Jakob Junis

P Texas Rangers

Age 34 RHP 6'3" / 238 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	31	MIX2 levels	26	8	71	2.65	3.44	20.4%	3.7%	1.0	K 20.2% · BB 3.2% · FIP 3.41
2025	32	MLBCLE	57	0	67	2.97	3.15	20.1%	5.8%	1.1
2026	33	MLBTEX	53	1	54	3.64	3.49	16.2%	6.4%	0.7
2027PROJ	34	MLBTEX	43	0	45	3.86	4.02	17.4%	6.1%	0.3
2028PROJ	35	MLBTEX	24	0	25	4.16	4.35	16.5%	6.6%	0.1
2029PROJ	36	MLBTEX	11	0	12	4.50	4.72	15.5%	7.2%	-0.0

Last 3 seasons WAR 2.8 · Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs41

FB Velo19

K%10

Form6.28 ERA (proj 3.86) · K 11.4% (proj 17.4%) · BB 15.7% (proj 6.1%) ▼▼ last 90d · 14 IP through Oct 27

CompsChris Jakubauskas (age 32) · Rolando Arrojo (age 34) · Nelson Figueroa (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 110102vR 94

ERA3.86

FIP4.02

K%17.4%

BB%6.1%

K-BB%11.4%

Proj IP45

Value2027 · WAR

Runs saved vs league avg · 45 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1992.3

Whiff%

p1018.3%

p1016.2%

xwOBA vs

p410.322

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	22	MIX MLE	2 levels	27	27	160	646	126	30	11	100%	5.9	15.2%	5.3%	9.8%	2.9%	3.78	4.07	—
2015	22	MIX RAW	2 levels	27	27	160	646	126	30	11	100%	5.9	19.5%	4.6%	14.9%	1.7%	3.78	2.98	—
2016	23	MIX MLE	2 levels	27	27	149	619	143	34	18	100%	5.5	19.0%	6.0%	13.0%	3.4%	4.05	4.13	—
2016	23	MIX RAW	2 levels	27	27	149	619	143	34	18	100%	5.5	23.1%	5.5%	17.6%	2.9%	4.05	3.44	—
2017	24	MIX MLE	2 levels	32	28	169	710	166	39	21	88%	5.3	21.9%	5.6%	16.3%	3.3%	3.72	3.74	+3.4
2017	24	MIX RAW	2 levels	32	28	169	710	166	39	21	88%	5.3	23.4%	5.5%	17.9%	3.0%	3.72	3.44	+4.1
2018	25	MLB MLE	Kansas City Royals	30	30	177	758	164	42	32	100%	5.9	21.6%	5.5%	16.1%	4.6%	4.37	4.48	+1.9
2018	25	MLB RAW	Kansas City Royals	30	30	177	758	164	42	32	100%	5.9	21.6%	5.5%	16.1%	4.2%	4.37	4.31	+2.3
2019	26	MLB MLE	Kansas City Royals	31	31	175	771	164	57	31	100%	5.7	21.3%	7.4%	13.9%	4.3%	5.24	4.63	+1.6
2019	26	MLB RAW	Kansas City Royals	31	31	175	771	164	57	31	100%	5.7	21.3%	7.4%	13.9%	4.0%	5.24	4.50	+1.8
2020	27	MLB MLE	Kansas City Royals	8	6	25	114	19	5	7	75%	3.2	16.7%	4.4%	12.3%	6.6%	6.39	5.89	-0.1
2020	27	MLB RAW	Kansas City Royals	8	6	25	114	19	5	7	75%	3.2	16.7%	4.4%	12.3%	6.1%	6.39	5.78	-0.1
2021	28	MIX MLE	3 levels	25	15	62	275	67	20	12	60%	2.5	22.6%	8.1%	14.5%	4.7%	5.34	4.81	+0.4
2021	28	MIX RAW	3 levels	25	15	62	275	67	20	12	60%	2.5	24.4%	7.3%	17.1%	4.4%	5.34	4.42	+0.7
2022	29	MIX MLE	3 levels	28	22	127	551	111	29	19	79%	4.5	19.9%	5.5%	14.4%	3.7%	4.74	4.14	+1.9
2022	29	MIX RAW	3 levels	28	22	127	551	111	29	19	79%	4.5	20.1%	5.3%	14.9%	3.4%	4.74	3.98	+2.2
2023	30	MLB MLE	San Francisco Giants	40	4	86	367	96	20	12	10%	2.1	26.2%	5.4%	20.7%	3.5%	3.87	3.52	+1.1
2023	30	MLB RAW	San Francisco Giants	40	4	86	367	96	20	12	10%	2.1	26.2%	5.4%	20.7%	3.3%	3.87	3.38	+1.2
2024	31	MIX MLE	2 levels	26	8	71	269	55	10	8	31%	2.7	20.2%	3.2%	17.0%	2.9%	2.65	3.41	+1.0
2024	31	MIX MLE	2 levels	26	8	71	269	55	10	8	31%	2.7	20.2%	3.2%	17.0%	2.9%	2.65	3.41	+1.0
2024	31	MIX RAW	2 levels	26	8	71	269	55	10	8	31%	2.7	20.4%	3.7%	16.7%	3.0%	2.65	3.44	+1.0
2025	32	MLB MLE	Cleveland Guardians	57	0	67	274	55	16	5	0%	1.2	20.1%	5.8%	14.2%	1.8%	2.97	3.15	+1.1
2025	32	MLB MLE	Cleveland Guardians	57	0	67	274	55	16	5	0%	1.2	20.1%	5.8%	14.2%	1.8%	2.97	3.15	+1.1
2025	32	MLB RAW	Cleveland Guardians	57	0	67	274	55	16	5	0%	1.2	20.1%	5.8%	14.2%	1.8%	2.97	3.15	+1.1
2026	33	MLB MLE	Texas Rangers	53	1	54	234	38	15	4	2%	1.0	16.2%	6.4%	9.8%	1.6%	3.64	3.41	+0.7
2026	33	MLB MLE	Texas Rangers	53	1	54	234	38	15	4	2%	1.0	16.2%	6.4%	9.8%	1.6%	3.64	3.41	+0.7
2026	33	MLB RAW	Texas Rangers	53	1	54	234	38	15	4	2%	1.0	16.2%	6.4%	9.8%	1.7%	3.64	3.49	+0.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			302	117	901	3839	806	224	134	39%	3.0	21.0%	5.8%	15.2%	3.5%	4.31	3.99	+6.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	TEX	43	0	45	190	33	12	4	0%	1.0	17.4%	6.1%	11.4%	2.3%	3.86	4.02	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	TEX	23	0	25	104	17	7	3	0%	1.1	16.5%	6.6%	9.9%	2.6%	4.16	4.35	+0.1
2029	36	MLB MLE	TEX	12	0	12	52	8	4	2	0%	1.0	15.5%	7.2%	8.2%	3.0%	4.50	4.72	-0.0
2028	35	MLB CTX	TEX	24	0	25	106	17	7	3	0%	1.0	16.5%	6.6%	9.9%	2.6%	4.16	4.35	+0.1
2029	36	MLB CTX	TEX	11	0	12	51	8	4	2	0%	1.1	15.5%	7.2%	8.2%	3.0%	4.50	4.72	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		337	117	938	3995	831	235	138	35%	2.8	20.8%	5.9%	14.9%	3.5%	4.31	3.99	+6.3
/ 200 IP	—	RAW		72	25	200	852	177	50	29	35%	2.8	20.8%	5.9%	14.9%	3.5%	4.31	3.99	+1.3
Career	—	MLE		384	199	1324	5588	1133	327	201	52%	3.4	20.3%	5.9%	14.4%	3.6%	4.19	4.11	+15.5
/ 200 IP	—	MLE		58	30	200	844	171	49	30	52%	3.4	20.3%	5.9%	14.4%	3.6%	4.19	4.11	+3.3

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.

2022 AAASacramento River Cats · 11 IPFIP 7.63 → 8.40 (+0.77)K% 16.4% → 14.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.36%	—	×0.880	14.40%
BB% (unintentional) /BF	7.27%	—	×1.050	7.64%
HR / BF /BF	9.09%	÷0.989	×1.100	10.11%

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 98).

Result: FIP 7.63 → 8.40, 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).

2021 AAAOmaha Storm Chasers · 18 IPFIP 5.13 → 5.60 (+0.47)K% 21.7% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.69%	—	×0.880	19.08%
BB% (unintentional) /BF	9.64%	—	×1.050	10.12%
HR / BF /BF	4.82%	÷1.027	×1.100	5.16%

Result: FIP 5.13 → 5.60, 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).

2021 ROKACL Royals Gold · 5 IPFIP 2.57 → 4.37 (+1.80)K% 33.3% → 22.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.680	22.67%
BB% (unintentional) /BF	0.00%	—	×1.250	0.00%
HR / BF /BF	4.17%	÷1.006	×1.400	5.80%

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 101).

Result: FIP 2.57 → 4.37, 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 AAAOmaha Storm Chasers · 71 IPFIP 2.38 → 2.89 (+0.51)K% 29.9% → 26.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.86%	—	×0.880	26.28%
BB% (unintentional) /BF	5.21%	—	×1.050	5.47%
HR / BF /BF	2.08%	÷0.956	×1.100	2.40%

Result: FIP 2.38 → 2.89, 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 AANorthwest Arkansas Naturals · 119 IPFIP 3.13 → 3.91 (+0.78)K% 24.1% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.07%	—	×0.810	19.50%
BB% (unintentional) /BF	5.56%	—	×1.100	6.11%
HR / BF /BF	2.47%	÷0.967	×1.200	3.06%

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 93).

Result: FIP 3.13 → 3.91, 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 AAAOmaha Storm Chasers · 30 IPFIP 4.59 → 4.93 (+0.34)K% 19.5% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.55%	—	×0.880	17.20%
BB% (unintentional) /BF	5.26%	—	×1.050	5.53%
HR / BF /BF	4.51%	÷1.054	×1.100	4.71%

Result: FIP 4.59 → 4.93, 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+Wilmington Blue Rocks · 156 IPFIP 2.99 → 4.07 (+1.07)K% 19.7% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.68%	—	×0.770	15.15%
BB% (unintentional) /BF	4.64%	—	×1.150	5.34%
HR / BF /BF	1.76%	÷0.769	×1.250	2.86%

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 54).

Result: FIP 2.99 → 4.07, 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 AANorthwest Arkansas Naturals · 4 IPFIP 2.50 → 2.79 (+0.29)K% 14.3% → 11.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.29%	—	×0.810	11.57%
BB% (unintentional) /BF	4.76%	—	×1.100	5.24%
HR / BF /BF	0.00%	÷0.883	×1.200	0.00%

Result: FIP 2.50 → 2.79, 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
Jakob Junis (OGILVIE)	—	—	—	—	—	2.3%	—	4.47	16.1%	9.3%	37	+0.45	—	—	—
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	63
Rolando Arrojo R 2002 · 3.91 FIP	34	SW	3.91	14.7%	7.5%	2.0%	.288	—	—	—	—	—	3.50	700	61
Nelson Figueroa R 2008 · 3.94 FIP	34	SW	3.94	17.1%	11.8%	1.4%	.306	3.96	17.5%	9.9%	192	+0.02	3.71	499	59
Juan Omar Espinoza R 2016 · 4.84 FIP	32	SP-	4.84	8.4%	9.2%	2.4%	.348	—	—	—	—	—	4.81	104	59
Tiago Da Silva R 2019 · 3.95 FIP	34	SW	3.95	15.0%	7.2%	2.2%	.365	—	—	—	—	—	2.21	287	58
Ivan Maldonado R 2015 · 4.60 FIP	35	SW	4.60	12.8%	8.7%	2.7%	.269	5.41	6.4%	-3.3%	104	+0.81	4.60	172	57
Marco Antonio Duarte R 2019 · 5.03 FIP	32	SW	5.03	12.2%	9.1%	3.3%	.420	—	—	—	—	—	4.07	203	57
Paolo Espino R 2021 · 4.32 FIP	34	SP-	4.32	20.2%	5.1%	4.2%	.277	4.81	16.6%	10.9%	260	+0.49	3.42	933	56
Fernando Rodriguez Jr. R 2018 · 4.07 FIP	34	SW	4.07	23.7%	9.9%	3.1%	.253	5.11	25.0%	13.8%	32	+1.04	3.34	335	56
Rudy Gonzalez R 2015 · 4.52 FIP	34	SW	4.52	13.5%	9.6%	2.4%	.347	3.84	14.0%	6.5%	79	-0.68	3.31	119	55
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	51
Jean Machi R 2016 · 4.36 FIP	34	SU	4.36	18.3%	6.6%	3.6%	.282	4.12	18.6%	12.0%	67	-0.24	2.16	306	50
Josh Lindblom R 2021 · 3.79 FIP	34	SP-	3.79	22.7%	6.9%	3.2%	.319	5.07	20.8%	12.0%	133	+1.28	1.83	480	50
Guillermo Moscoso R 2018 · 5.07 FIP	34	SP-	5.07	18.2%	8.4%	4.5%	.318	4.35	12.6%	5.9%	104	-0.72	3.56	486	49
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	48
Tim Dillard R 2017 · 3.62 FIP	34	SU	3.62	15.1%	8.8%	1.2%	.310	4.32	13.2%	7.1%	188	+0.70	2.92	432	48
Giovanni Carrara R 2002 · 4.78 FIP	34	SU	4.78	14.5%	7.2%	3.6%	.239	3.83	17.1%	7.9%	158	-0.95	2.50	462	47
Juan Delgadillo R 2017 · 4.47 FIP	34	SP	4.47	10.9%	6.2%	2.7%	.311	—	—	—	—	—	4.47	143	47
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	47
Angel Sánchez R 2024 · 3.77 FIP	34	SP	3.77	11.8%	5.0%	1.9%	.302	—	—	—	—	—	3.77	284	46

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	76	20.6%	6.1%	1.32	4.29
Yusmeiro Petit ('21)	4.5	70	20.7%	5.5%	1.36	3.93
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
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*
Scott Bankhead ('86-'95, 10 WAR*)	4.5	100	21.2%*	7.3%*	1.36*	4.16*