Ryan Boyer

P Erie SeaWolves

Age 30 RHP 6'2" / 225 lbs AA peak 0.1 · 2027

Overview

AAPROSPECT

ETA2029MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	26	MIX3 levels	25	0	34	1.83	2.05	32.1%	9.5%	—	K 25.9% · BB 7.9% · FIP 1.92
2024	27	MIX3 levels	36	0	36	3.96	3.43	29.6%	5.3%	—	K 24.1% · BB 3.6% · FIP 2.95
2025	28	MIX2 levels	37	0	59	5.01	4.43	25.6%	6.8%	—	K 21.2% · BB 7.4% · FIP 4.78
2026	29	No MLB games played
2027PROJ	30	MLBDET	1	0	1	4.22	4.22	21.9%	6.7%	0.0
2028PROJ31% MLB	31	MLBDET	16	0	17	4.35	4.43	21.7%	7.3%	0.0
2029PROJ51% MLB	32	MLBDET	23	0	24	4.53	4.65	21.3%	7.8%	-0.0

Next 3 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff50Control55Command55

CompsNeil Wagner (age 29) · Tony Barnette (age 32) · Mike Fyhrie (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 113105vR 97

ERA4.22

FIP4.22

K%21.9%

BB%6.7%

K-BB%15.2%

Proj IP68

Value2027 · WAR

Runs saved vs league avg · 68 IP

Grades · Contract

Scouting Grades

STUF5050

CTRL5555

CMD5555

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)
2022	25	MIX MLE	3 levels	24	0	30	124	45	7	4	0%	1.2	31.0%	4.8%	26.2%	3.6%	3.60	3.09	—
2022	25	MIX RAW	3 levels	24	0	30	124	45	7	4	0%	1.2	36.3%	5.6%	30.6%	3.2%	3.60	2.53	—
2023	26	MIX MLE	3 levels	25	0	34	137	44	13	1	0%	1.4	25.9%	7.9%	18.0%	0.0%	1.83	1.92	—
2023	26	MIX RAW	3 levels	25	0	34	137	44	13	1	0%	1.4	32.1%	9.5%	22.6%	0.7%	1.83	2.05	—
2024	27	MIX MLE	3 levels	36	0	36	152	45	8	6	0%	1.0	24.1%	3.6%	20.5%	2.6%	3.96	2.95	—
2024	27	MIX MLE	3 levels	36	0	36	152	45	8	6	0%	1.0	24.1%	3.6%	20.5%	2.6%	3.96	2.95	—
2024	27	MIX RAW	3 levels	36	0	36	152	45	8	6	0%	1.0	29.6%	5.3%	24.3%	3.9%	3.96	3.43	—
2025	28	MIX MLE	2 levels	37	0	59	250	64	17	12	0%	1.6	21.2%	7.4%	13.7%	4.6%	5.01	4.78	—
2025	28	MIX MLE	2 levels	37	0	59	250	64	17	12	0%	1.6	21.2%	7.4%	13.7%	4.6%	5.01	4.78	—
2025	28	MIX RAW	2 levels	37	0	59	250	64	17	12	0%	1.6	25.6%	6.8%	18.8%	4.8%	5.01	4.43	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	DET	1	0	1	4	1	0	0	0%	1.0	21.9%	6.7%	15.2%	3.2%	4.22	4.22	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	DET	53	0	55	234	51	17	8	0%	1.0	21.7%	7.3%	14.4%	3.4%	4.35	4.43	+0.1
2029	32	MLB MLE	DET	44	0	46	196	42	15	7	0%	1.1	21.3%	7.8%	13.5%	3.6%	4.53	4.65	-0.1
2028	31	MLB CTX	DET	16	0	17	72	16	5	2	0%	1.1	21.7%	7.3%	14.4%	3.4%	4.35	4.43	+0.0
2029	32	MLB CTX	DET	23	0	24	101	22	8	4	0%	1.0	21.3%	7.8%	13.5%	3.6%	4.53	4.65	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		39	0	41	174	37	13	6	0%	1.1	21.5%	7.6%	13.9%	3.5%	4.45	4.18	+0.2
/ 200 IP	—	RAW		190	0	200	844	180	63	29	0%	1.1	21.5%	7.6%	13.9%	3.5%	4.45	4.18	+0.9
Career	—	MLE		122	0	160	663	163	41	20	0%	1.3	24.7%	6.2%	18.5%	3.0%	3.83	3.45	+0.0
/ 200 IP	—	MLE		152	0	200	829	204	51	25	0%	1.3	24.7%	6.2%	18.5%	3.0%	3.83	3.45	+0.0

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 AAErie SeaWolves · 53 IPFIP 3.97 → 4.78 (+0.81)K% 26.1% → 21.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.13%	—	×0.810	21.16%
BB% (unintentional) /BF	6.76%	—	×1.100	7.43%
HR / BF /BF	4.05%	÷1.056	×1.200	4.60%

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 111).

Result: FIP 3.97 → 4.78, 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 AAAToledo Mud Hens · 6 IPFIP 8.08 → 9.78 (+1.70)K% 21.4% → 18.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.43%	—	×0.860	18.43%
BB% (unintentional) /BF	7.14%	—	×0.900	6.43%
HR / BF /BF	10.71%	÷0.872	×1.100	13.52%

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 74).

Result: FIP 8.08 → 9.78, 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 AANew Hampshire Fisher Cats · 30 IPFIP 2.37 → 2.95 (+0.58)K% 29.8% → 24.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.75%	—	×0.810	24.10%
BB% (unintentional) /BF	3.31%	—	×1.100	3.64%
HR / BF /BF	2.48%	÷1.145	×1.200	2.60%

Result: FIP 2.37 → 2.95, 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 AAABuffalo Bisons · 4 IPFIP 8.38 → 8.87 (+0.49)K% 16.7% → 14.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.860	14.33%
BB% (unintentional) /BF	16.67%	—	×0.900	15.00%
HR / BF /BF	8.33%	÷0.990	×1.100	9.26%

Result: FIP 8.38 → 8.87, 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+Vancouver Canadians · 31 IPFIP 1.13 → 1.92 (+0.78)K% 33.6% → 25.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.62%	—	×0.770	25.89%
BB% (unintentional) /BF	6.90%	—	×1.150	7.93%
HR / BF /BF	0.00%	÷1.253	×1.250	0.00%

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 151).

Result: FIP 1.13 → 1.92, 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 A+Vancouver Canadians · 18 IPFIP 1.75 → 3.09 (+1.34)K% 40.3% → 31.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.28%	—	×0.770	31.01%
BB% (unintentional) /BF	4.17%	—	×1.150	4.79%
HR / BF /BF	2.78%	÷0.952	×1.250	3.65%

Result: FIP 1.75 → 3.09, 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 ADunedin Blue Jays · 6 IPFIP 3.66 → 5.32 (+1.65)K% 30.0% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.740	22.20%
BB% (unintentional) /BF	10.00%	—	×1.180	11.80%
HR / BF /BF	3.33%	÷0.916	×1.300	4.73%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 83).

Result: FIP 3.66 → 5.32, 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 AANew Hampshire Fisher Cats · 5 IPFIP 3.48 → 4.47 (+0.99)K% 31.8% → 25.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.82%	—	×0.810	25.77%
BB% (unintentional) /BF	4.55%	—	×1.100	5.00%
HR / BF /BF	4.55%	÷1.027	×1.200	5.31%

Result: FIP 3.48 → 4.47, 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).

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
Ryan Boyer (OGILVIE)	—	—	4.22	21.9%	6.7%	3.2%	—	4.53	21.5%	14.0%	102	+0.31	—	—	—
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	87
Tony Barnette R 2016 · 3.05 FIP	32	SU	3.05	19.9%	6.1%	1.6%	.281	3.26	23.4%	17.0%	84	+0.21	2.61	144	85
Mike Fyhrie R 2000 · 3.08 FIP	30	SU	3.08	19.5%	5.0%	1.8%	.309	3.87	13.7%	4.4%	69	+0.79	3.08	173	83
Kiko Calero R 2005 · 3.51 FIP	30	SU	3.51	22.7%	7.0%	2.6%	.252	3.23	23.3%	13.9%	99	-0.28	2.27	298	83
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	81
Spencer Bivens R 2024 · 4.07 FIP	30	SU	4.07	18.0%	5.8%	3.2%	.265	3.88	18.2%	10.9%	157	-0.19	3.52	391	80
Gerardo Sanchez R 2019 · 3.58 FIP	30	SU	3.58	20.5%	5.4%	2.8%	.300	—	—	—	—	—	2.65	137	79
Adrian Ramirez R 2019 · 4.29 FIP	31	SU	4.29	21.7%	6.3%	4.0%	.327	—	—	—	—	—	3.04	137	79
Marc Magliaro R 2018 · 3.94 FIP	28	SU	3.94	19.6%	7.3%	2.9%	.307	—	—	—	—	—	3.94	125	78
Austin Adams R 2016 · 3.40 FIP	29	SU	3.40	21.1%	6.1%	2.4%	.376	5.80	20.8%	10.2%	60	+2.40	3.40	162	78
Blake Smith R 2016 · 3.59 FIP	28	SU	3.59	22.4%	8.6%	2.4%	.307	—	—	—	—	—	3.17	124	78
Mark Serrano R 2016 · 3.16 FIP	30	SU	3.16	17.7%	6.5%	1.3%	.290	4.36	21.1%	14.0%	88	+1.20	3.16	120	77
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	77
Rob Wooten R 2015 · 4.86 FIP	30	SU	4.86	19.3%	5.5%	4.9%	.299	3.87	17.7%	14.4%	111	-0.99	2.48	253	77
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
Cristhian Martinez R 2012 · 3.09 FIP	30	SU	3.09	20.8%	4.8%	2.1%	.326	—	—	—	—	—	3.09	204	77
Tyler Sturdevant R 2016 · 3.44 FIP	30	SU	3.44	23.8%	6.5%	2.8%	.323	4.48	13.5%	6.8%	60	+1.04	3.44	149	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
Manny Acosta R 2011 · 3.75 FIP	30	SU	3.75	22.5%	7.4%	2.9%	.321	4.49	21.3%	10.2%	47	+0.74	3.08	350	76
Jared Burton R 2012 · 3.09 FIP	31	SU	3.09	22.4%	6.1%	2.0%	.212	3.59	19.9%	12.6%	144	+0.50	3.09	367	76

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	0.8	57	21.7%	7.2%	1.30	4.35
Chris Hatcher ('18)	1.3	53	22.9%	8.4%	1.32	4.65
Michael Tonkin ('25)	1.6	55	23.2%	8.1%	1.28	4.23
Shawn Tolleson ('16)	1.1	54	23.1%	8.3%	1.28	3.92
Julio Navarro ('62-'70, 1 WAR*)	1.1	53	21.7%*	7.8%*	1.55*	4.13*
Randy St. Claire ('84-'94, -0 WAR*)	1.3	50	19.0%*	8.4%*	1.39*	4.61*
Rich Monteleone ('87-'96, 2 WAR*)	1.8	71	18.7%*	7.5%*	1.39*	4.03*