David Morgan

P San Diego Padres

Age 27 RHP 6'0" / 185 lbs MLB peak 0.3 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	MIX2 levels	40	0	61	5.04	3.33	24.4%	9.7%	—	K 19.2% · BB 10.9% · FIP 4.26
2025	25	MIX3 levels	55	2	62	3.65	3.52	30.9%	8.9%	0.8	K 25.9% · BB 11.4% · FIP 3.47
2026	26	MIX3 levels	50	0	52	4.47	3.21	23.7%	9.6%	0.8	K 22.6% · BB 8.9% · FIP 3.24
2027PROJ	27	MLBSD	32	0	34	3.95	3.99	25.7%	10.7%	0.2
2028PROJ88% MLB	28	MLBSD	32	0	34	4.06	4.11	25.3%	10.7%	0.2
2029PROJ90% MLB	29	MLBSD	29	0	30	4.20	4.28	24.6%	10.8%	0.1

Last 3 seasons WAR 1.6 · Next 3 projected WAR 0.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs61

K%60

CompsJT Chargois (age 27) · Taylor Williams (age 27) · Marcos Mateo (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10597vR 89

ERA3.95

FIP3.99

K%25.7%

BB%10.7%

K-BB%15.0%

Proj IP40

Value2027 · WAR

Runs saved vs league avg · 40 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL3535

CMD5050

K†6060

Statcast Percentiles

FB Velo

p9097.7

Whiff%

p8430.6%

p6024.1%

xwOBA vs

p610.301

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	ROK MLE	ACL Padres	3	0	3	13	4	1	0	0%	1.0	30.8%	7.7%	23.1%	0.0%	3.00	1.48	—
2022	22	ROK RAW	ACL Padres	3	0	3	13	4	1	0	0%	1.0	30.8%	7.7%	23.1%	0.0%	3.00	1.43	—
2023	23	MIX MLE	2 levels	28	0	45	191	46	13	2	0%	1.6	17.8%	8.8%	9.0%	1.3%	3.83	3.44	—
2023	23	MIX RAW	2 levels	28	0	45	191	46	13	2	0%	1.6	24.1%	6.8%	17.3%	1.0%	3.83	2.50	—
2024	24	MIX MLE	2 levels	40	0	61	258	63	25	5	0%	1.5	19.2%	10.9%	8.4%	2.6%	5.04	4.26	—
2024	24	MIX MLE	2 levels	40	0	61	258	63	25	5	0%	1.5	19.2%	10.9%	8.4%	2.6%	5.04	4.26	—
2024	24	MIX RAW	2 levels	40	0	61	258	63	25	5	0%	1.5	24.4%	9.7%	14.7%	1.9%	5.04	3.33	—
2025	25	MIX MLE	3 levels	55	2	62	259	80	23	9	4%	1.1	25.9%	11.4%	14.5%	2.0%	3.65	3.47	+0.8
2025	25	MIX MLE	3 levels	55	2	62	259	80	23	9	4%	1.1	25.9%	11.4%	14.5%	2.0%	3.65	3.47	+0.8
2025	25	MIX RAW	3 levels	55	2	62	259	80	23	9	4%	1.1	30.9%	8.9%	22.0%	3.5%	3.65	3.52	+0.8
2026	26	MIX MLE	3 levels	50	0	52	249	59	24	4	0%	1.0	22.6%	8.9%	13.7%	1.7%	4.47	3.24	+0.8
2026	26	MIX MLE	3 levels	50	0	52	249	59	24	4	0%	1.0	22.6%	8.9%	13.7%	1.7%	4.47	3.24	+0.8
2026	26	MIX RAW	3 levels	50	0	52	249	59	24	4	0%	1.0	23.7%	9.6%	14.1%	1.6%	4.47	3.21	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			73	2	81	355	89	41	6	3%	1.1	25.1%	11.5%	13.5%	1.7%	3.12	3.39	+1.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	SD	32	0	34	144	37	15	3	0%	1.1	25.7%	10.7%	15.0%	2.4%	3.95	3.99	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	SD	37	0	39	164	42	17	4	0%	1.0	25.3%	10.7%	14.7%	2.6%	4.06	4.11	+0.2
2029	29	MLB MLE	SD	31	0	33	139	34	15	4	0%	1.1	24.6%	10.8%	13.8%	2.8%	4.20	4.28	+0.1
2028	28	MLB CTX	SD	32	0	34	144	36	15	4	0%	1.1	25.3%	10.7%	14.7%	2.6%	4.06	4.11	+0.2
2029	29	MLB CTX	SD	29	0	30	127	31	14	3	0%	1.0	24.6%	10.8%	13.8%	2.8%	4.20	4.28	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		134	2	145	626	157	70	13	1%	1.1	25.0%	11.2%	13.9%	2.1%	3.57	3.56	+1.7
/ 200 IP	—	RAW		185	3	200	864	217	97	18	1%	1.1	25.0%	11.2%	13.9%	2.1%	3.57	3.56	+2.3
Career	—	MLE		176	2	222	970	211	97	19	1%	1.3	21.7%	10.0%	11.7%	1.9%	4.25	3.59	+1.4
/ 200 IP	—	MLE		158	2	200	873	190	88	17	1%	1.3	21.7%	10.0%	11.7%	1.9%	4.25	3.59	+1.9

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 AAAEl Paso Chihuahuas · 19 IPFIP 2.95 → 3.30 (+0.36)K% 22.9% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.89%	—	×0.860	19.69%
BB% (unintentional) /BF	3.61%	—	×0.900	3.25%
HR / BF /BF	2.41%	÷1.000	×1.100	2.65%

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 100).

Result: FIP 2.95 → 3.30, 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 AASan Antonio Missions · 9 IPFIP 3.21 → 5.15 (+1.94)K% 50.0% → 40.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	50.00%	—	×0.810	40.50%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	7.89%	÷0.950	×1.200	9.97%

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 90).

Result: FIP 3.21 → 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).

2025 AAAEl Paso Chihuahuas · 6 IPFIP 4.16 → 5.05 (+0.89)K% 39.3% → 33.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.29%	—	×0.860	33.79%
BB% (unintentional) /BF	3.57%	—	×0.900	3.21%
HR / BF /BF	7.14%	÷0.981	×1.100	8.01%

Result: FIP 4.16 → 5.05, 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 AASan Antonio Missions · 34 IPFIP 3.60 → 4.24 (+0.63)K% 18.9% → 15.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.88%	—	×0.810	15.29%
BB% (unintentional) /BF	10.49%	—	×1.100	11.54%
HR / BF /BF	1.40%	÷0.953	×1.200	1.76%

Result: FIP 3.60 → 4.24, 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+Fort Wayne TinCaps · 27 IPFIP 2.99 → 4.28 (+1.29)K% 31.3% → 24.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.30%	—	×0.770	24.10%
BB% (unintentional) /BF	8.70%	—	×1.150	10.00%
HR / BF /BF	2.61%	÷0.916	×1.250	3.56%

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 2.99 → 4.28, 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 ALake Elsinore Storm · 40 IPFIP 2.64 → 3.44 (+0.80)K% 24.0% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.00%	—	×0.740	17.76%
BB% (unintentional) /BF	7.43%	—	×1.180	8.77%
HR / BF /BF	1.14%	÷1.121	×1.300	1.33%

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 124).

Result: FIP 2.64 → 3.44, 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
David Morgan (OGILVIE)	—	—	3.99	25.7%	10.7%	2.4%	—	4.19	25.0%	14.3%	72	+0.20	—	—	—
JT Chargois R 2018 · 3.36 FIP	27	SU	3.36	24.4%	9.9%	2.0%	.282	3.48	25.9%	17.5%	108	+0.12	2.58	362	79
Taylor Williams R 2018 · 3.52 FIP	27	SU	3.52	24.2%	8.9%	2.4%	.309	4.35	22.5%	12.7%	108	+0.83	2.51	309	78
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	77
Heath Hembree R 2016 · 3.74 FIP	27	SU	3.74	21.1%	7.2%	2.8%	.292	4.03	27.3%	18.8%	162	+0.29	2.52	382	77
Ty Adcock R 2024 · 3.76 FIP	27	SU	3.76	20.8%	8.9%	2.4%	.405	4.85	20.4%	11.6%	66	+1.09	3.76	122	77
Tom Wilhelmsen R 2011 · 3.28 FIP	27	SU	3.28	22.1%	9.6%	1.5%	.253	3.18	22.9%	13.4%	218	-0.10	2.72	398	77
Fernando Rodriguez Jr. R 2011 · 3.85 FIP	27	SU	3.85	24.7%	10.8%	2.7%	.315	3.90	25.2%	16.5%	70	+0.05	3.34	335	76
Will Vest R 2022 · 3.43 FIP	27	SU	3.43	23.2%	8.1%	2.3%	.309	2.91	25.4%	18.5%	200	-0.52	2.56	448	76
Jake Barrett R 2018 · 3.31 FIP	27	SU	3.31	26.1%	13.0%	1.4%	.266	1.48	29.8%	22.7%	15	-1.83	1.48	230	75
Matt Ramsey R 2017 · 3.25 FIP	27	SU	3.25	24.2%	9.6%	1.8%	.330	3.21	23.6%	14.6%	81	-0.04	2.42	125	75
Zach Franklin R 2024 · 3.24 FIP	25	SU	3.24	26.4%	10.9%	1.8%	.301	4.15	27.5%	18.3%	117	+0.91	3.24	158	75
Jose Veras R 2008 · 3.67 FIP	27	SU	3.67	24.9%	9.1%	2.8%	.281	4.10	23.8%	11.7%	169	+0.43	3.37	422	75
Matt Pushard R 2024 · 3.87 FIP	26	SU	3.87	24.1%	8.9%	3.0%	.242	3.29	22.2%	14.0%	115	-0.58	2.84	220	74
José Ruiz R 2022 · 4.34 FIP	27	SU	4.34	25.7%	12.5%	3.3%	.284	4.18	21.5%	13.3%	171	-0.16	3.43	459	74
Blake Smith R 2015 · 3.17 FIP	27	SU	3.17	23.3%	10.5%	1.3%	.290	3.59	22.4%	13.8%	71	+0.42	3.17	124	74
Connor Sadzeck R 2019 · 4.20 FIP	27	SU	4.20	25.2%	13.1%	2.9%	.238	4.61	22.7%	10.8%	94	+0.41	3.20	549	74
Ryan Brasier R 2016 · 3.68 FIP	28	SU	3.68	25.0%	7.7%	3.1%	.289	3.09	24.2%	17.9%	130	-0.59	2.31	426	74
Grant Balfour R 2004 · 3.79 FIP	26	SU	3.79	24.4%	11.6%	2.3%	.292	4.01	24.8%	8.3%	25	+0.22	2.04	533	74
José A. Váldez R 2017 · 4.39 FIP	27	SU	4.39	21.6%	8.3%	3.8%	.325	4.15	18.8%	6.4%	153	-0.24	4.02	328	74
R.J. Alvarez R 2018 · 3.01 FIP	27	SU	3.01	21.0%	8.0%	1.2%	.306	4.64	24.0%	12.9%	89	+1.63	3.01	302	74

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	1.0	38	25.2%	11.5%	0.85	3.67
Chaz Roe ('21)	1.1	43	26.4%	11.1%	0.79	3.89
Michael Wuertz ('11)	2.1	52	25.3%	11.3%	0.96	3.71
Arodys Vizcaíno ('22)	1.0	50	25.7%	11.3%	0.85	3.10
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*
Dave Geisel ('78-'85, 2 WAR*)	1.0	42	23.5%*	10.6%*	0.84*	4.12*
Ed Vosberg ('86-'02, 1 WAR*)	1.2	47	21.0%*	9.6%*	0.86*	4.06*