Ryan Yarbrough

P New York Yankees

Age 35 LHP 6'5" / 205 lbs MLB

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	32	MLBTOR	44	0	99	3.19	4.11	16.3%	7.3%	0.5
2025	33	MIX2 levels	22	11	75	4.32	4.54	21.9%	6.5%	0.8	K 20.8% · BB 7.2% · FIP 4.78
2026	34	MLBNYY	38	2	63	3.69	3.21	18.3%	8.2%	1.0
2027PROJ	35	MLBNYY	45	0	47	4.07	4.47	16.6%	7.9%	0.0
2028PROJ	36	MLBNYY	20	0	21	4.53	4.84	15.6%	8.5%	-0.1
2029PROJ	37	MLBNYY	9	0	9	4.95	5.24	14.5%	9.1%	-0.1

Last 3 seasons WAR 2.3 · Next 3 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs78

K%23

FB Velo10

Form3.98 ERA (proj 4.07) · K 23.5% (proj 16.6%) · BB 6.2% (proj 7.9%) ▲▲ last 90d · 20 IP through Oct 27

CompsKwang Hyun Kim (age 33) · Juan Antonio Pena (age 37) · Chris Narveson (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9199vR 107

ERA4.07

FIP4.47

K%16.6%

BB%7.9%

K-BB%8.8%

Proj IP47

Value2027 · WAR

Runs saved vs league avg · 47 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1087.3

Whiff%

p4825.0%

p2318.3%

xwOBA vs

p780.285

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	23	MIX MLE	3 levels	22	22	97	421	88	23	7	100%	4.4	16.3%	5.9%	10.4%	2.3%	4.10	3.75	—
2015	23	MIX RAW	3 levels	22	22	97	421	88	23	7	100%	4.4	20.9%	5.5%	15.4%	1.7%	4.10	2.93	—
2016	24	AA MLE	Jackson Generals	25	25	128	522	99	31	7	100%	5.1	15.4%	6.5%	8.8%	1.6%	2.95	3.52	—
2016	24	AA RAW	Jackson Generals	25	25	128	522	99	31	7	100%	5.1	19.0%	5.9%	13.0%	1.3%	2.95	2.99	—
2017	25	AAA MLE	Durham Bulls	26	26	157	645	159	38	20	100%	6.1	21.7%	6.2%	15.5%	3.3%	3.43	3.84	—
2017	25	AAA RAW	Durham Bulls	26	26	157	645	159	38	20	100%	6.1	24.7%	5.9%	18.8%	3.1%	3.43	3.46	—
2018	26	MLB MLE	Tampa Bay Rays	38	6	147	628	128	44	18	16%	3.9	20.4%	7.0%	13.4%	2.9%	3.91	3.88	+1.2
2018	26	MLB RAW	Tampa Bay Rays	38	6	147	628	128	44	18	16%	3.9	20.4%	7.0%	13.4%	2.9%	3.91	3.85	+1.2
2019	27	MIX MLE	2 levels	33	18	168	670	152	21	17	55%	5.1	22.1%	3.2%	18.9%	2.6%	4.08	3.06	+4.9
2019	27	MIX RAW	2 levels	33	18	168	670	152	21	17	55%	5.1	22.7%	3.1%	19.6%	2.5%	4.08	2.98	+5.1
2020	28	MLB MLE	Tampa Bay Rays	11	9	56	234	44	11	5	82%	5.1	18.8%	4.7%	14.1%	2.2%	3.56	3.31	+1.4
2020	28	MLB RAW	Tampa Bay Rays	11	9	56	234	44	11	5	82%	5.1	18.8%	4.7%	14.1%	2.1%	3.56	3.28	+1.5
2021	29	MLB MLE	Tampa Bay Rays	30	21	155	653	117	25	25	70%	5.2	17.9%	3.8%	14.1%	3.9%	5.11	4.23	+2.1
2021	29	MLB RAW	Tampa Bay Rays	30	21	155	653	117	25	25	70%	5.2	17.9%	3.8%	14.1%	3.8%	5.11	4.17	+2.3
2022	30	MIX MLE	2 levels	27	16	108	483	87	34	15	59%	4.0	17.4%	7.2%	10.2%	3.2%	4.51	4.31	+1.4
2022	30	MIX RAW	2 levels	27	16	108	483	87	34	15	59%	4.0	18.0%	7.0%	11.0%	3.1%	4.51	4.24	+1.5
2023	31	MIX MLE	3 levels	29	13	107	444	87	16	15	45%	3.7	19.2%	3.7%	15.5%	3.2%	4.19	3.69	+1.1
2023	31	MIX RAW	3 levels	29	13	107	444	87	16	15	45%	3.7	19.6%	3.6%	16.0%	3.4%	4.19	3.74	+1.0
2024	32	MLB MLE	Toronto Blue Jays	44	0	99	398	65	29	11	0%	2.2	16.3%	7.3%	9.0%	2.8%	3.19	4.16	+0.5
2024	32	MLB MLE	Toronto Blue Jays	44	0	99	398	65	29	11	0%	2.2	16.3%	7.3%	9.0%	2.8%	3.19	4.16	+0.5
2024	32	MLB RAW	Toronto Blue Jays	44	0	99	398	65	29	11	0%	2.2	16.3%	7.3%	9.0%	2.8%	3.19	4.11	+0.5
2025	33	MIX MLE	2 levels	22	11	75	306	67	20	14	50%	3.4	20.8%	7.2%	13.6%	4.6%	4.32	4.78	+0.5
2025	33	MIX MLE	2 levels	22	11	75	306	67	20	14	50%	3.4	20.8%	7.2%	13.6%	4.6%	4.32	4.78	+0.5
2025	33	MIX RAW	2 levels	22	11	75	306	67	20	14	50%	3.4	21.9%	6.5%	15.4%	4.6%	4.32	4.54	+0.8
2026	34	MLB MLE	New York Yankees	38	2	63	268	49	22	3	5%	1.7	18.3%	8.2%	10.1%	1.0%	3.69	3.17	+1.0
2026	34	MLB MLE	New York Yankees	38	2	63	268	49	22	3	5%	1.7	18.3%	8.2%	10.1%	1.0%	3.69	3.17	+1.0
2026	34	MLB RAW	New York Yankees	38	2	63	268	49	22	3	5%	1.7	18.3%	8.2%	10.1%	1.1%	3.69	3.21	+1.0
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			253	78	895	3740	703	204	115	31%	3.5	18.8%	5.5%	13.3%	3.1%	4.18	3.88	+7.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	NYY	45	0	47	198	33	16	5	0%	1.0	16.6%	7.9%	8.8%	2.5%	4.07	4.47	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	NYY	20	0	21	89	14	8	3	0%	1.1	15.6%	8.5%	7.1%	2.9%	4.53	4.84	-0.1
2029	37	MLB MLE	NYY	8	0	9	38	5	3	1	0%	1.1	14.5%	9.1%	5.4%	3.3%	4.95	5.24	-0.1
2030	38	MLB MLE	NYY	4	0	4	16	2	2	1	0%	0.9	13.4%	9.8%	3.5%	3.8%	5.38	5.67	-0.0
2028	36	MLB CTX	NYY	20	0	21	89	14	8	3	0%	1.1	15.6%	8.5%	7.1%	2.9%	4.53	4.84	-0.1
2029	37	MLB CTX	NYY	9	0	9	38	6	3	1	0%	1.0	14.5%	9.1%	5.4%	3.3%	4.95	5.24	-0.1
2030	38	MLB CTX	NYY	4	0	4	17	2	2	1	0%	1.0	13.4%	9.8%	3.5%	3.8%	5.38	5.67	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		285	78	929	3882	724	216	119	27%	3.3	18.7%	5.6%	13.1%	3.1%	4.20	3.92	+7.0
/ 200 IP	—	RAW		61	17	200	836	156	47	26	27%	3.3	18.7%	5.6%	13.1%	3.1%	4.20	3.92	+1.5
Career	—	MLE		345	169	1360	5672	1072	324	163	49%	3.9	18.9%	5.7%	13.2%	2.9%	3.95	3.81	+6.9
/ 200 IP	—	MLE		51	25	200	834	158	48	24	49%	3.9	18.9%	5.7%	13.2%	2.9%	3.95	3.81	+1.5

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 AAAScranton/Wilkes-Barre RailRiders · 11 IPFIP 2.30 → 2.64 (+0.34)K% 28.6% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.860	24.57%
BB% (unintentional) /BF	2.38%	—	×0.900	2.14%
HR / BF /BF	2.38%	÷1.070	×1.100	2.45%

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 114).

Result: FIP 2.30 → 2.64, 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).

2023 AAAOmaha Storm Chasers · 15 IPFIP 1.75 → 2.27 (+0.53)K% 34.0% → 29.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.96%	—	×0.860	29.21%
BB% (unintentional) /BF	3.77%	—	×0.900	3.40%
HR / BF /BF	1.89%	÷0.942	×1.100	2.20%

Result: FIP 1.75 → 2.27, 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).

2022 AAADurham Bulls · 28 IPFIP 3.86 → 4.21 (+0.35)K% 20.3% → 17.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.31%	—	×0.880	17.88%
BB% (unintentional) /BF	9.38%	—	×1.050	9.84%
HR / BF /BF	2.34%	÷1.034	×1.100	2.49%

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 107).

Result: FIP 3.86 → 4.21, 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 AAADurham Bulls · 26 IPFIP 1.72 → 2.04 (+0.32)K% 32.7% → 28.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.71%	—	×0.880	28.79%
BB% (unintentional) /BF	2.80%	—	×1.050	2.94%
HR / BF /BF	1.87%	÷1.128	×1.100	1.82%

Result: FIP 1.72 → 2.04, 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 AAADurham Bulls · 157 IPFIP 3.47 → 3.84 (+0.37)K% 24.7% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.65%	—	×0.880	21.69%
BB% (unintentional) /BF	5.89%	—	×1.050	6.19%
HR / BF /BF	3.10%	÷1.049	×1.100	3.25%

Result: FIP 3.47 → 3.84, 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 AAJackson Generals · 128 IPFIP 2.99 → 3.52 (+0.53)K% 19.0% → 15.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.97%	—	×0.810	15.36%
BB% (unintentional) /BF	5.94%	—	×1.100	6.53%
HR / BF /BF	1.34%	÷0.992	×1.200	1.62%

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 98).

Result: FIP 2.99 → 3.52, 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 A+Bakersfield Blaze · 81 IPFIP 3.06 → 3.75 (+0.68)K% 21.1% → 16.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.14%	—	×0.770	16.28%
BB% (unintentional) /BF	5.14%	—	×1.150	5.91%
HR / BF /BF	2.00%	÷1.077	×1.250	2.32%

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 115).

Result: FIP 3.06 → 3.75, 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 ROKAZL Mariners · 10 IPFIP 0.67 → 1.63 (+0.96)K% 32.5% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.50%	—	×0.680	22.10%
BB% (unintentional) /BF	2.50%	—	×1.250	3.12%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 100).

Result: FIP 0.67 → 1.63, 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
Ryan Yarbrough (OGILVIE)	—	—	—	—	—	2.5%	—	4.84	15.6%	7.1%	21	+0.37	—	—	—
Kwang Hyun Kim L 2021 · 4.21 FIP	33	SP-	4.21	17.7%	8.2%	2.9%	.266	—	—	—	—	—	3.76	146	56
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	49
Chris Narveson L 2017 · 4.59 FIP	35	SP	4.59	12.2%	10.4%	2.2%	.264	—	—	—	—	—	4.00	625	46
Hisanori Takahashi L 2010 · 3.50 FIP	35	SW	3.50	22.1%	7.0%	2.5%	.292	3.81	21.1%	15.0%	118	+0.31	3.50	240	44
Tsuyoshi Wada L 2015 · 3.66 FIP	34	SP	3.66	16.8%	7.1%	2.0%	.307	—	—	—	—	—	3.60	188	43
Tomas Solis L 2017 · 5.48 FIP	34	SP	5.48	11.3%	7.9%	4.2%	.322	6.44	8.9%	1.4%	57	+0.96	4.20	327	40
Raul Valdes L 2013 · 4.07 FIP	35	SU	4.07	24.3%	5.3%	4.3%	.350	—	—	—	—	—	2.29	160	36
Francisley Bueno L 2016 · 3.48 FIP	35	SP	3.48	15.0%	4.6%	1.9%	.312	4.44	11.7%	3.3%	58	+0.96	2.43	188	35
Orlando Lara L 2019 · 4.09 FIP	34	SP	4.09	22.0%	7.8%	3.4%	.329	—	—	—	—	—	3.85	426	35
Oscar Rivera L 2016 · 4.10 FIP	35	SP	4.10	10.7%	7.2%	1.8%	.309	5.80	9.8%	2.0%	42	+1.70	3.68	237	34
Mauricio Lara L 2015 · 3.62 FIP	36	SP	3.62	13.9%	6.9%	1.5%	.310	3.69	12.1%	5.1%	220	+0.07	3.62	444	33
Luke Westphal L 2024 · 5.19 FIP	35	SW	5.19	15.7%	16.4%	2.4%	.299	—	—	—	—	—	2.97	294	33
Arturo Lopez L 2017 · 4.34 FIP	34	SP	4.34	10.3%	5.4%	2.6%	.342	5.46	12.2%	5.4%	117	+1.12	4.34	445	33
Daniel Rodriguez L 2019 · 4.77 FIP	34	SP	4.77	20.8%	7.8%	4.5%	.329	—	—	—	—	—	2.89	391	33
Andrew Albers L 2021 · 4.60 FIP	35	SP	4.60	16.9%	3.7%	4.5%	.312	—	—	—	—	—	2.98	568	32
Jorge Castillo L 2017 · 4.97 FIP	35	SP	4.97	11.2%	6.7%	3.6%	.337	4.91	19.6%	11.5%	119	-0.06	4.50	408	32
Brandon Mann L 2018 · 3.59 FIP	34	SU	3.59	17.8%	11.4%	1.0%	.232	—	—	—	—	—	3.59	190	31
Hideki Okajima L 2010 · 4.27 FIP	34	SU	4.27	15.5%	7.0%	2.9%	.333	—	—	—	—	—	3.05	238	31
Marlon Arias L 2019 · 4.83 FIP	34	SP	4.83	19.8%	11.2%	3.6%	.273	—	—	—	—	—	3.73	154	30
Heriberto Ruelas L 2017 · 3.42 FIP	35	SU	3.42	17.2%	6.7%	1.7%	.294	—	—	—	—	—	2.95	117	30

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	4.9	75	18.6%	6.1%	1.18	4.20
Dave Schmidt ('81-'92, 8 WAR*)	4.5	90	17.4%*	6.1%*	1.04*	4.13*
Tommy Hunter ('23)	4.6	71	16.7%	5.4%	1.27	4.07
Ross Stripling ('24)	4.2	94	20.9%	5.6%	1.32	4.17
Paul Lindblad ('65-'78, 6 WAR*)	6.1	93	18.3%*	7.4%*	1.22*	4.07*
Doug Bird ('73-'83, 10 WAR*)	6.1	110	20.3%*	5.8%*	1.41*	4.43*
Carlos Villanueva ('16)	5.0	91	20.4%	7.6%	1.25	4.31