Jordan Hicks

P Chicago White Sox

Age 30 RHP 6'2" / 217 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	27	MLBSF	29	20	110	4.10	4.06	20.0%	9.8%	1.7
2025	28	MIX2 levels	36	10	69	7.04	4.11	18.5%	10.2%	0.4	K 18.5% · BB 9.9% · FIP 3.87
2026	29	MIX2 levels	56	2	51	4.26	3.46	30.6%	13.5%	0.7	K 29.6% · BB 13.4% · FIP 3.30
2027PROJ	30	MLBCWS	42	0	44	4.35	4.15	24.8%	11.5%	0.2
2028PROJ	31	MLBCWS	33	0	35	4.37	4.38	24.0%	11.8%	0.1
2029PROJ	32	MLBCWS	24	0	25	4.53	4.64	23.2%	12.2%	-0.0

Last 3 seasons WAR 2.8 · Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

K%90

xwOBA vs90

Form5.29 ERA (proj 4.35) · K 35.4% (proj 24.8%) · BB 13.9% (proj 11.5%) ▲▲ last 90d · 17 IP through Oct 27

CompsTom Wilhelmsen (age 30) · Jhan Mariñez (age 29) · Kerry Ligtenberg (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9890vR 82

ERA4.35

FIP4.15

K%24.8%

BB%11.5%

K-BB%13.3%

Proj IP44

Value2027 · WAR

Runs saved vs league avg · 44 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL4040

CMD4040

K†6060

Statcast Percentiles

FB Velo

p9099.4

Whiff%

p6928.2%

p9029.6%

xwOBA vs

p900.262

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)
2016	19	ROK MLE	Johnson City Cardinals	6	6	30	132	20	13	1	100%	5.0	10.3%	12.3%	-2.0%	1.1%	4.20	4.37	—
2016	19	ROK RAW	Johnson City Cardinals	6	6	30	132	20	13	1	100%	5.0	15.2%	9.8%	5.3%	0.8%	4.20	3.50	—
2017	20	MIX MLE	2 levels	22	19	105	452	95	45	3	86%	4.8	15.8%	11.7%	4.1%	0.9%	2.74	3.74	—
2017	20	MIX RAW	2 levels	22	19	105	452	95	45	3	86%	4.8	21.0%	10.0%	11.1%	0.7%	2.74	2.95	—
2018	21	MLB MLE	St. Louis Cardinals	73	0	78	339	70	43	2	0%	1.1	20.6%	12.7%	8.0%	0.6%	3.59	3.31	+1.1
2018	21	MLB RAW	St. Louis Cardinals	73	0	78	339	70	43	2	0%	1.1	20.6%	12.7%	8.0%	0.6%	3.59	3.29	+1.2
2019	22	MLB MLE	St. Louis Cardinals	29	0	29	110	31	11	2	0%	1.0	28.2%	10.0%	18.2%	2.0%	3.14	3.06	+0.5
2019	22	MLB RAW	St. Louis Cardinals	29	0	29	110	31	11	2	0%	1.0	28.2%	10.0%	18.2%	1.8%	3.14	3.00	+0.5
2021	24	MIX MLE	2 levels	13	3	13	59	13	13	2	23%	1.0	22.0%	22.0%	0.0%	3.4%	7.62	5.89	-0.2
2021	24	MIX RAW	2 levels	13	3	13	59	13	13	2	23%	1.0	22.0%	22.0%	0.0%	3.4%	7.62	6.10	-0.2
2022	25	MIX MLE	2 levels	39	9	66	288	68	39	6	23%	1.7	24.0%	13.3%	10.6%	2.0%	5.43	3.88	+0.5
2022	25	MIX RAW	2 levels	39	9	66	288	68	39	6	23%	1.7	23.6%	13.5%	10.1%	2.1%	5.43	3.99	+0.4
2023	26	MLB MLE	Toronto Blue Jays	65	0	66	285	81	31	4	0%	1.0	28.4%	10.9%	17.5%	1.4%	3.29	2.85	+1.3
2023	26	MLB RAW	Toronto Blue Jays	65	0	66	285	81	31	4	0%	1.0	28.4%	10.9%	17.5%	1.4%	3.29	2.84	+1.3
2024	27	MLB MLE	San Francisco Giants	29	20	110	481	96	47	12	69%	3.8	20.0%	9.8%	10.2%	2.7%	4.10	4.13	+1.6
2024	27	MLB MLE	San Francisco Giants	29	20	110	481	96	47	12	69%	3.8	20.0%	9.8%	10.2%	2.7%	4.10	4.13	+1.6
2024	27	MLB RAW	San Francisco Giants	29	20	110	481	96	47	12	69%	3.8	20.0%	9.8%	10.2%	2.5%	4.10	4.06	+1.7
2025	28	MIX MLE	2 levels	36	10	69	325	60	33	7	28%	1.9	18.5%	9.9%	8.6%	2.0%	7.04	3.87	+0.6
2025	28	MIX MLE	2 levels	36	10	69	325	60	33	7	28%	1.9	18.5%	9.9%	8.6%	2.0%	7.04	3.87	+0.6
2025	28	MIX RAW	2 levels	36	10	69	325	60	33	7	28%	1.9	18.5%	10.2%	8.3%	2.2%	7.04	4.11	+0.4
2026	29	MIX MLE	2 levels	56	2	51	229	70	31	5	4%	0.9	29.6%	13.4%	16.2%	1.8%	4.26	3.30	+0.8
2026	29	MIX MLE	2 levels	56	2	51	229	70	31	5	4%	0.9	29.6%	13.4%	16.2%	1.8%	4.26	3.30	+0.8
2026	29	MIX RAW	2 levels	56	2	51	229	70	31	5	4%	0.9	30.6%	13.5%	17.0%	2.2%	4.26	3.46	+0.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			328	39	468	2052	473	237	35	12%	1.4	23.1%	11.5%	11.5%	1.7%	4.36	3.57	+5.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	30	MLB	CWS	42	0	44	186	46	21	4	0%	1.0	24.8%	11.5%	13.3%	2.4%	4.35	4.15	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	31	MLB MLE	CWS	33	0	35	147	35	17	4	0%	1.1	24.0%	11.8%	12.2%	2.6%	4.37	4.38	+0.1
2029	32	MLB MLE	CWS	24	0	25	105	24	13	3	0%	1.0	23.2%	12.2%	11.0%	2.9%	4.53	4.64	-0.0
2028	31	MLB CTX	CWS	33	0	35	148	35	17	4	0%	1.1	24.0%	11.8%	12.2%	2.6%	4.37	4.38	+0.1
2029	32	MLB CTX	CWS	24	0	25	106	25	13	3	0%	1.0	23.2%	12.2%	11.0%	2.9%	4.53	4.64	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		385	39	528	2303	533	267	42	10%	1.4	23.1%	11.6%	11.5%	1.8%	4.37	3.61	+5.9
/ 200 IP	—	RAW		146	15	200	873	202	101	16	10%	1.4	23.1%	11.6%	11.5%	1.8%	4.37	3.61	+2.2
Career	—	MLE		368	69	616	2700	573	315	45	19%	1.7	21.2%	11.7%	9.5%	1.7%	4.24	3.71	+5.5
/ 200 IP	—	MLE		120	22	200	877	186	102	15	19%	1.7	21.2%	11.7%	9.5%	1.7%	4.24	3.71	+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.

2022 AAAMemphis Redbirds · 5 IPFIP 5.63 → 6.05 (+0.41)K% 20.0% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.880	17.60%
BB% (unintentional) /BF	16.00%	—	×1.050	16.80%
HR / BF /BF	4.00%	÷1.048	×1.100	4.20%

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 110).

Result: FIP 5.63 → 6.05, 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).

2017 APeoria Chiefs · 78 IPFIP 3.47 → 4.28 (+0.82)K% 18.2% → 13.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.21%	—	×0.740	13.47%
BB% (unintentional) /BF	11.27%	—	×1.180	13.30%
HR / BF /BF	0.87%	÷0.970	×1.300	1.16%

Factors used: pitcher A level factors — K ×0.74, BB ×1.18, HR ×1.30. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 94).

Result: FIP 3.47 → 4.28, 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+Palm Beach Cardinals · 27 IPFIP 1.27 → 1.96 (+0.69)K% 30.2% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.19%	—	×0.770	23.25%
BB% (unintentional) /BF	5.66%	—	×1.150	6.51%
HR / BF /BF	0.00%	÷0.824	×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 65).

Result: FIP 1.27 → 1.96, 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 ROKJohnson City Cardinals · 30 IPFIP 3.48 → 4.37 (+0.89)K% 15.2% → 10.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.15%	—	×0.680	10.30%
BB% (unintentional) /BF	9.85%	—	×1.250	12.31%
HR / BF /BF	0.76%	÷1.000	×1.400	1.06%

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 3.48 → 4.37, 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
Jordan Hicks (OGILVIE)	—	—	4.15	24.8%	11.5%	2.4%	—	4.49	23.7%	11.7%	60	+0.34	—	—	—
Tom Wilhelmsen R 2014 · 3.45 FIP	30	SU	3.45	22.7%	9.5%	1.9%	.196	4.64	17.5%	7.8%	148	+1.19	2.72	398	70
Jhan Mariñez R 2018 · 4.09 FIP	29	SU	4.09	21.2%	7.9%	3.2%	.331	—	—	—	—	—	3.16	253	70
Kerry Ligtenberg R 2001 · 3.20 FIP	30	SU	3.20	22.0%	8.7%	1.6%	.267	3.95	18.5%	11.1%	181	+0.75	2.94	381	70
Danny Farquhar R 2017 · 3.62 FIP	30	SU	3.62	22.3%	11.0%	1.8%	.247	—	—	—	—	—	1.67	351	69
Fernando Rodriguez Jr. R 2015 · 3.34 FIP	31	SU	3.34	26.5%	9.6%	2.3%	.241	3.89	23.3%	13.2%	105	+0.55	3.34	335	69
Troy Percival R 2000 · 4.46 FIP	30	SU	4.46	22.2%	11.8%	3.2%	.252	3.15	28.2%	18.8%	163	-1.31	2.15	709	69
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	69
Britt Reames R 2002 · 3.76 FIP	28	SU	3.76	24.7%	10.4%	2.6%	.323	—	—	—	—	—	3.76	204	69
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	69
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	69
Jose Veras R 2011 · 3.37 FIP	30	SU	3.37	25.9%	10.2%	2.1%	.254	3.67	24.9%	13.6%	176	+0.30	3.37	422	68
Manny Acosta R 2011 · 3.75 FIP	30	SU	3.75	22.5%	7.4%	2.9%	.321	4.49	21.3%	10.2%	47	+0.74	3.08	350	68
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	67
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	67
AJ Ramos R 2017 · 3.86 FIP	30	SU	3.86	27.9%	12.8%	2.7%	.288	5.02	25.0%	8.0%	20	+1.16	2.55	410	67
Ryan Tepera R 2018 · 3.90 FIP	30	SU	3.90	25.9%	8.7%	3.4%	.282	3.20	28.4%	19.9%	104	-0.70	2.35	445	67
Joel Hanrahan R 2012 · 4.54 FIP	30	SU	4.54	26.4%	14.2%	3.4%	.224	—	—	—	—	—	2.08	397	67
Fernando Rodney R 2007 · 3.48 FIP	30	SU	3.48	24.2%	9.4%	2.2%	.287	3.90	19.7%	8.1%	184	+0.42	1.85	933	66
Tyler Yates R 2008 · 3.90 FIP	30	SU	3.90	19.0%	11.2%	1.8%	.293	5.06	16.1%	5.4%	12	+1.16	3.23	248	66
Francisco Cordero R 2005 · 2.97 FIP	30	SU	2.97	26.2%	9.3%	1.7%	.295	2.95	28.0%	18.8%	209	-0.02	1.98	822	66

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	2.9	57	23.2%	11.9%	0.72	4.37
Juan Cruz ('12)	3.3	60	23.1%	12.1%	0.92	4.05
Horacio Pina ('68-'78, 2 WAR*)	2.2	62	20.7%*	11.2%*	0.84*	4.01*
Dennys Reyes ('11)	3.6	56	19.8%	12.3%	0.76	4.21
Greg Cadaret ('87-'98, 4 WAR*)	3.6	80	22.3%*	12.1%*	0.88*	4.14*
Curt Leskanic ('04)	3.6	65	20.6%	11.6%	1.01	4.36
George Frazier ('78-'87, 2 WAR*)	3.4	68	21.6%*	10.5%*	0.92*	4.42*