Clay Holmes

P Chicago Cubs

Age 34 RHP 6'5" / 245 lbs MLB peak 0.3 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	31	MLBNYY	67	0	63	3.14	2.77	25.1%	7.7%	1.3
2025	32	MLBNYM	33	31	166	3.53	3.84	18.2%	9.3%	3.1
2026	33	MIX4 levels	21	21	113	3.35	3.49	18.3%	9.8%	2.6	K 18.6% · BB 10.0% · FIP 3.61
2027PROJ	34	MLBCHC	78	0	82	4.01	4.20	19.1%	9.6%	0.3
2028PROJ	35	MLBCHC	44	0	46	4.33	4.54	18.1%	10.2%	0.0
2029PROJ	36	MLBCHC	22	0	23	4.68	4.91	17.1%	10.8%	-0.1

Last 3 seasons WAR 7.1 · 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

xwOBA vs59

FB Velo27

K%25

Form6.31 ERA (proj 4.01) · K 19.8% (proj 19.1%) · BB 14.7% (proj 9.6%) ▼ last 60d · 26 IP through Oct 27

CompsRolando Arrojo (age 34) · Chris Jakubauskas (age 32) · Jean Machi (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 108100vR 92

ERA4.01

FIP4.20

K%19.1%

BB%9.6%

K-BB%9.4%

Proj IP82

Value2027 · WAR

Runs saved vs league avg · 82 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2792.9

Whiff%

p2621.9%

p2518.6%

xwOBA vs

p590.303

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	MIX MLE	2 levels	9	9	36	149	26	8	0	100%	4.0	12.8%	6.2%	6.6%	0.0%	2.48	2.81	—
2015	22	MIX RAW	2 levels	9	9	36	149	26	8	0	100%	4.0	17.4%	5.4%	12.1%	0.0%	2.48	2.33	—
2016	23	AA MLE	Altoona Curve	26	26	136	597	101	63	10	100%	5.2	13.7%	11.6%	2.1%	2.0%	4.22	4.50	—
2016	23	AA RAW	Altoona Curve	26	26	136	597	101	63	10	100%	5.2	16.9%	10.6%	6.4%	1.7%	4.22	3.96	—
2017	24	AAA MLE	Indianapolis Indians	25	24	113	475	99	59	4	96%	4.5	18.3%	13.0%	5.3%	1.1%	3.36	3.80	—
2017	24	AAA RAW	Indianapolis Indians	25	24	113	475	99	59	4	96%	4.5	20.8%	12.4%	8.4%	0.8%	3.36	3.38	—
2018	25	MIX MLE	3 levels	34	21	128	560	129	62	6	62%	3.8	20.3%	11.9%	8.4%	1.2%	4.02	3.55	+2.9
2018	25	MIX RAW	3 levels	34	21	128	560	129	62	6	62%	3.8	23.0%	11.1%	12.0%	1.1%	4.02	3.15	+3.5
2019	26	MIX MLE	3 levels	47	0	70	339	72	50	6	0%	1.5	21.1%	15.5%	5.5%	2.0%	5.81	4.41	+0.1
2019	26	MIX RAW	3 levels	47	0	70	339	72	50	6	0%	1.5	21.2%	14.7%	6.5%	1.8%	5.81	4.31	+0.2
2020	27	MLB MLE	Pittsburgh Pirates	1	0	1	6	1	0	0	0%	1.3	16.7%	0.0%	16.7%	0.0%	0.00	1.69	+0.0
2020	27	MLB RAW	Pittsburgh Pirates	1	0	1	6	1	0	0	0%	1.3	16.7%	0.0%	16.7%	0.0%	0.00	1.60	+0.0
2021	28	MLB MLE	New York Yankees	69	0	70	292	78	27	5	0%	1.0	26.7%	9.2%	17.5%	1.6%	3.60	2.90	+1.4
2021	28	MLB RAW	New York Yankees	69	0	70	292	78	27	5	0%	1.0	26.7%	9.2%	17.5%	1.7%	3.60	2.96	+1.3
2022	29	MLB MLE	New York Yankees	62	0	64	260	65	18	2	0%	1.0	25.0%	6.9%	18.1%	0.7%	2.54	2.26	+1.8
2022	29	MLB RAW	New York Yankees	62	0	64	260	65	18	2	0%	1.0	25.0%	6.9%	18.1%	0.8%	2.54	2.31	+1.7
2023	30	MLB MLE	New York Yankees	66	0	63	262	71	23	2	0%	1.0	27.1%	8.8%	18.3%	0.7%	2.86	2.32	+1.7
2023	30	MLB RAW	New York Yankees	66	0	63	262	71	23	2	0%	1.0	27.1%	8.8%	18.3%	0.8%	2.86	2.35	+1.7
2024	31	MLB MLE	New York Yankees	67	0	63	271	68	21	4	0%	0.9	25.1%	7.7%	17.3%	1.4%	3.14	2.72	+1.4
2024	31	MLB MLE	New York Yankees	67	0	63	271	68	21	4	0%	0.9	25.1%	7.7%	17.3%	1.4%	3.14	2.72	+1.4
2024	31	MLB RAW	New York Yankees	67	0	63	271	68	21	4	0%	0.9	25.1%	7.7%	17.3%	1.5%	3.14	2.77	+1.3
2025	32	MLB MLE	New York Mets	33	31	166	708	129	66	14	94%	5.0	18.2%	9.3%	8.9%	2.0%	3.53	3.83	+3.1
2025	32	MLB MLE	New York Mets	33	31	166	708	129	66	14	94%	5.0	18.2%	9.3%	8.9%	2.0%	3.53	3.83	+3.1
2025	32	MLB RAW	New York Mets	33	31	166	708	129	66	14	94%	5.0	18.2%	9.3%	8.9%	2.0%	3.53	3.84	+3.1
2026	33	MIX MLE	4 levels	21	21	113	469	86	46	6	100%	5.4	18.6%	10.0%	8.6%	1.5%	3.35	3.61	+2.4
2026	33	MIX MLE	4 levels	21	21	113	469	86	46	6	100%	5.4	18.6%	10.0%	8.6%	1.5%	3.35	3.61	+2.4
2026	33	MIX RAW	4 levels	21	21	113	469	86	46	6	100%	5.4	18.3%	9.8%	8.5%	1.3%	3.35	3.49	+2.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			362	53	604	2587	567	254	40	15%	1.7	21.9%	9.8%	12.1%	1.5%	3.59	3.34	+8.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	CHC	78	0	82	346	66	33	7	0%	1.1	19.1%	9.6%	9.4%	2.0%	4.01	4.20	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	CHC	44	0	46	193	35	20	5	0%	1.0	18.1%	10.2%	7.9%	2.4%	4.33	4.54	+0.0
2029	36	MLB MLE	CHC	22	0	23	98	17	11	3	0%	1.1	17.1%	10.8%	6.3%	2.7%	4.68	4.91	-0.1
2028	35	MLB CTX	CHC	44	0	46	194	35	20	5	0%	1.0	18.1%	10.2%	7.9%	2.4%	4.33	4.54	+0.0
2029	36	MLB CTX	CHC	22	0	23	97	17	10	3	0%	1.0	17.1%	10.8%	6.3%	2.7%	4.68	4.91	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		428	53	673	2878	619	284	47	12%	1.6	21.5%	9.9%	11.6%	1.6%	3.68	3.44	+8.9
/ 200 IP	—	RAW		127	16	200	855	184	84	14	12%	1.6	21.5%	9.9%	11.6%	1.6%	3.68	3.44	+2.7
Career	—	MLE		460	132	1022	4388	872	462	64	29%	2.2	19.9%	10.5%	9.4%	1.5%	3.64	3.55	+8.6
/ 200 IP	—	MLE		90	26	200	858	171	90	12	29%	2.2	19.9%	10.5%	9.4%	1.5%	3.64	3.55	+2.6

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.

2026 A+Brooklyn Cyclones · 5 IPFIP 2.00 → 2.25 (+0.25)K% 13.0% → 10.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.04%	—	×0.770	10.04%
BB% (unintentional) /BF	0.00%	—	×1.150	0.00%
HR / BF /BF	0.00%	÷1.000	×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 100).

Result: FIP 2.00 → 2.25, 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 AAAIndianapolis Indians · 16 IPFIP 4.63 → 5.01 (+0.38)K% 16.2% → 14.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.25%	—	×0.880	14.30%
BB% (unintentional) /BF	17.50%	—	×1.050	18.38%
HR / BF /BF	1.25%	÷0.953	×1.100	1.44%

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 91).

Result: FIP 4.63 → 5.01, 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).

2018 AAAIndianapolis Indians · 95 IPFIP 2.81 → 3.15 (+0.34)K% 24.4% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.45%	—	×0.880	21.52%
BB% (unintentional) /BF	9.78%	—	×1.050	10.27%
HR / BF /BF	0.98%	÷1.041	×1.100	1.03%

Result: FIP 2.81 → 3.15, 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 A+Bradenton Marauders · 6 IPFIP 0.03 → 0.74 (+0.71)K% 36.4% → 28.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.36%	—	×0.770	28.00%
BB% (unintentional) /BF	0.00%	—	×1.150	0.00%
HR / BF /BF	0.00%	÷1.066	×1.250	0.00%

Result: FIP 0.03 → 0.74, 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 AAAIndianapolis Indians · 113 IPFIP 3.38 → 3.80 (+0.43)K% 20.8% → 18.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.84%	—	×0.880	18.34%
BB% (unintentional) /BF	12.42%	—	×1.050	13.04%
HR / BF /BF	0.84%	÷0.850	×1.100	1.09%

Result: FIP 3.38 → 3.80, 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 AAAltoona Curve · 136 IPFIP 3.93 → 4.50 (+0.57)K% 16.9% → 13.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.92%	—	×0.810	13.70%
BB% (unintentional) /BF	10.55%	—	×1.100	11.61%
HR / BF /BF	1.68%	÷1.016	×1.200	1.98%

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 103).

Result: FIP 3.93 → 4.50, 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 A+Bradenton Marauders · 23 IPFIP 2.60 → 3.08 (+0.48)K% 17.2% → 13.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.20%	—	×0.770	13.25%
BB% (unintentional) /BF	7.53%	—	×1.150	8.66%
HR / BF /BF	0.00%	÷0.974	×1.250	0.00%

Result: FIP 2.60 → 3.08, 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 ROKGCL Pirates · 13 IPFIP 1.82 → 2.36 (+0.54)K% 17.9% → 12.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.86%	—	×0.680	12.14%
BB% (unintentional) /BF	1.79%	—	×1.250	2.23%
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 1.82 → 2.36, 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
Clay Holmes (OGILVIE)	—	—	—	—	—	2.0%	—	4.66	17.7%	7.4%	69	+0.46	—	—	—
Rolando Arrojo R 2002 · 3.91 FIP	34	SW	3.91	14.7%	7.5%	2.0%	.288	—	—	—	—	—	3.50	700	75
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	65
Jean Machi R 2016 · 4.36 FIP	34	SU	4.36	18.3%	6.6%	3.6%	.282	4.12	18.6%	12.0%	67	-0.24	2.16	306	62
Fernando Rodriguez Jr. R 2018 · 4.07 FIP	34	SW	4.07	23.7%	9.9%	3.1%	.253	5.11	25.0%	13.8%	32	+1.04	3.34	335	61
Matt Herges R 2004 · 4.18 FIP	34	SU	4.18	13.0%	5.6%	2.7%	.346	4.29	11.7%	4.5%	149	+0.11	2.90	691	58
Giovanni Carrara R 2002 · 4.78 FIP	34	SU	4.78	14.5%	7.2%	3.6%	.239	3.83	17.1%	7.9%	158	-0.95	2.50	462	58
Ivan Maldonado R 2015 · 4.60 FIP	35	SW	4.60	12.8%	8.7%	2.7%	.269	5.41	6.4%	-3.3%	104	+0.81	4.60	172	57
Anthony Telford R 2000 · 3.87 FIP	34	SU	3.87	20.6%	6.7%	3.0%	.287	4.54	16.2%	5.1%	24	+0.67	3.16	441	56
Tiago Da Silva R 2019 · 3.95 FIP	34	SW	3.95	15.0%	7.2%	2.2%	.365	—	—	—	—	—	2.21	287	55
Brendan Donnelly R 2005 · 4.02 FIP	34	SU	4.02	19.6%	5.9%	3.3%	.264	4.02	17.6%	8.5%	98	-0.00	2.23	385	55
Rudy Gonzalez R 2015 · 4.52 FIP	34	SW	4.52	13.5%	9.6%	2.4%	.347	3.84	14.0%	6.5%	79	-0.68	3.31	119	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 2024 · 4.56 FIP	34	SU	4.56	18.6%	12.2%	2.7%	.289	3.82	20.3%	13.1%	57	-0.74	3.55	429	53
Nelson Figueroa R 2008 · 3.94 FIP	34	SW	3.94	17.1%	11.8%	1.4%	.306	3.96	17.5%	9.9%	192	+0.02	3.71	499	53
Huascar Brazobán R 2024 · 3.08 FIP	34	SU	3.08	23.5%	11.5%	0.9%	.259	3.71	21.3%	11.7%	144	+0.63	3.08	370	53
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	52
Dale Thayer R 2015 · 4.34 FIP	34	SU	4.34	15.8%	7.0%	3.1%	.274	—	—	—	—	—	2.75	239	52
Junior Guerra R 2019 · 4.13 FIP	34	SU	4.13	22.4%	9.9%	3.0%	.212	4.12	20.0%	5.9%	89	-0.01	3.18	646	52
Marco Antonio Duarte R 2019 · 5.03 FIP	32	SW	5.03	12.2%	9.1%	3.3%	.420	—	—	—	—	—	4.07	203	51
Tim Dillard R 2017 · 3.62 FIP	34	SU	3.62	15.1%	8.8%	1.2%	.310	4.32	13.2%	7.1%	188	+0.70	2.92	432	51

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	3.8	69	21.2%	10.1%	0.65	3.71
Eddie Watt ('66-'75, 9 WAR*)	3.3	73	22.0%*	9.1%*	0.73*	3.64*
Joe Kelly ('24)	4.2	70	21.1%	9.8%	0.78	3.98
Zack Britton ('22)	3.2	71	20.1%	10.0%	0.55	3.13
Hector Carrasco ('07)	4.2	69	18.1%	10.6%	0.75	4.00
Darold Knowles ('65-'80, 10 WAR*)	5.5	78	20.1%*	10.2%*	0.78*	3.77*
Fred Gladding ('61-'73, 6 WAR*)	3.0	67	19.6%*	8.6%*	0.76*	3.76*