Michael Stryffeler

P Richmond Flying Squirrels

Age 31 RHP 6'2" / 210 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	26	MIX2 levels	53	0	52	4.01	3.27	36.6%	16.1%	—	K 30.4% · BB 17.3% · FIP 4.24
2023	27	MIX2 levels	7	0	7	3.86	2.24	33.3%	14.8%	—	K 33.3% · BB 14.8% · FIP 2.16
2025	29	AASF	25	0	33	4.13	4.39	24.0%	16.7%	—	K 19.4% · BB 18.3% · FIP 4.89
2026	30	No MLB games played
2027PROJ	31	MLBSF	—	—	0	5.17	5.17	21.1%	14.8%	—
2028PROJ31% MLB	32	MLBSF	5	0	5	5.22	5.31	20.9%	14.6%	-0.0

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

Contract

No contract data on file.

CompsJim Miller (age 30) · Neil Wagner (age 32) · Dustin Molleken (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 131123vR 115

ERA5.17

FIP5.17

K%21.1%

BB%14.8%

K-BB%6.3%

Proj IP18

Value2027 · WAR

Runs saved vs league avg · 18 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)
2019	23	MIX MLE	2 levels	12	0	14	60	23	5	0	0%	1.2	25.7%	11.8%	13.9%	0.0%	1.88	2.43	—
2019	23	MIX RAW	2 levels	12	0	14	60	23	5	0	0%	1.2	38.3%	8.3%	30.0%	0.0%	1.88	0.94	—
2021	25	AA MLE	Arkansas Travelers	41	0	43	182	71	36	3	0%	1.0	31.6%	21.8%	9.8%	2.2%	2.09	4.41	—
2021	25	AA RAW	Arkansas Travelers	41	0	43	182	71	36	3	0%	1.0	39.0%	19.8%	19.2%	1.6%	2.09	3.22	—
2022	26	MIX MLE	2 levels	53	0	52	224	82	36	5	0%	1.0	30.4%	17.3%	13.1%	2.8%	4.01	4.24	—
2022	26	MIX RAW	2 levels	53	0	52	224	82	36	5	0%	1.0	36.6%	16.1%	20.5%	2.2%	4.01	3.27	—
2023	27	MIX MLE	2 levels	7	0	7	27	9	4	0	0%	1.0	33.3%	14.8%	18.5%	0.0%	3.86	2.16	—
2023	27	MIX RAW	2 levels	7	0	7	27	9	4	0	0%	1.0	33.3%	14.8%	18.5%	0.0%	3.86	2.24	—
2025	29	AA MLE	Richmond Flying Squirrels	25	0	33	150	36	25	3	0%	1.3	19.4%	18.3%	1.1%	2.0%	4.13	4.89	—
2025	29	AA MLE	Richmond Flying Squirrels	25	0	33	150	36	25	3	0%	1.3	19.4%	18.3%	1.1%	2.0%	4.13	4.89	—
2025	29	AA RAW	Richmond Flying Squirrels	25	0	33	150	36	25	3	0%	1.3	24.0%	16.7%	7.3%	2.0%	4.13	4.39	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	SF	—	—	0	—	—	—	—	—	—	21.1%	14.8%	6.3%	2.9%	5.17	5.17	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	SF	15	0	16	66	14	10	2	0%	1.0	20.9%	14.6%	6.3%	3.2%	5.22	5.31	-0.1
2028	32	MLB CTX	SF	5	0	5	21	4	3	1	0%	1.0	20.9%	14.6%	6.3%	3.2%	5.22	5.31	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		5	0	5	21	4	3	1	0%	1.0	20.9%	14.6%	6.3%	3.2%	5.22	4.93	-0.0
/ 200 IP	—	RAW		190	0	200	844	163	122	41	0%	1.1	20.9%	14.6%	6.3%	3.2%	5.22	4.93	-0.9
Career	—	MLE		138	0	149	643	179	117	13	0%	1.1	27.9%	18.2%	9.7%	2.1%	3.27	4.18	-0.1
/ 200 IP	—	MLE		186	0	200	865	241	157	18	0%	1.1	27.9%	18.2%	9.7%	2.1%	3.27	4.18	-1.8

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 AARichmond Flying Squirrels · 33 IPFIP 4.28 → 4.89 (+0.61)K% 24.0% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.00%	—	×0.810	19.44%
BB% (unintentional) /BF	16.67%	—	×1.100	18.33%
HR / BF /BF	2.00%	÷1.183	×1.200	2.03%

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 137).

Result: FIP 4.28 → 4.89, 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 AAArkansas Travelers · 36 IPFIP 2.60 → 3.71 (+1.11)K% 37.7% → 30.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.75%	—	×0.810	30.58%
BB% (unintentional) /BF	12.58%	—	×1.100	13.84%
HR / BF /BF	1.99%	÷0.909	×1.200	2.62%

Result: FIP 2.60 → 3.71, 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 AAASacramento River Cats · 16 IPFIP 4.66 → 5.33 (+0.66)K% 34.2% → 30.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.25%	—	×0.880	30.14%
BB% (unintentional) /BF	23.29%	—	×1.050	24.45%
HR / BF /BF	2.74%	÷0.989	×1.100	3.05%

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 98).

Result: FIP 4.66 → 5.33, 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 AAArkansas Travelers · 43 IPFIP 3.22 → 4.41 (+1.19)K% 39.0% → 31.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.01%	—	×0.810	31.60%
BB% (unintentional) /BF	19.78%	—	×1.100	21.76%
HR / BF /BF	1.65%	÷0.892	×1.200	2.22%

Result: FIP 3.22 → 4.41, 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 ROKAZL Mariners · 13 IPFIP 1.11 → 2.43 (+1.32)K% 37.7% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.74%	—	×0.680	25.66%
BB% (unintentional) /BF	9.43%	—	×1.250	11.79%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

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 1.11 → 2.43, 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 Stryffeler (OGILVIE)	—	—	5.17	21.1%	14.8%	2.9%	—	5.31	20.9%	6.3%	16	+0.14	—	—	—
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	83
Neil Wagner R 2016 · 4.05 FIP	32	SU	4.05	19.8%	12.3%	1.9%	.360	3.11	20.7%	12.1%	54	-0.94	3.11	135	82
Dustin Molleken R 2016 · 4.04 FIP	31	SU	4.04	19.6%	12.1%	1.9%	.276	3.38	24.3%	11.9%	16	-0.66	3.38	129	79
Miguel Mejia R 2019 · 4.41 FIP	31	SU	4.41	15.7%	10.7%	2.3%	.341	—	—	—	—	—	3.37	108	78
Jeff Fulchino R 2011 · 4.44 FIP	31	SU	4.44	20.4%	11.1%	3.0%	.302	—	—	—	—	—	3.36	178	76
Mike Broadway R 2018 · 4.65 FIP	31	SU	4.65	23.0%	10.6%	3.9%	.282	—	—	—	—	—	1.74	195	76
Jason Bulger R 2010 · 4.36 FIP	31	SU	4.36	22.5%	13.5%	2.6%	.324	—	—	—	—	—	3.78	106	75
Fredy Quintero R 2019 · 4.62 FIP	31	SU	4.62	17.2%	13.7%	2.2%	.310	—	—	—	—	—	4.29	241	75
Dalier Hinojosa R 2016 · 4.30 FIP	30	SU	4.30	18.7%	12.6%	2.2%	.329	—	—	—	—	—	3.41	104	75
Dan Winkler R 2021 · 4.76 FIP	31	SU	4.76	21.1%	15.3%	2.7%	.233	3.87	24.4%	10.4%	46	-0.89	2.40	231	74
David Carpenter R 2016 · 4.49 FIP	31	SU	4.49	21.4%	12.8%	2.9%	.333	3.89	21.5%	11.9%	49	-0.60	2.66	275	73
Manny Acosta R 2012 · 4.49 FIP	31	SU	4.49	21.3%	11.1%	3.2%	.295	—	—	—	—	—	3.08	350	73
Antonio Guzman R 2019 · 4.96 FIP	31	SU	4.96	19.3%	14.7%	3.0%	.326	—	—	—	—	—	3.15	254	73
Arcenio Leon R 2017 · 4.42 FIP	30	SU	4.42	18.5%	13.7%	2.1%	.203	3.61	21.4%	13.3%	18	-0.81	3.00	115	71
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	71
Brian Sanches R 2010 · 4.24 FIP	31	SU	4.24	21.3%	10.6%	2.9%	.217	4.35	19.6%	8.5%	62	+0.11	3.39	229	71
Kane Davis R 2005 · 5.15 FIP	30	SU	5.15	15.7%	14.3%	2.9%	.170	4.82	16.9%	6.7%	11	-0.33	3.40	125	71
Luis Angel Daniel R 2016 · 4.56 FIP	31	SU	4.56	13.4%	8.9%	2.7%	.286	4.16	9.8%	0.3%	53	-0.40	4.16	156	70
Steve Delabar R 2014 · 5.10 FIP	31	SU	5.10	18.4%	16.7%	2.6%	.225	4.01	23.6%	12.0%	79	-1.09	2.43	229	70
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	69