Randy Labaut

P Indianapolis Indians

Age 30 LHP 6'2" / 205 lbs AAA

Overview

AAAPROSPECT

MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	26	MIX2 levels	33	1	58	3.70	4.27	20.6%	14.2%	—	K 17.0% · BB 13.9% · FIP 4.72
2024	27	AAACLE	36	3	61	4.57	4.85	18.6%	14.3%	—	K 16.0% · BB 12.9% · FIP 4.80
2025	28	MIX3 levels	24	3	40	4.69	4.34	26.2%	11.9%	—	K 22.2% · BB 11.3% · FIP 5.15
2026	29	No MLB games played
2027PROJ	30	MLBPIT	2	0	2	4.89	4.89	20.7%	11.2%	-0.0
2028PROJ37% MLB	31	MLBPIT	7	0	7	4.95	5.03	20.5%	11.3%	-0.0

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsRaul Valdes (age 32) · Andrew Barbosa (age 29) · Jose Meraz (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 110118vR 126

ERA4.89

FIP4.89

K%20.7%

BB%11.2%

K-BB%9.5%

Proj IP25

Value2027 · WAR

Runs saved vs league avg · 25 IP

Grades · Contract

Scouting Grades

K†5050

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)
2019	22	ROK MLE	AZL Indians Blue	11	0	31	122	39	5	0	0%	2.8	21.7%	5.1%	16.6%	0.0%	1.74	1.91	—
2019	22	ROK RAW	AZL Indians Blue	11	0	31	122	39	5	0	0%	2.8	32.0%	4.1%	27.9%	0.0%	1.74	1.07	—
2021	24	A MLE	Lynchburg Hillcats	22	3	44	185	58	22	5	14%	2.0	23.2%	14.0%	9.2%	3.7%	3.09	4.95	—
2021	24	A RAW	Lynchburg Hillcats	22	3	44	185	58	22	5	14%	2.0	31.4%	11.9%	19.5%	2.7%	3.09	3.44	—
2022	25	MIX MLE	2 levels	38	0	70	288	89	35	8	0%	1.9	24.3%	13.6%	10.7%	3.1%	3.33	4.49	—
2022	25	MIX RAW	2 levels	38	0	70	288	89	35	8	0%	1.9	30.9%	12.2%	18.8%	2.8%	3.33	3.54	—
2023	26	MIX MLE	2 levels	33	1	58	247	51	35	5	3%	1.8	17.0%	13.9%	3.1%	2.4%	3.70	4.72	—
2023	26	MIX RAW	2 levels	33	1	58	247	51	35	5	3%	1.8	20.6%	14.2%	6.5%	2.0%	3.70	4.27	—
2024	27	AAA MLE	Columbus Clippers	36	3	61	280	52	40	7	8%	1.7	16.0%	12.9%	3.1%	2.6%	4.57	4.80	—
2024	27	AAA MLE	Columbus Clippers	36	3	61	280	52	40	7	8%	1.7	16.0%	12.9%	3.1%	2.6%	4.57	4.80	—
2024	27	AAA RAW	Columbus Clippers	36	3	61	280	52	40	7	8%	1.7	18.6%	14.3%	4.3%	2.5%	4.57	4.85	—
2025	28	MIX MLE	3 levels	24	3	40	168	44	20	6	12%	1.7	22.2%	11.3%	10.9%	4.5%	4.69	5.15	—
2025	28	MIX MLE	3 levels	24	3	40	168	44	20	6	12%	1.7	22.2%	11.3%	10.9%	4.5%	4.69	5.15	—
2025	28	MIX RAW	3 levels	24	3	40	168	44	20	6	12%	1.7	26.2%	11.9%	14.3%	3.6%	4.69	4.34	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	PIT	2	0	2	8	2	1	0	0%	1.0	20.7%	11.2%	9.5%	3.2%	4.89	4.89	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	PIT	18	0	19	82	17	9	3	0%	1.1	20.5%	11.3%	9.2%	3.4%	4.95	5.03	-0.1
2028	31	MLB CTX	PIT	7	0	7	30	6	3	1	0%	1.0	20.5%	11.3%	9.2%	3.4%	4.95	5.03	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		7	0	7	30	6	3	1	0%	1.0	20.5%	11.3%	9.2%	3.4%	4.95	4.65	-0.0
/ 200 IP	—	RAW		190	0	200	844	168	84	28	0%	1.1	20.5%	11.3%	9.2%	3.4%	4.95	4.65	-0.2
Career	—	MLE		164	10	305	1290	263	161	37	6%	1.9	20.4%	12.5%	8.0%	2.8%	3.63	4.51	-0.1
/ 200 IP	—	MLE		108	7	200	847	173	105	24	6%	1.9	20.4%	12.5%	8.0%	2.8%	3.63	4.51	-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 AAAIndianapolis Indians · 37 IPFIP 4.59 → 5.15 (+0.56)K% 25.8% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.79%	—	×0.860	22.18%
BB% (unintentional) /BF	12.58%	—	×0.900	11.32%
HR / BF /BF	3.77%	÷0.915	×1.100	4.54%

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 83).

Result: FIP 4.59 → 5.15, 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 AAAColumbus Clippers · 61 IPFIP 4.71 → 4.80 (+0.09)K% 18.6% → 16.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.57%	—	×0.860	15.97%
BB% (unintentional) /BF	14.29%	—	×0.900	12.86%
HR / BF /BF	2.50%	÷1.060	×1.100	2.59%

Result: FIP 4.71 → 4.80, 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).

2023 AAAkron RubberDucks · 35 IPFIP 2.92 → 3.73 (+0.81)K% 26.8% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.81%	—	×0.810	21.72%
BB% (unintentional) /BF	10.14%	—	×1.100	11.16%
HR / BF /BF	1.45%	÷0.909	×1.200	1.91%

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 82).

Result: FIP 2.92 → 3.73, 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 AAAColumbus Clippers · 24 IPFIP 5.97 → 5.97 (−0.00)K% 12.8% → 11.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.84%	—	×0.860	11.05%
BB% (unintentional) /BF	19.27%	—	×0.900	17.34%
HR / BF /BF	2.75%	÷1.037	×1.100	2.92%

Result: FIP 5.97 → 5.97, 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+Lake County Captains · 38 IPFIP 3.02 → 4.02 (+1.01)K% 34.4% → 26.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.44%	—	×0.770	26.52%
BB% (unintentional) /BF	7.95%	—	×1.150	9.14%
HR / BF /BF	3.31%	÷1.134	×1.250	3.65%

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 127).

Result: FIP 3.02 → 4.02, 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 AAAkron RubberDucks · 32 IPFIP 4.15 → 5.01 (+0.87)K% 27.0% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.01%	—	×0.810	21.88%
BB% (unintentional) /BF	16.79%	—	×1.100	18.47%
HR / BF /BF	2.19%	÷1.014	×1.200	2.59%

Result: FIP 4.15 → 5.01, 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 ALynchburg Hillcats · 44 IPFIP 3.44 → 4.95 (+1.51)K% 31.4% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.35%	—	×0.740	23.20%
BB% (unintentional) /BF	11.89%	—	×1.180	14.03%
HR / BF /BF	2.70%	÷0.950	×1.300	3.70%

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 90).

Result: FIP 3.44 → 4.95, 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).

2019 ROKAZL Indians Blue · 31 IPFIP 0.92 → 1.91 (+0.99)K% 32.0% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.97%	—	×0.680	21.74%
BB% (unintentional) /BF	4.10%	—	×1.250	5.12%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

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 0.92 → 1.91, 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
Randy Labaut (OGILVIE)	—	—	4.89	20.7%	11.2%	3.2%	—	5.03	20.5%	9.2%	19	+0.14	—	—	—
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	86
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	80
Jose Meraz L 2017 · 4.34 FIP	29	SU	4.34	16.4%	10.3%	2.4%	.282	3.80	10.5%	-2.0%	30	-0.54	3.78	110	75
Juan Pablo Perez L 2007 · 5.32 FIP	28	SU	5.32	17.5%	14.0%	3.5%	.324	—	—	—	—	—	1.75	104	75
Keith Hessler L 2018 · 4.96 FIP	29	SU	4.96	19.8%	13.3%	3.4%	.348	—	—	—	—	—	3.31	228	75
Brian Shouse L 2002 · 5.54 FIP	33	SU	5.54	15.5%	11.3%	4.2%	.245	3.31	16.3%	10.7%	159	-2.23	2.32	339	74
Jack Taschner L 2008 · 4.09 FIP	30	SU	4.09	17.2%	9.7%	2.2%	.323	4.73	15.2%	3.0%	49	+0.64	3.14	189	73
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	73
Tommy Phelps L 2004 · 4.80 FIP	30	SW	4.80	19.4%	8.3%	4.2%	.286	3.98	13.2%	5.7%	23	-0.82	3.39	120	72
Aaron Brown L 2022 · 5.38 FIP	30	SU	5.38	21.3%	12.5%	4.5%	.253	—	—	—	—	—	4.04	269	72
James Jones L 2019 · 4.28 FIP	30	SU	4.28	21.9%	13.0%	2.5%	.230	4.65	23.9%	8.8%	35	+0.37	3.58	127	72
Buddy Baumann L 2018 · 4.87 FIP	30	SU	4.87	20.5%	11.9%	3.6%	.258	—	—	—	—	—	2.92	155	71
Clay Rapada L 2011 · 4.34 FIP	30	SU	4.34	26.1%	10.1%	3.9%	.268	3.00	24.5%	14.2%	38	-1.34	3.00	120	71
Armando Almanza L 2003 · 4.95 FIP	30	SU	4.95	21.3%	10.0%	4.3%	.331	5.29	24.1%	14.8%	12	+0.34	2.90	211	71
Kelly Wunsch L 2002 · 4.60 FIP	30	SU	4.60	15.9%	13.0%	2.2%	.242	3.54	20.8%	8.3%	60	-1.06	3.36	175	70
Tom Martin L 2000 · 4.16 FIP	30	SU	4.16	14.7%	9.1%	2.1%	.274	4.29	21.8%	12.3%	68	+0.12	3.40	303	70
Jimmy Reyes L 2017 · 4.92 FIP	28	SU	4.92	13.8%	8.0%	3.6%	.294	—	—	—	—	—	3.02	200	69
John Rheinecker L 2007 · 5.13 FIP	28	SW	5.13	16.7%	10.9%	3.8%	.317	—	—	—	—	—	4.14	121	69
Mike Zagurski L 2012 · 4.48 FIP	29	SU	4.48	20.9%	11.7%	3.0%	.305	—	—	—	—	—	2.94	120	68
Randy Keisler L 2005 · 5.07 FIP	29	SW	5.07	16.4%	9.9%	3.8%	.295	5.51	6.5%	0.0%	17	+0.44	5.07	135	68