Justin Dunn

P Salt Lake Bees

Age 31 RHP 6'2" / 210 lbs AAA peak 0.1 · 2027

Overview

AAASP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	27	MIX2 levels	3	2	3	5.40	5.80	33.3%	13.3%	—	K 33.3% · BB 13.3% · FIP 5.63
2025	29	MIX2 levels	19	14	54	9.67	5.66	20.3%	16.0%	—	K 18.8% · BB 14.7% · FIP 5.29
2026	30	AAALAA	27	2	40	6.13	4.97	20.2%	15.0%	—	K 17.4% · BB 13.5% · FIP 4.91
2027PROJ	31	MLBLAA	1	1	6	5.72	5.73	17.5%	15.0%	-0.0
2028PROJ37% MLB	32	MLBLAA	2	2	15	5.88	5.99	16.7%	15.4%	-0.1
2029PROJ55% MLB	33	MLBLAA	3	3	18	6.11	6.27	15.9%	15.9%	-0.2

Next 3 projected WAR -0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2022

FB Velo16

K%10

xwOBA vs10

CompsDerek Eitel (age 29) · Miguel Ruiz (age 31) · Scott Copeland (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 142134vR 126

ERA5.72

FIP5.73

K%17.5%

BB%15.0%

K-BB%2.5%

Proj IP55

Value2027 · WAR

Runs saved vs league avg · 55 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL3030

CMD3535

K†4040

Statcast Percentiles

FB Velo

p1692.1

Whiff%

p2922.4%

p1015.2%

xwOBA vs

p100.431

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)
2017	21	A+ MLE	St. Lucie Mets	20	16	95	433	75	46	5	80%	4.8	13.3%	12.2%	1.1%	1.6%	5.00	4.39	—
2017	21	A+ RAW	St. Lucie Mets	20	16	95	433	75	46	5	80%	4.8	17.3%	10.6%	6.7%	1.2%	5.00	3.66	—
2018	22	MIX MLE	2 levels	24	24	135	576	156	51	9	100%	5.6	21.6%	9.9%	11.7%	1.8%	3.59	3.54	—
2018	22	MIX RAW	2 levels	24	24	135	576	156	51	9	100%	5.6	27.1%	8.9%	18.2%	1.6%	3.59	2.79	—
2019	23	MIX MLE	2 levels	29	29	138	583	163	48	13	100%	4.8	23.1%	7.8%	15.4%	3.6%	3.51	4.10	+2.1
2019	23	MIX RAW	2 levels	29	29	138	583	163	48	13	100%	4.8	28.0%	8.2%	19.7%	2.2%	3.51	3.01	+4.1
2020	24	MLB MLE	Seattle Mariners	10	10	46	198	38	30	10	100%	4.6	19.2%	15.2%	4.0%	5.2%	4.34	6.24	-0.4
2020	24	MLB RAW	Seattle Mariners	10	10	46	198	38	30	10	100%	4.6	19.2%	15.2%	4.0%	5.1%	4.34	6.25	-0.4
2021	25	MIX MLE	2 levels	12	11	50	219	49	30	6	92%	4.2	22.5%	13.3%	9.2%	2.8%	3.75	4.44	+0.6
2021	25	MIX RAW	2 levels	12	11	50	219	49	30	6	92%	4.2	22.4%	13.7%	8.7%	2.7%	3.75	4.49	+0.5
2022	26	MIX MLE	3 levels	16	16	62	287	49	36	15	100%	3.9	16.1%	12.9%	3.2%	4.9%	6.10	6.06	-0.5
2022	26	MIX RAW	3 levels	16	16	62	287	49	36	15	100%	3.9	17.1%	12.5%	4.5%	5.2%	6.10	6.41	-0.7
2023	27	MIX MLE	2 levels	3	2	3	15	5	2	1	67%	1.1	33.3%	13.3%	20.0%	6.7%	5.40	5.63	—
2023	27	MIX RAW	2 levels	3	2	3	15	5	2	1	67%	1.1	33.3%	13.3%	20.0%	6.7%	5.40	5.80	—
2025	29	MIX MLE	2 levels	19	14	54	281	57	45	9	74%	2.8	18.8%	14.7%	4.1%	3.5%	9.67	5.29	—
2025	29	MIX MLE	2 levels	19	14	54	281	57	45	9	74%	2.8	18.8%	14.7%	4.1%	3.5%	9.67	5.29	—
2025	29	MIX RAW	2 levels	19	14	54	281	57	45	9	74%	2.8	20.3%	16.0%	4.3%	3.2%	9.67	5.66	—
2026	30	AAA MLE	Salt Lake Bees	27	2	40	193	39	29	5	7%	1.5	17.4%	13.5%	3.9%	2.8%	6.13	4.91	—
2026	30	AAA MLE	Salt Lake Bees	27	2	40	193	39	29	5	7%	1.5	17.4%	13.5%	3.9%	2.8%	6.13	4.91	—
2026	30	AAA RAW	Salt Lake Bees	27	2	40	193	39	29	5	7%	1.5	20.2%	15.0%	5.2%	2.6%	6.13	4.97	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			32	32	134	584	113	85	27	100%	4.2	19.3%	14.6%	4.8%	4.6%	4.44	5.94	-0.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	LAA	1	1	6	25	4	4	1	100%	6.0	17.5%	15.0%	2.5%	3.3%	5.72	5.73	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	LAA	7	7	41	172	29	27	6	100%	5.8	16.7%	15.4%	1.3%	3.6%	5.88	5.99	-0.3
2029	33	MLB MLE	LAA	5	5	33	139	22	22	5	100%	6.6	15.9%	15.9%	-0.0%	3.9%	6.11	6.27	-0.3
2030	34	MLB MLE	LAA	5	5	28	117	18	19	5	100%	5.6	15.0%	16.4%	-1.4%	4.2%	6.39	6.57	-0.3
2028	32	MLB CTX	LAA	2	2	15	63	11	10	2	100%	7.5	16.7%	15.4%	1.3%	3.6%	5.88	5.99	-0.1
2029	33	MLB CTX	LAA	3	3	18	76	12	12	3	100%	6.0	15.9%	15.9%	-0.0%	3.9%	6.11	6.27	-0.2
2030	34	MLB CTX	LAA	2	2	14	59	9	10	2	100%	7.0	15.0%	16.4%	-1.4%	4.2%	6.39	6.57	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		40	40	181	783	145	117	35	100%	4.5	18.5%	14.9%	3.6%	4.4%	4.88	5.86	-0.9
/ 200 IP	—	RAW		44	44	200	866	160	129	39	100%	4.5	18.5%	14.9%	3.6%	4.4%	4.88	5.86	-1.0
Career	—	MLE		160	124	624	2785	542	321	85	78%	3.9	19.5%	11.5%	7.9%	3.1%	4.80	4.60	-1.7
/ 200 IP	—	MLE		51	40	200	893	174	103	27	78%	3.9	19.5%	11.5%	7.9%	3.1%	4.80	4.60	-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.

2026 AAASalt Lake Bees · 40 IPFIP 4.72 → 4.91 (+0.19)K% 20.2% → 17.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.21%	—	×0.860	17.38%
BB% (unintentional) /BF	15.03%	—	×0.900	13.52%
HR / BF /BF	2.59%	÷1.000	×1.100	2.85%

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 4.72 → 4.91, 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).

2025 AAAOmaha Storm Chasers · 49 IPFIP 5.07 → 5.29 (+0.22)K% 21.9% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.91%	—	×0.860	18.84%
BB% (unintentional) /BF	16.33%	—	×0.900	14.70%
HR / BF /BF	3.19%	÷1.003	×1.100	3.50%

Result: FIP 5.07 → 5.29, 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).

2025 ROKACL Royals · 5 IPFIP 6.06 → 7.56 (+1.50)K% 6.7% → 4.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	6.67%	—	×0.680	4.53%
BB% (unintentional) /BF	13.33%	—	×1.250	16.67%
HR / BF /BF	3.33%	÷0.939	×1.400	4.97%

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 88).

Result: FIP 6.06 → 7.56, 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 AAALouisville Bats · 29 IPFIP 4.67 → 4.72 (+0.05)K% 19.3% → 17.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.29%	—	×0.880	16.97%
BB% (unintentional) /BF	12.86%	—	×1.050	13.50%
HR / BF /BF	2.86%	÷1.282	×1.100	2.45%

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 156).

Result: FIP 4.67 → 4.72, 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 AAArkansas Travelers · 132 IPFIP 2.87 → 4.10 (+1.23)K% 28.6% → 23.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.810	23.14%
BB% (unintentional) /BF	7.05%	—	×1.100	7.76%
HR / BF /BF	2.35%	÷0.784	×1.200	3.60%

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 57).

Result: FIP 2.87 → 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).

2018 AABinghamton Rumble Ponies · 90 IPFIP 2.98 → 3.73 (+0.75)K% 27.7% → 22.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.70%	—	×0.810	22.44%
BB% (unintentional) /BF	9.50%	—	×1.100	10.45%
HR / BF /BF	1.85%	÷1.013	×1.200	2.19%

Result: FIP 2.98 → 3.73, 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+St. Lucie Mets · 46 IPFIP 2.44 → 3.17 (+0.74)K% 25.9% → 19.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.89%	—	×0.770	19.93%
BB% (unintentional) /BF	7.61%	—	×1.150	8.76%
HR / BF /BF	1.02%	÷1.074	×1.250	1.18%

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 2.44 → 3.17, 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+St. Lucie Mets · 95 IPFIP 3.62 → 4.39 (+0.77)K% 17.3% → 13.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.32%	—	×0.770	13.34%
BB% (unintentional) /BF	10.62%	—	×1.150	12.22%
HR / BF /BF	1.15%	÷0.911	×1.250	1.58%

Result: FIP 3.62 → 4.39, 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 Dunn (OGILVIE)	—	—	5.73	17.5%	15.0%	3.3%	—	6.24	16.0%	0.1%	102	+0.51	—	—	—
Derek Eitel R 2017 · 4.72 FIP	29	SP-	4.72	17.2%	16.0%	1.9%	.273	—	—	—	—	—	3.96	138	73
Miguel Ruiz R 2017 · 5.60 FIP	31	SP	5.60	10.6%	11.0%	3.6%	.279	—	—	—	—	—	5.60	117	72
Scott Copeland R 2019 · 5.62 FIP	31	SP-	5.62	17.1%	12.4%	4.4%	.311	—	—	—	—	—	3.55	569	71
Hector Galvan R 2019 · 4.84 FIP	31	SP-	4.84	14.3%	12.2%	2.6%	.357	—	—	—	—	—	3.59	406	69
Luis Mateo R 2019 · 5.55 FIP	29	SP-	5.55	8.6%	13.4%	2.7%	.303	—	—	—	—	—	3.48	160	69
Reinier Roibal R 2019 · 4.91 FIP	30	SP	4.91	17.5%	11.8%	3.3%	.426	—	—	—	—	—	3.11	133	69
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	69
Daniel Guerrero R 2016 · 4.81 FIP	31	SP	4.81	12.4%	11.6%	2.3%	.328	4.93	11.7%	2.0%	175	+0.12	4.61	269	65
David Fletcher R 2024 · 5.67 FIP	30	SP-	5.67	7.5%	10.3%	3.5%	.294	—	—	—	—	—	5.67	100	65
Walter Silva R 2009 · 5.72 FIP	32	SP	5.72	9.1%	12.4%	3.3%	.330	—	—	—	—	—	3.67	453	64
Juan Omar Espinoza R 2015 · 4.81 FIP	31	SP-	4.81	8.9%	6.6%	3.0%	.346	4.84	8.4%	-0.8%	52	+0.03	4.81	104	64
Alex Caldera R 2016 · 4.54 FIP	30	SP	4.54	11.9%	11.1%	1.9%	.280	—	—	—	—	—	4.54	120	64
Jose A. Flores R 2018 · 4.77 FIP	29	SP-	4.77	13.7%	10.3%	2.8%	.350	—	—	—	—	—	2.94	237	63
Chris Heston R 2017 · 5.88 FIP	29	SP-	5.88	13.6%	10.0%	4.9%	.368	—	—	—	—	—	3.81	321	61
Wes Obermueller R 2007 · 4.83 FIP	30	SW	4.83	12.6%	11.2%	2.5%	.317	—	—	—	—	—	4.72	308	61
Luke Farrell R 2022 · 4.87 FIP	31	SP-	4.87	17.8%	11.0%	3.4%	.289	4.85	17.3%	7.9%	55	-0.02	3.69	584	60
Seth Simmons R 2018 · 6.20 FIP	30	SW	6.20	13.3%	10.9%	5.2%	.242	—	—	—	—	—	3.07	251	60
André Rienzo R 2019 · 5.33 FIP	31	SP	5.33	17.9%	11.8%	4.1%	.337	—	—	—	—	—	3.53	321	59
Ariel Peña R 2019 · 6.07 FIP	30	SP	6.07	19.9%	25.6%	2.6%	.293	—	—	—	—	—	3.74	223	59
Robert Ellis R 2001 · 4.84 FIP	30	SP	4.84	9.9%	7.7%	2.9%	.287	8.08	9.0%	-2.2%	18	+3.24	4.84	110	59

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	1.5	41	17.8%	15.2%	1.59	5.26
Richie Lewis ('98)	1.5	59	18.1%	14.1%	1.38	4.88
Doug Creek ('05)	1.4	58	22.2%	15.1%	1.52	5.32
Jeff Jones ('80-'84, -2 WAR*)	1.0	41	22.0%*	12.4%*	1.48*	4.53*
Doug Bochtler ('00)	1.3	65	18.6%	14.4%	1.25	4.57
Daryl Patterson ('68-'74, -1 WAR*)	1.2	46	18.3%*	11.2%*	1.27*	5.03*
Bill Laxton ('70-'77, -3 WAR*)	1.2	61	26.1%*	14.4%*	1.87*	5.39*