Cody Morris

P Scranton/Wilkes-Barre RailRiders

Age 30 RHP 6'4" / 205 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	25	MIX3 levels	16	11	45	2.00	2.99	34.4%	10.0%	1.3	K 31.3% · BB 11.6% · FIP 3.74
2023	26	MIX3 levels	27	4	47	3.83	5.12	26.7%	16.3%	-0.3	K 23.9% · BB 15.6% · FIP 5.08
2024	27	AAAScranton/Wilk…	26	2	38	4.03	3.76	27.1%	15.3%	—	K 23.3% · BB 13.8% · FIP 3.90
2025	28	No MLB games played
2026	29	No MLB games played
2027PROJ	30	MLBNYY	6	0	6	4.63	4.70	24.7%	12.0%	-0.0
2028PROJ37% MLB	31	MLBNYY	16	0	17	4.77	4.89	23.8%	12.1%	-0.1
2029PROJ55% MLB	32	MLBNYY	19	0	20	4.96	5.11	23.0%	12.3%	-0.1

Last 3 seasons WAR 1.0 · Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2022

K%54

FB Velo51

xwOBA vs20

CompsJosh Zeid (age 30) · Elian Leyva (age 30) · Fabio Castillo (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 122114vR 106

ERA4.63

FIP4.70

K%24.7%

BB%12.0%

K-BB%12.7%

Proj IP58

Value2027 · WAR

Runs saved vs league avg · 58 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL3535

CMD4545

K†6060

Statcast Percentiles

FB Velo

p5194.6

Whiff%

p7228.7%

p5423.0%

xwOBA vs

p200.348

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	MIX MLE	2 levels	21	20	89	388	111	27	7	95%	4.2	21.6%	8.1%	13.5%	2.6%	4.35	3.73	—
2019	22	MIX RAW	2 levels	21	20	89	388	111	27	7	95%	4.2	28.6%	7.0%	21.6%	1.8%	4.35	2.54	—
2021	24	MIX MLE	3 levels	15	14	61	240	93	20	3	93%	4.1	30.9%	9.1%	21.9%	1.1%	1.62	2.23	—
2021	24	MIX RAW	3 levels	15	14	61	240	93	20	3	93%	4.1	38.8%	8.3%	30.4%	1.2%	1.62	1.67	—
2022	25	MIX MLE	3 levels	16	11	45	180	62	18	5	69%	2.8	31.3%	11.6%	19.7%	3.3%	2.00	3.74	+0.9
2022	25	MIX RAW	3 levels	16	11	45	180	62	18	5	69%	2.8	34.4%	10.0%	24.4%	2.8%	2.00	2.99	+1.3
2023	26	MIX MLE	3 levels	27	4	47	202	54	33	8	15%	1.7	23.9%	15.6%	8.3%	3.7%	3.83	5.08	-0.3
2023	26	MIX RAW	3 levels	27	4	47	202	54	33	8	15%	1.7	26.7%	16.3%	10.4%	4.0%	3.83	5.12	-0.3
2024	27	AAA MLE	Scranton/Wilkes-Barre RailRiders	26	2	38	170	46	26	3	8%	1.5	23.3%	13.8%	9.5%	1.9%	4.03	3.90	—
2024	27	AAA MLE	Scranton/Wilkes-Barre RailRiders	26	2	38	170	46	26	3	8%	1.5	23.3%	13.8%	9.5%	1.9%	4.03	3.90	—
2024	27	AAA RAW	Scranton/Wilkes-Barre RailRiders	26	2	38	170	46	26	3	8%	1.5	27.1%	15.3%	11.8%	1.8%	4.03	3.76	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			13	5	32	138	32	18	6	38%	2.4	23.2%	13.0%	10.1%	4.3%	3.41	5.25	-0.3
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	NYY	6	0	6	25	6	3	1	0%	1.0	24.7%	12.0%	12.7%	3.3%	4.63	4.70	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	NYY	43	0	45	190	45	23	7	0%	1.0	23.8%	12.1%	11.7%	3.5%	4.77	4.89	-0.2
2029	32	MLB MLE	NYY	35	0	37	155	36	19	6	0%	1.1	23.0%	12.3%	10.7%	3.7%	4.96	5.11	-0.2
2028	31	MLB CTX	NYY	16	0	17	72	17	9	2	0%	1.1	23.8%	12.1%	11.7%	3.5%	4.77	4.89	-0.1
2029	32	MLB CTX	NYY	19	0	20	84	19	10	3	0%	1.1	23.0%	12.3%	10.7%	3.7%	4.96	5.11	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		48	5	69	294	68	37	12	10%	1.4	23.3%	12.6%	10.7%	3.9%	4.20	4.89	-0.3
/ 200 IP	—	RAW		140	15	200	857	198	108	35	10%	1.4	23.3%	12.6%	10.7%	3.9%	4.20	4.89	-0.8
Career	—	MLE		105	51	280	1180	302	129	29	49%	2.7	25.6%	10.9%	14.7%	2.5%	3.25	3.68	-0.7
/ 200 IP	—	MLE		75	36	200	843	216	92	21	49%	2.7	25.6%	10.9%	14.7%	2.5%	3.25	3.68	-1.2

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 AAAScranton/Wilkes-Barre RailRiders · 38 IPFIP 3.72 → 3.90 (+0.18)K% 27.1% → 23.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.06%	—	×0.860	23.27%
BB% (unintentional) /BF	15.29%	—	×0.900	13.76%
HR / BF /BF	1.76%	÷1.045	×1.100	1.86%

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 109).

Result: FIP 3.72 → 3.90, 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 · 34 IPFIP 4.86 → 5.08 (+0.22)K% 27.8% → 23.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.78%	—	×0.860	23.89%
BB% (unintentional) /BF	17.36%	—	×0.900	15.62%
HR / BF /BF	3.47%	÷1.037	×1.100	3.68%

Result: FIP 4.86 → 5.08, 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 · 5 IPFIP 2.26 → 2.78 (+0.53)K% 25.0% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.810	20.25%
BB% (unintentional) /BF	10.00%	—	×1.100	11.00%
HR / BF /BF	0.00%	÷0.909	×1.200	0.00%

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 2.26 → 2.78, 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).

2022 AAAColumbus Clippers · 15 IPFIP 1.94 → 2.71 (+0.78)K% 51.7% → 45.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	51.72%	—	×0.880	45.52%
BB% (unintentional) /BF	10.34%	—	×1.050	10.86%
HR / BF /BF	3.45%	÷1.001	×1.100	3.79%

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 100).

Result: FIP 1.94 → 2.71, 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 ROKACL Guardians · 6 IPFIP -0.35 → 0.75 (+1.11)K% 40.9% → 27.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.91%	—	×0.680	27.82%
BB% (unintentional) /BF	0.00%	—	×1.250	0.00%
HR / BF /BF	0.00%	÷0.915	×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 83).

Result: FIP -0.35 → 0.75, 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 AAAColumbus Clippers · 37 IPFIP 1.49 → 1.93 (+0.44)K% 36.1% → 31.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.11%	—	×0.880	31.78%
BB% (unintentional) /BF	8.33%	—	×1.050	8.75%
HR / BF /BF	0.69%	÷1.034	×1.100	0.74%

Result: FIP 1.49 → 1.93, 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).

2021 AAAkron RubberDucks · 20 IPFIP 1.83 → 2.76 (+0.93)K% 36.2% → 29.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.25%	—	×0.810	29.36%
BB% (unintentional) /BF	8.75%	—	×1.100	9.62%
HR / BF /BF	1.25%	÷0.893	×1.200	1.68%

Result: FIP 1.83 → 2.76, 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+Lynchburg Hillcats · 44 IPFIP 3.50 → 4.94 (+1.45)K% 27.1% → 20.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.09%	—	×0.770	20.86%
BB% (unintentional) /BF	8.37%	—	×1.150	9.63%
HR / BF /BF	2.96%	÷0.850	×1.250	4.34%

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 70).

Result: FIP 3.50 → 4.94, 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
Cody Morris (OGILVIE)	—	—	4.70	24.7%	12.0%	3.3%	—	4.99	23.5%	11.3%	82	+0.29	—	—	—
Josh Zeid R 2017 · 5.35 FIP	30	SW	5.35	18.3%	10.8%	4.4%	.334	—	—	—	—	—	4.00	313	66
Elian Leyva R 2019 · 4.30 FIP	30	SP-	4.30	17.0%	8.5%	2.9%	.323	—	—	—	—	—	3.10	158	66
Fabio Castillo R 2017 · 3.98 FIP	28	SP-	3.98	20.9%	8.8%	2.8%	.291	—	—	—	—	—	3.13	195	65
Aldo Montes R 2019 · 4.69 FIP	29	SW	4.69	18.2%	10.6%	3.3%	.336	—	—	—	—	—	4.69	210	64
Edgar Alberto Garcia R 2017 · 4.01 FIP	29	SP-	4.01	17.3%	10.9%	1.8%	.305	—	—	—	—	—	4.01	272	63
Yoanner Negrin R 2015 · 3.79 FIP	31	SW	3.79	15.1%	7.5%	1.9%	.256	3.29	15.0%	8.5%	270	-0.50	3.15	485	63
Deck McGuire R 2018 · 5.28 FIP	29	SW	5.28	18.4%	13.2%	3.8%	.262	—	—	—	—	—	3.41	622	62
Aaron Wilkerson R 2019 · 4.67 FIP	30	SP-	4.67	20.9%	9.8%	3.8%	.291	3.33	24.6%	19.0%	112	-1.34	2.75	902	60
Mike Burns R 2009 · 4.90 FIP	31	SP-	4.90	17.2%	6.6%	4.4%	.307	—	—	—	—	—	3.10	104	60
Jared Fernandez R 2002 · 4.23 FIP	30	SP-	4.23	15.6%	10.0%	2.2%	.323	3.57	11.8%	5.6%	38	-0.66	3.57	101	59
Dustin Molleken R 2015 · 3.74 FIP	30	SU	3.74	20.4%	11.7%	1.6%	.287	3.90	20.6%	8.4%	77	+0.16	3.38	129	59
Hector Velázquez R 2019 · 4.82 FIP	30	SW	4.82	19.6%	12.3%	3.3%	.292	3.81	19.2%	10.9%	25	-1.01	2.75	536	59
Chris Mazza R 2020 · 3.94 FIP	30	SP-	3.94	21.3%	11.0%	2.3%	.348	3.95	21.4%	13.5%	111	+0.01	3.29	563	59
Felipe Arredondo R 2017 · 4.76 FIP	30	SW	4.76	14.8%	7.3%	3.6%	.311	4.65	16.2%	9.1%	64	-0.11	3.92	337	58
Britt Reames R 2002 · 3.76 FIP	28	SU	3.76	24.7%	10.4%	2.6%	.323	—	—	—	—	—	3.76	204	58
Derek Eitel R 2017 · 4.72 FIP	29	SP-	4.72	17.2%	16.0%	1.9%	.273	—	—	—	—	—	3.96	138	57
Murphy Smith R 2018 · 4.57 FIP	30	SW	4.57	13.6%	9.3%	2.6%	.273	—	—	—	—	—	3.16	300	57
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	57
Mike Broadway R 2017 · 4.73 FIP	30	SU	4.73	18.3%	10.2%	3.4%	.245	4.65	23.0%	12.4%	49	-0.08	1.74	195	57
Fredy Quintero R 2017 · 4.45 FIP	29	SW	4.45	14.0%	8.7%	2.6%	.335	4.62	17.2%	3.5%	43	+0.17	4.29	241	57

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	0.9	43	23.8%	12.3%	1.39	4.51
John Hudek ('99)	1.0	40	23.0%	13.7%	1.30	4.43
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*
J.J. Hoover ('18)	1.4	57	23.8%	11.4%	1.42	4.17
Jeff Jones ('80-'84, -2 WAR*)	1.0	41	22.0%*	12.4%*	1.48*	4.53*
Bill Risley ('98)	1.1	55	21.9%	10.9%	1.26	3.98