Bryan Hudson

P Chicago White Sox

Age 30 LHP 6'8" / 248 lbs MLB peak 0.3 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MIX2 levels	51	0	71	1.77	3.07	28.0%	7.1%	1.3	K 26.8% · BB 7.4% · FIP 3.35
2025	28	MIX2 levels	46	0	47	5.59	3.96	23.5%	11.7%	0.3	K 21.4% · BB 11.1% · FIP 3.84
2026	29	MLBCWS	70	9	67	3.74	3.03	20.9%	5.7%	1.2
2027PROJ	30	MLBCWS	50	0	53	3.77	4.05	19.6%	7.1%	0.3
2028PROJ95% MLB	31	MLBCWS	32	0	34	4.08	4.28	18.9%	7.4%	0.1
2029PROJ95% MLB	32	MLBCWS	21	0	22	4.35	4.53	18.1%	7.8%	0.0

Last 3 seasons WAR 2.8 · Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs86

K%41

FB Velo14

Form5.40 ERA (proj 3.77) · K 17.2% (proj 19.6%) · BB 1.1% (proj 7.1%) ▲ last 90d · 22 IP through Oct 27

CompsRaul Valdes (age 32) · Matt Thornton (age 30) · Andrew Barbosa (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8492vR 100

ERA3.77

FIP4.05

K%19.6%

BB%7.1%

K-BB%12.5%

Proj IP53

Value2027 · WAR

Runs saved vs league avg · 53 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL4545

CMD5555

K†4545

Statcast Percentiles

FB Velo

p1491.9

Whiff%

p3523.1%

p4120.9%

xwOBA vs

p860.277

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	18	ROK MLE	AZL Cubs	5	0	7	31	5	2	0	0%	1.3	16.1%	6.5%	9.7%	0.0%	2.70	2.56	—
2015	18	ROK RAW	AZL Cubs	5	0	7	31	5	2	0	0%	1.3	16.1%	6.5%	9.7%	0.0%	2.70	2.50	—
2017	20	A MLE	South Bend Cubs	24	24	124	539	81	52	10	100%	5.2	11.1%	11.4%	-0.3%	2.8%	3.91	5.13	—
2017	20	A RAW	South Bend Cubs	24	24	124	539	81	52	10	100%	5.2	15.0%	9.6%	5.4%	1.9%	3.91	4.10	—
2018	21	A+ MLE	Myrtle Beach Pelicans	23	23	113	499	78	58	4	100%	4.9	12.0%	13.4%	-1.3%	1.1%	4.70	4.37	—
2018	21	A+ RAW	Myrtle Beach Pelicans	23	23	113	499	78	58	4	100%	4.9	15.6%	11.6%	4.0%	0.8%	4.70	3.72	—
2019	22	MIX MLE	2 levels	11	9	37	157	32	8	5	82%	3.3	14.6%	7.2%	7.3%	5.2%	4.66	5.62	—
2019	22	MIX RAW	2 levels	11	9	37	157	32	8	5	82%	3.3	20.4%	5.1%	15.3%	3.2%	4.66	3.78	—
2021	24	MIX MLE	2 levels	35	0	59	249	55	30	5	0%	1.7	18.9%	11.1%	7.8%	1.0%	4.76	3.48	—
2021	24	MIX RAW	2 levels	35	0	59	249	55	30	5	0%	1.7	22.1%	12.0%	10.0%	2.0%	4.76	3.87	—
2022	25	MIX MLE	2 levels	39	0	59	256	76	27	5	0%	1.5	25.6%	11.2%	14.5%	2.2%	3.66	3.55	—
2022	25	MIX RAW	2 levels	39	0	59	256	76	27	5	0%	1.5	29.7%	10.5%	19.1%	2.0%	3.66	3.00	—
2023	26	MIX MLE	2 levels	52	8	64	285	93	29	4	15%	1.2	30.7%	9.3%	21.4%	1.4%	3.08	2.44	+1.6
2023	26	MIX RAW	2 levels	52	8	64	285	93	29	4	15%	1.2	32.6%	10.2%	22.5%	1.4%	3.08	2.37	+1.7
2024	27	MIX MLE	2 levels	51	0	71	268	75	19	7	0%	1.4	26.8%	7.4%	19.5%	2.9%	1.77	3.35	+1.0
2024	27	MIX MLE	2 levels	51	0	71	268	75	19	7	0%	1.4	26.8%	7.4%	19.5%	2.9%	1.77	3.35	+1.0
2024	27	MIX RAW	2 levels	51	0	71	268	75	19	7	0%	1.4	28.0%	7.1%	20.9%	2.6%	1.77	3.07	+1.3
2025	28	MIX MLE	2 levels	46	0	47	213	50	25	5	0%	1.0	21.4%	11.1%	10.4%	2.1%	5.59	3.84	+0.4
2025	28	MIX MLE	2 levels	46	0	47	213	50	25	5	0%	1.0	21.4%	11.1%	10.4%	2.1%	5.59	3.84	+0.4
2025	28	MIX RAW	2 levels	46	0	47	213	50	25	5	0%	1.0	23.5%	11.7%	11.7%	2.3%	5.59	3.96	+0.3
2026	29	MLB MLE	Chicago White Sox	70	9	67	282	59	16	5	13%	1.0	20.9%	5.7%	15.2%	1.7%	3.74	3.01	+1.2
2026	29	MLB MLE	Chicago White Sox	70	9	67	282	59	16	5	13%	1.0	20.9%	5.7%	15.2%	1.7%	3.74	3.01	+1.2
2026	29	MLB RAW	Chicago White Sox	70	9	67	282	59	16	5	13%	1.0	20.9%	5.7%	15.2%	1.8%	3.74	3.03	+1.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			135	9	153	632	147	48	15	7%	1.1	23.3%	7.6%	15.7%	2.4%	3.23	3.39	+2.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	CWS	50	0	53	224	44	16	5	0%	1.1	19.6%	7.1%	12.5%	2.4%	3.77	4.05	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	CWS	34	0	35	149	28	11	4	0%	1.0	18.9%	7.4%	11.4%	2.6%	4.08	4.28	+0.1
2029	32	MLB MLE	CWS	22	0	24	99	18	8	3	0%	1.1	18.1%	7.8%	10.2%	2.9%	4.35	4.53	+0.0
2028	31	MLB CTX	CWS	32	0	34	144	27	11	4	0%	1.1	18.9%	7.4%	11.4%	2.6%	4.08	4.28	+0.1
2029	32	MLB CTX	CWS	21	0	22	93	17	7	3	0%	1.0	18.1%	7.8%	10.2%	2.9%	4.35	4.53	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		188	9	209	868	191	66	21	5%	1.1	22.0%	7.6%	14.4%	2.5%	3.48	3.56	+2.5
/ 200 IP	—	RAW		180	9	200	830	183	63	20	5%	1.1	22.0%	7.6%	14.4%	2.5%	3.48	3.56	+2.3
Career	—	MLE		356	73	648	2779	524	283	58	21%	1.8	18.9%	10.2%	8.7%	2.1%	3.92	3.94	+2.2
/ 200 IP	—	MLE		110	23	200	858	162	88	18	21%	1.8	18.9%	10.2%	8.7%	2.1%	3.92	3.94	+2.1

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.

2025 AAACharlotte Knights · 32 IPFIP 3.68 → 3.62 (−0.06)K% 22.5% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.46%	—	×0.860	19.32%
BB% (unintentional) /BF	10.14%	—	×0.900	9.13%
HR / BF /BF	2.17%	÷1.320	×1.100	1.81%

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 164).

Result: FIP 3.68 → 3.62, 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 AAANashville Sounds · 9 IPFIP 0.82 → 1.16 (+0.35)K% 35.1% → 30.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.14%	—	×0.860	30.22%
BB% (unintentional) /BF	5.41%	—	×0.900	4.86%
HR / BF /BF	0.00%	÷0.909	×1.100	0.00%

Result: FIP 0.82 → 1.16, 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 AAAOklahoma City Dodgers · 56 IPFIP 2.08 → 2.44 (+0.35)K% 35.7% → 30.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.68%	—	×0.860	30.69%
BB% (unintentional) /BF	10.37%	—	×0.900	9.34%
HR / BF /BF	1.24%	÷1.006	×1.100	1.36%

Result: FIP 2.08 → 2.44, 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).

2022 AAAIowa Cubs · 46 IPFIP 3.14 → 3.61 (+0.47)K% 28.4% → 25.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.43%	—	×0.880	25.02%
BB% (unintentional) /BF	10.78%	—	×1.050	11.32%
HR / BF /BF	1.96%	÷0.996	×1.100	2.16%

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 99).

Result: FIP 3.14 → 3.61, 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 AATennessee Smokies · 13 IPFIP 2.45 → 3.33 (+0.88)K% 34.6% → 28.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.62%	—	×0.810	28.04%
BB% (unintentional) /BF	9.62%	—	×1.100	10.58%
HR / BF /BF	1.92%	÷1.010	×1.200	2.28%

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 102).

Result: FIP 2.45 → 3.33, 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 AATennessee Smokies · 55 IPFIP 2.91 → 3.48 (+0.57)K% 23.3% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.29%	—	×0.810	18.86%
BB% (unintentional) /BF	10.05%	—	×1.100	11.05%
HR / BF /BF	0.91%	÷1.050	×1.200	1.04%

Result: FIP 2.91 → 3.48, 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 AAAIowa Cubs · 4 IPFIP 10.84 → 11.24 (+0.40)K% 13.3% → 11.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.33%	—	×0.880	11.73%
BB% (unintentional) /BF	26.67%	—	×1.050	28.00%
HR / BF /BF	10.00%	÷1.081	×1.100	10.18%

Result: FIP 10.84 → 11.24, 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).

2019 A+Myrtle Beach Pelicans · 29 IPFIP 4.46 → 5.62 (+1.15)K% 18.9% → 14.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.90%	—	×0.770	14.55%
BB% (unintentional) /BF	6.30%	—	×1.150	7.24%
HR / BF /BF	3.94%	÷0.956	×1.250	5.15%

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 91).

Result: FIP 4.46 → 5.62, 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
Bryan Hudson (OGILVIE)	—	—	4.05	19.6%	7.1%	2.4%	—	4.38	18.6%	11.0%	59	+0.33	—	—	—
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	75
Matt Thornton L 2007 · 3.13 FIP	30	SU	3.13	22.1%	8.0%	1.6%	.324	2.19	30.7%	24.4%	200	-0.94	1.71	663	74
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	72
Cesár Ramos L 2014 · 3.93 FIP	30	SU	3.93	18.3%	8.9%	2.3%	.256	4.24	16.7%	9.0%	225	+0.31	2.56	463	72
Arturo Barradas L 2017 · 3.42 FIP	29	SU	3.42	18.1%	7.0%	1.8%	.288	3.24	16.1%	9.5%	34	-0.18	3.24	163	71
Brian Tallet L 2008 · 3.36 FIP	30	SU	3.36	19.6%	7.9%	1.7%	.282	5.06	16.0%	6.3%	251	+1.70	2.59	443	70
Buddy Boshers L 2018 · 3.22 FIP	30	SU	3.22	22.9%	6.6%	2.2%	.314	3.73	25.3%	14.9%	55	+0.51	2.76	239	70
Jack Taschner L 2008 · 4.09 FIP	30	SU	4.09	17.2%	9.7%	2.2%	.323	4.73	15.2%	3.0%	49	+0.64	3.14	189	69
Dan Jennings L 2017 · 4.32 FIP	30	SU	4.32	19.1%	9.0%	3.1%	.245	3.88	16.6%	8.5%	64	-0.44	2.60	344	69
Daniel Rodriguez L 2015 · 2.89 FIP	30	SU	2.89	20.2%	9.4%	0.6%	.256	4.17	16.3%	5.9%	234	+1.28	2.89	391	69
Jeff Ibarra L 2017 · 3.64 FIP	29	SU	3.64	18.1%	7.0%	2.1%	.342	2.85	20.6%	17.2%	29	-0.79	2.85	101	69
Jeremy Bleich L 2017 · 3.20 FIP	30	SU	3.20	17.6%	5.0%	1.7%	.307	3.97	20.2%	13.5%	84	+0.77	3.20	241	69
Tommy Phelps L 2004 · 4.80 FIP	30	SW	4.80	19.4%	8.3%	4.2%	.286	3.98	13.2%	5.7%	23	-0.82	3.39	120	69
Hideki Okajima L 2007 · 3.05 FIP	31	SU	3.05	23.2%	5.5%	2.2%	.234	3.87	20.2%	12.6%	169	+0.82	3.05	238	69
Doug Slaten L 2010 · 3.22 FIP	30	SU	3.22	20.7%	9.8%	1.1%	.269	4.85	13.5%	3.1%	29	+1.63	3.22	139	68
Zach Phillips L 2017 · 2.85 FIP	30	SU	2.85	19.2%	8.6%	0.5%	.301	3.67	19.1%	10.6%	36	+0.82	2.33	202	68
Jorge Ibarra L 2017 · 3.09 FIP	29	SU	3.09	19.3%	6.5%	1.4%	.351	—	—	—	—	—	2.55	146	68
Scott Downs L 2006 · 3.97 FIP	30	SU	3.97	18.7%	7.3%	2.8%	.275	2.95	21.5%	14.2%	175	-1.02	2.68	748	68
Chad Bell L 2018 · 4.38 FIP	29	SU	4.38	18.8%	7.4%	3.5%	.341	—	—	—	—	—	3.39	393	68
Ryan Buchter L 2017 · 4.45 FIP	30	SU	4.45	24.3%	9.3%	4.0%	.202	4.19	25.3%	15.6%	85	-0.26	2.37	296	68

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	1.3	53	21.4%	7.9%	0.95	3.55
Blaine Hardy ('21)	1.5	48	18.5%	8.3%	0.99	3.78
Jose Alvarez ('22)	2.1	60	20.0%	7.6%	0.83	3.47
Todd Worrell ('97)	1.7	55	23.4%	7.5%	0.95	3.58
Mike Perez ('90-'97, 3 WAR*)	1.7	58	18.5%*	8.2%*	0.88*	3.86*
Gary Wayne ('89-'94, 2 WAR*)	1.2	62	19.9%*	9.4%*	1.05*	3.88*
Carlos Diaz ('82-'86, 2 WAR*)	1.3	52	25.1%*	8.7%*	0.91*	3.84*