Michael Boyle

P St. Paul Saints

Age 33 LHP 6'3" / 200 lbs AAA

Overview

AAAPROSPECT

MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	28	MIX3 levels	10	1	11	7.15	6.36	18.2%	10.9%	—	K 18.2% · BB 10.9% · FIP 5.94
2023	29	MIX2 levels	49	1	64	4.08	4.91	24.1%	12.8%	—	K 20.2% · BB 12.2% · FIP 5.22
2024	30	AAAMIN	10	0	9	7.71	7.28	8.0%	14.0%	—	K 6.9% · BB 12.6% · FIP 6.44
2025	31	No MLB games played
2026	32	No MLB games played
2027PROJ	33	MLBMIN	—	—	0	4.66	4.66	20.3%	9.7%	—
2028PROJ6% MLB	34	MLBMIN	1	0	1	4.79	4.90	20.0%	10.2%	-0.0

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

Contract

No contract data on file.

CompsJuan Pablo Perez (age 34) · Brandon Mann (age 33) · Adam Liberatore (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 105114vR 122

ERA4.66

FIP4.66

K%20.3%

BB%9.7%

K-BB%10.6%

Proj IP5

Value2027 · WAR

Runs saved vs league avg · 5 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)
2015	21	MIX MLE	2 levels	15	10	47	199	46	11	1	67%	3.1	16.2%	6.7%	9.4%	0.7%	1.91	2.97	—
2015	21	MIX RAW	2 levels	15	10	47	199	46	11	1	67%	3.1	23.1%	5.5%	17.6%	0.5%	1.91	2.12	—
2016	22	MIX MLE	2 levels	26	24	135	568	109	38	10	92%	5.2	14.4%	7.9%	6.5%	2.3%	3.53	4.12	—
2016	22	MIX RAW	2 levels	26	24	135	568	109	38	10	92%	5.2	19.2%	6.7%	12.5%	1.8%	3.53	3.29	—
2017	23	A+ MLE	Rancho Cucamonga Quakes	19	7	53	229	37	16	4	37%	2.8	12.4%	8.0%	4.4%	2.0%	4.75	4.17	—
2017	23	A+ RAW	Rancho Cucamonga Quakes	19	7	53	229	37	16	4	37%	2.8	16.2%	7.0%	9.2%	1.7%	4.75	3.59	—
2018	24	MIX MLE	2 levels	33	0	44	187	44	19	6	0%	1.3	18.3%	11.4%	6.8%	3.5%	2.68	4.93	—
2018	24	MIX RAW	2 levels	33	0	44	187	44	19	6	0%	1.3	23.5%	10.2%	13.4%	3.2%	2.68	4.18	—
2019	25	AA MLE	Tulsa Drillers	40	1	54	235	57	26	8	2%	1.3	19.6%	12.2%	7.5%	4.5%	3.02	5.45	—
2019	25	AA RAW	Tulsa Drillers	40	1	54	235	57	26	8	2%	1.3	24.3%	11.1%	13.2%	3.4%	3.02	4.37	—
2022	28	MIX MLE	3 levels	10	1	11	55	10	6	3	10%	1.1	18.2%	10.9%	7.3%	5.5%	7.15	5.94	—
2022	28	MIX RAW	3 levels	10	1	11	55	10	6	3	10%	1.1	18.2%	10.9%	7.3%	5.5%	7.15	6.36	—
2023	29	MIX MLE	2 levels	49	1	64	274	66	35	11	2%	1.3	20.2%	12.2%	8.0%	4.2%	4.08	5.22	—
2023	29	MIX RAW	2 levels	49	1	64	274	66	35	11	2%	1.3	24.1%	12.8%	11.3%	4.0%	4.08	4.91	—
2024	30	AAA MLE	St. Paul Saints	10	0	9	50	4	7	2	0%	0.9	6.9%	12.6%	-5.7%	4.2%	7.71	6.44	—
2024	30	AAA MLE	St. Paul Saints	10	0	9	50	4	7	2	0%	0.9	6.9%	12.6%	-5.7%	4.2%	7.71	6.44	—
2024	30	AAA RAW	St. Paul Saints	10	0	9	50	4	7	2	0%	0.9	8.0%	14.0%	-6.0%	4.0%	7.71	7.28	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	MIN	—	—	0	—	—	—	—	—	—	20.3%	9.7%	10.6%	3.0%	4.66	4.66	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	MIN	16	0	17	71	14	7	2	0%	1.0	20.0%	10.2%	9.9%	3.3%	4.79	4.90	-0.1
2028	34	MLB CTX	MIN	1	0	1	4	1	0	0	0%	1.0	20.0%	10.2%	9.9%	3.3%	4.79	4.90	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		1	0	1	4	1	0	0	0%	0.9	20.0%	10.2%	9.9%	3.3%	4.79	4.52	+0.0
/ 200 IP	—	RAW		190	0	200	844	218	0	0	0%	1.1	20.0%	10.2%	9.9%	3.3%	4.79	4.52	+0.1
Career	—	MLE		202	44	417	1797	291	172	52	22%	2.1	16.2%	9.6%	6.6%	2.9%	3.63	4.55	-0.1
/ 200 IP	—	MLE		97	21	200	862	140	83	25	22%	2.1	16.2%	9.6%	6.6%	2.9%	3.63	4.55	-0.8

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.

2024 AAASt. Paul Saints · 9 IPFIP 6.39 → 6.44 (+0.05)K% 8.0% → 6.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	8.00%	—	×0.860	6.88%
BB% (unintentional) /BF	14.00%	—	×0.900	12.60%
HR / BF /BF	4.00%	÷1.039	×1.100	4.24%

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 108).

Result: FIP 6.39 → 6.44, 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).

2023 AAASt. Paul Saints · 37 IPFIP 5.22 → 5.47 (+0.25)K% 22.7% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.70%	—	×0.860	19.52%
BB% (unintentional) /BF	15.95%	—	×0.900	14.36%
HR / BF /BF	3.68%	÷1.009	×1.100	4.01%

Result: FIP 5.22 → 5.47, 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 AAWichita Wind Surge · 27 IPFIP 4.39 → 4.85 (+0.46)K% 26.1% → 21.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.13%	—	×0.810	21.16%
BB% (unintentional) /BF	8.11%	—	×1.100	8.92%
HR / BF /BF	4.50%	÷1.231	×1.200	4.39%

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 146).

Result: FIP 4.39 → 4.85, 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 AAWichita Wind Surge · 5 IPFIP 9.73 → 11.27 (+1.53)K% 7.1% → 5.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	7.14%	—	×0.810	5.79%
BB% (unintentional) /BF	10.71%	—	×1.100	11.79%
HR / BF /BF	10.71%	÷0.985	×1.200	13.05%

Result: FIP 9.73 → 11.27, 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 AATulsa Drillers · 54 IPFIP 4.32 → 5.45 (+1.13)K% 24.3% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.26%	—	×0.810	19.65%
BB% (unintentional) /BF	11.06%	—	×1.100	12.17%
HR / BF /BF	3.40%	÷0.909	×1.200	4.49%

Result: FIP 4.32 → 5.45, 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+Rancho Cucamonga Quakes · 31 IPFIP 2.50 → 3.37 (+0.87)K% 29.1% → 22.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.13%	—	×0.770	22.43%
BB% (unintentional) /BF	7.87%	—	×1.150	9.06%
HR / BF /BF	1.57%	÷1.058	×1.250	1.86%

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 112).

Result: FIP 2.50 → 3.37, 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 AATulsa Drillers · 13 IPFIP 7.67 → 8.22 (+0.55)K% 11.7% → 9.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	11.67%	—	×0.810	9.45%
BB% (unintentional) /BF	15.00%	—	×1.100	16.50%
HR / BF /BF	6.67%	÷1.147	×1.200	6.98%

Result: FIP 7.67 → 8.22, 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+Rancho Cucamonga Quakes · 53 IPFIP 3.58 → 4.17 (+0.59)K% 16.2% → 12.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.16%	—	×0.770	12.44%
BB% (unintentional) /BF	6.99%	—	×1.150	8.03%
HR / BF /BF	1.75%	÷1.085	×1.250	2.01%

Result: FIP 3.58 → 4.17, 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).

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 Boyle (OGILVIE)	—	—	4.66	20.3%	9.7%	3.0%	—	4.90	20.0%	9.9%	17	+0.24	—	—	—
Juan Pablo Perez L 2013 · 3.68 FIP	34	SU	3.68	25.2%	11.5%	2.3%	.250	3.68	26.5%	11.2%	51	-0.00	1.75	104	71
Brandon Mann L 2017 · 4.24 FIP	33	SU	4.24	19.8%	11.3%	2.5%	.286	3.59	17.8%	6.4%	52	-0.65	3.59	190	70
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	70
Zach Phillips L 2019 · 3.67 FIP	32	SU	3.67	19.1%	8.5%	2.0%	.355	—	—	—	—	—	2.33	202	69
Kelly Wunsch L 2005 · 3.82 FIP	33	SU	3.82	21.0%	11.4%	1.9%	.261	—	—	—	—	—	3.36	175	69
Alejandro Soto L 2019 · 4.03 FIP	33	SW	4.03	17.1%	6.9%	2.7%	.376	—	—	—	—	—	3.96	280	69
Colt Hynes L 2017 · 3.75 FIP	32	SW	3.75	14.1%	4.6%	2.3%	.306	—	—	—	—	—	3.42	169	68
Brian Shouse L 2002 · 5.54 FIP	33	SU	5.54	15.5%	11.3%	4.2%	.245	3.31	16.3%	10.7%	159	-2.23	2.32	339	68
Brian Moran L 2022 · 4.20 FIP	33	SU	4.20	21.7%	7.0%	3.7%	.304	3.72	21.5%	14.5%	152	-0.48	1.80	421	67
Doug Creek L 2002 · 4.99 FIP	33	SU	4.99	21.4%	12.6%	3.8%	.288	5.79	17.1%	7.9%	36	+0.80	4.48	277	66
Ryan Sherriff L 2023 · 3.78 FIP	33	SU	3.78	21.9%	11.0%	2.1%	.297	—	—	—	—	—	2.96	245	66
Marlon Arias L 2017 · 3.73 FIP	32	SU	3.73	17.2%	7.7%	2.0%	.443	4.83	19.8%	8.6%	74	+1.10	3.73	154	65
Hideki Okajima L 2009 · 3.95 FIP	33	SU	3.95	20.5%	7.0%	3.1%	.268	4.27	15.5%	8.5%	46	+0.32	3.05	238	65
Clay Rapada L 2015 · 3.84 FIP	34	SU	3.84	14.3%	8.0%	1.7%	.299	—	—	—	—	—	3.00	120	65
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	65
Edward Paredes L 2019 · 5.10 FIP	32	SU	5.10	21.5%	7.3%	5.3%	.364	—	—	—	—	—	2.36	130	65
Tommy Phelps L 2005 · 3.98 FIP	31	SU	3.98	13.2%	7.5%	1.9%	.280	—	—	—	—	—	3.39	120	64
Scott Sauerbeck L 2005 · 3.75 FIP	33	SU	3.75	22.3%	8.9%	2.5%	.298	5.05	14.0%	0.0%	25	+1.30	2.79	386	64
Anthony Gose L 2024 · 3.78 FIP	33	SU	3.78	26.4%	11.3%	2.7%	.279	5.07	21.5%	10.1%	97	+1.29	3.78	233	61
Luke Westphal L 2022 · 4.35 FIP	33	SW	4.35	24.2%	13.4%	2.9%	.302	5.19	15.7%	-0.7%	26	+0.84	2.97	294	61