Kyle Leahy

P St. Louis Cardinals

Age 30 RHP 6'5" / 225 lbs MLB peak 0.7 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MIX2 levels	49	0	77	3.99	3.83	20.5%	5.8%	0.7	K 19.1% · BB 5.5% · FIP 4.08
2025	28	MLBSTL	62	1	88	3.07	2.94	22.0%	7.4%	1.7
2026	29	MLBSTL	30	30	140	3.66	3.49	22.1%	6.9%	3.3
2027PROJ	30	MLBSTL	105	0	110	4.02	4.03	21.9%	7.2%	0.7
2028PROJ95% MLB	31	MLBSTL	71	0	75	4.17	4.26	21.1%	7.5%	0.3
2029PROJ95% MLB	32	MLBSTL	52	0	55	4.38	4.52	20.3%	7.9%	0.0

Last 3 seasons WAR 5.6 · Next 3 projected WAR 1.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%50

FB Velo47

xwOBA vs29

Form8.56 ERA (proj 4.02) · K 26.2% (proj 21.9%) · BB 6.2% (proj 7.2%) ▲ last 60d · 14 IP through Oct 27

CompsKenta Maeda (age 30) · Odrisamer Despaigne (age 30) · Tiago Da Silva (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114106vR 98

ERA4.02

FIP4.03

K%21.9%

BB%7.2%

K-BB%14.7%

Proj IP110

Value2027 · WAR

Runs saved vs league avg · 110 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p4794.3

Whiff%

p3222.7%

p5022.1%

xwOBA vs

p290.337

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)
2018	21	ROK MLE	Johnson City Cardinals	13	10	59	277	45	11	7	77%	4.5	11.0%	5.0%	6.1%	3.5%	5.68	4.74	—
2018	21	ROK RAW	Johnson City Cardinals	13	10	59	277	45	11	7	77%	4.5	16.2%	4.0%	12.3%	2.5%	5.68	3.68	—
2019	22	MIX MLE	3 levels	23	22	123	519	102	32	7	96%	5.3	14.7%	7.4%	7.3%	1.7%	3.73	3.75	—
2019	22	MIX RAW	3 levels	23	22	123	519	102	32	7	96%	5.3	19.7%	6.2%	13.5%	1.3%	3.73	2.96	—
2021	24	AA MLE	Springfield Cardinals	25	10	87	422	63	44	17	40%	3.5	12.1%	11.5%	0.6%	4.9%	8.20	6.21	—
2021	24	AA RAW	Springfield Cardinals	25	10	87	422	63	44	17	40%	3.5	14.9%	10.4%	4.5%	4.0%	8.20	5.72	—
2022	25	MIX MLE	2 levels	28	26	145	654	146	72	23	93%	5.2	18.3%	12.2%	6.0%	3.9%	5.29	5.23	—
2022	25	MIX RAW	2 levels	28	26	145	654	146	72	23	93%	5.2	22.3%	11.0%	11.3%	3.5%	5.29	4.64	—
2023	26	MIX MLE	2 levels	49	4	85	401	94	44	16	8%	1.7	20.5%	9.1%	11.4%	4.5%	6.56	4.98	-0.4
2023	26	MIX RAW	2 levels	49	4	85	401	94	44	16	8%	1.7	23.4%	11.0%	12.5%	4.0%	6.56	4.89	-0.3
2024	27	MIX MLE	2 levels	49	0	77	312	64	18	10	0%	1.6	19.1%	5.5%	13.6%	3.5%	3.99	4.08	+0.4
2024	27	MIX MLE	2 levels	49	0	77	312	64	18	10	0%	1.6	19.1%	5.5%	13.6%	3.5%	3.99	4.08	+0.4
2024	27	MIX RAW	2 levels	49	0	77	312	64	18	10	0%	1.6	20.5%	5.8%	14.7%	3.2%	3.99	3.83	+0.7
2025	28	MLB MLE	St. Louis Cardinals	62	1	88	363	80	27	5	2%	1.4	22.0%	7.4%	14.6%	1.5%	3.07	2.99	+1.6
2025	28	MLB MLE	St. Louis Cardinals	62	1	88	363	80	27	5	2%	1.4	22.0%	7.4%	14.6%	1.5%	3.07	2.99	+1.6
2025	28	MLB RAW	St. Louis Cardinals	62	1	88	363	80	27	5	2%	1.4	22.0%	7.4%	14.6%	1.4%	3.07	2.94	+1.7
2026	29	MLB MLE	St. Louis Cardinals	30	30	140	596	132	41	15	100%	4.7	22.1%	6.9%	15.3%	2.7%	3.66	3.59	+3.1
2026	29	MLB MLE	St. Louis Cardinals	30	30	140	596	132	41	15	100%	4.7	22.1%	6.9%	15.3%	2.7%	3.66	3.59	+3.1
2026	29	MLB RAW	St. Louis Cardinals	30	30	140	596	132	41	15	100%	4.7	22.1%	6.9%	15.3%	2.5%	3.66	3.49	+3.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			128	31	278	1165	247	83	25	24%	2.2	21.2%	7.1%	14.1%	2.1%	3.65	3.39	+3.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	STL	105	0	110	464	102	33	13	0%	1.0	21.9%	7.2%	14.7%	2.7%	4.02	4.03	+0.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	STL	76	0	79	335	71	25	10	0%	1.0	21.1%	7.5%	13.6%	2.9%	4.17	4.26	+0.3
2029	32	MLB MLE	STL	55	0	58	243	49	19	8	0%	1.0	20.3%	7.9%	12.4%	3.2%	4.38	4.52	+0.0
2028	31	MLB CTX	STL	71	0	75	317	67	24	9	0%	1.1	21.1%	7.5%	13.6%	2.9%	4.17	4.26	+0.3
2029	32	MLB CTX	STL	52	0	55	232	47	18	7	0%	1.1	20.3%	7.9%	12.4%	3.2%	4.38	4.52	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		252	31	408	1714	361	125	42	12%	1.6	21.1%	7.3%	13.8%	2.4%	3.85	3.58	+4.7
/ 200 IP	—	RAW		123	15	200	839	177	61	21	12%	1.6	21.1%	7.3%	13.8%	2.4%	3.85	3.58	+2.3
Career	—	MLE		279	103	803	3544	631	302	115	37%	2.9	17.8%	8.5%	9.3%	3.2%	4.88	4.46	+4.2
/ 200 IP	—	MLE		70	26	200	883	157	75	29	37%	2.9	17.8%	8.5%	9.3%	3.2%	4.88	4.46	+2.0

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 · 28 IPFIP 4.50 → 4.93 (+0.43)K% 25.6% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.62%	—	×0.860	22.03%
BB% (unintentional) /BF	6.61%	—	×0.900	5.95%
HR / BF /BF	4.96%	÷1.020	×1.100	5.34%

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 4.50 → 4.93, 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 · 83 IPFIP 4.50 → 4.98 (+0.48)K% 23.8% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.83%	—	×0.860	20.50%
BB% (unintentional) /BF	10.10%	—	×0.900	9.09%
HR / BF /BF	3.89%	÷0.955	×1.100	4.48%

Result: FIP 4.50 → 4.98, 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 AASpringfield Cardinals · 141 IPFIP 4.49 → 5.23 (+0.73)K% 22.5% → 18.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.54%	—	×0.810	18.25%
BB% (unintentional) /BF	11.11%	—	×1.100	12.22%
HR / BF /BF	3.44%	÷1.069	×1.200	3.86%

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 114).

Result: FIP 4.49 → 5.23, 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 · 87 IPFIP 5.37 → 6.21 (+0.84)K% 14.9% → 12.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.93%	—	×0.810	12.09%
BB% (unintentional) /BF	10.43%	—	×1.100	11.47%
HR / BF /BF	4.03%	÷0.990	×1.200	4.88%

Result: FIP 5.37 → 6.21, 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 APeoria Chiefs · 90 IPFIP 2.59 → 3.32 (+0.73)K% 22.2% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.19%	—	×0.740	16.42%
BB% (unintentional) /BF	6.03%	—	×1.180	7.11%
HR / BF /BF	1.10%	÷1.105	×1.300	1.29%

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 121).

Result: FIP 2.59 → 3.32, 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 A+Palm Beach Cardinals · 30 IPFIP 4.02 → 4.83 (+0.82)K% 13.3% → 10.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.29%	—	×0.770	10.23%
BB% (unintentional) /BF	6.99%	—	×1.150	8.04%
HR / BF /BF	2.10%	÷0.912	×1.250	2.88%

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 82).

Result: FIP 4.02 → 4.83, 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).

2018 ROKJohnson City Cardinals · 59 IPFIP 3.62 → 4.74 (+1.12)K% 16.2% → 11.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.25%	—	×0.680	11.05%
BB% (unintentional) /BF	3.97%	—	×1.250	4.96%
HR / BF /BF	2.53%	÷1.000	×1.400	3.54%

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 3.62 → 4.74, 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
Kyle Leahy (OGILVIE)	—	—	—	—	—	2.7%	—	4.37	20.8%	13.1%	137	+0.34	—	—	—
Kenta Maeda R 2018 · 2.89 FIP	30	SP-	2.89	28.8%	7.3%	2.3%	.312	3.55	27.4%	20.7%	327	+0.66	2.87	1,092	68
Odrisamer Despaigne R 2017 · 4.09 FIP	30	SW	4.09	13.7%	8.7%	1.9%	.270	3.53	19.1%	10.5%	220	-0.56	3.13	689	65
Tiago Da Silva R 2015 · 3.10 FIP	30	SW	3.10	19.6%	4.5%	2.0%	.278	2.38	21.4%	18.5%	98	-0.72	2.21	287	64
Jorge Sosa R 2007 · 4.06 FIP	30	SW	4.06	14.3%	8.1%	2.1%	.273	4.99	12.8%	4.5%	81	+0.93	4.06	773	64
Josh A. Smith R 2018 · 2.91 FIP	30	SP-	2.91	21.7%	5.2%	1.8%	.337	4.28	19.5%	12.2%	157	+1.37	2.91	598	63
Todd Redmond R 2015 · 3.86 FIP	30	SW	3.86	15.4%	7.1%	2.1%	.311	—	—	—	—	—	3.21	247	61
Ross Ohlendorf R 2013 · 4.01 FIP	30	SW	4.01	18.2%	5.3%	3.2%	.265	4.18	23.5%	14.4%	122	+0.17	3.30	617	61
Chris Sampson R 2008 · 3.42 FIP	30	SW	3.42	12.8%	3.8%	1.7%	.281	4.08	12.7%	6.7%	86	+0.66	3.19	359	61
Miguel Batista R 2001 · 4.28 FIP	30	SW	4.28	15.5%	10.0%	2.2%	.238	4.03	14.5%	5.7%	577	-0.25	3.39	1,943	60
Mike Bolsinger R 2017 · 4.26 FIP	29	SW	4.26	20.2%	9.4%	3.0%	.317	—	—	—	—	—	3.42	379	60
Scott Carroll R 2015 · 3.92 FIP	30	SW	3.92	12.7%	7.7%	1.7%	.286	5.07	11.2%	2.6%	128	+1.15	3.92	377	59
Luke Hudson R 2006 · 3.81 FIP	29	SP-	3.81	14.5%	8.4%	1.6%	.307	—	—	—	—	—	3.81	235	59
Sean Lowe R 2001 · 3.93 FIP	30	SW	3.93	13.4%	5.7%	2.3%	.267	4.38	15.7%	7.0%	124	+0.45	3.93	435	58
Jose Contreras R 2003 · 3.03 FIP	31	SP-	3.03	24.6%	9.9%	1.4%	.255	4.30	17.9%	9.5%	571	+1.27	2.43	1,279	57
Casey Lawrence R 2018 · 3.77 FIP	30	SP-	3.77	18.1%	4.4%	3.0%	.305	4.08	20.0%	14.8%	86	+0.31	3.57	1,374	56
Hisashi Iwakuma R 2012 · 4.28 FIP	31	SP-	4.28	19.5%	7.7%	3.4%	.277	3.45	21.5%	17.8%	528	-0.83	3.19	884	56
Jake Brigham R 2015 · 3.09 FIP	27	SW	3.09	15.0%	6.5%	0.8%	.318	—	—	—	—	—	3.09	108	56
Jim Brower R 2003 · 3.97 FIP	30	SU	3.97	15.8%	9.0%	1.9%	.272	4.22	16.5%	6.9%	173	+0.25	3.67	571	55
Jorge Campillo R 2008 · 3.93 FIP	29	SP-	3.93	16.3%	5.5%	2.8%	.283	—	—	—	—	—	3.93	172	54
Zach Neal R 2018 · 4.44 FIP	29	SW	4.44	13.8%	3.6%	3.7%	.307	—	—	—	—	—	4.24	732	54

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	2.6	88	21.2%	7.3%	0.97	3.89
Bob Reynolds ('69-'75, 4 WAR*)	1.3	85	23.1%*	7.4%*	0.94*	3.63*
Bob Apodaca ('74-'77, 4 WAR*)	1.8	90	19.2%*	8.2%*	0.92*	3.43*
Tom Johnson ('74-'78, 3 WAR*)	1.4	68	21.7%*	7.8%*	0.86*	3.69*
Alfredo Aceves ('14)	1.9	76	17.6%	8.1%	1.14	3.83
Justin Duchscherer ('10)	2.3	91	18.7%	6.5%	0.93	3.13
Zach McAllister ('23)	3.0	85	21.1%	7.8%	1.07	4.15