Mason Fluharty

P Toronto Blue Jays

Age 25 LHP 6'2" / 215 lbs MLB

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	22	AAATOR	56	0	67	3.63	3.46	27.4%	8.7%	—	K 23.6% · BB 7.8% · FIP 3.86
2025	23	MIX2 levels	66	0	64	4.24	3.37	26.6%	10.1%	0.9	K 25.8% · BB 9.9% · FIP 3.40
2026	24	MLBTOR	83	4	63	3.30	3.51	24.7%	9.9%	0.8
2027PROJ	25	MLBTOR	42	0	44	3.92	5.38	10.4%	10.2%	-0.4
2028PROJ93% MLB	26	MLBTOR	20	0	21	4.64	5.12	13.4%	10.1%	-0.1
2029PROJ95% MLB	27	MLBTOR	12	0	13	4.95	5.16	13.4%	10.2%	-0.1

Last 3 seasons WAR 1.7 · 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

xwOBA vs90

K%64

Form0.92 ERA (proj 3.92) · K 18.4% (proj 10.4%) · BB 13.2% (proj 10.2%) ▲ last 90d · 20 IP through Oct 27

CompsJoely Rodríguez (age 25) · Jake Palisch (age 25) · Justin Bruihl (age 25)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 97105vR 113

ERA3.92

FIP5.38

K%10.4%

BB%10.2%

K-BB%0.3%

Proj IP48

Value2027 · WAR

Runs saved vs league avg · 48 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL3535

CMD5050

K†3030

Statcast Percentiles

Whiff%

p7228.7%

p6424.7%

xwOBA vs

p900.261

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	20	A+ MLE	Vancouver Canadians	10	0	15	65	21	7	2	0%	1.5	24.9%	12.4%	12.5%	4.0%	3.52	4.79	—
2022	20	A+ RAW	Vancouver Canadians	10	0	15	65	21	7	2	0%	1.5	32.3%	10.8%	21.5%	3.1%	3.52	3.43	—
2023	21	MIX MLE	2 levels	48	0	58	251	75	19	7	0%	1.2	23.9%	8.4%	15.4%	3.3%	3.28	3.94	—
2023	21	MIX RAW	2 levels	48	0	58	251	75	19	7	0%	1.2	29.9%	7.6%	22.3%	2.8%	3.28	3.07	—
2024	22	AAA MLE	Buffalo Bisons	56	0	67	277	76	24	8	0%	1.2	23.6%	7.8%	15.8%	3.2%	3.63	3.86	—
2024	22	AAA MLE	Buffalo Bisons	56	0	67	277	76	24	8	0%	1.2	23.6%	7.8%	15.8%	3.2%	3.63	3.86	—
2024	22	AAA RAW	Buffalo Bisons	56	0	67	277	76	24	8	0%	1.2	27.4%	8.7%	18.8%	2.9%	3.63	3.46	—
2025	23	MIX MLE	2 levels	66	0	64	267	71	27	6	0%	1.0	25.8%	9.9%	16.0%	2.2%	4.24	3.40	+0.9
2025	23	MIX MLE	2 levels	66	0	64	267	71	27	6	0%	1.0	25.8%	9.9%	16.0%	2.2%	4.24	3.40	+0.9
2025	23	MIX RAW	2 levels	66	0	64	267	71	27	6	0%	1.0	26.6%	10.1%	16.5%	2.2%	4.24	3.37	+0.9
2026	24	MLB MLE	Toronto Blue Jays	83	4	63	263	65	26	6	5%	0.8	24.7%	9.9%	14.8%	2.3%	3.30	3.52	+0.8
2026	24	MLB MLE	Toronto Blue Jays	83	4	63	263	65	26	6	5%	0.8	24.7%	9.9%	14.8%	2.3%	3.30	3.52	+0.8
2026	24	MLB RAW	Toronto Blue Jays	83	4	63	263	65	26	6	5%	0.8	24.7%	9.9%	14.8%	2.3%	3.30	3.51	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			138	4	115	478	121	46	12	3%	0.8	25.3%	9.6%	15.7%	2.5%	3.82	3.55	+1.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	25	MLB	TOR	42	0	44	186	19	19	5	0%	1.0	10.4%	10.2%	0.3%	2.7%	3.92	5.38	-0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	26	MLB MLE	TOR	22	0	23	97	13	10	3	0%	1.0	13.4%	10.1%	3.2%	2.7%	4.64	5.12	-0.1
2029	27	MLB MLE	TOR	13	0	14	57	8	6	2	0%	1.0	13.4%	10.2%	3.2%	2.8%	4.95	5.16	-0.1
2030	28	MLB MLE	TOR	9	0	9	40	5	4	1	0%	1.0	13.3%	10.4%	2.9%	2.9%	5.13	5.25	-0.1
2031	29	MLB MLE	TOR	7	0	7	31	4	3	1	0%	1.0	12.7%	10.6%	2.1%	3.0%	5.30	5.41	-0.1
2028	26	MLB CTX	TOR	20	0	21	89	12	9	2	0%	1.1	13.4%	10.1%	3.2%	2.7%	4.64	5.12	-0.1
2029	27	MLB CTX	TOR	12	0	13	55	7	6	2	0%	1.1	13.4%	10.2%	3.2%	2.8%	4.95	5.16	-0.1
2030	28	MLB CTX	TOR	9	0	9	38	5	4	1	0%	1.0	13.3%	10.4%	2.9%	2.9%	5.13	5.25	-0.1
2031	29	MLB CTX	TOR	7	0	7	30	4	3	1	0%	1.0	12.7%	10.6%	2.1%	3.0%	5.30	5.41	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		186	4	166	690	149	68	18	2%	0.9	21.6%	9.8%	11.8%	2.6%	4.15	3.94	+1.2
/ 200 IP	—	RAW		225	5	200	834	180	82	22	2%	0.9	21.6%	9.8%	11.8%	2.6%	4.15	3.94	+1.4
Career	—	MLE		263	4	266	1123	275	103	32	2%	1.0	24.5%	9.2%	15.3%	2.8%	3.62	3.74	+1.0
/ 200 IP	—	MLE		197	3	200	843	207	77	24	2%	1.0	24.5%	9.2%	15.3%	2.8%	3.62	3.74	+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.

2025 AAABuffalo Bisons · 11 IPFIP 2.37 → 2.54 (+0.17)K% 28.8% → 24.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.85%	—	×0.860	24.81%
BB% (unintentional) /BF	13.46%	—	×0.900	12.12%
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 2.37 → 2.54, 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 · 67 IPFIP 3.47 → 3.86 (+0.39)K% 27.4% → 23.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.44%	—	×0.860	23.60%
BB% (unintentional) /BF	8.66%	—	×0.900	7.80%
HR / BF /BF	2.89%	÷0.990	×1.100	3.21%

Result: FIP 3.47 → 3.86, 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).

2023 AANew Hampshire Fisher Cats · 42 IPFIP 3.36 → 4.24 (+0.88)K% 28.0% → 22.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.98%	—	×0.810	22.66%
BB% (unintentional) /BF	7.25%	—	×1.100	7.98%
HR / BF /BF	3.11%	÷1.000	×1.200	3.73%

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 3.36 → 4.24, 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 · 15 IPFIP 2.08 → 2.95 (+0.86)K% 36.2% → 27.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.21%	—	×0.770	27.88%
BB% (unintentional) /BF	8.62%	—	×1.150	9.91%
HR / BF /BF	1.72%	÷1.253	×1.250	1.72%

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 2.08 → 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).

2022 A+Vancouver Canadians · 15 IPFIP 3.42 → 4.79 (+1.36)K% 32.3% → 24.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.31%	—	×0.770	24.88%
BB% (unintentional) /BF	10.77%	—	×1.150	12.38%
HR / BF /BF	3.08%	÷0.952	×1.250	4.04%

Result: FIP 3.42 → 4.79, 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
Mason Fluharty (OGILVIE)	—	—	5.38	10.4%	10.2%	2.7%	—	5.13	13.4%	3.2%	36	-0.25	—	—	—
Joely Rodríguez L 2017 · 5.43 FIP	25	SU	5.43	13.9%	11.7%	3.7%	.315	2.48	23.3%	14.5%	62	-2.95	1.56	449	75
Jake Palisch L 2024 · 5.25 FIP	25	SU	5.25	13.6%	7.4%	4.3%	.288	4.51	10.7%	2.3%	224	-0.74	3.88	317	73
Justin Bruihl L 2022 · 4.58 FIP	25	SU	4.58	14.0%	10.2%	2.5%	.271	3.17	22.0%	14.1%	153	-1.41	2.03	407	72
Kyle Ryan L 2017 · 4.66 FIP	25	SU	4.66	16.2%	12.4%	2.5%	.350	4.45	21.0%	12.2%	143	-0.21	2.86	489	69
Dan Jennings L 2012 · 5.13 FIP	25	SU	5.13	9.3%	11.6%	2.5%	.242	3.04	20.5%	12.4%	137	-2.09	2.60	344	68
Cody Stull L 2017 · 4.73 FIP	25	SU	4.73	11.9%	10.4%	2.4%	.310	3.16	16.7%	8.1%	81	-1.57	2.57	264	67
Justin Thomas L 2016 · 4.25 FIP	25	SU	4.25	13.1%	10.4%	1.7%	.268	—	—	—	—	—	3.09	105	67
Jose Meraz L 2015 · 4.71 FIP	27	SU	4.71	13.3%	12.0%	2.2%	.331	4.06	15.3%	6.7%	44	-0.65	3.78	110	66
Estevan Gonzalez L 2017 · 5.21 FIP	25	SU	5.21	12.1%	13.3%	2.6%	.291	—	—	—	—	—	4.58	195	65
Bryan Harper L 2015 · 4.90 FIP	25	SU	4.90	15.2%	9.4%	3.5%	.236	4.38	17.1%	5.3%	92	-0.52	4.13	133	65
Frank Garces L 2015 · 5.65 FIP	25	SU	5.65	16.7%	13.3%	4.2%	.267	4.32	16.9%	8.9%	114	-1.33	4.32	306	65
Will Lamb L 2016 · 5.66 FIP	25	SU	5.66	15.3%	11.7%	4.3%	.280	5.78	20.0%	6.0%	67	+0.12	3.64	178	64
Chris Reed L 2015 · 4.92 FIP	25	SU	4.92	15.4%	14.5%	2.3%	.284	4.67	16.4%	6.6%	81	-0.25	4.67	137	64
Orleny Quiroz L 2018 · 4.55 FIP	25	SU	4.55	12.7%	8.6%	2.6%	.309	—	—	—	—	—	2.58	136	64
Jeremy Horst L 2011 · 4.39 FIP	25	SU	4.39	13.0%	7.2%	2.7%	.302	3.18	25.2%	15.5%	57	-1.21	2.12	181	63
Daniel Gibson L 2017 · 4.44 FIP	25	SU	4.44	13.2%	13.5%	1.3%	.336	3.73	18.6%	9.9%	28	-0.71	2.95	159	63
Kyle Hunter L 2015 · 4.32 FIP	26	SW	4.32	12.5%	11.5%	1.5%	.258	4.49	12.6%	4.6%	144	+0.17	4.32	188	63
Cam Alldred L 2021 · 4.79 FIP	25	SU	4.79	17.7%	11.9%	3.0%	.213	4.24	18.8%	9.5%	206	-0.55	3.70	332	63
Chandler Hawkins L 2017 · 4.76 FIP	24	SU	4.76	13.5%	11.7%	2.4%	.255	—	—	—	—	—	3.51	115	63
Kumar Nambiar L 2023 · 4.94 FIP	25	SU	4.94	14.0%	13.0%	2.5%	.260	—	—	—	—	—	3.10	221	63

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	1.1	31	18.9%	10.6%	1.02	4.11
Chris Ray ('11)	1.4	47	19.1%	10.2%	1.12	4.10
Manny Acosta ('12)	1.2	41	20.5%	10.9%	1.09	3.99
Mike Munoz ('89-'00, 1 WAR*)	1.8	46	17.6%*	10.2%*	0.93*	4.46*
Kevin Hickey ('81-'91, 0 WAR*)	1.2	47	17.7%*	10.2%*	1.10*	4.27*
Brad Clontz ('00)	1.4	56	17.4%	9.9%	0.97	4.34
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*