Grant Judkins

P Lansing Lugnuts

Age 29 RHP 6'3" / 215 lbs A+ peak 0.3 · 2027

Overview

A+PROSPECT

ETA2029MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	26	A+OAK	25	12	83	3.56	3.83	21.9%	7.9%	—	K 16.8% · BB 9.1% · FIP 5.13
2025	27	A+OAK	24	24	124	4.73	4.56	19.4%	7.9%	—	K 14.9% · BB 9.1% · FIP 5.80
2027PROJ	29	MLBOAK	—	—	0	5.43	5.43	14.1%	10.0%	—
2028PROJ30% MLB	30	MLBOAK	3	3	18	5.42	5.50	14.7%	10.2%	-0.0
2029PROJ51% MLB	31	MLBOAK	4	4	25	5.50	5.61	15.0%	10.5%	-0.1

Next 3 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff35Control50Command40

CompsTravis Smith (age 29) · Zack Segovia (age 32) · Miguel Ruiz (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 136128vR 120

ERA5.43

FIP5.43

K%14.1%

BB%10.0%

K-BB%4.2%

Proj IP78

Value2027 · WAR

Runs saved vs league avg · 78 IP

Grades · Contract

Scouting Grades

STUF3535

CTRL5050

CMD4040

K†3030

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)
2021	23	A MLE	Stockton Ports	14	11	49	222	63	21	11	79%	3.5	21.0%	11.2%	9.8%	5.5%	6.29	5.77	—
2021	23	A RAW	Stockton Ports	14	11	49	222	63	21	11	79%	3.5	28.4%	9.5%	18.9%	5.0%	6.29	4.74	—
2022	24	A MLE	Stockton Ports	15	13	66	309	66	35	12	87%	4.4	15.8%	13.4%	2.4%	5.0%	5.43	6.22	—
2022	24	A RAW	Stockton Ports	15	13	66	309	66	35	12	87%	4.4	21.4%	11.3%	10.0%	3.9%	5.43	5.04	—
2024	26	A+ MLE	Lansing Lugnuts	25	12	83	343	75	27	10	48%	3.3	16.8%	9.1%	7.8%	4.2%	3.56	5.13	—
2024	26	A+ MLE	Lansing Lugnuts	25	12	83	343	75	27	10	48%	3.3	16.8%	9.1%	7.8%	4.2%	3.56	5.13	—
2024	26	A+ RAW	Lansing Lugnuts	25	12	83	343	75	27	10	48%	3.3	21.9%	7.9%	14.0%	2.9%	3.56	3.83	—
2025	27	A+ MLE	Lansing Lugnuts	24	24	124	531	103	42	20	100%	5.2	14.9%	9.1%	5.8%	5.1%	4.73	5.80	—
2025	27	A+ MLE	Lansing Lugnuts	24	24	124	531	103	42	20	100%	5.2	14.9%	9.1%	5.8%	5.1%	4.73	5.80	—
2025	27	A+ RAW	Lansing Lugnuts	24	24	124	531	103	42	20	100%	5.2	19.4%	7.9%	11.5%	3.8%	4.73	4.56	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	OAK	—	—	0	—	—	—	—	—	—	14.1%	10.0%	4.2%	3.4%	5.43	5.43	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	OAK	10	10	59	248	37	25	9	100%	5.9	14.7%	10.2%	4.5%	3.6%	5.42	5.50	-0.1
2029	31	MLB MLE	OAK	8	8	49	205	31	22	8	100%	6.1	15.0%	10.5%	4.5%	3.8%	5.50	5.61	-0.1
2028	30	MLB CTX	OAK	3	3	18	76	11	8	3	100%	6.0	14.7%	10.2%	4.5%	3.6%	5.42	5.50	-0.0
2029	31	MLB CTX	OAK	4	4	25	106	16	11	4	100%	6.2	15.0%	10.5%	4.5%	3.8%	5.50	5.61	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		7	7	42	179	27	19	7	100%	6.1	14.9%	10.4%	4.5%	3.7%	5.47	5.18	+0.1
/ 200 IP	—	RAW		33	33	200	844	127	90	33	100%	6.0	14.9%	10.4%	4.5%	3.7%	5.47	5.18	+0.5
Career	—	MLE		78	60	322	1405	233	145	69	77%	4.1	16.5%	10.4%	6.2%	4.9%	4.81	5.72	-0.2
/ 200 IP	—	MLE		48	37	200	873	144	90	43	77%	4.1	16.5%	10.4%	6.2%	4.9%	4.81	5.72	-0.3

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 A+Lansing Lugnuts · 124 IPFIP 4.53 → 5.80 (+1.27)K% 19.4% → 14.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.40%	—	×0.770	14.94%
BB% (unintentional) /BF	7.91%	—	×1.150	9.10%
HR / BF /BF	3.77%	÷0.921	×1.250	5.11%

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 84).

Result: FIP 4.53 → 5.80, 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+Lansing Lugnuts · 83 IPFIP 3.85 → 5.13 (+1.28)K% 21.9% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.87%	—	×0.770	16.84%
BB% (unintentional) /BF	7.87%	—	×1.150	9.05%
HR / BF /BF	2.92%	÷0.867	×1.250	4.21%

Result: FIP 3.85 → 5.13, 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 AStockton Ports · 66 IPFIP 4.86 → 6.22 (+1.36)K% 21.4% → 15.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.36%	—	×0.740	15.81%
BB% (unintentional) /BF	11.33%	—	×1.180	13.37%
HR / BF /BF	3.88%	÷1.003	×1.300	5.04%

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 101).

Result: FIP 4.86 → 6.22, 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 AStockton Ports · 49 IPFIP 4.62 → 5.77 (+1.15)K% 28.4% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.38%	—	×0.740	21.00%
BB% (unintentional) /BF	9.46%	—	×1.180	11.16%
HR / BF /BF	4.95%	÷1.165	×1.300	5.53%

Result: FIP 4.62 → 5.77, 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
Grant Judkins (OGILVIE)	—	—	5.43	14.1%	10.0%	3.4%	—	5.55	14.8%	4.5%	107	+0.12	—	—	—
Travis Smith R 2002 · 5.28 FIP	29	SP	5.28	13.1%	8.2%	4.1%	.324	5.71	15.0%	9.0%	51	+0.43	3.67	105	86
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	83
Miguel Ruiz R 2015 · 5.77 FIP	29	SP-	5.77	8.8%	8.0%	4.4%	.346	5.60	10.6%	-0.4%	53	-0.17	5.60	117	82
Luke Irvine R 2016 · 4.31 FIP	27	SP-	4.31	13.4%	9.8%	2.0%	.302	—	—	—	—	—	4.31	104	82
David Fletcher R 2024 · 5.67 FIP	30	SP-	5.67	7.5%	10.3%	3.5%	.294	—	—	—	—	—	5.67	100	81
Robert Ellis R 2001 · 4.84 FIP	30	SP	4.84	9.9%	7.7%	2.9%	.287	8.08	9.0%	-2.2%	18	+3.24	4.84	110	80
Scott McGregor R 2015 · 4.37 FIP	28	SP-	4.37	11.8%	7.6%	2.4%	.293	—	—	—	—	—	4.37	107	79
Travis Driskill R 2002 · 4.95 FIP	30	SP-	4.95	13.2%	8.0%	3.6%	.291	4.26	15.3%	12.0%	48	-0.69	4.26	181	78
Juan Omar Espinoza R 2015 · 4.81 FIP	31	SP-	4.81	8.9%	6.6%	3.0%	.346	4.84	8.4%	-0.8%	52	+0.03	4.81	104	78
Andres Ivan Meza R 2015 · 4.45 FIP	28	SP	4.45	10.9%	5.6%	2.9%	.322	4.14	12.5%	4.0%	85	-0.31	4.14	275	77
Daniel Guerrero R 2016 · 4.81 FIP	31	SP	4.81	12.4%	11.6%	2.3%	.328	4.93	11.7%	2.0%	175	+0.12	4.61	269	77
Frank Batista R 2015 · 4.29 FIP	26	SP-	4.29	12.1%	8.2%	2.1%	.280	—	—	—	—	—	4.29	118	77
David Griffin R 2023 · 5.83 FIP	27	SP-	5.83	14.2%	9.2%	5.0%	.306	—	—	—	—	—	4.43	239	77
Jordan Cooper R 2015 · 5.01 FIP	26	SP	5.01	9.2%	10.2%	2.5%	.305	—	—	—	—	—	5.01	106	75
Colin Bates R 2015 · 4.57 FIP	27	SP-	4.57	10.5%	6.5%	2.8%	.301	—	—	—	—	—	4.57	111	74
Caleb Clay R 2015 · 4.67 FIP	27	SP	4.67	9.9%	6.9%	2.8%	.324	—	—	—	—	—	4.67	111	73
Reinier Roibal R 2019 · 4.91 FIP	30	SP	4.91	17.5%	11.8%	3.3%	.426	—	—	—	—	—	3.11	133	73
Kyle Friedrichs R 2021 · 4.73 FIP	29	SP-	4.73	13.9%	6.2%	3.7%	.286	—	—	—	—	—	3.53	603	72
Scott Carroll R 2014 · 4.35 FIP	29	SP-	4.35	11.2%	7.7%	2.2%	.296	4.52	11.9%	3.7%	248	+0.17	3.92	377	72
Marco Quevedo R 2016 · 4.57 FIP	29	SP	4.57	13.6%	6.2%	3.3%	.258	5.02	11.5%	3.2%	107	+0.45	4.13	300	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	0.9	62	14.5%	10.2%	1.38	5.45
Brian Stokes ('10)	1.0	52	14.5%	10.1%	1.22	5.27
Rodney Myers ('04)	1.2	60	15.0%	10.3%	1.05	5.07
Luther Hackman ('03)	1.1	71	13.3%	10.9%	1.10	5.09
Daryl Patterson ('68-'74, -1 WAR*)	1.2	46	18.3%*	11.2%*	1.27*	5.03*
Bob Macdonald ('90-'96, 1 WAR*)	1.2	59	18.3%*	9.8%*	1.20*	4.32*
Dennis Powell ('85-'93, -2 WAR*)	1.7	49	17.6%*	10.1%*	1.18*	5.31*