Braydon Fisher

P Toronto Blue Jays

Age 27 RHP 6'4" / 180 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	MIX2 levels	46	0	54	5.03	2.86	30.4%	13.4%	—	K 25.4% · BB 13.0% · FIP 3.31
2025	25	MIX2 levels	66	1	67	2.43	2.40	31.2%	8.6%	1.7	K 30.1% · BB 8.4% · FIP 2.44
2026	26	MLBTOR	75	10	76	3.21	4.06	23.9%	12.6%	0.4
2027PROJ	27	MLBTOR	56	0	59	3.79	4.11	26.6%	10.7%	0.3
2028PROJ95% MLB	28	MLBTOR	44	0	46	4.03	4.20	26.5%	10.8%	0.2
2029PROJ95% MLB	29	MLBTOR	34	0	36	4.23	4.36	25.9%	11.0%	0.1

Last 3 seasons WAR 2.2 · Next 3 projected WAR 0.6Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%59

FB Velo47

xwOBA vs46

Form1.14 ERA (proj 3.79) · K 22.9% (proj 26.6%) · BB 18.8% (proj 10.7%) ▼▼ last 90d · 24 IP through Oct 27

CompsJuan Minaya (age 27) · Kyle Barraclough (age 27) · Spencer Patton (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 110102vR 94

ERA3.79

FIP4.11

K%26.6%

BB%10.7%

K-BB%15.9%

Proj IP62

Value2027 · WAR

Runs saved vs league avg · 62 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL4545

CMD6565

K†6060

Statcast Percentiles

FB Velo

p4794.3

Whiff%

p6928.3%

p5923.9%

xwOBA vs

p460.316

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)
2018	18	ROK MLE	AZL Dodgers	11	9	22	94	19	9	2	82%	2.0	13.7%	12.0%	1.8%	3.0%	2.05	5.09	—
2018	18	ROK RAW	AZL Dodgers	11	9	22	94	19	9	2	82%	2.0	20.2%	9.6%	10.6%	2.1%	2.05	3.78	—
2021	21	A MLE	Rancho Cucamonga Quakes	23	2	70	324	83	26	8	9%	3.0	19.0%	9.5%	9.5%	3.3%	6.56	4.53	—
2021	21	A RAW	Rancho Cucamonga Quakes	23	2	70	324	83	26	8	9%	3.0	25.6%	8.0%	17.6%	2.5%	6.56	3.33	—
2022	22	MIX MLE	2 levels	39	0	53	234	72	39	3	0%	1.4	23.4%	19.6%	3.7%	1.8%	4.61	4.58	—
2022	22	MIX RAW	2 levels	39	0	53	234	72	39	3	0%	1.4	30.8%	16.7%	14.1%	1.3%	4.61	3.33	—
2023	23	MIX MLE	2 levels	46	1	65	275	90	34	9	2%	1.4	25.4%	14.3%	11.1%	3.8%	2.77	4.86	—
2023	23	MIX RAW	2 levels	46	1	65	275	90	34	9	2%	1.4	32.7%	12.4%	20.4%	3.3%	2.77	3.70	—
2024	24	MIX MLE	2 levels	46	0	54	253	77	34	3	0%	1.2	25.4%	13.0%	12.4%	1.3%	5.03	3.31	—
2024	24	MIX MLE	2 levels	46	0	54	253	77	34	3	0%	1.2	25.4%	13.0%	12.4%	1.3%	5.03	3.31	—
2024	24	MIX RAW	2 levels	46	0	54	253	77	34	3	0%	1.2	30.4%	13.4%	17.0%	1.2%	5.03	2.86	—
2025	25	MIX MLE	2 levels	66	1	67	269	84	23	4	2%	1.0	30.1%	8.4%	21.7%	1.5%	2.43	2.44	+1.7
2025	25	MIX MLE	2 levels	66	1	67	269	84	23	4	2%	1.0	30.1%	8.4%	21.7%	1.5%	2.43	2.44	+1.7
2025	25	MIX RAW	2 levels	66	1	67	269	84	23	4	2%	1.0	31.2%	8.6%	22.7%	1.5%	2.43	2.40	+1.7
2026	26	MLB MLE	Toronto Blue Jays	75	10	76	310	74	39	8	13%	1.0	23.9%	12.6%	11.3%	2.6%	3.21	4.09	+0.4
2026	26	MLB MLE	Toronto Blue Jays	75	10	76	310	74	39	8	13%	1.0	23.9%	12.6%	11.3%	2.6%	3.21	4.09	+0.4
2026	26	MLB RAW	Toronto Blue Jays	75	10	76	310	74	39	8	13%	1.0	23.9%	12.6%	11.3%	2.6%	3.21	4.06	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			127	11	126	512	136	57	12	9%	1.0	26.6%	11.1%	15.4%	2.3%	3.01	3.54	+1.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	TOR	56	0	59	249	66	27	7	0%	1.1	26.6%	10.7%	15.9%	2.8%	3.79	4.11	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	TOR	46	0	48	202	54	22	6	0%	1.0	26.5%	10.8%	15.7%	2.9%	4.03	4.20	+0.2
2029	29	MLB MLE	TOR	36	0	38	160	42	18	5	0%	1.1	25.9%	11.0%	14.8%	3.0%	4.23	4.36	+0.1
2028	28	MLB CTX	TOR	44	0	46	194	51	21	6	0%	1.0	26.5%	10.8%	15.7%	2.9%	4.03	4.20	+0.2
2029	29	MLB CTX	TOR	34	0	36	152	39	17	5	0%	1.1	25.9%	11.0%	14.8%	3.0%	4.23	4.36	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		205	11	207	857	226	95	22	5%	1.0	26.4%	11.0%	15.4%	2.6%	3.45	3.69	+2.1
/ 200 IP	—	RAW		198	11	200	827	218	92	21	5%	1.0	26.4%	11.0%	15.4%	2.6%	3.45	3.69	+2.0
Career	—	MLE		306	23	406	1759	418	221	43	8%	1.3	23.8%	12.6%	11.2%	2.5%	3.95	4.04	+1.8
/ 200 IP	—	MLE		151	11	200	867	206	109	21	8%	1.3	23.8%	12.6%	11.2%	2.5%	3.95	4.04	+1.7

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 AAABuffalo Bisons · 17 IPFIP 1.27 → 1.57 (+0.29)K% 32.8% → 28.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.84%	—	×0.860	28.24%
BB% (unintentional) /BF	7.46%	—	×0.900	6.72%
HR / BF /BF	0.00%	÷0.847	×1.100	0.00%

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 69).

Result: FIP 1.27 → 1.57, 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).

2024 AAABuffalo Bisons · 30 IPFIP 3.70 → 3.89 (+0.18)K% 25.2% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.17%	—	×0.860	21.65%
BB% (unintentional) /BF	15.65%	—	×0.900	14.08%
HR / BF /BF	1.36%	÷0.990	×1.100	1.51%

Result: FIP 3.70 → 3.89, 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).

2024 AANew Hampshire Fisher Cats · 24 IPFIP 1.75 → 2.51 (+0.76)K% 37.7% → 30.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.74%	—	×0.810	30.57%
BB% (unintentional) /BF	10.38%	—	×1.100	11.42%
HR / BF /BF	0.94%	÷1.145	×1.200	0.99%

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 129).

Result: FIP 1.75 → 2.51, 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 AATulsa Drillers · 61 IPFIP 3.99 → 4.86 (+0.87)K% 31.3% → 25.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.30%	—	×0.810	25.35%
BB% (unintentional) /BF	12.98%	—	×1.100	14.27%
HR / BF /BF	3.44%	÷1.083	×1.200	3.81%

Result: FIP 3.99 → 4.86, 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+Great Lakes Loons · 47 IPFIP 3.48 → 4.58 (+1.10)K% 30.3% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.33%	—	×0.770	23.36%
BB% (unintentional) /BF	17.06%	—	×1.150	19.62%
HR / BF /BF	1.42%	÷1.013	×1.250	1.76%

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 103).

Result: FIP 3.48 → 4.58, 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 ARancho Cucamonga Quakes · 5 IPFIP 1.81 → 2.88 (+1.06)K% 34.8% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.78%	—	×0.740	25.74%
BB% (unintentional) /BF	13.04%	—	×1.180	15.39%
HR / BF /BF	0.00%	÷1.247	×1.300	0.00%

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 149).

Result: FIP 1.81 → 2.88, 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 ARancho Cucamonga Quakes · 70 IPFIP 3.31 → 4.53 (+1.22)K% 25.6% → 19.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.62%	—	×0.740	18.96%
BB% (unintentional) /BF	8.02%	—	×1.180	9.47%
HR / BF /BF	2.47%	÷0.965	×1.300	3.33%

Result: FIP 3.31 → 4.53, 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 ROKAZL Dodgers · 22 IPFIP 3.77 → 5.09 (+1.32)K% 20.2% → 13.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.21%	—	×0.680	13.74%
BB% (unintentional) /BF	9.57%	—	×1.250	11.97%
HR / BF /BF	2.13%	÷1.000	×1.400	2.98%

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 3.77 → 5.09, 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).

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
Braydon Fisher (OGILVIE)	—	—	4.11	26.6%	10.7%	2.8%	—	4.27	26.2%	15.3%	86	+0.16	—	—	—
Juan Minaya R 2018 · 3.40 FIP	27	SU	3.40	27.0%	11.4%	2.1%	.282	3.82	24.0%	13.2%	131	+0.42	3.20	446	82
Kyle Barraclough R 2017 · 3.42 FIP	27	SU	3.42	26.6%	12.2%	1.8%	.282	4.99	24.5%	12.2%	89	+1.57	1.95	523	80
Spencer Patton R 2015 · 3.47 FIP	27	SU	3.47	26.8%	9.6%	2.6%	.295	2.79	30.3%	18.1%	57	-0.68	2.79	239	79
Mychal Givens R 2017 · 3.28 FIP	27	SU	3.28	27.9%	7.6%	2.9%	.244	3.48	28.4%	19.3%	162	+0.20	1.77	526	78
Tyler Kinley R 2018 · 3.36 FIP	27	SU	3.36	27.2%	11.8%	1.9%	.331	4.14	23.5%	11.8%	159	+0.78	1.58	602	76
Jason Adam R 2019 · 3.39 FIP	27	SU	3.39	24.2%	9.0%	2.2%	.237	2.64	33.1%	24.9%	88	-0.75	2.42	396	75
Alan Busenitz R 2018 · 4.37 FIP	27	SU	4.37	23.2%	7.5%	4.1%	.322	—	—	—	—	—	2.92	436	75
Hunter Strickland R 2016 · 2.93 FIP	27	SU	2.93	22.8%	6.4%	1.7%	.266	4.09	19.7%	10.6%	131	+1.16	2.30	523	74
Shintaro Fujinami R 2023 · 4.09 FIP	29	SU	4.09	23.2%	12.3%	2.5%	.290	4.49	22.0%	0.2%	51	+0.40	4.09	130	74
Derek Law R 2018 · 3.22 FIP	27	SU	3.22	22.1%	7.3%	1.9%	.305	4.02	22.5%	10.2%	104	+0.80	2.49	520	74
Fernando Rodriguez Jr. R 2011 · 3.85 FIP	27	SU	3.85	24.7%	10.8%	2.7%	.315	3.90	25.2%	16.5%	70	+0.05	3.34	335	74
Jose Veras R 2008 · 3.67 FIP	27	SU	3.67	24.9%	9.1%	2.8%	.281	4.10	23.8%	11.7%	169	+0.43	3.37	422	74
Brad Boxberger R 2015 · 3.93 FIP	27	SU	3.93	27.3%	10.0%	3.4%	.280	4.09	28.5%	17.2%	107	+0.16	2.63	478	74
Ryan Garton R 2017 · 3.67 FIP	27	SU	3.67	25.2%	11.3%	2.3%	.252	4.15	23.5%	10.8%	108	+0.48	3.14	307	74
Cole Sulser R 2017 · 3.52 FIP	27	SU	3.52	22.6%	10.2%	1.9%	.321	2.85	28.2%	18.4%	140	-0.67	2.38	554	73
Ryan Brasier R 2016 · 3.68 FIP	28	SU	3.68	25.0%	7.7%	3.1%	.289	3.09	24.2%	17.9%	130	-0.59	2.31	426	73
Trevor Stephan R 2023 · 3.15 FIP	27	SU	3.15	25.5%	8.5%	2.1%	.303	7.09	15.9%	4.0%	40	+3.94	1.88	440	73
Seth Simmons R 2015 · 3.07 FIP	27	SU	3.07	23.8%	9.7%	1.4%	.277	4.87	16.6%	7.6%	175	+1.80	3.07	251	72
Simon Castro R 2015 · 3.28 FIP	27	SU	3.28	26.4%	8.5%	2.4%	.320	3.58	26.0%	17.6%	128	+0.30	3.01	185	72
Austin Ross R 2016 · 3.81 FIP	27	SU	3.81	22.9%	8.0%	3.0%	.295	3.67	12.4%	5.2%	127	-0.14	3.16	269	72

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	1.4	55	26.4%	11.5%	1.02	3.53
Arodys Vizcaíno ('22)	1.0	50	25.7%	11.3%	0.85	3.10
Kevin Siegrist ('17)	1.2	49	27.9%	11.3%	0.92	3.04
Michael Wuertz ('11)	2.1	52	25.3%	11.3%	0.96	3.71
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Rod Scurry ('80-'88, 5 WAR*)	2.3	58	29.6%*	13.6%*	0.91*	3.67*