Erick Fedde

P Chicago White Sox

Age 34 RHP 6'4" / 205 lbs MLB peak 0.3 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2024	31	MLBSTL	31	31	177	3.30	3.71	21.2%	7.2%	3.6
2025	32	MLBMIL	32	24	141	5.49	5.08	13.3%	10.6%	0.5
2026	33	MLBCWS	33	15	141	3.83	4.88	16.3%	8.3%	-0.5
2027PROJ	34	MLBCWS	16	16	96	4.74	5.07	16.0%	8.8%	0.4
2028PROJ	35	MLBCWS	9	9	53	5.14	5.41	15.0%	9.4%	-0.0
2029PROJ	36	MLBCWS	4	4	27	5.52	5.78	14.0%	10.0%	-0.1

Last 3 seasons WAR 3.6 · Next 3 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo44

xwOBA vs31

K%10

Form1.99 ERA (proj 4.74) · K 16.0% (proj 16.0%) · BB 9.6% (proj 8.8%) · last 60d · 23 IP through Oct 27

CompsMasato Yoshii (age 35) · Kenshin Kawakami (age 34) · Jarrett Grube (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 126118vR 110

ERA4.74

FIP5.07

K%16.0%

BB%8.8%

K-BB%7.1%

Proj IP96

Value2027 · WAR

Runs saved vs league avg · 96 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p4494.1

Whiff%

p1018.2%

p1016.3%

xwOBA vs

p310.334

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	22	A MLE	Hagerstown Suns	6	6	29	119	23	8	1	100%	4.8	14.3%	7.9%	6.4%	1.1%	4.34	3.51	—
2015	22	A RAW	Hagerstown Suns	6	6	29	119	23	8	1	100%	4.8	19.3%	6.7%	12.6%	0.8%	4.34	2.79	—
2016	23	MIX MLE	2 levels	23	22	121	501	123	29	8	96%	5.3	19.1%	6.6%	12.6%	1.9%	3.12	3.34	—
2016	23	MIX RAW	2 levels	23	22	121	501	123	29	8	96%	5.3	24.6%	5.8%	18.8%	1.6%	3.12	2.65	—
2017	24	MIX MLE	3 levels	32	16	106	446	94	28	12	50%	3.3	18.1%	6.7%	11.4%	3.0%	4.51	4.06	+1.7
2017	24	MIX RAW	3 levels	32	16	106	446	94	28	12	50%	3.3	21.1%	6.3%	14.8%	2.7%	4.51	3.59	+2.3
2018	25	MIX MLE	3 levels	25	25	122	522	120	41	11	100%	4.9	21.3%	7.9%	13.4%	2.1%	4.81	3.47	+2.9
2018	25	MIX RAW	3 levels	25	25	122	522	120	41	11	100%	4.9	23.0%	7.9%	15.1%	2.1%	4.81	3.31	+3.1
2019	26	MIX MLE	3 levels	28	18	113	484	78	40	18	64%	4.0	14.8%	8.4%	6.4%	3.9%	4.79	5.06	+0.4
2019	26	MIX RAW	3 levels	28	18	113	484	78	40	18	64%	4.0	16.1%	8.3%	7.9%	3.7%	4.79	4.86	+0.7
2020	27	MLB MLE	Washington Nationals	11	8	50	222	28	20	10	73%	4.6	12.6%	9.0%	3.6%	4.5%	4.29	5.65	-0.1
2020	27	MLB RAW	Washington Nationals	11	8	50	222	28	20	10	73%	4.6	12.6%	9.0%	3.6%	4.5%	4.29	5.76	-0.2
2021	28	MIX MLE	2 levels	30	28	138	611	131	43	23	93%	4.6	21.7%	7.1%	14.6%	3.9%	5.40	4.31	+1.8
2021	28	MIX RAW	2 levels	30	28	138	611	131	43	23	93%	4.6	21.4%	7.0%	14.4%	3.8%	5.40	4.30	+1.8
2022	29	MIX MLE	2 levels	28	28	131	589	98	59	21	100%	4.7	16.4%	10.1%	6.3%	3.7%	5.63	5.01	+0.6
2022	29	MIX RAW	2 levels	28	28	131	589	98	59	21	100%	4.7	16.6%	10.0%	6.6%	3.6%	5.63	5.04	+0.5
2024	31	MLB MLE	St. Louis Cardinals	31	31	177	726	154	52	20	100%	5.7	21.2%	7.2%	14.0%	3.0%	3.30	3.84	+3.3
2024	31	MLB MLE	St. Louis Cardinals	31	31	177	726	154	52	20	100%	5.7	21.2%	7.2%	14.0%	3.0%	3.30	3.84	+3.3
2024	31	MLB RAW	St. Louis Cardinals	31	31	177	726	154	52	20	100%	5.7	21.2%	7.2%	14.0%	2.8%	3.30	3.71	+3.6
2025	32	MLB MLE	Milwaukee Brewers	32	24	141	623	83	66	19	75%	4.4	13.3%	10.6%	2.7%	2.9%	5.49	4.91	+0.8
2025	32	MLB MLE	Milwaukee Brewers	32	24	141	623	83	66	19	75%	4.4	13.3%	10.6%	2.7%	2.9%	5.49	4.91	+0.8
2025	32	MLB RAW	Milwaukee Brewers	32	24	141	623	83	66	19	75%	4.4	13.3%	10.6%	2.7%	3.0%	5.49	5.08	+0.5
2026	33	MLB MLE	Chicago White Sox	33	15	141	606	99	50	23	45%	4.3	16.3%	8.3%	8.1%	3.7%	3.83	4.81	-0.4
2026	33	MLB MLE	Chicago White Sox	33	15	141	606	99	50	23	45%	4.3	16.3%	8.3%	8.1%	3.7%	3.83	4.81	-0.4
2026	33	MLB RAW	Chicago White Sox	33	15	141	606	99	50	23	45%	4.3	16.3%	8.3%	8.1%	3.8%	3.83	4.88	-0.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			198	158	914	3967	688	346	140	80%	4.6	17.3%	8.7%	8.6%	3.5%	4.77	4.72	+7.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	CWS	16	16	96	405	65	36	13	100%	6.0	16.0%	8.8%	7.1%	3.3%	4.74	5.07	+0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	CWS	9	9	53	223	33	21	8	100%	5.9	15.0%	9.4%	5.6%	3.7%	5.14	5.41	-0.0
2029	36	MLB MLE	CWS	5	5	27	115	16	12	5	100%	5.5	14.0%	10.0%	4.0%	4.0%	5.52	5.78	-0.1
2028	35	MLB CTX	CWS	9	9	53	224	34	21	8	100%	5.9	15.0%	9.4%	5.6%	3.7%	5.14	5.41	-0.0
2029	36	MLB CTX	CWS	4	4	27	114	16	11	5	100%	6.8	14.0%	10.0%	4.0%	4.0%	5.52	5.78	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		211	171	994	4305	738	378	153	81%	4.7	17.1%	8.8%	8.3%	3.5%	4.81	4.71	+7.9
/ 200 IP	—	RAW		43	34	200	866	149	76	31	81%	4.7	17.1%	8.8%	8.3%	3.5%	4.81	4.71	+1.6
Career	—	MLE		279	221	1269	5449	970	445	172	79%	4.5	17.8%	8.2%	9.6%	3.1%	4.50	4.36	+7.0
/ 200 IP	—	MLE		44	35	200	859	153	70	27	79%	4.5	17.8%	8.2%	9.6%	3.1%	4.50	4.36	+1.4

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.

2021 A+Wilmington Blue Rocks · 5 IPFIP 2.50 → 2.86 (+0.37)K% 14.3% → 11.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.29%	—	×0.770	11.00%
BB% (unintentional) /BF	4.76%	—	×1.150	5.48%
HR / BF /BF	0.00%	÷0.906	×1.250	0.00%

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 81).

Result: FIP 2.50 → 2.86, 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 AAHarrisburg Senators · 25 IPFIP 2.55 → 3.14 (+0.59)K% 27.3% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.27%	—	×0.810	22.09%
BB% (unintentional) /BF	5.05%	—	×1.100	5.56%
HR / BF /BF	2.02%	÷1.111	×1.200	2.18%

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 122).

Result: FIP 2.55 → 3.14, 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 AAAFresno Grizzlies · 10 IPFIP 7.82 → 8.84 (+1.02)K% 19.6% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.61%	—	×0.880	17.25%
BB% (unintentional) /BF	7.84%	—	×1.050	8.24%
HR / BF /BF	9.80%	÷0.962	×1.100	11.21%

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 7.82 → 8.84, 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 AAASyracuse Chiefs · 67 IPFIP 2.41 → 2.65 (+0.24)K% 24.3% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.31%	—	×0.880	21.39%
BB% (unintentional) /BF	6.25%	—	×1.050	6.56%
HR / BF /BF	1.04%	÷1.205	×1.100	0.95%

Result: FIP 2.41 → 2.65, 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 AAHarrisburg Senators · 56 IPFIP 3.01 → 3.71 (+0.70)K% 23.7% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.68%	—	×0.810	19.18%
BB% (unintentional) /BF	7.46%	—	×1.100	8.20%
HR / BF /BF	1.75%	÷0.970	×1.200	2.17%

Result: FIP 3.01 → 3.71, 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).

2017 AAASyracuse Chiefs · 34 IPFIP 3.22 → 3.56 (+0.34)K% 17.6% → 15.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.61%	—	×0.880	15.49%
BB% (unintentional) /BF	3.52%	—	×1.050	3.70%
HR / BF /BF	2.11%	÷0.980	×1.100	2.37%

Result: FIP 3.22 → 3.56, 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).

2016 A+Potomac Nationals · 92 IPFIP 2.62 → 3.44 (+0.82)K% 25.5% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.54%	—	×0.770	19.66%
BB% (unintentional) /BF	5.11%	—	×1.150	5.87%
HR / BF /BF	1.88%	÷1.025	×1.250	2.29%

Result: FIP 2.62 → 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).

2016 AAHarrisburg Senators · 29 IPFIP 2.67 → 3.05 (+0.37)K% 21.7% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.71%	—	×0.810	17.58%
BB% (unintentional) /BF	7.75%	—	×1.100	8.53%
HR / BF /BF	0.78%	÷1.445	×1.200	0.64%

Result: FIP 2.67 → 3.05, 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).

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
Erick Fedde (OGILVIE)	—	—	—	—	—	3.3%	—	5.54	14.7%	5.1%	80	+0.47	—	—	—
Masato Yoshii R 2000 · 5.32 FIP	35	SP	5.32	12.1%	6.5%	4.4%	.302	4.38	13.1%	8.0%	244	-0.94	4.15	757	65
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	49
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	46
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	45
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	44
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	44
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	43
Michael Nix R 2016 · 4.60 FIP	33	SP	4.60	11.9%	11.6%	1.9%	.322	—	—	—	—	—	3.82	234	41
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	40
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	39
Guillermo Moscoso R 2018 · 5.07 FIP	34	SP-	5.07	18.2%	8.4%	4.5%	.318	4.35	12.6%	5.9%	104	-0.72	3.56	486	39
Francisco Rodriguez R 2017 · 4.43 FIP	34	SP	4.43	11.4%	8.2%	2.3%	.305	5.18	10.7%	1.4%	83	+0.75	3.16	334	39
David Buchanan R 2024 · 4.26 FIP	35	SP-	4.26	14.7%	6.5%	2.9%	.337	5.56	12.2%	4.1%	29	+1.30	3.88	560	38
Jesse Estrada R 2017 · 5.39 FIP	33	SP	5.39	8.3%	5.6%	4.2%	.285	4.34	14.4%	7.8%	85	-1.05	3.80	257	37
Marco Carrillo R 2019 · 6.08 FIP	32	SP	6.08	9.9%	9.1%	4.9%	.334	—	—	—	—	—	5.33	194	36
Francisco Cordova R 2016 · 4.59 FIP	32	SP	4.59	7.1%	7.7%	2.0%	.300	—	—	—	—	—	4.59	115	36
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	36
Casey Lawrence R 2022 · 4.55 FIP	34	SP-	4.55	18.6%	3.9%	4.6%	.237	4.99	13.4%	8.1%	414	+0.44	3.57	1,374	34
Juan Delgadillo R 2017 · 4.47 FIP	34	SP	4.47	10.9%	6.2%	2.7%	.311	—	—	—	—	—	4.47	143	32
Juan Omar Espinoza R 2016 · 4.84 FIP	32	SP-	4.84	8.4%	9.2%	2.4%	.348	—	—	—	—	—	4.81	104	31

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	5.4	99	17.0%	9.1%	1.37	4.80
José Ureña ('25)	5.0	91	15.4%	8.7%	1.23	4.75
Lew Krausse ('61-'74, 0 WAR*)	6.4	128	17.0%*	8.7%*	1.31*	4.97*
Allen Watson ('00)	4.5	112	15.0%	8.9%	1.40	5.03
Claudio Vargas ('10)	3.8	109	16.2%	8.7%	1.49	4.83
Bob Hendley ('61-'67, 2 WAR*)	4.4	126	18.0%*	9.5%*	1.36*	4.71*
Bob Owchinko ('76-'86, 0 WAR*)	4.5	127	18.6%*	9.3%*	1.25*	5.02*