Justin Steele

P Iowa Cubs

Age 32 LHP 6'2" / 205 lbs AAA peak 0.4 · 2027 ILInjured 60-Day (D60)

Overview

AAASPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MIX2 levels	25	25	138	3.20	3.04	24.3%	6.6%	4.1	K 24.3% · BB 6.7% · FIP 3.12
2025	30	MLBCHC	4	4	23	4.76	4.78	22.8%	5.4%	0.2
2026	31	AAACHC	4	3	8	4.70	0.62	32.4%	2.9%	—	K 32.4% · BB 2.9% · FIP 0.74
2027PROJ	32	MLBCHC	2	2	14	3.83	4.13	22.6%	6.7%	0.2
2028PROJ	33	MLBCHC	7	7	44	4.18	4.41	21.7%	7.2%	0.5

Last 3 seasons WAR 4.2 · Next 2 projected WAR 0.7Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

K%53

xwOBA vs24

FB Velo10

CompsTsuyoshi Wada (age 33) · Brandon Mann (age 32) · Kwang Hyun Kim (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 95104vR 112

ERA3.83

FIP4.13

K%22.6%

BB%6.7%

K-BB%15.8%

Proj IP29

Value2027 · WAR

Runs saved vs league avg · 29 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1090.8

Whiff%

p1018.5%

p5322.8%

xwOBA vs

p240.343

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)
2016	21	A MLE	South Bend Cubs	19	19	77	361	76	39	3	100%	4.1	15.6%	12.7%	2.8%	1.1%	5.00	3.99	—
2016	21	A RAW	South Bend Cubs	19	19	77	361	76	39	3	100%	4.1	21.1%	10.8%	10.2%	0.8%	5.00	3.15	—
2017	22	A+ MLE	Myrtle Beach Pelicans	20	20	99	432	82	36	6	100%	4.9	14.6%	9.6%	5.0%	1.9%	2.92	4.10	—
2017	22	A+ RAW	Myrtle Beach Pelicans	20	20	99	432	82	36	6	100%	4.9	19.0%	8.3%	10.6%	1.4%	2.92	3.32	—
2018	23	MIX MLE	3 levels	11	11	47	185	53	13	2	100%	4.2	23.1%	8.3%	14.7%	1.0%	2.31	2.74	—
2018	23	MIX RAW	3 levels	11	11	47	185	53	13	2	100%	4.2	28.6%	7.0%	21.6%	1.1%	2.31	2.22	—
2019	24	AA MLE	Tennessee Smokies	11	11	39	170	42	20	3	100%	3.5	20.0%	12.9%	7.1%	1.9%	5.59	4.07	—
2019	24	AA RAW	Tennessee Smokies	11	11	39	170	42	20	3	100%	3.5	24.7%	11.8%	12.9%	1.8%	5.59	3.49	—
2021	26	MIX MLE	2 levels	29	14	84	358	88	37	13	48%	2.9	23.6%	10.5%	13.1%	3.8%	3.31	4.50	+0.0
2021	26	MIX RAW	2 levels	29	14	84	358	88	37	13	48%	2.9	24.6%	10.3%	14.2%	3.6%	3.31	4.33	+0.2
2022	27	MLB MLE	Chicago Cubs	24	24	119	512	126	50	8	100%	5.0	24.6%	9.8%	14.8%	1.6%	3.18	3.15	+3.3
2022	27	MLB RAW	Chicago Cubs	24	24	119	512	126	50	8	100%	5.0	24.6%	9.8%	14.8%	1.6%	3.18	3.12	+3.4
2023	28	MLB MLE	Chicago Cubs	30	30	173	716	176	36	14	100%	5.8	24.6%	5.0%	19.6%	2.0%	3.06	2.78	+5.8
2023	28	MLB RAW	Chicago Cubs	30	30	173	716	176	36	14	100%	5.8	24.6%	5.0%	19.6%	2.0%	3.06	2.74	+5.9
2024	29	MIX MLE	2 levels	25	25	138	572	139	38	12	100%	5.5	24.3%	6.7%	17.7%	2.2%	3.20	3.12	+3.9
2024	29	MIX MLE	2 levels	25	25	138	572	139	38	12	100%	5.5	24.3%	6.7%	17.7%	2.2%	3.20	3.12	+3.9
2024	29	MIX RAW	2 levels	25	25	138	572	139	38	12	100%	5.5	24.3%	6.6%	17.7%	2.1%	3.20	3.04	+4.1
2025	30	MLB MLE	Chicago Cubs	4	4	23	92	21	5	5	100%	5.7	22.8%	5.4%	17.4%	5.6%	4.76	4.96	+0.1
2025	30	MLB MLE	Chicago Cubs	4	4	23	92	21	5	5	100%	5.7	22.8%	5.4%	17.4%	5.6%	4.76	4.96	+0.1
2025	30	MLB RAW	Chicago Cubs	4	4	23	92	21	5	5	100%	5.7	22.8%	5.4%	17.4%	5.4%	4.76	4.78	+0.2
2026	31	AAA MLE	Iowa Cubs	4	3	8	34	11	1	0	75%	1.9	32.4%	2.9%	29.4%	0.0%	4.70	0.74	—
2026	31	AAA MLE	Iowa Cubs	4	3	8	34	11	1	0	75%	1.9	32.4%	2.9%	29.4%	0.0%	4.70	0.74	—
2026	31	AAA RAW	Iowa Cubs	4	3	8	34	11	1	0	75%	1.9	32.4%	2.9%	29.4%	0.0%	4.70	0.62	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			102	91	507	2123	517	153	51	89%	5.0	24.4%	7.2%	17.1%	2.4%	3.30	3.27	+13.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	CHC	2	2	14	59	13	4	2	100%	7.0	22.6%	6.7%	15.8%	3.1%	3.83	4.13	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	CHC	7	7	44	187	41	13	6	100%	6.3	21.7%	7.2%	14.5%	3.4%	4.18	4.41	+0.5
2028	33	MLB CTX	CHC	7	7	44	186	40	13	6	100%	6.3	21.7%	7.2%	14.5%	3.4%	4.18	4.41	+0.5
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		109	98	551	2310	558	166	57	90%	5.1	24.1%	7.2%	16.9%	2.5%	3.37	3.34	+14.0
/ 200 IP	—	RAW		40	36	200	839	203	60	21	90%	5.0	24.1%	7.2%	16.9%	2.5%	3.37	3.34	+5.1
Career	—	MLE		177	161	806	3432	754	293	71	91%	4.6	22.0%	8.5%	13.4%	2.1%	3.44	3.47	+13.7
/ 200 IP	—	MLE		44	40	200	851	187	73	18	91%	4.6	22.0%	8.5%	13.4%	2.1%	3.44	3.47	+5.0

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 AAAIowa Cubs · 8 IPFIP 0.74 → 1.09 (+0.35)K% 32.4% → 27.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.35%	—	×0.860	27.82%
BB% (unintentional) /BF	2.94%	—	×0.900	2.65%
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 0.74 → 1.09, 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 AAAIowa Cubs · 27 IPFIP 2.75 → 3.10 (+0.35)K% 26.4% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.36%	—	×0.880	23.20%
BB% (unintentional) /BF	10.91%	—	×1.050	11.45%
HR / BF /BF	0.91%	÷1.081	×1.100	0.93%

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 116).

Result: FIP 2.75 → 3.10, 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 AATennessee Smokies · 39 IPFIP 3.47 → 4.07 (+0.59)K% 24.7% → 20.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.71%	—	×0.810	20.01%
BB% (unintentional) /BF	11.76%	—	×1.100	12.94%
HR / BF /BF	1.76%	÷1.142	×1.200	1.85%

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 128).

Result: FIP 3.47 → 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).

2018 A+Myrtle Beach Pelicans · 18 IPFIP 1.96 → 2.61 (+0.65)K% 25.7% → 19.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.68%	—	×0.770	19.77%
BB% (unintentional) /BF	8.11%	—	×1.150	9.32%
HR / BF /BF	0.00%	÷0.933	×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 87).

Result: FIP 1.96 → 2.61, 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 ROKAZL Cubs 1 · 18 IPFIP 1.33 → 2.88 (+1.56)K% 39.1% → 26.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.13%	—	×0.680	26.61%
BB% (unintentional) /BF	5.80%	—	×1.250	7.25%
HR / BF /BF	1.45%	÷1.000	×1.400	2.03%

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 1.33 → 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).

2018 AATennessee Smokies · 10 IPFIP 3.90 → 4.61 (+0.71)K% 16.7% → 13.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.810	13.50%
BB% (unintentional) /BF	7.14%	—	×1.100	7.86%
HR / BF /BF	2.38%	÷0.947	×1.200	3.02%

Result: FIP 3.90 → 4.61, 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 A+Myrtle Beach Pelicans · 99 IPFIP 3.32 → 4.10 (+0.78)K% 19.0% → 14.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.98%	—	×0.770	14.62%
BB% (unintentional) /BF	8.33%	—	×1.150	9.58%
HR / BF /BF	1.39%	÷0.936	×1.250	1.86%

Result: FIP 3.32 → 4.10, 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 ASouth Bend Cubs · 77 IPFIP 3.15 → 3.99 (+0.85)K% 21.1% → 15.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.05%	—	×0.740	15.58%
BB% (unintentional) /BF	10.80%	—	×1.180	12.75%
HR / BF /BF	0.83%	÷0.997	×1.300	1.08%

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 99).

Result: FIP 3.15 → 3.99, 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 Steele (OGILVIE)	—	—	—	—	—	3.1%	—	4.41	21.7%	14.5%	44	+0.28	—	—	—
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	71
Brandon Mann L 2016 · 4.72 FIP	32	SP	4.72	19.4%	7.9%	4.1%	.320	3.97	19.0%	7.6%	128	-0.75	3.59	190	68
Kwang Hyun Kim L 2020 · 3.76 FIP	32	SP	3.76	15.6%	6.5%	2.1%	.214	4.21	17.7%	9.5%	107	+0.45	3.76	146	67
Jorge Castillo L 2015 · 4.50 FIP	33	SP	4.50	16.8%	9.2%	3.0%	.292	4.95	12.4%	7.2%	186	+0.45	4.50	408	59
Mike Montgomery L 2021 · 4.49 FIP	32	SP	4.49	18.5%	12.4%	2.5%	.347	4.44	15.5%	3.4%	147	-0.05	3.43	764	58
Ryan Verdugo L 2017 · 3.36 FIP	30	SP-	3.36	22.7%	5.4%	2.7%	.333	—	—	—	—	—	3.36	153	58
Tomas Solis L 2015 · 4.20 FIP	32	SP	4.20	15.0%	6.2%	2.9%	.264	5.09	12.0%	3.7%	159	+0.89	4.20	327	57
Matt Dermody L 2022 · 4.22 FIP	32	SP-	4.22	17.9%	5.5%	3.5%	.302	3.87	20.7%	16.1%	49	-0.35	2.38	348	56
Jose De Paula L 2019 · 3.31 FIP	31	SP	3.31	19.0%	7.6%	1.6%	.394	—	—	—	—	—	3.31	118	55
Arturo Lopez L 2015 · 4.80 FIP	32	SP	4.80	13.7%	6.2%	3.8%	.301	4.80	10.9%	4.2%	217	+0.00	4.34	445	55
Orlando Lara L 2017 · 4.39 FIP	32	SP	4.39	14.7%	6.5%	3.1%	.346	4.09	22.0%	14.2%	104	-0.30	3.85	426	55
Oscar Rivera L 2015 · 3.68 FIP	34	SP	3.68	14.2%	6.9%	1.7%	.298	4.66	10.4%	3.0%	129	+0.98	3.68	237	54
Roy Merritt L 2017 · 3.95 FIP	31	SP	3.95	16.2%	5.8%	2.7%	.339	—	—	—	—	—	3.93	129	52
Sean Nolin L 2021 · 4.54 FIP	31	SP-	4.54	20.4%	8.9%	3.7%	.316	5.87	19.3%	12.9%	23	+1.33	3.79	309	52
Santiago Gutierrez L 2017 · 3.95 FIP	31	SP-	3.95	15.0%	8.2%	2.0%	.366	6.54	17.1%	3.7%	46	+2.59	3.90	341	51
Alejandro Astorga L 2017 · 4.01 FIP	30	SP	4.01	14.4%	8.2%	2.0%	.306	—	—	—	—	—	3.72	311	51
Everett Teaford L 2015 · 4.42 FIP	31	SP-	4.42	13.9%	7.6%	2.8%	.324	—	—	—	—	—	4.42	208	50
Enrique Lechuga L 2017 · 3.78 FIP	31	SP	3.78	10.5%	6.1%	1.5%	.390	—	—	—	—	—	3.78	137	49
Eric Stults L 2012 · 3.71 FIP	32	SP-	3.71	13.3%	6.5%	1.7%	.262	4.01	14.8%	9.6%	517	+0.30	3.50	773	49
Luke Westphal L 2021 · 4.70 FIP	32	SP-	4.70	22.7%	13.1%	3.4%	.321	4.58	21.9%	7.6%	95	-0.12	2.97	294	48

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	2.9	83	24.0%	7.3%	0.97	3.40
Julio Urías ('23)	3.6	102	24.3%	6.6%	1.05	3.11
Kris Medlen ('18)	3.0	100	20.1%	6.6%	0.75	3.33
Andy McGaffigan ('81-'91, 10 WAR*)	4.2	104	22.6%*	8.2%*	0.87*	3.85*
Alejandro Peña ('81-'96, 15 WAR*)	5.3	81	24.7%*	7.5%*	0.96*	3.61*
Bob Reynolds ('69-'75, 4 WAR*)	1.3	85	23.1%*	7.4%*	0.94*	3.63*
Collin McHugh ('23)	5.0	90	23.3%	6.7%	0.96	3.72