Cody Scroggins

P Bowie Baysox

Age 30 RHP 6'0" / 195 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	25	MIX2 levels	44	51	5.12	3.81	26.6%	13.7%	—	K 20.5% · BB 16.2% · FIP 4.50
2023	26	MIX2 levels	11	18	4.00	5.27	17.7%	6.3%	—	K 17.7% · BB 6.3% · FIP 5.18
2024	27	MIX3 levels	18	27	5.60	4.45	15.3%	13.0%	—	K 14.2% · BB 9.6% · FIP 3.78
2027PROJ	30	MLBBOS	10	10	4.46	4.46	21.6%	9.3%	0.0
2028PROJ31% MLB	31	MLBBOS	6	6	4.57	4.65	21.4%	9.6%	-0.0

Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsNeil Wagner (age 29) · Joe Nelson (age 31) · Jim Miller (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 118110vR 102

ERA4.46

FIP4.46

K%21.6%

BB%9.3%

K-BB%12.3%

Proj IP10

Value2027 · WAR

Runs saved vs league avg · 10 IP

Grades · Contract

Scouting Grades

K†5050

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 industry-standard fWAR.

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	24	MIX MLE	2 levels	37	0	49	216	50	28	5	0%	1.3	17.4%	15.1%	2.3%	3.3%	3.33	5.35	—
2021	24	MIX RAW	2 levels	37	0	49	216	50	28	5	0%	1.3	23.1%	13.0%	10.2%	2.3%	3.33	4.11	—
2022	25	MIX MLE	2 levels	44	0	51	241	64	33	5	0%	1.2	20.5%	16.2%	4.3%	2.0%	5.12	4.50	—
2022	25	MIX RAW	2 levels	44	0	51	241	64	33	5	0%	1.2	26.6%	13.7%	12.9%	2.1%	5.12	3.81	—
2023	26	MIX MLE	2 levels	11	0	18	79	14	5	4	0%	1.6	17.7%	6.3%	11.4%	5.1%	4.00	5.18	—
2023	26	MIX RAW	2 levels	11	0	18	79	14	5	4	0%	1.6	17.7%	6.3%	11.4%	5.1%	4.00	5.27	—
2024	27	MIX MLE	3 levels	18	0	27	131	20	17	2	0%	1.5	14.2%	9.6%	4.5%	1.2%	5.60	3.78	—
2024	27	MIX MLE	3 levels	18	0	27	131	20	17	2	0%	1.5	14.2%	9.6%	4.5%	1.2%	5.60	3.78	—
2024	27	MIX RAW	3 levels	18	0	27	131	20	17	2	0%	1.5	15.3%	13.0%	2.3%	1.5%	5.60	4.45	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	BOS	10	0	10	42	9	4	1	0%	1.0	21.6%	9.3%	12.3%	3.0%	4.46	4.46	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	BOS	19	0	20	84	18	8	3	0%	1.1	21.4%	9.6%	11.8%	3.2%	4.57	4.65	-0.0
2028	31	MLB CTX	BOS	6	0	6	25	5	2	1	0%	1.0	21.4%	9.6%	11.8%	3.2%	4.57	4.65	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		6	0	6	26	6	3	1	0%	1.0	21.4%	9.6%	11.8%	3.2%	4.57	4.27	+0.0
/ 200 IP	—	RAW		190	0	200	844	192	96	32	0%	1.1	21.4%	9.6%	11.8%	3.2%	4.57	4.27	+0.7
Career	—	MLE		110	0	145	667	119	89	17	0%	1.3	17.9%	13.4%	4.5%	2.6%	4.47	4.71	-0.0
/ 200 IP	—	MLE		152	0	200	920	165	123	24	0%	1.3	17.9%	13.4%	4.5%	2.6%	4.47	4.71	-0.2

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 Scroggins (OGILVIE)	—	—	4.46	21.6%	9.3%	3.0%	—	4.65	21.4%	11.8%	20	+0.19	—	—	—
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	87
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	86
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	82
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	82
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	80
Scott Atchison R 2007 · 4.55 FIP	31	SU	4.55	19.1%	7.6%	3.8%	.297	4.59	16.2%	9.5%	60	+0.04	2.52	340	80
Esteban Haro R 2016 · 4.55 FIP	30	SU	4.55	17.1%	10.2%	2.9%	.338	3.83	11.6%	6.3%	75	-0.72	3.76	164	79
Brian Schlitter R 2016 · 3.28 FIP	30	SU	3.28	20.6%	10.8%	1.0%	.333	3.81	14.4%	6.6%	110	+0.53	3.27	300	77
Erik Davis R 2017 · 4.25 FIP	30	SU	4.25	22.2%	11.1%	3.0%	.349	3.52	17.4%	8.5%	65	-0.73	3.05	227	77
Jose Manuel Lopez R 2015 · 5.21 FIP	30	SU	5.21	18.6%	7.6%	4.9%	.373	3.56	18.5%	9.3%	93	-1.65	3.50	170	76
Andrew Robinson R 2017 · 4.32 FIP	29	SU	4.32	23.4%	8.8%	3.8%	.245	—	—	—	—	—	3.19	160	76
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	76
Marc Magliaro R 2018 · 3.94 FIP	28	SU	3.94	19.6%	7.3%	2.9%	.307	—	—	—	—	—	3.94	125	76
Adrian Ramirez R 2019 · 4.29 FIP	31	SU	4.29	21.7%	6.3%	4.0%	.327	—	—	—	—	—	3.04	137	75
Kiko Calero R 2005 · 3.51 FIP	30	SU	3.51	22.7%	7.0%	2.6%	.252	3.23	23.3%	13.9%	99	-0.28	2.27	298	75
Francisco Mendoza R 2016 · 3.81 FIP	28	SU	3.81	17.9%	7.7%	2.3%	.344	—	—	—	—	—	3.13	120	75
Jeremy Fikac R 2004 · 4.91 FIP	29	SU	4.91	19.6%	8.0%	4.5%	.276	—	—	—	—	—	3.10	136	75
Adrian Garza R 2017 · 4.47 FIP	29	SU	4.47	16.0%	7.0%	3.4%	.351	5.18	9.7%	4.1%	113	+0.71	3.94	229	75
Greg Burke R 2013 · 3.70 FIP	30	SU	3.70	17.9%	8.3%	1.9%	.357	3.61	22.1%	14.8%	62	-0.09	3.61	139	74
Grant Sides R 2019 · 3.31 FIP	30	SU	3.31	21.7%	9.8%	1.5%	.267	—	—	—	—	—	3.31	159	74