.6%	1.16	3.17
Julio Urías ('23)	3.6	102	24.3%	6.6%	1.05	3.11
James Paxton ('24)	4.8	95	25.3%	7.9%	1.05	3.77
David Price ('22)	10.7	165	23.6%	6.4%	0.93	3.32
John Candelaria ('75-'93, 42 WAR*)	12.6	140	22.5%*	5.7%*	1.34*	3.73*
Jose Rijo ('84-'02, 33 WAR*)	9.4	145	25.9%*	8.4%*	0.93*	3.56*
Dick Hughes ('66-'68, 5 WAR*)	1.5	102	24.1%*	7.1%*	1.51*	3.68*