Parker Dunshee

P Rochester Red Wings

Age 32 RHP 6'0" / 215 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	28	MIX2 levels	34	5	65	3.18	4.01	26.6%	11.0%	—	K 21.9% · BB 11.4% · FIP 4.69
2024	29	MIX3 levels	38	0	62	3.94	2.35	31.4%	10.6%	1.6	K 26.9% · BB 9.7% · FIP 2.29
2025	30	AAAWSH	33	0	39	5.12	4.63	18.9%	11.4%	—	K 16.2% · BB 10.3% · FIP 4.83
2026	31	No MLB games played
2027PROJ	32	MLBWSH	2	0	2	4.60	4.60	20.2%	9.8%	-0.0
2028PROJ37% MLB	33	MLBWSH	7	0	7	4.72	4.82	20.0%	10.2%	-0.0

Last 3 seasons WAR 1.6 · Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsElian Leyva (age 30) · Marco Quevedo (age 32) · Angel Sánchez (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 121112vR 104

ERA4.60

FIP4.60

K%20.2%

BB%9.8%

K-BB%10.3%

Proj IP24

Value2027 · WAR

Runs saved vs league avg · 24 IP

Grades · Contract

Scouting Grades

K†5050

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	22	ROK MLE	AZL Athletics	1	0	2	11	3	0	1	0%	2.0	27.3%	0.0%	27.3%	9.1%	13.50	5.79	—
2017	22	ROK RAW	AZL Athletics	1	0	2	11	3	0	1	0%	2.0	27.3%	0.0%	27.3%	9.1%	13.50	6.60	—
2018	23	MIX MLE	2 levels	24	22	151	587	163	29	12	92%	6.3	21.9%	5.6%	16.4%	2.3%	2.33	3.22	—
2018	23	MIX RAW	2 levels	24	22	151	587	163	29	12	92%	6.3	27.8%	4.9%	22.8%	2.0%	2.33	2.55	—
2019	24	MIX MLE	2 levels	26	25	130	547	124	48	22	96%	5.0	19.5%	9.3%	10.2%	4.0%	4.36	4.83	—
2019	24	MIX RAW	2 levels	26	25	130	547	124	48	22	96%	5.0	22.7%	8.8%	13.9%	4.0%	4.36	4.50	—
2021	26	MIX MLE	2 levels	11	10	45	207	41	16	7	91%	4.1	16.8%	8.4%	8.4%	4.1%	6.55	5.01	—
2021	26	MIX RAW	2 levels	11	10	45	207	41	16	7	91%	4.1	19.8%	7.7%	12.1%	3.4%	6.55	4.36	—
2022	27	AAA MLE	Las Vegas Aviators	31	20	111	537	92	56	38	65%	3.6	15.1%	10.9%	4.1%	7.2%	9.22	7.19	—
2022	27	AAA RAW	Las Vegas Aviators	31	20	111	537	92	56	38	65%	3.6	17.1%	10.4%	6.7%	7.1%	9.22	7.39	—
2023	28	MIX MLE	2 levels	34	5	65	290	77	32	9	15%	1.9	21.9%	11.4%	10.4%	3.6%	3.18	4.69	—
2023	28	MIX RAW	2 levels	34	5	65	290	77	32	9	15%	1.9	26.6%	11.0%	15.5%	3.1%	3.18	4.01	—
2024	29	MIX MLE	3 levels	38	0	62	274	86	29	3	0%	1.6	26.9%	9.7%	17.3%	0.4%	3.94	2.29	+1.7
2024	29	MIX MLE	3 levels	38	0	62	274	86	29	3	0%	1.6	26.9%	9.7%	17.3%	0.4%	3.94	2.29	+1.7
2024	29	MIX RAW	3 levels	38	0	62	274	86	29	3	0%	1.6	31.4%	10.6%	20.8%	1.1%	3.94	2.35	+1.6
2025	30	AAA MLE	Rochester Red Wings	33	0	39	175	33	20	5	0%	1.2	16.2%	10.3%	5.9%	3.3%	5.12	4.83	—
2025	30	AAA MLE	Rochester Red Wings	33	0	39	175	33	20	5	0%	1.2	16.2%	10.3%	5.9%	3.3%	5.12	4.83	—
2025	30	AAA RAW	Rochester Red Wings	33	0	39	175	33	20	5	0%	1.2	18.9%	11.4%	7.4%	2.9%	5.12	4.63	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			1	0	2	14	3	2	2	0%	2.3	21.4%	14.3%	7.1%	14.3%	19.29	14.24	-0.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	WSH	2	0	2	8	2	1	0	0%	1.0	20.2%	9.8%	10.3%	2.9%	4.60	4.60	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	WSH	18	0	18	78	16	8	2	0%	1.0	20.0%	10.2%	9.8%	3.2%	4.72	4.82	-0.1
2028	33	MLB CTX	WSH	7	0	7	30	6	3	1	0%	1.0	20.0%	10.2%	9.8%	3.2%	4.72	4.82	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		7	0	9	43	9	5	3	0%	1.3	20.5%	11.6%	8.9%	6.8%	8.45	6.58	-0.2
/ 200 IP	—	RAW		164	0	200	936	198	110	66	0%	1.2	20.5%	11.6%	8.9%	6.8%	8.45	6.58	-4.7
Career	—	MLE		198	82	605	2628	520	238	101	41%	3.1	19.8%	9.0%	10.7%	3.9%	4.82	4.69	-0.3
/ 200 IP	—	MLE		65	27	200	869	172	79	33	41%	3.1	19.8%	9.0%	10.7%	3.9%	4.82	4.69	-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.

2025 AAARochester Red Wings · 39 IPFIP 4.52 → 4.83 (+0.31)K% 18.9% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.86%	—	×0.860	16.22%
BB% (unintentional) /BF	11.43%	—	×0.900	10.29%
HR / BF /BF	2.86%	÷0.960	×1.100	3.27%

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 92).

Result: FIP 4.52 → 4.83, 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 AAAGwinnett Stripers · 45 IPFIP 2.50 → 2.72 (+0.22)K% 27.8% → 23.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.78%	—	×0.860	23.89%
BB% (unintentional) /BF	11.62%	—	×0.900	10.45%
HR / BF /BF	0.51%	÷0.968	×1.100	0.57%

Result: FIP 2.50 → 2.72, 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).

2024 AAMississippi Braves · 15 IPFIP 0.10 → 0.91 (+0.81)K% 45.2% → 36.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	45.16%	—	×0.810	36.58%
BB% (unintentional) /BF	6.45%	—	×1.100	7.10%
HR / BF /BF	0.00%	÷0.906	×1.200	0.00%

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 81).

Result: FIP 0.10 → 0.91, 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).

2023 AARichmond Flying Squirrels · 46 IPFIP 3.27 → 4.20 (+0.93)K% 27.2% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.23%	—	×0.810	22.05%
BB% (unintentional) /BF	10.89%	—	×1.100	11.98%
HR / BF /BF	1.98%	÷0.906	×1.200	2.62%

Result: FIP 3.27 → 4.20, 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 AAASacramento River Cats · 19 IPFIP 5.55 → 5.83 (+0.28)K% 25.0% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.860	21.50%
BB% (unintentional) /BF	11.36%	—	×0.900	10.23%
HR / BF /BF	5.68%	÷1.057	×1.100	5.91%

Result: FIP 5.55 → 5.83, 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 AAALas Vegas Aviators · 111 IPFIP 6.86 → 7.19 (+0.33)K% 17.1% → 15.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.13%	—	×0.880	15.08%
BB% (unintentional) /BF	10.43%	—	×1.050	10.95%
HR / BF /BF	7.08%	÷1.075	×1.100	7.24%

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 115).

Result: FIP 6.86 → 7.19, 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 AAALas Vegas Aviators · 43 IPFIP 4.44 → 5.01 (+0.57)K% 19.1% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.10%	—	×0.880	16.80%
BB% (unintentional) /BF	8.04%	—	×1.050	8.44%
HR / BF /BF	3.52%	÷0.941	×1.100	4.11%

Result: FIP 4.44 → 5.01, 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 AAALas Vegas Aviators · 92 IPFIP 5.26 → 5.50 (+0.24)K% 22.6% → 19.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.56%	—	×0.880	19.85%
BB% (unintentional) /BF	9.27%	—	×1.050	9.74%
HR / BF /BF	5.26%	÷1.119	×1.100	5.17%

Result: FIP 5.26 → 5.50, 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
Parker Dunshee (OGILVIE)	—	—	4.60	20.2%	9.8%	2.9%	—	4.82	20.0%	9.8%	18	+0.22	—	—	—
Elian Leyva R 2019 · 4.30 FIP	30	SP-	4.30	17.0%	8.5%	2.9%	.323	—	—	—	—	—	3.10	158	70
Marco Quevedo R 2019 · 5.17 FIP	32	SP-	5.17	13.6%	11.4%	3.2%	.344	—	—	—	—	—	4.13	300	70
Angel Sánchez R 2023 · 4.90 FIP	33	SW	4.90	19.0%	10.5%	3.8%	.333	3.77	11.8%	6.8%	27	-1.13	3.77	284	68
Jared Fernandez R 2003 · 3.57 FIP	31	SP-	3.57	11.8%	6.2%	1.2%	.269	—	—	—	—	—	3.57	101	67
Mitch Lively R 2017 · 3.47 FIP	31	SP-	3.47	17.0%	6.4%	1.8%	.304	—	—	—	—	—	2.39	158	66
Mike Burns R 2009 · 4.90 FIP	31	SP-	4.90	17.2%	6.6%	4.4%	.307	—	—	—	—	—	3.10	104	66
Marco Antonio Duarte R 2019 · 5.03 FIP	32	SW	5.03	12.2%	9.1%	3.3%	.420	—	—	—	—	—	4.07	203	65
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	65
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	65
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	64
Juan Omar Espinoza R 2016 · 4.84 FIP	32	SP-	4.84	8.4%	9.2%	2.4%	.348	—	—	—	—	—	4.81	104	63
Mike Fyhrie R 2002 · 3.86 FIP	32	SW	3.86	13.7%	9.0%	1.4%	.267	—	—	—	—	—	3.08	173	63
Bradin Hagens R 2019 · 5.46 FIP	30	SW	5.46	13.2%	12.6%	3.4%	.303	—	—	—	—	—	3.79	254	63
Austin Bibens-Dirkx R 2017 · 5.03 FIP	32	SW	5.03	14.0%	6.6%	4.2%	.268	5.28	17.5%	9.7%	166	+0.25	3.97	567	62
Tim Dillard R 2015 · 3.58 FIP	32	SW	3.58	15.9%	7.7%	1.5%	.330	3.85	16.7%	8.1%	144	+0.27	2.92	432	62
Cody Satterwhite R 2017 · 4.22 FIP	30	SU	4.22	17.0%	7.1%	3.0%	.310	—	—	—	—	—	3.69	147	62
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	60
Jason Urquidez R 2015 · 3.59 FIP	32	SU	3.59	15.6%	9.3%	1.1%	.386	1.81	25.5%	20.7%	90	-1.78	1.25	123	59
Todd Redmond R 2015 · 3.86 FIP	30	SW	3.86	15.4%	7.1%	2.1%	.311	—	—	—	—	—	3.21	247	58
Brian Sanches R 2011 · 4.35 FIP	32	SU	4.35	19.6%	11.1%	2.7%	.250	—	—	—	—	—	3.39	229	57