Justin Bruihl

P St. Louis Cardinals

Age 30 LHP 6'2" / 215 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MIX2 levels	44	47	3.80	2.99	24.3%	9.3%	0.9	K 21.6% · BB 9.6% · FIP 3.20
2025	28	MIX2 levels	54	56	3.88	2.83	27.8%	7.5%	1.1	K 25.0% · BB 6.9% · FIP 3.21
2026	29	MLBSTL	73	78	3.59	3.41	15.3%	7.5%	1.1
2027PROJ	30	MLBSTL	57	60	4.02	4.19	16.9%	7.3%	0.3
2028PROJ95% MLB	31	MLBSTL	31	33	4.26	4.42	16.1%	7.7%	0.1
2029PROJ95% MLB	32	MLBSTL	19	20	4.51	4.68	15.3%	8.1%	-0.0

Last 3 seasons WAR 3.1 · 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 vs39

FB Velo11

K%10

Form3.55 ERA (proj 4.02) · K 13.4% (proj 16.9%) · BB 7.1% (proj 7.3%) ▼ last 90d · 25 IP through Oct 27

CompsJack Taschner (age 30) · Arturo Barradas (age 29) · Raul Valdes (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 97105vR 113

ERA4.02

FIP4.19

K%16.9%

BB%7.3%

K-BB%9.5%

Proj IP60

Value2027 · WAR

Runs saved vs league avg · 60 IP

Grades · Statcast · Contract

Scouting Grades

STUF5555

CTRL5050

CMD5555

K†4040

Statcast Percentiles

FB Velo

p1191.7

Whiff%

p1017.8%

p1015.3%

xwOBA vs

p390.324

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)
2018	21	ROK MLE	Ogden Raptors	21	0	32	159	44	24	1	0%	1.5	18.8%	18.9%	-0.1%	0.9%	8.24	4.38	—
2018	21	ROK RAW	Ogden Raptors	21	0	32	159	44	24	1	0%	1.5	27.7%	15.1%	12.6%	0.6%	8.24	3.01	—
2019	22	MIX MLE	2 levels	25	0	42	163	51	9	0	0%	1.7	23.2%	7.0%	16.1%	0.0%	1.30	2.03	—
2019	22	MIX RAW	2 levels	25	0	42	163	51	9	0	0%	1.7	31.3%	5.5%	25.8%	0.0%	1.30	1.30	—
2021	24	MIX MLE	3 levels	47	3	56	221	61	13	4	6%	1.2	24.3%	6.1%	18.1%	2.0%	2.72	2.92	+1.1
2021	24	MIX RAW	3 levels	47	3	56	221	61	13	4	6%	1.2	27.6%	5.9%	21.7%	1.8%	2.72	2.55	+1.4
2022	25	MIX MLE	2 levels	49	1	54	244	37	24	6	2%	1.1	14.0%	10.2%	3.7%	2.5%	3.67	4.58	-0.0
2022	25	MIX RAW	2 levels	49	1	54	244	37	24	6	2%	1.1	15.2%	9.8%	5.3%	2.5%	3.67	4.51	+0.0
2023	26	MIX MLE	3 levels	52	1	56	239	50	19	3	2%	1.1	19.2%	7.6%	11.5%	1.2%	4.31	3.10	+1.0
2023	26	MIX RAW	3 levels	52	1	56	239	50	19	3	2%	1.1	20.9%	7.9%	13.0%	1.3%	4.31	3.03	+1.0
2024	27	MIX MLE	2 levels	44	0	47	214	52	20	3	0%	1.1	21.6%	9.6%	12.0%	1.3%	3.80	3.20	+0.8
2024	27	MIX MLE	2 levels	44	0	47	214	52	20	3	0%	1.1	21.6%	9.6%	12.0%	1.3%	3.80	3.20	+0.8
2024	27	MIX RAW	2 levels	44	0	47	214	52	20	3	0%	1.1	24.3%	9.3%	15.0%	1.4%	3.80	2.99	+0.9
2025	28	MIX MLE	2 levels	54	0	56	241	67	18	5	0%	1.0	25.0%	6.9%	18.1%	2.4%	3.88	3.21	+0.9
2025	28	MIX MLE	2 levels	54	0	56	241	67	18	5	0%	1.0	25.0%	6.9%	18.1%	2.4%	3.88	3.21	+0.9
2025	28	MIX RAW	2 levels	54	0	56	241	67	18	5	0%	1.0	27.8%	7.5%	20.3%	2.1%	3.88	2.83	+1.1
2026	29	MLB MLE	St. Louis Cardinals	73	0	78	346	53	26	4	0%	1.1	15.3%	7.5%	7.8%	1.2%	3.59	3.44	+1.0
2026	29	MLB MLE	St. Louis Cardinals	73	0	78	346	53	26	4	0%	1.1	15.3%	7.5%	7.8%	1.2%	3.59	3.44	+1.0
2026	29	MLB RAW	St. Louis Cardinals	73	0	78	346	53	26	4	0%	1.1	15.3%	7.5%	7.8%	1.2%	3.59	3.41	+1.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			167	2	167	732	122	48	14	1%	1.0	16.7%	6.6%	10.1%	1.9%	4.20	3.59	+1.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	STL	57	0	60	253	43	19	6	0%	1.1	16.9%	7.3%	9.5%	2.2%	4.02	4.19	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	STL	33	0	35	148	24	11	4	0%	1.1	16.1%	7.7%	8.4%	2.4%	4.26	4.42	+0.1
2029	32	MLB MLE	STL	20	0	21	89	14	7	2	0%	1.1	15.3%	8.1%	7.2%	2.7%	4.51	4.68	-0.0
2028	31	MLB CTX	STL	31	0	33	139	22	11	3	0%	1.1	16.1%	7.7%	8.4%	2.4%	4.26	4.42	+0.1
2029	32	MLB CTX	STL	19	0	20	84	13	7	2	0%	1.1	15.3%	8.1%	7.2%	2.7%	4.51	4.68	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		218	2	221	957	158	66	20	1%	1.0	16.5%	6.9%	9.6%	2.1%	4.23	3.71	+2.2
/ 200 IP	—	RAW		197	2	200	868	143	60	18	1%	1.0	16.5%	6.9%	9.6%	2.1%	4.23	3.71	+2.0
Career	—	MLE		365	5	421	1827	361	161	28	1%	1.2	19.7%	8.8%	10.9%	1.5%	3.77	3.38	+1.9
/ 200 IP	—	MLE		174	2	200	869	171	77	13	1%	1.2	19.7%	8.8%	10.9%	1.5%	3.77	3.38	+1.7

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 AAABuffalo Bisons · 42 IPFIP 2.69 → 3.20 (+0.51)K% 27.8% → 23.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.84%	—	×0.860	23.94%
BB% (unintentional) /BF	7.95%	—	×0.900	7.16%
HR / BF /BF	1.70%	÷0.847	×1.100	2.21%

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 69).

Result: FIP 2.69 → 3.20, 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 AAAIndianapolis Indians · 42 IPFIP 2.92 → 3.20 (+0.29)K% 25.1% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.13%	—	×0.860	21.61%
BB% (unintentional) /BF	10.70%	—	×0.900	9.63%
HR / BF /BF	1.07%	÷0.909	×1.100	1.29%

Result: FIP 2.92 → 3.20, 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 AAAAlbuquerque Isotopes · 27 IPFIP 2.70 → 2.88 (+0.18)K% 23.5% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.48%	—	×0.860	20.19%
BB% (unintentional) /BF	8.70%	—	×0.900	7.83%
HR / BF /BF	0.87%	÷1.062	×1.100	0.90%

Result: FIP 2.70 → 2.88, 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 AAAOklahoma City Dodgers · 30 IPFIP 4.21 → 4.55 (+0.34)K% 16.7% → 14.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.880	14.67%
BB% (unintentional) /BF	13.89%	—	×1.050	14.58%
HR / BF /BF	1.39%	÷0.997	×1.100	1.53%

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 99).

Result: FIP 4.21 → 4.55, 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 AAAOklahoma City Dodgers · 23 IPFIP 2.25 → 2.70 (+0.45)K% 31.9% → 28.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.91%	—	×0.880	28.09%
BB% (unintentional) /BF	5.32%	—	×1.050	5.59%
HR / BF /BF	2.13%	÷1.016	×1.100	2.30%

Result: FIP 2.25 → 2.70, 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 AATulsa Drillers · 15 IPFIP 1.69 → 2.65 (+0.95)K% 37.0% → 30.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.04%	—	×0.810	30.00%
BB% (unintentional) /BF	5.56%	—	×1.100	6.11%
HR / BF /BF	1.85%	÷0.935	×1.200	2.38%

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 87).

Result: FIP 1.69 → 2.65, 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 AGreat Lakes Loons · 34 IPFIP 1.21 → 2.03 (+0.82)K% 31.3% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.34%	—	×0.740	23.19%
BB% (unintentional) /BF	5.97%	—	×1.180	7.04%
HR / BF /BF	0.00%	÷1.018	×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 104).

Result: FIP 1.21 → 2.03, 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).

2019 A+Rancho Cucamonga Quakes · 7 IPFIP 0.92 → 1.58 (+0.67)K% 31.0% → 23.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.03%	—	×0.770	23.90%
BB% (unintentional) /BF	3.45%	—	×1.150	3.97%
HR / BF /BF	0.00%	÷0.969	×1.250	0.00%

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 94).

Result: FIP 0.92 → 1.58, 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
Justin Bruihl (OGILVIE)	—	—	4.19	16.9%	7.3%	2.2%	—	4.52	15.8%	8.0%	56	+0.33	—	—	—
Jack Taschner L 2008 · 4.09 FIP	30	SU	4.09	17.2%	9.7%	2.2%	.323	4.73	15.2%	3.0%	49	+0.64	3.14	189	77
Arturo Barradas L 2017 · 3.42 FIP	29	SU	3.42	18.1%	7.0%	1.8%	.288	3.24	16.1%	9.5%	34	-0.18	3.24	163	76
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	76
Jeff Ibarra L 2017 · 3.64 FIP	29	SU	3.64	18.1%	7.0%	2.1%	.342	2.85	20.6%	17.2%	29	-0.79	2.85	101	75
Richard Bleier L 2017 · 3.39 FIP	30	SU	3.39	12.4%	3.2%	1.7%	.250	3.03	12.9%	10.1%	105	-0.36	2.28	591	75
Matt Thornton L 2007 · 3.13 FIP	30	SU	3.13	22.1%	8.0%	1.6%	.324	2.19	30.7%	24.4%	200	-0.94	1.71	663	74
Dan Jennings L 2017 · 4.32 FIP	30	SU	4.32	19.1%	9.0%	3.1%	.245	3.88	16.6%	8.5%	64	-0.44	2.60	344	73
Jorge Ibarra L 2017 · 3.09 FIP	29	SU	3.09	19.3%	6.5%	1.4%	.351	—	—	—	—	—	2.55	146	73
Brian Tallet L 2008 · 3.36 FIP	30	SU	3.36	19.6%	7.9%	1.7%	.282	5.06	16.0%	6.3%	251	+1.70	2.59	443	73
Scott Rice L 2013 · 3.10 FIP	31	SU	3.10	19.2%	10.8%	0.5%	.277	3.85	22.1%	5.0%	53	+0.75	3.10	104	73
Doug Slaten L 2010 · 3.22 FIP	30	SU	3.22	20.7%	9.8%	1.1%	.269	4.85	13.5%	3.1%	29	+1.63	3.22	139	72
Kraig Sitton L 2017 · 3.87 FIP	29	SU	3.87	14.7%	6.1%	2.2%	.312	—	—	—	—	—	2.81	166	72
Jeremy Bleich L 2017 · 3.20 FIP	30	SU	3.20	17.6%	5.0%	1.7%	.307	3.97	20.2%	13.5%	84	+0.77	3.20	241	71
Tom Martin L 2000 · 4.16 FIP	30	SU	4.16	14.7%	9.1%	2.1%	.274	4.29	21.8%	12.3%	68	+0.12	3.40	303	71
Aaron Fultz L 2004 · 4.16 FIP	30	SU	4.16	17.1%	9.7%	2.3%	.294	3.54	18.9%	11.5%	181	-0.62	3.46	480	71
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	70
Josh Edgin L 2017 · 4.02 FIP	30	SU	4.02	16.3%	10.2%	1.8%	.303	3.63	29.2%	20.0%	43	-0.39	2.62	195	70
Pedro Feliciano L 2007 · 3.07 FIP	30	SU	3.07	22.2%	9.8%	1.1%	.239	3.28	21.8%	14.5%	175	+0.21	2.69	378	70
Chad Zerbe L 2002 · 3.87 FIP	30	SU	3.87	10.8%	7.9%	1.2%	.255	3.89	7.8%	2.3%	50	+0.02	3.75	145	70
Jose Meraz L 2017 · 4.34 FIP	29	SU	4.34	16.4%	10.3%	2.4%	.282	3.80	10.5%	-2.0%	30	-0.54	3.78	110	69

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 1,234 reliever careers (754 from 1990-2025 + 480 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	1.4	47	16.5%	7.9%	0.81	4.19
Ramon Troncoso ('13)	1.1	45	16.0%	8.5%	0.83	4.00
Jeremy Accardo ('12)	1.4	47	16.8%	9.1%	0.76	4.30
Dave Leiper ('84-'96, 0 WAR*)	1.4	46	15.6%*	9.1%*	0.94*	4.28*
Ramon Pena ('11)	1.9	63	16.0%	8.0%	0.90	4.43
Mike Perez ('90-'97, 3 WAR*)	1.7	58	18.5%*	8.2%*	0.88*	3.86*
Bill Landrum ('86-'93, 4 WAR*)	1.8	60	18.2%*	8.1%*	0.64*	3.86*