Andre Pallante

P St. Louis Cardinals

Age 28 RHP 6'0" / 215 lbs MLB peak 0.2 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	25	MIX2 levels	33	23	138	3.60	3.69	17.6%	10.4%	2.8	K 17.4% · BB 10.2% · FIP 3.73
2025	26	MLBSTL	31	31	163	5.31	4.56	15.5%	8.7%	1.6
2026	27	MLBSTL	29	29	162	3.83	3.63	16.2%	7.4%	3.5
2027PROJ	28	MLBSTL	19	19	119	4.38	4.41	16.7%	8.0%	1.4
2028PROJ	29	MLBSTL	11	11	70	4.49	4.57	16.1%	8.3%	0.7
2029PROJ	30	MLBSTL	7	7	44	4.66	4.77	15.4%	8.6%	0.3

Last 3 seasons WAR 7.9 · Next 3 projected WAR 2.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo60

xwOBA vs49

K%10

Form5.19 ERA (proj 4.38) · K 11.3% (proj 16.7%) · BB 7.8% (proj 8.0%) ▼ last 60d · 26 IP through Oct 27

CompsRodrigo Lopez (age 28) · Jorge Sosa (age 28) · David Phelps (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 113105vR 97

ERA4.38

FIP4.41

K%16.7%

BB%8.0%

K-BB%8.6%

Proj IP119

Value2027 · WAR

Runs saved vs league avg · 119 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6095.2

Whiff%

p1921.0%

p1016.2%

xwOBA vs

p490.313

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	22	MIX MLE	2 levels	23	22	99	449	86	46	8	96%	4.3	15.7%	10.9%	4.8%	2.3%	3.81	4.42	—
2021	22	MIX RAW	2 levels	23	22	99	449	86	46	8	96%	4.3	19.2%	10.2%	8.9%	1.8%	3.81	3.80	—
2022	23	MLB MLE	St. Louis Cardinals	47	10	108	457	73	38	9	21%	2.3	16.0%	8.3%	7.7%	2.1%	3.17	3.97	+0.7
2022	23	MLB RAW	St. Louis Cardinals	47	10	108	457	73	38	9	21%	2.3	16.0%	8.3%	7.7%	2.0%	3.17	3.89	+0.9
2023	24	MIX MLE	2 levels	67	0	78	340	57	32	6	0%	1.2	14.2%	9.9%	4.3%	2.1%	4.52	4.33	+0.2
2023	24	MIX RAW	2 levels	67	0	78	340	57	32	6	0%	1.2	16.8%	9.4%	7.4%	1.8%	4.52	3.87	+0.6
2024	25	MIX MLE	2 levels	33	23	138	584	103	61	8	70%	4.2	17.4%	10.2%	7.2%	1.5%	3.60	3.73	+2.8
2024	25	MIX MLE	2 levels	33	23	138	584	103	61	8	70%	4.2	17.4%	10.2%	7.2%	1.5%	3.60	3.73	+2.8
2024	25	MIX RAW	2 levels	33	23	138	584	103	61	8	70%	4.2	17.6%	10.4%	7.2%	1.4%	3.60	3.69	+2.8
2025	26	MLB MLE	St. Louis Cardinals	31	31	163	715	111	62	21	100%	5.2	15.5%	8.7%	6.9%	3.2%	5.31	4.62	+1.5
2025	26	MLB MLE	St. Louis Cardinals	31	31	163	715	111	62	21	100%	5.2	15.5%	8.7%	6.9%	3.2%	5.31	4.62	+1.5
2025	26	MLB RAW	St. Louis Cardinals	31	31	163	715	111	62	21	100%	5.2	15.5%	8.7%	6.9%	2.9%	5.31	4.56	+1.6
2026	27	MLB MLE	St. Louis Cardinals	29	29	162	679	110	50	12	100%	5.6	16.2%	7.4%	8.8%	1.9%	3.83	3.71	+3.3
2026	27	MLB MLE	St. Louis Cardinals	29	29	162	679	110	50	12	100%	5.6	16.2%	7.4%	8.8%	1.9%	3.83	3.71	+3.3
2026	27	MLB RAW	St. Louis Cardinals	29	29	162	679	110	50	12	100%	5.6	16.2%	7.4%	8.8%	1.8%	3.83	3.63	+3.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			198	90	622	2662	431	228	56	45%	3.1	16.2%	8.6%	7.6%	2.1%	4.20	3.98	+4.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	STL	19	19	119	502	84	40	12	100%	6.3	16.7%	8.0%	8.6%	2.4%	4.38	4.41	+1.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	STL	11	11	70	296	48	24	8	100%	6.4	16.1%	8.3%	7.8%	2.5%	4.49	4.57	+0.7
2029	30	MLB MLE	STL	7	7	44	187	29	16	5	100%	6.3	15.4%	8.6%	6.8%	2.8%	4.66	4.77	+0.3
2028	29	MLB CTX	STL	11	11	70	296	48	24	8	100%	6.4	16.1%	8.3%	7.8%	2.5%	4.49	4.57	+0.7
2029	30	MLB CTX	STL	7	7	44	186	29	16	5	100%	6.3	15.4%	8.6%	6.8%	2.8%	4.66	4.77	+0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		216	108	736	3145	507	269	69	50%	3.4	16.1%	8.5%	7.6%	2.2%	4.25	4.02	+12.1
/ 200 IP	—	RAW		59	29	200	854	138	73	19	50%	3.4	16.1%	8.5%	7.6%	2.2%	4.25	4.02	+3.3
Career	—	MLE		230	115	747	3224	515	293	71	50%	3.2	16.0%	9.1%	6.9%	2.2%	4.08	4.12	+11.4
/ 200 IP	—	MLE		62	31	200	863	138	78	19	50%	3.2	16.0%	9.1%	6.9%	2.2%	4.08	4.12	+3.1

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.

2024 AAAMemphis Redbirds · 16 IPFIP 4.28 → 4.20 (−0.08)K% 12.0% → 10.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.00%	—	×0.860	10.32%
BB% (unintentional) /BF	17.33%	—	×0.900	15.60%
HR / BF /BF	0.00%	÷1.020	×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 104).

Result: FIP 4.28 → 4.20, 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 AAAMemphis Redbirds · 10 IPFIP 0.66 → 1.02 (+0.37)K% 36.8% → 31.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.84%	—	×0.860	31.68%
BB% (unintentional) /BF	5.26%	—	×0.900	4.74%
HR / BF /BF	0.00%	÷0.955	×1.100	0.00%

Result: FIP 0.66 → 1.02, 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).

2021 AASpringfield Cardinals · 94 IPFIP 3.76 → 4.42 (+0.66)K% 19.4% → 15.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.43%	—	×0.810	15.74%
BB% (unintentional) /BF	9.95%	—	×1.100	10.95%
HR / BF /BF	1.90%	÷0.990	×1.200	2.30%

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 98).

Result: FIP 3.76 → 4.42, 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 AAAMemphis Redbirds · 5 IPFIP 3.73 → 3.97 (+0.24)K% 14.8% → 13.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.81%	—	×0.880	13.04%
BB% (unintentional) /BF	14.81%	—	×1.050	15.56%
HR / BF /BF	0.00%	÷0.949	×1.100	0.00%

Factors used: pitcher AAA level factors — K ×0.88, BB ×1.05, 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 90).

Result: FIP 3.73 → 3.97, 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).

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
Andre Pallante (OGILVIE)	—	—	—	—	—	2.4%	—	4.65	15.8%	7.4%	114	+0.24	—	—	—
Rodrigo Lopez R 2004 · 4.21 FIP	28	SP-	4.21	16.9%	7.3%	2.9%	.275	4.57	14.2%	7.8%	478	+0.36	4.06	1,344	62
Jorge Sosa R 2005 · 4.23 FIP	28	SW	4.23	14.7%	9.7%	2.1%	.259	4.99	14.0%	6.8%	252	+0.76	4.06	773	61
David Phelps R 2015 · 3.94 FIP	28	SP	3.94	16.0%	6.8%	2.4%	.299	2.82	29.9%	20.6%	142	-1.12	2.50	672	57
Tanner Roark R 2015 · 4.46 FIP	28	SW	4.46	15.0%	4.9%	3.6%	.286	3.78	20.2%	12.9%	572	-0.68	2.40	1,194	55
Josh Fogg R 2005 · 4.85 FIP	28	SP-	4.85	11.5%	5.7%	3.6%	.287	4.77	12.4%	5.7%	416	-0.08	0.87	1,159	54
Joaquín Benoit R 2005 · 3.96 FIP	28	SW	3.96	21.1%	10.3%	2.4%	.246	3.39	24.2%	13.9%	207	-0.57	1.99	1,064	52
Hector Noesí R 2015 · 4.37 FIP	28	SP-	4.37	17.6%	8.1%	3.2%	.261	—	—	—	—	—	3.73	613	52
Lucas Harrell R 2013 · 5.12 FIP	28	SP-	5.12	12.6%	11.7%	2.9%	.299	4.18	15.5%	2.7%	121	-0.94	2.81	541	51
Chase Anderson R 2016 · 4.83 FIP	28	SP	4.83	18.5%	8.2%	4.1%	.285	4.25	21.3%	13.3%	438	-0.58	3.30	1,260	50
Gary Knotts R 2004 · 4.95 FIP	27	SP-	4.95	13.5%	9.2%	3.3%	.275	—	—	—	—	—	4.95	261	50
Jason Hammel R 2011 · 4.52 FIP	28	SP	4.52	12.7%	8.8%	2.5%	.275	3.89	20.3%	13.2%	434	-0.63	3.10	1,810	50
Victor Santos R 2005 · 4.67 FIP	28	SP	4.67	13.9%	8.1%	3.1%	.278	4.90	16.0%	7.8%	179	+0.23	4.09	602	49
Sonny Gray R 2018 · 3.79 FIP	28	SP-	3.79	21.1%	9.8%	2.2%	.320	3.21	28.5%	19.1%	367	-0.58	2.55	2,087	49
Odrisamer Despaigne R 2015 · 4.37 FIP	28	SP-	4.37	12.6%	5.3%	3.0%	.289	3.81	15.5%	7.0%	329	-0.56	3.13	689	48
Taylor Jungmann R 2017 · 3.88 FIP	27	SP-	3.88	18.8%	11.1%	1.8%	.279	—	—	—	—	—	3.59	435	47
Tyler Wilson R 2017 · 4.45 FIP	27	SP-	4.45	12.1%	7.0%	2.7%	.311	—	—	—	—	—	3.62	377	47
Cha Seung Baek R 2008 · 4.29 FIP	28	SP-	4.29	15.3%	6.6%	3.0%	.283	—	—	—	—	—	3.35	280	47
Jesse Hahn R 2017 · 3.66 FIP	28	SP	3.66	16.6%	9.7%	1.3%	.327	2.19	29.2%	16.9%	17	-1.47	2.19	499	47
Bud Norris R 2013 · 3.68 FIP	28	SP	3.68	19.0%	8.7%	2.0%	.330	4.05	20.0%	11.9%	361	+0.37	3.51	1,221	47
Brad Peacock R 2016 · 3.97 FIP	28	SP-	3.97	20.9%	8.7%	2.8%	.302	3.45	29.4%	20.4%	289	-0.52	2.97	748	46

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	4.3	107	16.2%	8.5%	0.86	4.27
Omar Daal ('03)	6.0	120	15.6%	8.5%	1.05	4.55
Don Hood ('73-'83, 4 WAR*)	4.2	85	16.0%*	9.9%*	0.85*	4.22*
Chris Hammond ('06)	5.6	94	14.8%	8.0%	0.84	4.14
Cal Koonce ('62-'71, 5 WAR*)	4.9	97	15.6%*	9.4%*	1.09*	4.34*
Jim Acker ('83-'92, 5 WAR*)	4.5	90	16.7%*	8.3%*	1.06*	4.12*
Kendall Graveman ('25)	3.3	96	17.6%	8.1%	1.06	4.03