Chris Flexen

P Chicago Cubs

Age 33 RHP 6'3" / 220 lbs MLB peak 0.3 · 2027

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	29	MIX2 levels	31	18	112	6.37	5.76	16.9%	8.5%	-0.5	K 15.9% · BB 8.2% · FIP 5.40
2024	30	MLBCWS	33	30	160	4.95	4.67	17.4%	8.8%	1.3
2025	31	MIX2 levels	26	6	67	2.42	4.07	15.8%	7.4%	0.4	K 14.7% · BB 7.1% · FIP 4.22
2026	32	No MLB games played
2027PROJ	33	MLBCHC	9	9	52	4.77	4.89	15.6%	8.1%	0.3
2028PROJ	34	MLBCHC	6	6	33	5.02	5.19	14.7%	8.6%	0.1
2029PROJ	35	MLBCHC	3	3	19	5.32	5.53	13.8%	9.1%	-0.0

Last 3 seasons WAR 1.3 · Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

FB Velo21

xwOBA vs21

K%10

CompsOsvaldo Fernandez (age 32) · Miguel Ruiz (age 31) · Andres Ivan Meza (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 127119vR 111

ERA4.77

FIP4.89

K%15.6%

BB%8.1%

K-BB%7.6%

Proj IP52

Value2027 · WAR

Runs saved vs league avg · 52 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2192.4

Whiff%

p1016.7%

p1012.4%

xwOBA vs

p210.346

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	MIX MLE	2 levels	9	7	40	154	38	8	0	78%	4.4	18.4%	6.2%	12.2%	0.0%	1.59	2.34	—
2015	21	MIX RAW	2 levels	9	7	40	154	38	8	0	78%	4.4	24.7%	5.2%	19.5%	0.0%	1.59	1.79	—
2016	22	A+ MLE	St. Lucie Mets	25	25	134	572	95	51	6	100%	5.4	12.8%	10.3%	2.5%	1.4%	3.56	4.08	—
2016	22	A+ RAW	St. Lucie Mets	25	25	134	572	95	51	6	100%	5.4	16.6%	8.9%	7.7%	1.0%	3.56	3.41	—
2017	23	MIX MLE	3 levels	24	19	109	465	99	45	16	79%	4.6	18.6%	9.9%	8.7%	3.4%	4.45	4.66	+0.9
2017	23	MIX RAW	3 levels	24	19	109	465	99	45	16	79%	4.6	21.3%	9.7%	11.6%	3.4%	4.45	4.43	+1.2
2018	24	MIX MLE	2 levels	22	18	98	437	81	35	13	82%	4.5	17.3%	7.9%	9.4%	3.0%	4.94	4.30	+1.3
2018	24	MIX RAW	2 levels	22	18	98	437	81	35	13	82%	4.5	18.5%	8.0%	10.5%	3.0%	4.94	4.24	+1.3
2019	25	MIX MLE	2 levels	35	15	92	415	102	32	12	43%	2.6	21.9%	8.0%	14.0%	3.3%	4.78	4.06	+0.5
2019	25	MIX RAW	2 levels	35	15	92	415	102	32	12	43%	2.6	24.6%	7.7%	16.9%	2.9%	4.78	3.62	+1.0
2021	27	MLB MLE	Seattle Mariners	31	31	180	741	125	40	19	100%	5.8	16.9%	5.4%	11.5%	2.6%	3.61	3.80	+3.5
2021	27	MLB RAW	Seattle Mariners	31	31	180	741	125	40	19	100%	5.8	16.9%	5.4%	11.5%	2.6%	3.61	3.75	+3.6
2022	28	MLB MLE	Seattle Mariners	33	22	138	590	95	48	17	67%	4.2	16.1%	8.1%	8.0%	3.0%	3.73	4.39	+1.6
2022	28	MLB RAW	Seattle Mariners	33	22	138	590	95	48	17	67%	4.2	16.1%	8.1%	8.0%	2.9%	3.73	4.37	+1.7
2023	29	MIX MLE	2 levels	31	18	112	503	85	43	26	58%	3.6	15.9%	8.2%	7.7%	4.8%	6.37	5.40	-0.0
2023	29	MIX RAW	2 levels	31	18	112	503	85	43	26	58%	3.6	16.9%	8.5%	8.3%	5.2%	6.37	5.76	-0.5
2024	30	MLB MLE	Chicago White Sox	33	30	160	707	123	62	24	91%	4.8	17.4%	8.8%	8.6%	3.3%	4.95	4.57	+1.5
2024	30	MLB MLE	Chicago White Sox	33	30	160	707	123	62	24	91%	4.8	17.4%	8.8%	8.6%	3.3%	4.95	4.57	+1.5
2024	30	MLB RAW	Chicago White Sox	33	30	160	707	123	62	24	91%	4.8	17.4%	8.8%	8.6%	3.4%	4.95	4.67	+1.3
2025	31	MIX MLE	2 levels	26	6	67	272	43	20	7	23%	2.6	14.7%	7.1%	7.7%	2.7%	2.42	4.22	+0.3
2025	31	MIX MLE	2 levels	26	6	67	272	43	20	7	23%	2.6	14.7%	7.1%	7.7%	2.7%	2.42	4.22	+0.3
2025	31	MIX RAW	2 levels	26	6	67	272	43	20	7	23%	2.6	15.8%	7.4%	8.5%	2.6%	2.42	4.07	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			174	111	691	3025	488	251	106	64%	4.0	16.1%	8.3%	7.8%	3.5%	4.83	4.77	+5.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	CHC	9	9	52	220	34	18	7	100%	5.8	15.6%	8.1%	7.6%	3.1%	4.77	4.89	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	CHC	5	5	33	139	20	12	5	100%	6.6	14.7%	8.6%	6.2%	3.4%	5.02	5.19	+0.1
2029	35	MLB MLE	CHC	3	3	19	79	11	7	3	100%	6.2	13.8%	9.1%	4.7%	3.7%	5.32	5.53	-0.0
2028	34	MLB CTX	CHC	6	6	33	139	20	12	5	100%	5.5	14.7%	8.6%	6.2%	3.4%	5.02	5.19	+0.1
2029	35	MLB CTX	CHC	3	3	19	80	11	7	3	100%	6.3	13.8%	9.1%	4.7%	3.7%	5.32	5.53	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		183	120	743	3242	519	270	114	66%	4.1	16.0%	8.3%	7.7%	3.5%	4.85	4.73	+5.8
/ 200 IP	—	RAW		49	32	200	873	140	73	31	65%	4.1	16.0%	8.3%	7.7%	3.5%	4.85	4.73	+1.6
Career	—	MLE		269	191	1130	4856	817	392	142	71%	4.2	16.8%	8.1%	8.8%	2.9%	4.23	4.31	+5.1
/ 200 IP	—	MLE		48	34	200	860	145	69	25	71%	4.2	16.8%	8.1%	8.8%	2.9%	4.23	4.31	+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.

2025 AAAIowa Cubs · 23 IPFIP 2.29 → 2.45 (+0.16)K% 22.3% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.34%	—	×0.860	19.21%
BB% (unintentional) /BF	8.51%	—	×0.900	7.66%
HR / BF /BF	0.00%	÷1.125	×1.100	0.00%

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 125).

Result: FIP 2.29 → 2.45, 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 AAAAlbuquerque Isotopes · 9 IPFIP 3.78 → 4.02 (+0.23)K% 29.7% → 25.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.73%	—	×0.860	25.57%
BB% (unintentional) /BF	13.51%	—	×0.900	12.16%
HR / BF /BF	2.70%	÷1.062	×1.100	2.80%

Result: FIP 3.78 → 4.02, 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 AAASyracuse Mets · 79 IPFIP 3.37 → 3.93 (+0.56)K% 26.7% → 23.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.67%	—	×0.880	23.47%
BB% (unintentional) /BF	6.09%	—	×1.050	6.39%
HR / BF /BF	3.19%	÷0.960	×1.100	3.65%

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 3.37 → 3.93, 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 AAALas Vegas 51s · 92 IPFIP 3.92 → 4.30 (+0.38)K% 19.6% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.65%	—	×0.880	17.29%
BB% (unintentional) /BF	7.56%	—	×1.050	7.93%
HR / BF /BF	2.77%	÷1.009	×1.100	3.02%

Result: FIP 3.92 → 4.30, 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 AABinghamton Rumble Ponies · 49 IPFIP 2.46 → 2.81 (+0.35)K% 27.9% → 22.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.93%	—	×0.810	22.63%
BB% (unintentional) /BF	3.91%	—	×1.100	4.30%
HR / BF /BF	2.23%	÷1.365	×1.200	1.96%

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 173).

Result: FIP 2.46 → 2.81, 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+St. Lucie Mets · 13 IPFIP 2.78 → 3.75 (+0.97)K% 24.5% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.53%	—	×0.770	18.89%
BB% (unintentional) /BF	5.66%	—	×1.150	6.51%
HR / BF /BF	1.89%	÷0.911	×1.250	2.59%

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 82).

Result: FIP 2.78 → 3.75, 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+St. Lucie Mets · 134 IPFIP 3.40 → 4.08 (+0.68)K% 16.6% → 12.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.61%	—	×0.770	12.79%
BB% (unintentional) /BF	8.92%	—	×1.150	10.25%
HR / BF /BF	1.05%	÷0.943	×1.250	1.39%

Result: FIP 3.40 → 4.08, 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).

2015 ASavannah Sand Gnats · 34 IPFIP 1.67 → 2.34 (+0.66)K% 24.8% → 18.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.81%	—	×0.740	18.36%
BB% (unintentional) /BF	5.26%	—	×1.180	6.21%
HR / BF /BF	0.00%	÷0.804	×1.300	0.00%

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 61).

Result: FIP 1.67 → 2.34, 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
Chris Flexen (OGILVIE)	—	—	—	—	—	3.1%	—	5.31	14.4%	5.6%	52	+0.42	—	—	—
Osvaldo Fernandez R 2001 · 4.53 FIP	32	SP-	4.53	9.6%	8.2%	2.2%	.324	—	—	—	—	—	4.25	387	70
Miguel Ruiz R 2017 · 5.60 FIP	31	SP	5.60	10.6%	11.0%	3.6%	.279	—	—	—	—	—	5.60	117	65
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	64
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	63
Marco Quevedo R 2019 · 5.17 FIP	32	SP-	5.17	13.6%	11.4%	3.2%	.344	—	—	—	—	—	4.13	300	63
Deck McGuire R 2022 · 5.12 FIP	33	SP-	5.12	15.7%	13.1%	3.1%	.279	—	—	—	—	—	3.41	622	61
Rolando Valdez R 2019 · 5.36 FIP	33	SP-	5.36	11.7%	7.6%	4.2%	.360	—	—	—	—	—	3.06	258	61
David Fletcher R 2024 · 5.67 FIP	30	SP-	5.67	7.5%	10.3%	3.5%	.294	—	—	—	—	—	5.67	100	61
Austin Bibens-Dirkx R 2018 · 4.89 FIP	33	SP-	4.89	17.9%	7.6%	4.3%	.322	4.82	15.9%	9.5%	147	-0.07	3.97	567	61
Mike Burns R 2009 · 4.90 FIP	31	SP-	4.90	17.2%	6.6%	4.4%	.307	—	—	—	—	—	3.10	104	60
Juan Delgadillo R 2015 · 4.54 FIP	32	SP	4.54	15.5%	7.2%	3.3%	.316	4.47	10.9%	4.7%	39	-0.07	4.47	143	60
Juan Omar Espinoza R 2016 · 4.84 FIP	32	SP-	4.84	8.4%	9.2%	2.4%	.348	—	—	—	—	—	4.81	104	60
Elih Villanueva R 2017 · 5.48 FIP	31	SP	5.48	14.1%	6.0%	5.1%	.365	—	—	—	—	—	3.83	225	60
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	59
Alfredo Figaro R 2016 · 3.75 FIP	32	SP-	3.75	10.2%	5.0%	1.6%	.270	—	—	—	—	—	3.75	143	59
Scott Carroll R 2017 · 5.71 FIP	32	SP	5.71	14.4%	8.6%	5.0%	.300	—	—	—	—	—	3.92	377	58
Walter Silva R 2009 · 5.72 FIP	32	SP	5.72	9.1%	12.4%	3.3%	.330	—	—	—	—	—	3.67	453	57
Diego Moreno R 2019 · 4.08 FIP	32	SP	4.08	12.4%	6.9%	2.1%	.376	4.75	18.6%	9.2%	30	+0.67	2.29	187	57
Thad Weber R 2017 · 5.25 FIP	32	SP-	5.25	13.2%	4.4%	4.9%	.398	—	—	—	—	—	4.40	378	56
Yohan Pino R 2017 · 5.33 FIP	33	SP-	5.33	15.2%	3.6%	5.6%	.296	—	—	—	—	—	3.84	193	55

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	4.0	88	16.0%	8.5%	1.35	4.85
Ross Ohlendorf ('16)	2.9	84	17.2%	8.6%	1.38	4.82
Mark Grant ('84-'93, 0 WAR*)	3.2	91	18.0%*	8.5%*	1.41*	4.84*
Amaury Telemaco ('05)	2.8	70	15.0%	8.0%	1.48	4.94
Willie Fraser ('86-'95, 0 WAR*)	3.3	110	15.2%*	8.2%*	1.47*	4.73*
Scott Bailes ('86-'98, -3 WAR*)	3.4	76	15.4%*	8.2%*	1.35*	5.02*
Claudio Vargas ('10)	3.8	109	16.2%	8.7%	1.49	4.83