Brant Hurter

P Detroit Tigers

Age 28 LHP 6'6" / 250 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	25	MIX2 levels	29	19	117	4.55	3.33	21.7%	5.8%	2.9	K 19.7% · BB 5.4% · FIP 3.80
2025	26	MIX2 levels	46	4	67	2.83	2.86	23.8%	8.2%	1.4	K 24.5% · BB 8.7% · FIP 2.95
2026	27	MIX3 levels	38	2	43	4.81	3.33	17.6%	11.2%	0.6	K 16.8% · BB 9.8% · FIP 3.32
2027PROJ	28	MLBDET	32	0	34	3.72	4.05	19.1%	8.1%	0.2
2028PROJ95% MLB	29	MLBDET	23	0	24	4.02	4.21	18.5%	8.3%	0.1
2029PROJ95% MLB	30	MLBDET	16	0	17	4.26	4.42	17.8%	8.6%	0.0

Last 3 seasons WAR 4.9 · 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 vs66

K%13

FB Velo11

CompsTommy Phelps (age 29) · Hector Hernandez (age 28) · Mark Hendrickson (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 94102vR 110

ERA3.72

FIP4.05

K%19.1%

BB%8.1%

K-BB%11.1%

Proj IP34

Value2027 · WAR

Runs saved vs league avg · 34 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL5555

CMD5555

K†4545

Statcast Percentiles

FB Velo

p1191.7

Whiff%

p1820.8%

p1316.8%

xwOBA vs

p660.296

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)
2022	23	MIX MLE	3 levels	25	18	107	437	136	21	7	72%	4.3	23.7%	5.5%	18.2%	1.9%	3.71	2.84	—
2022	23	MIX RAW	3 levels	25	18	107	437	136	21	7	72%	4.3	31.1%	4.8%	26.3%	1.6%	3.71	1.99	—
2023	24	AA MLE	Erie SeaWolves	26	26	118	498	133	33	7	100%	4.5	21.6%	7.3%	14.3%	1.6%	3.28	3.07	—
2023	24	AA RAW	Erie SeaWolves	26	26	118	498	133	33	7	100%	4.5	26.7%	6.6%	20.1%	1.4%	3.28	2.46	—
2024	25	MIX MLE	2 levels	29	19	117	498	108	29	12	66%	4.0	19.7%	5.4%	14.4%	3.1%	4.55	3.80	+2.2
2024	25	MIX MLE	2 levels	29	19	117	498	108	29	12	66%	4.0	19.7%	5.4%	14.4%	3.1%	4.55	3.80	+2.2
2024	25	MIX RAW	2 levels	29	19	117	498	108	29	12	66%	4.0	21.7%	5.8%	15.9%	2.4%	4.55	3.33	+2.9
2025	26	MIX MLE	2 levels	46	4	67	294	70	24	4	9%	1.4	24.5%	8.7%	15.9%	1.5%	2.83	2.95	+1.3
2025	26	MIX MLE	2 levels	46	4	67	294	70	24	4	9%	1.4	24.5%	8.7%	15.9%	1.5%	2.83	2.95	+1.3
2025	26	MIX RAW	2 levels	46	4	67	294	70	24	4	9%	1.4	23.8%	8.2%	15.6%	1.4%	2.83	2.86	+1.4
2026	27	MIX MLE	3 levels	38	2	43	188	33	21	1	5%	1.1	16.8%	9.8%	7.0%	0.7%	4.81	3.32	+0.6
2026	27	MIX MLE	3 levels	38	2	43	188	33	21	1	5%	1.1	16.8%	9.8%	7.0%	0.7%	4.81	3.32	+0.6
2026	27	MIX RAW	3 levels	38	2	43	188	33	21	1	5%	1.1	17.6%	11.2%	6.4%	0.5%	4.81	3.33	+0.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			82	5	144	595	130	44	9	6%	1.8	21.8%	7.4%	14.5%	1.5%	2.63	3.02	+2.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	DET	32	0	34	144	27	12	3	0%	1.1	19.1%	8.1%	11.1%	2.1%	3.72	4.05	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	DET	24	0	26	108	20	9	2	0%	1.1	18.5%	8.3%	10.3%	2.3%	4.02	4.21	+0.1
2029	30	MLB MLE	DET	17	0	18	77	14	7	2	0%	1.1	17.8%	8.6%	9.3%	2.5%	4.26	4.42	+0.0
2028	29	MLB CTX	DET	23	0	24	101	19	8	2	0%	1.0	18.5%	8.3%	10.3%	2.3%	4.02	4.21	+0.1
2029	30	MLB CTX	DET	16	0	17	72	13	6	2	0%	1.1	17.8%	8.6%	9.3%	2.5%	4.26	4.42	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		122	5	185	771	162	59	13	4%	1.5	21.0%	7.6%	13.4%	1.7%	2.97	3.23	+2.9
/ 200 IP	—	RAW		131	5	200	832	175	64	14	4%	1.5	21.0%	7.6%	13.4%	1.7%	2.97	3.23	+3.2
Career	—	MLE		164	69	451	1915	413	131	37	42%	2.8	21.6%	6.8%	14.7%	2.0%	3.79	3.21	+2.8
/ 200 IP	—	MLE		73	31	200	849	183	58	17	42%	2.7	21.6%	6.8%	14.7%	2.0%	3.79	3.21	+3.0

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.

2026 AAAToledo Mud Hens · 7 IPFIP 3.31 → 3.35 (+0.05)K% 19.5% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.51%	—	×0.860	16.78%
BB% (unintentional) /BF	14.63%	—	×0.900	13.17%
HR / BF /BF	0.00%	÷1.000	×1.100	0.00%

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 100).

Result: FIP 3.31 → 3.35, 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 AAAToledo Mud Hens · 71 IPFIP 3.53 → 4.23 (+0.69)K% 21.7% → 18.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.67%	—	×0.860	18.64%
BB% (unintentional) /BF	7.12%	—	×0.900	6.41%
HR / BF /BF	2.48%	÷0.792	×1.100	3.44%

Result: FIP 3.53 → 4.23, 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 AAErie SeaWolves · 118 IPFIP 2.46 → 3.07 (+0.62)K% 26.7% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.71%	—	×0.810	21.63%
BB% (unintentional) /BF	6.63%	—	×1.100	7.29%
HR / BF /BF	1.41%	÷1.055	×1.200	1.60%

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 111).

Result: FIP 2.46 → 3.07, 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).

2022 A+West Michigan Whitecaps · 51 IPFIP 1.75 → 2.70 (+0.95)K% 30.4% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.39%	—	×0.770	23.40%
BB% (unintentional) /BF	5.39%	—	×1.150	6.20%
HR / BF /BF	0.98%	÷0.847	×1.250	1.45%

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 69).

Result: FIP 1.75 → 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).

2022 ALakeland Flying Tigers · 42 IPFIP 2.00 → 2.84 (+0.84)K% 33.7% → 25.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.73%	—	×0.740	24.96%
BB% (unintentional) /BF	3.55%	—	×1.180	4.19%
HR / BF /BF	2.37%	÷1.279	×1.300	2.41%

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 156).

Result: FIP 2.00 → 2.84, 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).

2022 AAErie SeaWolves · 14 IPFIP 2.51 → 3.27 (+0.76)K% 26.6% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.56%	—	×0.810	21.52%
BB% (unintentional) /BF	6.25%	—	×1.100	6.88%
HR / BF /BF	1.56%	÷0.926	×1.200	2.02%

Result: FIP 2.51 → 3.27, 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).

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
Brant Hurter (OGILVIE)	—	—	4.05	19.1%	8.1%	2.1%	—	4.30	18.2%	9.8%	44	+0.25	—	—	—
Tommy Phelps L 2003 · 3.39 FIP	29	SW	3.39	15.6%	8.0%	1.1%	.322	4.47	16.9%	8.9%	57	+1.08	3.39	120	75
Hector Hernandez L 2019 · 3.39 FIP	28	SW	3.39	20.2%	9.0%	1.5%	.290	—	—	—	—	—	2.17	121	73
Mark Hendrickson L 2002 · 3.04 FIP	28	SW	3.04	14.8%	6.3%	0.7%	.216	4.51	10.9%	5.5%	520	+1.47	3.04	1,169	72
John Rheinecker L 2007 · 5.13 FIP	28	SW	5.13	16.7%	10.9%	3.8%	.317	—	—	—	—	—	4.14	121	72
Matt Whitehouse L 2019 · 4.07 FIP	28	SW	4.07	20.1%	10.9%	2.3%	.339	—	—	—	—	—	3.37	353	70
Chad Bell L 2017 · 4.57 FIP	28	SW	4.57	19.4%	8.9%	3.6%	.341	4.38	18.8%	11.4%	57	-0.19	3.39	393	70
Kyle Hunter L 2017 · 4.50 FIP	28	SU	4.50	14.0%	6.1%	3.3%	.335	—	—	—	—	—	4.32	188	68
Ryan Verdugo L 2015 · 3.61 FIP	28	SU	3.61	19.8%	10.8%	1.5%	.281	3.36	22.7%	17.3%	82	-0.25	3.36	153	68
Edgar Olmos L 2017 · 3.64 FIP	27	SW	3.64	18.9%	9.7%	1.7%	.252	—	—	—	—	—	3.19	203	67
Yohan Flande L 2014 · 3.65 FIP	28	SP-	3.65	14.1%	5.8%	1.8%	.266	3.90	14.1%	7.9%	312	+0.25	3.18	394	67
Randy Keisler L 2005 · 5.07 FIP	29	SW	5.07	16.4%	9.9%	3.8%	.295	5.51	6.5%	0.0%	17	+0.44	5.07	135	66
Jimmy Reyes L 2017 · 4.92 FIP	28	SU	4.92	13.8%	8.0%	3.6%	.294	—	—	—	—	—	3.02	200	65
David Huff L 2013 · 4.42 FIP	28	SU	4.42	20.5%	5.3%	4.3%	.248	3.76	17.1%	11.5%	169	-0.66	3.40	540	65
Brian Johnson L 2019 · 4.49 FIP	28	SW	4.49	18.8%	11.2%	2.8%	.349	4.94	15.5%	6.4%	61	+0.45	3.39	506	64
Austin Warner L 2022 · 4.78 FIP	28	SW	4.78	16.9%	9.0%	3.6%	.319	—	—	—	—	—	3.81	491	64
Kelvin Marte L 2016 · 3.44 FIP	28	SU	3.44	16.6%	7.0%	1.6%	.276	4.97	14.3%	3.1%	157	+1.53	3.44	360	63
Brendan Nail L 2023 · 4.36 FIP	27	SU	4.36	19.3%	10.5%	2.8%	.262	—	—	—	—	—	3.45	165	63
Chad Zerbe L 2001 · 3.75 FIP	29	SU	3.75	13.6%	6.2%	1.8%	.299	3.88	9.4%	2.6%	106	+0.13	3.75	145	63
Cesár Ramos L 2012 · 3.06 FIP	28	SU	3.06	24.2%	8.3%	1.7%	.215	3.47	18.6%	11.4%	202	+0.41	2.56	463	63
Carson Teel L 2022 · 4.26 FIP	26	SU	4.26	17.7%	7.1%	3.2%	.355	—	—	—	—	—	3.43	248	63

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	1.1	37	20.7%	8.1%	0.69	3.09
Alex Colomé ('23)	2.7	60	21.0%	8.3%	0.83	3.36
Jared Burton ('14)	1.8	60	19.9%	8.9%	0.77	3.44
Dylan Floro ('24)	2.0	58	20.8%	7.2%	0.54	3.49
Steve Olin ('89-'92, 4 WAR*)	1.4	68	20.3%*	7.5%*	0.64*	3.30*
Bill Landrum ('86-'93, 4 WAR*)	1.8	60	18.2%*	8.1%*	0.64*	3.86*
Mike Perez ('90-'97, 3 WAR*)	1.7	58	18.5%*	8.2%*	0.88*	3.86*