Cody Stashak

P Sacramento River Cats

Age 33 RHP 6'2" / 180 lbs AAA

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	28	MIX2 levels	12	0	17	3.63	1.89	25.0%	0.0%	0.6	K 23.1% · BB 0.0% · FIP 1.99
2023	29	AAASF	5	0	5	12.60	9.90	17.4%	4.3%	—	K 17.4% · BB 4.3% · FIP 9.34
2024	30	AAASF	27	0	36	5.45	4.53	27.2%	7.4%	—	K 23.4% · BB 6.7% · FIP 4.92
2025	31	No MLB games played
2026	32	No MLB games played
2027PROJ	33	MLBSF	13	0	14	4.06	4.23	23.9%	7.5%	0.1
2028PROJ6% MLB	34	MLBSF	6	0	6	4.35	4.54	22.8%	8.0%	0.0
2029PROJMLE	35	MLBSF	1	0	1	4.64	4.85	21.8%	8.5%	-0.0

Last 3 seasons WAR 0.6 · Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2022

xwOBA vs88

K%55

FB Velo10

CompsGiovanni Carrara (age 33) · Tony Barnette (age 33) · Rafael Martín (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114105vR 97

ERA4.06

FIP4.23

K%23.9%

BB%7.5%

K-BB%16.5%

Proj IP142

Value2027 · WAR

Runs saved vs league avg · 142 IP

Grades · Statcast · Contract

Scouting Grades

STUF6565

CTRL5555

CMD7070

K†5555

Statcast Percentiles

FB Velo

p1090.6

Whiff%

p6728.0%

p5523.1%

xwOBA vs

p880.275

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)
2015	21	ROK MLE	Elizabethton Twins	10	10	45	183	53	11	4	100%	4.5	19.7%	7.5%	12.2%	3.1%	3.43	4.07	—
2015	21	ROK RAW	Elizabethton Twins	10	10	45	183	53	11	4	100%	4.5	29.0%	6.0%	23.0%	2.2%	3.43	2.63	—
2016	22	MIX MLE	2 levels	21	20	122	488	90	33	4	95%	5.8	13.7%	8.0%	5.7%	0.9%	2.80	3.43	—
2016	22	MIX RAW	2 levels	21	20	122	488	90	33	4	95%	5.8	18.4%	6.8%	11.7%	0.8%	2.80	2.86	—
2017	23	MIX MLE	3 levels	22	16	95	380	90	20	7	73%	4.3	16.5%	6.8%	9.7%	2.8%	3.60	4.09	—
2017	23	MIX RAW	3 levels	22	16	95	380	90	20	7	73%	4.3	23.7%	5.3%	18.4%	1.8%	3.60	2.79	—
2018	24	MIX MLE	2 levels	37	2	60	237	74	15	5	5%	1.6	25.3%	6.5%	18.8%	2.2%	2.87	3.01	—
2018	24	MIX RAW	2 levels	37	2	60	237	74	15	5	5%	1.6	31.2%	6.3%	24.9%	2.1%	2.87	2.46	—
2019	25	MIX MLE	3 levels	51	3	78	321	99	10	8	6%	1.5	27.2%	3.3%	23.9%	2.8%	3.22	2.75	+1.7
2019	25	MIX RAW	3 levels	51	3	78	321	99	10	8	6%	1.5	30.8%	3.1%	27.7%	2.5%	3.22	2.28	+2.2
2020	26	MLB MLE	Minnesota Twins	11	0	15	57	17	3	2	0%	1.4	29.8%	5.3%	24.6%	3.5%	3.00	3.16	+0.2
2020	26	MLB RAW	Minnesota Twins	11	0	15	57	17	3	2	0%	1.4	29.8%	5.3%	24.6%	3.5%	3.00	3.17	+0.2
2021	27	MIX MLE	2 levels	17	0	18	84	29	10	2	0%	1.1	34.7%	13.3%	21.3%	2.7%	6.00	3.32	+0.3
2021	27	MIX RAW	2 levels	17	0	18	84	29	10	2	0%	1.1	34.5%	11.9%	22.6%	2.4%	6.00	2.99	+0.3
2022	28	MIX MLE	2 levels	12	0	17	68	17	0	1	0%	1.4	23.1%	0.0%	23.1%	1.5%	3.63	1.99	+0.5
2022	28	MIX RAW	2 levels	12	0	17	68	17	0	1	0%	1.4	25.0%	0.0%	25.0%	1.5%	3.63	1.89	+0.6
2023	29	AAA MLE	Sacramento River Cats	5	0	5	23	4	1	3	0%	1.0	17.4%	4.3%	13.0%	13.0%	12.60	9.34	—
2023	29	AAA RAW	Sacramento River Cats	5	0	5	23	4	1	3	0%	1.0	17.4%	4.3%	13.0%	13.0%	12.60	9.90	—
2024	30	AAA MLE	Sacramento River Cats	27	0	36	162	44	12	8	0%	1.3	23.4%	6.7%	16.7%	5.4%	5.45	4.92	—
2024	30	AAA MLE	Sacramento River Cats	27	0	36	162	44	12	8	0%	1.3	23.4%	6.7%	16.7%	5.4%	5.45	4.92	—
2024	30	AAA RAW	Sacramento River Cats	27	0	36	162	44	12	8	0%	1.3	27.2%	7.4%	19.8%	4.9%	5.45	4.53	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			55	1	72	301	83	14	8	2%	1.3	27.6%	4.7%	22.9%	2.7%	4.12	2.82	+1.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	SF	13	0	14	59	14	4	2	0%	1.1	23.9%	7.5%	16.5%	3.3%	4.06	4.23	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	SF	105	0	110	464	106	37	17	0%	1.0	22.8%	8.0%	14.8%	3.6%	4.35	4.54	+0.0
2029	35	MLB MLE	SF	83	0	87	367	80	31	14	0%	1.0	21.8%	8.5%	13.3%	3.9%	4.64	4.85	-0.3
2028	34	MLB CTX	SF	6	0	6	25	6	2	1	0%	1.0	22.8%	8.0%	14.8%	3.6%	4.35	4.54	+0.0
2029	35	MLB CTX	SF	1	0	1	4	1	0	0	0%	1.0	21.8%	8.5%	13.3%	3.9%	4.64	4.85	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		62	1	79	330	90	16	9	2%	1.3	27.1%	5.0%	22.2%	2.7%	4.15	2.94	+1.5
/ 200 IP	—	RAW		156	3	200	837	228	41	23	2%	1.3	27.1%	5.0%	22.2%	2.7%	4.15	2.94	+3.9
Career	—	MLE		213	51	491	2003	417	131	52	24%	2.3	20.8%	6.5%	14.3%	2.6%	3.54	3.58	+1.2
/ 200 IP	—	MLE		87	21	200	815	170	53	21	24%	2.3	20.8%	6.5%	14.3%	2.6%	3.54	3.58	+0.9

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.

2024 AAASacramento River Cats · 36 IPFIP 4.46 → 4.92 (+0.47)K% 27.2% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.16%	—	×0.860	23.36%
BB% (unintentional) /BF	7.41%	—	×0.900	6.67%
HR / BF /BF	4.94%	÷1.010	×1.100	5.38%

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 102).

Result: FIP 4.46 → 4.92, 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 AAASacramento River Cats · 5 IPFIP 9.34 → 9.79 (+0.44)K% 17.4% → 15.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.39%	—	×0.860	14.96%
BB% (unintentional) /BF	4.35%	—	×0.900	3.91%
HR / BF /BF	13.04%	÷1.057	×1.100	13.58%

Result: FIP 9.34 → 9.79, 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 AAPensacola Blue Wahoos · 28 IPFIP 2.64 → 3.60 (+0.95)K% 33.3% → 27.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.810	27.00%
BB% (unintentional) /BF	4.17%	—	×1.100	4.58%
HR / BF /BF	3.33%	÷1.000	×1.200	4.00%

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 100).

Result: FIP 2.64 → 3.60, 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 AAARochester Red Wings · 25 IPFIP 1.23 → 1.69 (+0.47)K% 35.1% → 30.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.05%	—	×0.880	30.85%
BB% (unintentional) /BF	4.12%	—	×1.050	4.33%
HR / BF /BF	1.03%	÷0.958	×1.100	1.18%

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 92).

Result: FIP 1.23 → 1.69, 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 AAChattanooga Lookouts · 56 IPFIP 2.20 → 3.01 (+0.80)K% 31.2% → 25.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.22%	—	×0.810	25.29%
BB% (unintentional) /BF	5.88%	—	×1.100	6.47%
HR / BF /BF	1.81%	÷0.975	×1.200	2.23%

Result: FIP 2.20 → 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).

2017 A+Fort Myers Miracle · 83 IPFIP 3.19 → 4.09 (+0.90)K% 21.4% → 16.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.43%	—	×0.770	16.50%
BB% (unintentional) /BF	5.95%	—	×1.150	6.85%
HR / BF /BF	2.08%	÷0.942	×1.250	2.76%

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 88).

Result: FIP 3.19 → 4.09, 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).

2017 AAChattanooga Lookouts · 6 IPFIP -0.74 → -0.01 (+0.73)K% 45.5% → 36.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	45.45%	—	×0.810	36.82%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	0.00%	÷0.975	×1.200	0.00%

Result: FIP -0.74 → -0.01, 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).

2017 ROKGCL Twins · 6 IPFIP 0.03 → 1.01 (+0.98)K% 36.4% → 24.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.36%	—	×0.680	24.73%
BB% (unintentional) /BF	0.00%	—	×1.250	0.00%
HR / BF /BF	0.00%	÷1.000	×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 100).

Result: FIP 0.03 → 1.01, 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).

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 Stashak (OGILVIE)	—	—	4.23	23.9%	7.5%	3.3%	—	4.68	22.4%	14.2%	197	+0.45	—	—	—
Giovanni Carrara R 2001 · 4.06 FIP	33	SU	4.06	20.1%	6.0%	3.5%	.249	4.28	15.7%	7.9%	173	+0.22	2.50	462	61
Tony Barnette R 2017 · 3.56 FIP	33	SU	3.56	22.6%	7.1%	2.7%	.335	2.61	25.0%	20.2%	26	-0.95	2.61	144	60
Rafael Martín R 2017 · 3.40 FIP	33	SU	3.40	20.4%	5.1%	2.5%	.305	4.71	22.5%	16.5%	47	+1.31	2.94	227	60
Jim Miller R 2015 · 3.95 FIP	33	SU	3.95	18.8%	4.1%	3.5%	.275	5.43	20.4%	6.3%	17	+1.48	3.95	140	57
Blake Parker R 2018 · 4.12 FIP	33	SU	4.12	25.4%	6.5%	4.3%	.290	4.12	24.9%	16.5%	121	+0.00	2.52	406	57
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	57
Rafael Betancourt R 2008 · 4.12 FIP	33	SU	4.12	20.7%	6.5%	3.6%	.304	2.37	31.4%	27.3%	181	-1.75	1.79	686	57
Aquilino López R 2008 · 3.70 FIP	33	SU	3.70	17.7%	5.2%	2.6%	.301	—	—	—	—	—	3.10	207	56
Felipe Arredondo R 2019 · 4.65 FIP	32	SU	4.65	16.2%	7.1%	3.7%	.272	—	—	—	—	—	3.92	337	56
Dale Thayer R 2014 · 3.56 FIP	33	SU	3.56	23.4%	4.9%	3.3%	.243	4.34	15.8%	8.8%	38	+0.78	2.75	239	56
Joe Nelson R 2008 · 3.08 FIP	33	SU	3.08	26.1%	7.8%	2.2%	.252	5.35	19.8%	5.5%	40	+2.27	3.08	139	55
Kazuhisa Makita R 2018 · 3.93 FIP	33	SU	3.93	21.6%	7.9%	3.0%	.270	2.81	18.9%	15.2%	70	-1.12	2.81	132	55
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	55
Tim Dillard R 2016 · 3.13 FIP	33	SU	3.13	22.6%	8.2%	1.7%	.358	4.14	13.7%	6.9%	250	+1.01	2.92	432	55
Casey Lawrence R 2021 · 4.08 FIP	33	SP-	4.08	20.0%	5.2%	3.7%	.284	4.99	15.9%	10.6%	440	+0.91	3.57	1,374	54
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	54
Scott McGough R 2023 · 4.57 FIP	33	SU	4.57	28.6%	9.3%	4.9%	.266	4.23	19.4%	9.4%	126	-0.34	3.55	429	54
Akinori Otsuka R 2005 · 3.05 FIP	33	SU	3.05	21.7%	9.4%	1.1%	.278	2.59	19.4%	14.2%	92	-0.46	2.39	232	53
Bob Wells R 2000 · 3.93 FIP	33	SU	3.93	21.7%	3.7%	4.0%	.266	4.57	14.2%	8.6%	127	+0.64	3.93	627	53
D.J. Carrasco R 2010 · 3.45 FIP	33	SU	3.45	19.7%	9.4%	1.5%	.275	4.47	12.0%	6.7%	49	+1.02	3.23	490	53

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	2.1	103	23.8%	7.4%	1.32	4.27
Kenta Maeda ('25)	4.9	123	25.8%	7.0%	1.30	4.20
Dick Hughes ('66-'68, 5 WAR*)	1.5	102	24.1%*	7.1%*	1.51*	3.68*
Mel Queen ('66-'72, 4 WAR*)	1.9	97	24.2%*	9.0%*	1.27*	3.77*
Luis Garcia ('25)	1.8	120	25.3%	7.7%	1.20	3.60
Enrique Romo ('77-'82, 6 WAR*)	3.0	100	26.0%*	8.2%*	1.33*	3.81*
Ross Stripling ('24)	4.2	94	20.9%	5.6%	1.32	4.17