Kyle Luckham

P Harrisburg Senators

Age 27 RHP 6'2" / 205 lbs AA peak 0.8 · 2027

Overview

AAPROSPECT

ETA2029MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	MIX2 levels	27	25	144	5.06	4.20	16.6%	5.7%	—	K 13.6% · BB 5.9% · FIP 4.81
2025	25	MIX2 levels	27	25	134	4.09	3.55	16.0%	7.4%	—	K 13.2% · BB 7.9% · FIP 4.03
2026	26	AAWSH	28	10	98	5.05	3.24	18.5%	6.8%	—	K 15.0% · BB 7.4% · FIP 3.78
2027PROJ	27	MLBWSH	1	1	2	4.92	4.92	12.0%	8.5%	0.0
2028PROJ38% MLB	28	MLBWSH	4	4	23	4.91	4.95	13.0%	8.7%	0.1
2029PROJ56% MLB	29	MLBWSH	5	5	29	4.96	5.03	13.5%	9.0%	0.1

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

Contract

No contract data on file.

Scouting grades · 20-80

Stuff30Control55Command35

Career WAR0.0 WAR median (P10 -0.0 / P90 0.0, comp-based) · star 0% · didn't stick 98%

CompsBryce Morrow (age 27) · Luke Irvine (age 27) · Edgar Alberto Garcia (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 127119vR 110

ERA4.92

FIP4.92

K%12.0%

BB%8.5%

K-BB%3.5%

Proj IP79

Value2027 · WAR

Runs saved vs league avg · 79 IP

Grades · Contract

Scouting Grades

STUF3030

CTRL5555

CMD3535

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)
2022	22	MIX MLE	2 levels	7	0	11	44	9	1	0	0%	1.6	20.5%	2.3%	18.2%	0.0%	3.97	1.66	—
2022	22	MIX RAW	2 levels	7	0	11	44	9	1	0	0%	1.6	20.5%	2.3%	18.2%	0.0%	3.97	1.78	—
2023	23	MIX MLE	2 levels	26	25	129	553	106	30	12	96%	5.0	14.5%	6.5%	8.0%	3.1%	4.87	4.40	—
2023	23	MIX RAW	2 levels	26	25	129	553	106	30	12	96%	5.0	19.2%	5.4%	13.7%	2.2%	4.87	3.36	—
2024	24	MIX MLE	2 levels	27	25	144	619	103	35	20	93%	5.3	13.6%	5.9%	7.7%	3.8%	5.06	4.81	—
2024	24	MIX MLE	2 levels	27	25	144	619	103	35	20	93%	5.3	13.6%	5.9%	7.7%	3.8%	5.06	4.81	—
2024	24	MIX RAW	2 levels	27	25	144	619	103	35	20	93%	5.3	16.6%	5.7%	11.0%	3.2%	5.06	4.20	—
2025	25	MIX MLE	2 levels	27	25	134	581	93	43	9	93%	5.0	13.2%	7.9%	5.3%	1.9%	4.09	4.03	—
2025	25	MIX MLE	2 levels	27	25	134	581	93	43	9	93%	5.0	13.2%	7.9%	5.3%	1.9%	4.09	4.03	—
2025	25	MIX RAW	2 levels	27	25	134	581	93	43	9	93%	5.0	16.0%	7.4%	8.6%	1.5%	4.09	3.55	—
2026	26	AA MLE	Harrisburg Senators	28	10	98	459	85	31	7	36%	3.5	15.0%	7.4%	7.6%	1.8%	5.05	3.78	—
2026	26	AA MLE	Harrisburg Senators	28	10	98	459	85	31	7	36%	3.5	15.0%	7.4%	7.6%	1.8%	5.05	3.78	—
2026	26	AA RAW	Harrisburg Senators	28	10	98	459	85	31	7	36%	3.5	18.5%	6.8%	11.8%	1.5%	5.05	3.24	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	WSH	1	1	2	8	1	1	0	100%	2.0	12.0%	8.5%	3.5%	2.5%	4.92	4.92	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	WSH	9	9	60	255	33	22	7	100%	6.7	13.0%	8.7%	4.3%	2.7%	4.91	4.95	+0.3
2029	29	MLB MLE	WSH	8	8	51	217	29	19	6	100%	6.4	13.5%	9.0%	4.6%	2.8%	4.96	5.03	+0.2
2030	30	MLB MLE	WSH	7	7	45	191	26	18	6	100%	6.5	13.7%	9.3%	4.4%	3.0%	5.07	5.17	+0.1
2028	28	MLB CTX	WSH	4	4	23	97	13	8	3	100%	5.8	13.0%	8.7%	4.3%	2.7%	4.91	4.95	+0.1
2029	29	MLB CTX	WSH	5	5	29	122	17	11	3	100%	5.8	13.5%	9.0%	4.6%	2.8%	4.96	5.03	+0.1
2030	30	MLB CTX	WSH	5	5	31	131	18	12	4	100%	6.2	13.7%	9.3%	4.4%	3.0%	5.07	5.17	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		13	13	82	347	47	31	10	100%	6.3	13.4%	9.0%	4.4%	2.9%	4.99	4.68	+0.7
/ 200 IP	—	RAW		31	31	200	844	114	75	24	100%	6.4	13.4%	9.0%	4.4%	2.9%	4.99	4.68	+1.7
Career	—	MLE		115	85	517	2256	319	153	60	74%	4.5	14.1%	6.8%	7.3%	2.7%	4.74	4.24	+0.6
/ 200 IP	—	MLE		44	33	200	873	123	59	23	74%	4.5	14.1%	6.8%	7.3%	2.7%	4.74	4.24	+0.8

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 AAHarrisburg Senators · 98 IPFIP 3.23 → 3.78 (+0.55)K% 18.5% → 15.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.52%	—	×0.810	15.00%
BB% (unintentional) /BF	6.75%	—	×1.100	7.43%
HR / BF /BF	1.53%	÷1.000	×1.200	1.83%

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 100).

Result: FIP 3.23 → 3.78, 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 AAHarrisburg Senators · 103 IPFIP 3.76 → 4.36 (+0.60)K% 15.9% → 12.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.87%	—	×0.810	12.86%
BB% (unintentional) /BF	7.94%	—	×1.100	8.73%
HR / BF /BF	1.81%	÷0.963	×1.200	2.26%

Result: FIP 3.76 → 4.36, 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 AAARochester Red Wings · 31 IPFIP 2.83 → 3.01 (+0.18)K% 16.4% → 14.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.43%	—	×0.860	14.13%
BB% (unintentional) /BF	5.71%	—	×0.900	5.14%
HR / BF /BF	0.71%	÷0.960	×1.100	0.82%

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 92).

Result: FIP 2.83 → 3.01, 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).

2024 AAHarrisburg Senators · 129 IPFIP 3.75 → 4.41 (+0.66)K% 17.2% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.16%	—	×0.810	13.90%
BB% (unintentional) /BF	4.61%	—	×1.100	5.07%
HR / BF /BF	2.77%	÷0.990	×1.200	3.36%

Result: FIP 3.75 → 4.41, 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 AAARochester Red Wings · 15 IPFIP 7.21 → 7.61 (+0.40)K% 13.0% → 11.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.99%	—	×0.860	11.17%
BB% (unintentional) /BF	12.99%	—	×0.900	11.69%
HR / BF /BF	6.49%	÷0.986	×1.100	7.24%

Result: FIP 7.21 → 7.61, 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).

2023 A+Wilmington Blue Rocks · 124 IPFIP 3.47 → 4.40 (+0.93)K% 18.9% → 14.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.87%	—	×0.770	14.53%
BB% (unintentional) /BF	5.66%	—	×1.150	6.51%
HR / BF /BF	2.26%	÷0.913	×1.250	3.10%

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 83).

Result: FIP 3.47 → 4.40, 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 AAHarrisburg Senators · 5 IPFIP 0.90 → 1.32 (+0.42)K% 26.1% → 21.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.09%	—	×0.810	21.13%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	0.00%	÷1.115	×1.200	0.00%

Result: FIP 0.90 → 1.32, 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).

2022 AFredericksburg Nationals · 7 IPFIP 2.18 → 2.59 (+0.41)K% 15.6% → 11.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.62%	—	×0.740	11.56%
BB% (unintentional) /BF	3.12%	—	×1.180	3.69%
HR / BF /BF	0.00%	÷1.074	×1.300	0.00%

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 115).

Result: FIP 2.18 → 2.59, 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 Luckham (OGILVIE)	—	—	4.92	12.0%	8.5%	2.5%	—	5.04	13.4%	4.4%	157	+0.12	—	—	—
Bryce Morrow R 2015 · 4.31 FIP	27	SP-	4.31	12.8%	7.9%	2.4%	.316	5.44	10.1%	-0.2%	50	+1.13	4.31	125	90
Luke Irvine R 2016 · 4.31 FIP	27	SP-	4.31	13.4%	9.8%	2.0%	.302	—	—	—	—	—	4.31	104	87
Edgar Alberto Garcia R 2015 · 5.24 FIP	27	SP-	5.24	11.6%	9.8%	3.4%	.263	4.28	15.3%	5.2%	167	-0.96	4.01	272	87
Frank Batista R 2015 · 4.29 FIP	26	SP-	4.29	12.1%	8.2%	2.1%	.280	—	—	—	—	—	4.29	118	87
Scott McGregor R 2015 · 4.37 FIP	28	SP-	4.37	11.8%	7.6%	2.4%	.293	—	—	—	—	—	4.37	107	86
Mike McCarthy R 2015 · 4.78 FIP	27	SP-	4.78	9.4%	9.1%	2.4%	.280	5.03	6.2%	-3.5%	37	+0.25	4.78	153	85
Colin Bates R 2015 · 4.57 FIP	27	SP-	4.57	10.5%	6.5%	2.8%	.301	—	—	—	—	—	4.57	111	83
Mike Augliera R 2015 · 4.27 FIP	25	SP-	4.27	12.0%	7.2%	2.3%	.350	—	—	—	—	—	4.27	114	82
Terance Marin R 2016 · 4.91 FIP	26	SP-	4.91	11.2%	7.7%	3.2%	.294	4.22	12.4%	7.5%	88	-0.69	3.54	322	82
Tony Bucciferro R 2015 · 5.11 FIP	25	SP-	5.11	6.8%	6.2%	3.3%	.324	—	—	—	—	—	5.11	107	81
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	80
Ivan Zavala R 2015 · 4.51 FIP	28	SP-	4.51	7.0%	4.7%	2.6%	.312	4.73	12.2%	4.3%	50	+0.22	4.27	209	80
Jordan Cooper R 2015 · 5.01 FIP	26	SP	5.01	9.2%	10.2%	2.5%	.305	—	—	—	—	—	5.01	106	80
Fredy Quintero R 2015 · 4.55 FIP	27	SP-	4.55	11.3%	8.9%	2.3%	.375	4.38	15.1%	7.3%	113	-0.17	4.29	241	80
Justin Topa R 2018 · 4.15 FIP	27	SP-	4.15	15.1%	7.9%	2.4%	.374	2.69	19.2%	12.6%	40	-1.46	2.67	284	80
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	80
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	79
Brannon Easterling R 2017 · 4.40 FIP	26	SP-	4.40	12.7%	8.2%	2.4%	.322	—	—	—	—	—	3.50	334	79
Shawn Haviland R 2015 · 3.95 FIP	29	SP-	3.95	14.6%	6.0%	2.4%	.303	4.67	16.1%	8.8%	169	+0.72	3.69	283	79
Nestor Molina R 2016 · 4.83 FIP	27	SP	4.83	15.0%	8.0%	3.6%	.304	3.61	17.0%	10.2%	289	-1.22	3.01	401	78

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	1.2	59	12.9%	8.8%	1.05	4.95
Eric Gunderson ('00)	1.1	76	13.3%	8.1%	1.14	4.95
Brian Stokes ('10)	1.0	52	14.5%	10.1%	1.22	5.27
Vic Darensbourg ('05)	1.5	50	17.0%	9.3%	1.01	4.96
Greg Booker ('83-'90, -1 WAR*)	1.3	53	13.3%*	10.2%*	1.07*	4.51*
Dave Leiper ('84-'96, 0 WAR*)	1.4	46	15.6%*	9.1%*	0.94*	4.28*
Joe Grzenda ('61-'72, 0 WAR*)	1.5	51	17.2%*	8.6%*	0.76*	4.83*