Brandyn Garcia

P Arizona Diamondbacks

Age 27 LHP 6'4" / 235 lbs MLB peak 0.6 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	MIX2 levels	27	25	116	2.25	2.45	27.0%	9.5%	—	K 21.2% · BB 10.7% · FIP 3.27
2025	25	MIX3 levels	52	0	59	3.99	3.49	26.3%	12.2%	0.7	K 22.8% · BB 12.2% · FIP 4.18
2026	26	MIX2 levels	67	0	64	1.98	2.41	31.6%	8.1%	1.6	K 30.6% · BB 7.7% · FIP 2.44
2027PROJ	27	MLBARI	42	0	44	3.59	3.68	27.4%	9.7%	0.5
2028PROJ86% MLB	28	MLBARI	40	0	42	3.76	3.83	26.9%	9.8%	0.4
2029PROJ89% MLB	29	MLBARI	35	0	37	3.93	4.03	26.0%	10.0%	0.2

Last 3 seasons WAR 2.4 · Next 3 projected WAR 1.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%90

xwOBA vs90

FB Velo88

Form1.61 ERA (proj 3.59) · K 30.5% (proj 27.4%) · BB 4.9% (proj 9.7%) ▲▲ last 90d · 22 IP through Oct 27

CompsJustin Wilson (age 27) · Fabian Williamson (age 27) · Justin King (age 26)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 6775vR 83

ERA3.59

FIP3.68

K%27.4%

BB%9.7%

K-BB%17.8%

Proj IP54

Value2027 · WAR

Runs saved vs league avg · 54 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p8897.2

Whiff%

p6026.9%

p9031.3%

xwOBA vs

p900.225

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)
2023	23	MIX MLE	2 levels	9	0	10	39	8	3	1	0%	1.1	20.5%	7.7%	12.8%	2.6%	1.80	3.75	—
2023	23	MIX RAW	2 levels	9	0	10	39	8	3	1	0%	1.1	20.5%	7.7%	12.8%	2.6%	1.80	3.70	—
2024	24	MIX MLE	2 levels	27	25	116	496	134	47	4	93%	4.3	21.2%	10.7%	10.5%	1.1%	2.25	3.27	—
2024	24	MIX MLE	2 levels	27	25	116	496	134	47	4	93%	4.3	21.2%	10.7%	10.5%	1.1%	2.25	3.27	—
2024	24	MIX RAW	2 levels	27	25	116	496	134	47	4	93%	4.3	27.0%	9.5%	17.5%	0.8%	2.25	2.45	—
2025	25	MIX MLE	3 levels	52	0	59	262	69	32	5	0%	1.1	22.8%	12.2%	10.6%	2.6%	3.99	4.18	+0.3
2025	25	MIX MLE	3 levels	52	0	59	262	69	32	5	0%	1.1	22.8%	12.2%	10.6%	2.6%	3.99	4.18	+0.3
2025	25	MIX RAW	3 levels	52	0	59	262	69	32	5	0%	1.1	26.3%	12.2%	14.1%	1.9%	3.99	3.49	+0.7
2026	26	MIX MLE	2 levels	67	0	64	247	78	20	4	0%	1.0	30.6%	7.7%	22.9%	1.7%	1.98	2.44	+1.6
2026	26	MIX MLE	2 levels	67	0	64	247	78	20	4	0%	1.0	30.6%	7.7%	22.9%	1.7%	1.98	2.44	+1.6
2026	26	MIX RAW	2 levels	67	0	64	247	78	20	4	0%	1.0	31.6%	8.1%	23.5%	1.6%	1.98	2.41	+1.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			71	0	66	258	74	18	3	0%	0.9	28.7%	7.0%	21.7%	1.2%	2.74	2.26	+1.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	ARI	42	0	44	186	51	18	4	0%	1.0	27.4%	9.7%	17.8%	2.4%	3.59	3.68	+0.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	ARI	46	0	49	206	55	20	5	0%	1.1	26.9%	9.8%	17.1%	2.5%	3.76	3.83	+0.4
2029	29	MLB MLE	ARI	39	0	41	174	45	17	5	0%	1.1	26.0%	10.0%	16.1%	2.7%	3.93	4.03	+0.3
2030	30	MLB MLE	ARI	32	0	34	142	36	15	4	0%	1.1	25.2%	10.2%	15.0%	2.9%	4.13	4.25	+0.1
2028	28	MLB CTX	ARI	40	0	42	177	48	17	4	0%	1.1	26.9%	9.8%	17.1%	2.5%	3.76	3.83	+0.4
2029	29	MLB CTX	ARI	35	0	37	156	41	16	4	0%	1.1	26.0%	10.0%	16.1%	2.7%	3.93	4.03	+0.2
2030	30	MLB CTX	ARI	30	0	31	131	33	13	4	0%	1.0	25.2%	10.2%	15.0%	2.9%	4.13	4.25	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		175	0	175	720	195	64	15	0%	1.0	27.0%	8.9%	18.2%	2.1%	3.48	3.12	+3.0
/ 200 IP	—	RAW		200	0	200	822	223	73	17	0%	1.0	27.0%	8.9%	18.2%	2.1%	3.48	3.12	+3.4
Career	—	MLE		155	25	248	1044	248	107	18	16%	1.6	23.8%	10.2%	13.5%	1.7%	2.57	3.32	+2.6
/ 200 IP	—	MLE		125	20	200	841	200	86	14	16%	1.6	23.8%	10.2%	13.5%	1.7%	2.57	3.32	+2.7

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.

2026 AAAReno Aces · 12 IPFIP 3.64 → 3.89 (+0.26)K% 32.7% → 28.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.73%	—	×0.860	28.15%
BB% (unintentional) /BF	18.18%	—	×0.900	16.36%
HR / BF /BF	1.82%	÷1.000	×1.100	2.00%

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 100).

Result: FIP 3.64 → 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).

2025 AAArkansas Travelers · 25 IPFIP 3.45 → 4.81 (+1.37)K% 30.0% → 24.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.810	24.30%
BB% (unintentional) /BF	10.91%	—	×1.100	12.00%
HR / BF /BF	2.73%	÷0.800	×1.200	4.09%

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 60).

Result: FIP 3.45 → 4.81, 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 AAAReno Aces · 19 IPFIP 3.98 → 4.39 (+0.40)K% 25.6% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.58%	—	×0.860	22.00%
BB% (unintentional) /BF	13.95%	—	×0.900	12.56%
HR / BF /BF	2.33%	÷0.904	×1.100	2.83%

Result: FIP 3.98 → 4.39, 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 A+Everett AquaSox · 68 IPFIP 1.94 → 2.70 (+0.76)K% 29.5% → 22.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.51%	—	×0.770	22.73%
BB% (unintentional) /BF	9.03%	—	×1.150	10.38%
HR / BF /BF	0.35%	÷1.175	×1.250	0.37%

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 135).

Result: FIP 1.94 → 2.70, 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 AAArkansas Travelers · 48 IPFIP 3.18 → 4.05 (+0.87)K% 23.6% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.56%	—	×0.810	19.08%
BB% (unintentional) /BF	10.10%	—	×1.100	11.11%
HR / BF /BF	1.44%	÷0.820	×1.200	2.11%

Result: FIP 3.18 → 4.05, 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 AModesto Nuts · 7 IPFIP 4.76 → 6.16 (+1.40)K% 14.3% → 10.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.29%	—	×0.740	10.57%
BB% (unintentional) /BF	7.14%	—	×1.180	8.43%
HR / BF /BF	3.57%	÷0.883	×1.300	5.26%

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 77).

Result: FIP 4.76 → 6.16, 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
Brandyn Garcia (OGILVIE)	—	—	—	—	—	2.4%	—	4.01	26.1%	16.2%	124	+0.33	—	—	—
Justin Wilson L 2015 · 2.49 FIP	27	SU	2.49	27.0%	8.2%	1.1%	.297	3.28	29.1%	18.1%	171	+0.79	2.49	554	77
Fabian Williamson L 2016 · 3.39 FIP	27	SU	3.39	23.8%	9.0%	2.1%	.259	5.26	16.5%	-3.3%	54	+1.87	3.39	154	77
Justin King L 2024 · 3.26 FIP	26	SU	3.26	29.5%	11.8%	2.1%	.346	4.16	22.4%	3.6%	65	+0.90	3.26	133	76
Jacob Latz L 2023 · 3.06 FIP	27	SU	3.06	28.1%	9.8%	2.0%	.366	3.36	25.0%	15.5%	216	+0.30	2.39	480	75
Darin Downs L 2012 · 2.83 FIP	27	SU	2.83	23.3%	8.1%	1.2%	.293	3.64	19.8%	11.9%	86	+0.81	2.83	107	75
Jack Sellinger L 2024 · 2.65 FIP	24	SU	2.65	26.4%	9.7%	1.0%	.284	3.24	24.0%	9.1%	114	+0.59	2.65	148	75
Sammy Solís L 2016 · 2.51 FIP	27	SU	2.51	27.3%	11.0%	0.6%	.286	4.90	23.7%	13.5%	101	+2.39	2.51	190	74
Conor Lillis-White L 2018 · 3.48 FIP	26	SU	3.48	26.8%	11.1%	2.3%	.322	—	—	—	—	—	1.80	182	74
Matt Purke L 2017 · 3.01 FIP	27	SU	3.01	25.1%	10.1%	1.4%	.335	4.29	16.1%	0.1%	47	+1.28	3.01	226	74
Aaron Fultz L 2001 · 3.62 FIP	27	SU	3.62	22.3%	6.0%	3.0%	.296	4.09	17.4%	9.2%	159	+0.47	3.46	480	73
Michael Gonzalez L 2005 · 3.04 FIP	27	SU	3.04	27.4%	13.7%	0.9%	.268	3.04	27.1%	16.4%	105	-0.00	1.45	454	73
Jordan Mills L 2019 · 3.28 FIP	27	SU	3.28	23.2%	9.3%	1.8%	.325	—	—	—	—	—	2.64	210	72
Jose Mijares L 2012 · 2.79 FIP	27	SU	2.79	23.6%	7.4%	1.3%	.287	2.78	22.9%	16.1%	49	-0.01	2.78	249	72
James Pazos L 2018 · 3.21 FIP	27	SU	3.21	21.3%	6.6%	1.9%	.291	4.60	20.6%	9.4%	97	+1.39	2.55	261	72
Luis Perez L 2012 · 3.32 FIP	27	SU	3.32	22.3%	9.1%	1.7%	.299	4.00	15.2%	2.6%	66	+0.68	3.32	173	71
Jesse Carlson L 2008 · 3.28 FIP	27	SU	3.28	23.2%	5.9%	2.5%	.216	4.00	16.8%	10.8%	81	+0.72	3.28	141	71
Colton Johnson L 2024 · 3.92 FIP	26	SU	3.92	20.8%	11.0%	2.1%	.309	4.53	12.2%	-0.4%	48	+0.61	3.92	106	71
Williams Jerez L 2019 · 3.58 FIP	27	SU	3.58	22.8%	7.1%	2.7%	.263	7.03	18.7%	7.0%	21	+3.45	3.58	364	71
Jeff Malm L 2016 · 3.13 FIP	25	SU	3.13	22.0%	7.2%	1.8%	.354	—	—	—	—	—	3.13	101	71
John Grabow L 2006 · 3.66 FIP	27	SU	3.66	21.8%	8.9%	2.3%	.300	4.10	18.7%	8.3%	200	+0.44	3.66	471	71

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	1.2	49	27.1%	9.2%	0.86	3.53
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Josh Fields ('18)	1.4	46	27.5%	8.0%	0.98	3.71
Kiko Calero ('09)	1.5	50	26.0%	10.1%	0.71	3.24
Kazuhiro Sasaki ('03)	1.1	56	26.2%	8.3%	0.97	3.14
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*
Dave Baldwin ('66-'73, 2 WAR*)	1.1	56	22.2%*	9.2%*	0.96*	3.97*