Michael Flynn

P El Paso Chihuahuas

Age 30 RHP 6'4" / 185 lbs AAA

Overview

AAAPROSPECT

MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MIX2 levels	37	1	48	4.50	3.29	33.0%	8.7%	—	K 27.6% · BB 8.3% · FIP 3.99
2025	28	MIX2 levels	24	1	22	3.68	6.01	15.3%	14.3%	—	K 11.6% · BB 14.6% · FIP 5.74
2026	29	MIX2 levels	32	1	34	5.88	3.87	22.0%	11.9%	—	K 18.4% · BB 11.4% · FIP 4.16
2027PROJ	30	MLBSD	2	0	2	4.88	4.88	18.8%	11.1%	-0.0
2028PROJ37% MLB	31	MLBSD	6	0	6	4.95	5.03	18.8%	11.2%	-0.0

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

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 0.0 / P90 0.0, comp-based) · star 0% · didn't stick 100%

CompsWirfin Obispo (age 30) · Esteban Haro (age 30) · Jim Miller (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 126118vR 110

ERA4.88

FIP4.88

K%18.8%

BB%11.1%

K-BB%7.7%

Proj IP17

Value2027 · WAR

Runs saved vs league avg · 17 IP

Grades · Contract

Scouting Grades

K†4545

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)
2019	22	ROK MLE	GCL Pirates	3	0	4	16	4	0	0	0%	1.3	25.0%	0.0%	25.0%	0.0%	2.25	0.99	—
2019	22	ROK RAW	GCL Pirates	3	0	4	16	4	0	0	0%	1.3	25.0%	0.0%	25.0%	0.0%	2.25	1.10	—
2022	25	A+ MLE	Everett AquaSox	21	4	36	179	33	13	3	19%	1.7	14.2%	8.4%	5.8%	1.7%	7.50	3.89	—
2022	25	A+ RAW	Everett AquaSox	21	4	36	179	33	13	3	19%	1.7	18.4%	7.3%	11.2%	1.7%	7.50	3.43	—
2023	26	MIX MLE	2 levels	36	0	40	181	61	18	5	0%	1.1	26.5%	11.2%	15.3%	3.7%	6.75	4.31	—
2023	26	MIX RAW	2 levels	36	0	40	181	61	18	5	0%	1.1	33.7%	9.9%	23.8%	2.8%	6.75	3.02	—
2024	27	MIX MLE	2 levels	37	1	48	206	68	18	7	3%	1.3	27.6%	8.3%	19.3%	3.9%	4.50	3.99	—
2024	27	MIX MLE	2 levels	37	1	48	206	68	18	7	3%	1.3	27.6%	8.3%	19.3%	3.9%	4.50	3.99	—
2024	27	MIX RAW	2 levels	37	1	48	206	68	18	7	3%	1.3	33.0%	8.7%	24.3%	3.4%	4.50	3.29	—
2025	28	MIX MLE	2 levels	24	1	22	98	15	14	4	4%	0.9	11.6%	14.6%	-3.0%	3.2%	3.68	5.74	—
2025	28	MIX MLE	2 levels	24	1	22	98	15	14	4	4%	0.9	11.6%	14.6%	-3.0%	3.2%	3.68	5.74	—
2025	28	MIX RAW	2 levels	24	1	22	98	15	14	4	4%	0.9	15.3%	14.3%	1.0%	4.1%	3.68	6.01	—
2026	29	MIX MLE	2 levels	32	1	34	159	35	19	3	3%	1.1	18.4%	11.4%	7.0%	2.1%	5.88	4.16	—
2026	29	MIX MLE	2 levels	32	1	34	159	35	19	3	3%	1.1	18.4%	11.4%	7.0%	2.1%	5.88	4.16	—
2026	29	MIX RAW	2 levels	32	1	34	159	35	19	3	3%	1.1	22.0%	11.9%	10.1%	1.9%	5.88	3.87	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	SD	2	0	2	8	2	1	0	0%	1.0	18.8%	11.1%	7.7%	2.9%	4.88	4.88	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	SD	15	0	16	68	13	8	2	0%	1.1	18.8%	11.2%	7.6%	3.1%	4.95	5.03	-0.1
2028	31	MLB CTX	SD	6	0	6	25	5	3	1	0%	1.0	18.8%	11.2%	7.6%	3.1%	4.95	5.03	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		6	0	6	25	5	3	1	0%	1.0	18.8%	11.2%	7.6%	3.1%	4.95	4.65	-0.0
/ 200 IP	—	RAW		190	0	200	844	168	101	34	0%	1.1	18.8%	11.2%	7.6%	3.1%	4.95	4.65	-0.2
Career	—	MLE		153	7	184	839	175	85	24	5%	1.2	20.8%	10.1%	10.7%	2.9%	5.68	4.22	-0.1
/ 200 IP	—	MLE		167	8	200	914	190	92	27	5%	1.2	20.8%	10.1%	10.7%	2.9%	5.68	4.22	-1.1

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 AAAEl Paso Chihuahuas · 21 IPFIP 4.42 → 4.57 (+0.14)K% 17.6% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.65%	—	×0.860	15.18%
BB% (unintentional) /BF	13.73%	—	×0.900	12.35%
HR / BF /BF	1.96%	÷1.000	×1.100	2.16%

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 4.42 → 4.57, 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).

2026 AASan Antonio Missions · 13 IPFIP 2.66 → 3.44 (+0.78)K% 29.8% → 24.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.82%	—	×0.810	24.16%
BB% (unintentional) /BF	8.77%	—	×1.100	9.65%
HR / BF /BF	1.75%	÷1.000	×1.200	2.11%

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 100).

Result: FIP 2.66 → 3.44, 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 AAADurham Bulls · 16 IPFIP 6.24 → 5.74 (−0.50)K% 13.5% → 11.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.51%	—	×0.860	11.62%
BB% (unintentional) /BF	16.22%	—	×0.900	14.59%
HR / BF /BF	4.05%	÷1.380	×1.100	3.23%

Result: FIP 6.24 → 5.74, 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 ROKFCL Rays · 6 IPFIP 4.68 → 7.18 (+2.50)K% 20.8% → 14.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.83%	—	×0.680	14.17%
BB% (unintentional) /BF	8.33%	—	×1.250	10.42%
HR / BF /BF	4.17%	÷0.809	×1.400	7.21%

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 62).

Result: FIP 4.68 → 7.18, 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 AAADurham Bulls · 42 IPFIP 3.65 → 3.99 (+0.34)K% 32.1% → 27.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.07%	—	×0.860	27.58%
BB% (unintentional) /BF	9.24%	—	×0.900	8.32%
HR / BF /BF	3.80%	÷1.061	×1.100	3.94%

Result: FIP 3.65 → 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).

2024 AATulsa Drillers · 6 IPFIP 0.22 → 0.94 (+0.71)K% 40.9% → 33.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.91%	—	×0.810	33.14%
BB% (unintentional) /BF	4.55%	—	×1.100	5.00%
HR / BF /BF	0.00%	÷1.063	×1.200	0.00%

Result: FIP 0.22 → 0.94, 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 A+Everett AquaSox · 22 IPFIP 2.08 → 3.31 (+1.23)K% 40.0% → 30.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.00%	—	×0.770	30.80%
BB% (unintentional) /BF	9.47%	—	×1.150	10.89%
HR / BF /BF	2.11%	÷1.012	×1.250	2.60%

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 102).

Result: FIP 2.08 → 3.31, 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 AAArkansas Travelers · 18 IPFIP 4.08 → 5.42 (+1.34)K% 26.7% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.74%	—	×0.810	21.66%
BB% (unintentional) /BF	10.47%	—	×1.100	11.51%
HR / BF /BF	3.49%	÷0.853	×1.200	4.91%

Result: FIP 4.08 → 5.42, 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
Michael Flynn (OGILVIE)	—	—	4.88	18.8%	11.1%	2.9%	—	5.03	18.8%	7.6%	16	+0.15	—	—	—
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	93
Esteban Haro R 2016 · 4.55 FIP	30	SU	4.55	17.1%	10.2%	2.9%	.338	3.83	11.6%	6.3%	75	-0.72	3.76	164	86
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	86
Dustin Molleken R 2015 · 3.74 FIP	30	SU	3.74	20.4%	11.7%	1.6%	.287	3.90	20.6%	8.4%	77	+0.16	3.38	129	85
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	83
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	81
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	80
Carlos Fisher R 2011 · 4.33 FIP	28	SU	4.33	15.5%	9.1%	2.5%	.275	—	—	—	—	—	2.48	245	78
Erik Davis R 2017 · 4.25 FIP	30	SU	4.25	22.2%	11.1%	3.0%	.349	3.52	17.4%	8.5%	65	-0.73	3.05	227	77
Ivan Zavala R 2017 · 5.02 FIP	30	SU	5.02	12.8%	8.0%	3.6%	.286	5.15	12.7%	5.3%	61	+0.13	4.27	209	77
Scott Atchison R 2007 · 4.55 FIP	31	SU	4.55	19.1%	7.6%	3.8%	.297	4.59	16.2%	9.5%	60	+0.04	2.52	340	77
Mike Broadway R 2017 · 4.73 FIP	30	SU	4.73	18.3%	10.2%	3.4%	.245	4.65	23.0%	12.4%	49	-0.08	1.74	195	76
Ronald Peña R 2021 · 4.49 FIP	29	SU	4.49	16.5%	10.4%	2.7%	.288	—	—	—	—	—	3.72	187	76
Adrian Garza R 2017 · 4.47 FIP	29	SU	4.47	16.0%	7.0%	3.4%	.351	5.18	9.7%	4.1%	113	+0.71	3.94	229	76
Dalier Hinojosa R 2016 · 4.30 FIP	30	SU	4.30	18.7%	12.6%	2.2%	.329	—	—	—	—	—	3.41	104	75
Francisco Mendoza R 2016 · 3.81 FIP	28	SU	3.81	17.9%	7.7%	2.3%	.344	—	—	—	—	—	3.13	120	75
Jose Manuel Lopez R 2015 · 5.21 FIP	30	SU	5.21	18.6%	7.6%	4.9%	.373	3.56	18.5%	9.3%	93	-1.65	3.50	170	75
Greg Burke R 2013 · 3.70 FIP	30	SU	3.70	17.9%	8.3%	1.9%	.357	3.61	22.1%	14.8%	62	-0.09	3.61	139	75
Ryan Doolittle R 2016 · 4.38 FIP	28	SU	4.38	13.9%	7.1%	2.8%	.269	—	—	—	—	—	4.03	101	74
Brandon Puffer R 2004 · 5.23 FIP	28	SU	5.23	13.5%	11.2%	3.4%	.328	—	—	—	—	—	3.55	108	74