Joshua Quezada

P Wilson Warbirds

Age 23 RHP 6'2" / 185 lbs A peak 0.2 · 2029

Overview

APROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	20	ROKDSL Brewers 1	9	5	34	3.44	3.13	26.8%	9.4%	—	K 18.3% · BB 11.7% · FIP 4.46
2025	21	MIX2 levels	18	3	46	6.80	2.78	24.4%	7.7%	—	K 15.8% · BB 8.4% · FIP 4.28
2026	22	MIX4 levels	39	0	61	5.16	3.03	29.4%	9.2%	—	K 22.5% · BB 9.1% · FIP 3.83
2027PROJ	23	MLBMIL	—	—	0	4.41	4.41	21.9%	9.2%	—
2028PROJ43% MLB	24	MLBMIL	18	0	19	4.34	4.30	22.6%	8.8%	0.1
2029PROJ66% MLB	25	MLBMIL	26	0	27	4.28	4.23	23.1%	8.7%	0.1

Next 3 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 -0.0 / P90 0.4, comp-based) · star 0% · didn't stick 94%

CompsJonathan Papelbon (age 24) · Heath Holder (age 23) · Ryan Conroy (age 24)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.41

FIP4.41

K%21.9%

BB%9.2%

K-BB%12.8%

Proj IP24

Value2027 · WAR

Runs saved vs league avg · 24 IP

Grades · Contract

Scouting Grades

K†5055

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	19	ROK MLE	DSL NYY Yankees	11	4	46	192	48	18	2	36%	4.2	17.0%	11.7%	5.3%	0.8%	3.69	3.61	—
2023	19	ROK RAW	DSL NYY Yankees	11	4	46	192	48	18	2	36%	4.2	25.0%	9.4%	15.6%	1.0%	3.69	2.75	—
2024	20	ROK MLE	DSL Brewers 1	9	5	34	149	40	14	3	56%	3.8	18.3%	11.7%	6.5%	2.6%	3.44	4.46	—
2024	20	ROK MLE	DSL Brewers 1	9	5	34	149	40	14	3	56%	3.8	18.3%	11.7%	6.5%	2.6%	3.44	4.46	—
2024	20	ROK RAW	DSL Brewers 1	9	5	34	149	40	14	3	56%	3.8	26.8%	9.4%	17.4%	2.0%	3.44	3.13	—
2025	21	MIX MLE	2 levels	18	3	46	209	51	16	3	17%	2.6	15.8%	8.4%	7.4%	2.6%	6.80	4.28	—
2025	21	MIX MLE	2 levels	18	3	46	209	51	16	3	17%	2.6	15.8%	8.4%	7.4%	2.6%	6.80	4.28	—
2025	21	MIX RAW	2 levels	18	3	46	209	51	16	3	17%	2.6	24.4%	7.7%	16.7%	1.4%	6.80	2.78	—
2026	22	MIX MLE	4 levels	39	0	61	262	77	24	6	0%	1.6	22.5%	9.1%	13.4%	2.7%	5.16	3.83	—
2026	22	MIX MLE	4 levels	39	0	61	262	77	24	6	0%	1.6	22.5%	9.1%	13.4%	2.7%	5.16	3.83	—
2026	22	MIX RAW	4 levels	39	0	61	262	77	24	6	0%	1.6	29.4%	9.2%	20.2%	2.3%	5.16	3.03	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	23	MLB	MIL	—	—	0	—	—	—	—	—	—	21.9%	9.2%	12.8%	3.0%	4.41	4.41	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	24	MLB MLE	MIL	43	0	45	190	43	17	6	0%	1.0	22.6%	8.8%	13.8%	2.9%	4.34	4.30	+0.1
2029	25	MLB MLE	MIL	39	0	41	175	40	15	5	0%	1.1	23.1%	8.7%	14.4%	2.9%	4.28	4.23	+0.2
2030	26	MLB MLE	MIL	39	0	40	171	40	15	5	0%	1.0	23.3%	8.6%	14.6%	2.9%	4.25	4.22	+0.2
2031	27	MLB MLE	MIL	37	0	39	165	38	14	5	0%	1.1	23.3%	8.7%	14.5%	3.0%	4.25	4.26	+0.1
2028	24	MLB CTX	MIL	18	0	19	80	18	7	2	0%	1.1	22.6%	8.8%	13.8%	2.9%	4.34	4.30	+0.1
2029	25	MLB CTX	MIL	26	0	27	114	26	10	3	0%	1.0	23.1%	8.7%	14.4%	2.9%	4.28	4.23	+0.1
2030	26	MLB CTX	MIL	30	0	32	135	31	12	4	0%	1.1	23.3%	8.6%	14.6%	2.9%	4.25	4.22	+0.1
2031	27	MLB CTX	MIL	31	0	33	139	32	12	4	0%	1.1	23.3%	8.7%	14.5%	3.0%	4.25	4.26	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		107	0	112	473	109	41	14	0%	1.0	23.1%	8.7%	14.4%	2.9%	4.27	3.87	+0.9
/ 200 IP	—	RAW		190	0	200	844	194	73	25	0%	1.1	23.1%	8.7%	14.4%	2.9%	4.27	3.87	+1.6
Career	—	MLE		77	12	188	812	152	81	18	16%	2.4	18.7%	10.0%	8.7%	2.2%	4.89	4.01	+0.6
/ 200 IP	—	MLE		82	13	200	865	162	87	19	16%	2.4	18.7%	10.0%	8.7%	2.2%	4.89	4.01	+0.7

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 AWilson Warbirds · 45 IPFIP 2.64 → 3.83 (+1.18)K% 30.4% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.41%	—	×0.740	22.51%
BB% (unintentional) /BF	7.73%	—	×1.180	9.12%
HR / BF /BF	2.06%	÷1.000	×1.300	2.68%

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 100).

Result: FIP 2.64 → 3.83, 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).

2026 A+Wisconsin Timber Rattlers · 11 IPFIP 5.26 → 6.68 (+1.42)K% 26.7% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.67%	—	×0.770	20.53%
BB% (unintentional) /BF	15.56%	—	×1.150	17.89%
HR / BF /BF	4.44%	÷1.000	×1.250	5.56%

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 100).

Result: FIP 5.26 → 6.68, 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 ROKACL Brewers · 35 IPFIP 3.00 → 4.28 (+1.28)K% 23.2% → 15.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.17%	—	×0.680	15.76%
BB% (unintentional) /BF	6.71%	—	×1.250	8.38%
HR / BF /BF	1.83%	÷0.971	×1.400	2.64%

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 94).

Result: FIP 3.00 → 4.28, 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 ACarolina Mudcats · 11 IPFIP 2.07 → 2.96 (+0.89)K% 28.9% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.89%	—	×0.740	21.38%
BB% (unintentional) /BF	11.11%	—	×1.180	13.11%
HR / BF /BF	0.00%	÷1.214	×1.300	0.00%

Result: FIP 2.07 → 2.96, 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 ROKDSL Brewers 1 · 34 IPFIP 3.13 → 4.46 (+1.33)K% 26.8% → 18.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.85%	—	×0.680	18.26%
BB% (unintentional) /BF	9.40%	—	×1.250	11.74%
HR / BF /BF	2.01%	÷1.098	×1.400	2.57%

Result: FIP 3.13 → 4.46, 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 ROKDSL NYY Yankees · 46 IPFIP 2.75 → 3.61 (+0.87)K% 25.0% → 17.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.680	17.00%
BB% (unintentional) /BF	9.38%	—	×1.250	11.72%
HR / BF /BF	1.04%	÷1.724	×1.400	0.85%

Result: FIP 2.75 → 3.61, 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
Joshua Quezada (OGILVIE)	—	—	4.41	21.9%	9.2%	3.0%	—	4.25	23.0%	14.3%	127	-0.16	—	—	—
Jonathan Papelbon R 2005 · 3.93 FIP	24	SW	3.93	23.0%	10.1%	2.7%	.305	1.90	31.3%	26.8%	196	-2.03	1.33	726	87
Heath Holder R 2016 · 4.16 FIP	23	SU	4.16	21.1%	10.1%	2.8%	.287	3.30	22.3%	13.1%	247	-0.86	2.94	367	87
Ryan Conroy R 2021 · 4.01 FIP	24	SU	4.01	22.2%	9.8%	2.8%	.353	4.75	19.3%	6.7%	54	+0.74	4.01	106	87
Michael Gomez R 2019 · 3.32 FIP	22	SU	3.32	22.6%	9.2%	1.8%	.283	3.81	17.9%	8.3%	110	+0.49	3.27	286	86
Colby Morris R 2021 · 4.11 FIP	24	SU	4.11	20.4%	10.7%	2.5%	.291	5.76	16.2%	4.4%	54	+1.65	4.11	105	85
Kevin Coulter R 2019 · 3.27 FIP	22	SU	3.27	21.0%	7.4%	1.8%	.287	4.51	16.1%	7.6%	92	+1.24	3.27	128	85
Owen Holt R 2022 · 3.72 FIP	23	SU	3.72	20.7%	10.2%	2.0%	.258	4.51	18.7%	6.4%	107	+0.79	3.72	145	85
Parker Curry R 2017 · 3.73 FIP	23	SU	3.73	22.5%	8.2%	2.7%	.341	4.62	18.6%	11.6%	193	+0.89	3.73	256	85
Tyler Bray R 2015 · 4.07 FIP	23	SU	4.07	22.3%	9.1%	3.1%	.356	3.21	17.9%	7.6%	114	-0.86	2.82	152	85
Zac Ryan R 2017 · 3.83 FIP	23	SU	3.83	20.5%	10.9%	2.0%	.233	3.93	21.6%	6.9%	124	+0.10	3.83	184	85
Josh Advocate R 2018 · 3.78 FIP	24	SU	3.78	18.8%	7.3%	2.4%	.291	3.71	20.4%	10.1%	75	-0.07	3.71	118	84
Alex Klonowski R 2015 · 3.58 FIP	23	SU	3.58	21.8%	9.4%	2.1%	.331	4.69	14.3%	7.1%	413	+1.11	3.58	539	84
Ricardo Velez R 2023 · 4.44 FIP	24	SU	4.44	22.4%	10.9%	3.4%	.261	4.09	18.8%	8.1%	138	-0.35	3.83	166	84
Tyler Mitzel R 2019 · 4.34 FIP	23	SU	4.34	24.1%	12.0%	3.2%	.245	4.10	22.4%	14.3%	96	-0.24	4.03	142	84
Matt Pidich R 2018 · 4.42 FIP	23	SU	4.42	17.1%	8.3%	3.1%	.303	4.08	20.8%	13.3%	115	-0.34	4.05	196	83
Luke Bell R 2024 · 3.62 FIP	23	SU	3.62	20.4%	9.9%	1.8%	.295	5.69	13.1%	1.3%	66	+2.07	3.62	110	83
Kyle Martin R 2015 · 3.61 FIP	24	SU	3.61	21.2%	9.6%	2.0%	.345	3.51	22.4%	12.5%	172	-0.10	3.01	214	83
Luis Romero R 2017 · 5.00 FIP	23	SU	5.00	22.1%	11.9%	4.1%	.367	4.26	21.6%	12.5%	132	-0.74	4.00	293	83
Brandon Beckel R 2024 · 3.75 FIP	22	SU	3.75	19.4%	10.3%	1.8%	.250	4.23	19.5%	10.5%	147	+0.48	3.75	194	83
Todd Peterson R 2021 · 5.11 FIP	23	SW	5.11	18.4%	10.3%	4.1%	.279	4.83	15.0%	4.3%	124	-0.28	4.45	175	83

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	1.0	38	22.9%	8.8%	1.14	4.30
Yency Almonte ('24)	1.1	45	22.5%	9.9%	1.13	4.44
Ian Krol ('21)	1.0	42	21.2%	8.8%	1.25	4.49
Kevin Quackenbush ('21)	1.0	52	21.0%	9.2%	1.04	4.41
Dave Baldwin ('66-'73, 2 WAR*)	1.1	56	22.2%*	9.2%*	0.96*	3.97*
Ed Vosberg ('86-'02, 1 WAR*)	1.2	47	21.0%*	9.6%*	0.86*	4.06*
Carlos Diaz ('82-'86, 2 WAR*)	1.3	52	25.1%*	8.7%*	0.91*	3.84*