Taylor Clarke

P Arizona Diamondbacks

Age 34 RHP 6'3" / 210 lbs MLB peak 0.1 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	31	AAAMIL	22	15	68	4.90	4.31	19.1%	5.8%	—	K 16.4% · BB 5.2% · FIP 4.87
2025	32	MIX2 levels	61	0	70	3.49	3.86	20.0%	5.3%	0.6	K 19.5% · BB 5.1% · FIP 4.12
2026	33	MLBARI	71	2	73	2.96	3.61	15.6%	4.2%	0.8
2027PROJ	34	MLBARI	49	0	51	4.18	4.38	17.8%	5.5%	0.1
2028PROJ	35	MLBARI	28	0	29	4.40	4.62	16.9%	6.0%	-0.1
2029PROJ	36	MLBARI	14	0	15	4.73	4.97	15.8%	6.6%	-0.1

Last 3 seasons WAR 1.4 · Next 3 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo50

xwOBA vs41

K%10

Form4.38 ERA (proj 4.18) · K 19.4% (proj 17.8%) · BB 4.9% (proj 5.5%) · last 90d · 25 IP through Oct 27

CompsJean Machi (age 34) · Chris Jakubauskas (age 32) · Brendan Donnelly (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 115107vR 99

ERA4.18

FIP4.38

K%17.8%

BB%5.5%

K-BB%12.3%

Proj IP51

Value2027 · WAR

Runs saved vs league avg · 51 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p5094.5

Whiff%

p3222.7%

p1015.6%

xwOBA vs

p410.322

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	23	MIX MLE	3 levels	27	27	149	619	118	33	13	100%	5.5	15.0%	6.0%	9.0%	2.3%	3.31	3.83	—
2016	23	MIX RAW	3 levels	27	27	149	619	118	33	13	100%	5.5	19.1%	5.3%	13.7%	2.1%	3.31	3.31	—
2017	24	MIX MLE	2 levels	27	27	145	599	138	52	15	100%	5.4	19.0%	9.4%	9.6%	2.8%	3.35	4.24	—
2017	24	MIX RAW	2 levels	27	27	145	599	138	52	15	100%	5.4	23.0%	8.7%	14.4%	2.5%	3.35	3.62	—
2018	25	AAA MLE	Reno Aces	27	27	152	637	125	43	12	100%	5.6	17.3%	7.1%	10.2%	2.0%	4.03	3.63	—
2018	25	AAA RAW	Reno Aces	27	27	152	637	125	43	12	100%	5.6	19.6%	6.8%	12.9%	1.9%	4.03	3.33	—
2019	26	MIX MLE	3 levels	32	24	127	553	99	47	29	75%	4.0	17.4%	9.0%	8.4%	5.7%	5.44	5.91	-0.7
2019	26	MIX RAW	3 levels	32	24	127	553	99	47	29	75%	4.0	17.9%	8.5%	9.4%	5.2%	5.44	5.61	-0.3
2020	27	MLB MLE	Arizona Diamondbacks	12	5	43	183	40	19	8	42%	3.6	21.9%	10.4%	11.5%	4.6%	4.36	5.11	-0.3
2020	27	MLB RAW	Arizona Diamondbacks	12	5	43	183	40	19	8	42%	3.6	21.9%	10.4%	11.5%	4.4%	4.36	4.97	-0.2
2021	28	MIX MLE	3 levels	51	1	51	228	48	16	4	2%	1.0	20.1%	6.2%	13.9%	2.2%	5.12	3.38	+0.7
2021	28	MIX RAW	3 levels	51	1	51	228	48	16	4	2%	1.0	21.1%	7.0%	14.0%	1.8%	5.12	3.18	+0.8
2022	29	MIX MLE	2 levels	48	0	50	207	49	8	6	0%	1.0	23.6%	3.9%	19.7%	3.2%	3.96	3.36	+0.7
2022	29	MIX RAW	2 levels	48	0	50	207	49	8	6	0%	1.0	23.7%	3.9%	19.8%	2.9%	3.96	3.18	+0.8
2023	30	MLB MLE	Kansas City Royals	58	2	59	266	65	23	12	3%	1.0	24.4%	8.6%	15.8%	4.9%	5.95	4.81	-0.2
2023	30	MLB RAW	Kansas City Royals	58	2	59	266	65	23	12	3%	1.0	24.4%	8.6%	15.8%	4.5%	5.95	4.71	-0.1
2024	31	AAA MLE	Nashville Sounds	22	15	68	293	56	17	11	68%	3.1	16.4%	5.2%	11.2%	4.5%	4.90	4.87	—
2024	31	AAA MLE	Nashville Sounds	22	15	68	293	56	17	11	68%	3.1	16.4%	5.2%	11.2%	4.5%	4.90	4.87	—
2024	31	AAA RAW	Nashville Sounds	22	15	68	293	56	17	11	68%	3.1	19.1%	5.8%	13.3%	3.8%	4.90	4.31	—
2025	32	MIX MLE	2 levels	61	0	70	265	53	14	9	0%	1.1	19.5%	5.1%	14.4%	3.7%	3.49	4.12	+0.4
2025	32	MIX MLE	2 levels	61	0	70	265	53	14	9	0%	1.1	19.5%	5.1%	14.4%	3.7%	3.49	4.12	+0.4
2025	32	MIX RAW	2 levels	61	0	70	265	53	14	9	0%	1.1	20.0%	5.3%	14.7%	3.4%	3.49	3.86	+0.6
2026	33	MLB MLE	Arizona Diamondbacks	71	2	73	288	45	12	7	3%	1.0	15.6%	4.2%	11.5%	2.6%	2.96	3.72	+0.7
2026	33	MLB MLE	Arizona Diamondbacks	71	2	73	288	45	12	7	3%	1.0	15.6%	4.2%	11.5%	2.6%	2.96	3.72	+0.7
2026	33	MLB RAW	Arizona Diamondbacks	71	2	73	288	45	12	7	3%	1.0	15.6%	4.2%	11.5%	2.4%	2.96	3.61	+0.8
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			305	24	408	1709	349	113	68	8%	1.3	20.4%	6.6%	13.8%	4.0%	4.42	4.39	+0.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	ARI	49	0	51	215	38	12	7	0%	1.0	17.8%	5.5%	12.3%	3.1%	4.18	4.38	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	ARI	28	0	29	123	21	7	4	0%	1.0	16.9%	6.0%	10.8%	3.5%	4.50	4.72	-0.1
2029	36	MLB MLE	ARI	15	0	15	65	10	4	2	0%	1.0	15.8%	6.6%	9.2%	3.8%	4.85	5.09	-0.1
2030	37	MLB MLE	ARI	7	0	7	31	5	2	1	0%	1.1	14.7%	7.3%	7.4%	4.2%	5.23	5.49	-0.1
2028	35	MLB CTX	ARI	28	0	29	122	21	7	4	0%	1.0	16.9%	6.0%	10.8%	3.3%	4.40	4.62	-0.1
2029	36	MLB CTX	ARI	14	0	15	63	10	4	2	0%	1.1	15.8%	6.6%	9.2%	3.6%	4.73	4.97	-0.1
2030	37	MLB CTX	ARI	7	0	7	30	4	2	1	0%	1.0	14.7%	7.3%	7.4%	4.0%	5.10	5.36	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		354	24	460	1928	385	127	76	7%	1.3	19.9%	6.6%	13.4%	3.9%	4.44	4.40	+0.8
/ 200 IP	—	RAW		154	10	200	839	168	55	33	7%	1.3	19.9%	6.6%	13.4%	3.9%	4.44	4.40	+0.3
Career	—	MLE		436	130	988	4138	758	293	139	30%	2.3	18.3%	7.1%	11.2%	3.4%	4.13	4.29	+0.5
/ 200 IP	—	MLE		88	26	200	838	153	59	28	30%	2.3	18.3%	7.1%	11.2%	3.4%	4.13	4.29	+0.2

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 AAAOmaha Storm Chasers · 14 IPFIP 3.82 → 3.98 (+0.16)K% 15.3% → 13.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.25%	—	×0.860	13.12%
BB% (unintentional) /BF	8.47%	—	×0.900	7.63%
HR / BF /BF	1.69%	÷1.003	×1.100	1.86%

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 101).

Result: FIP 3.82 → 3.98, 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 · 68 IPFIP 4.28 → 4.87 (+0.58)K% 19.1% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.11%	—	×0.860	16.44%
BB% (unintentional) /BF	5.80%	—	×0.900	5.22%
HR / BF /BF	3.75%	÷0.909	×1.100	4.54%

Result: FIP 4.28 → 4.87, 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 AAAReno Aces · 7 IPFIP 1.54 → 1.88 (+0.35)K% 29.6% → 26.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.63%	—	×0.880	26.07%
BB% (unintentional) /BF	7.41%	—	×1.050	7.78%
HR / BF /BF	0.00%	÷0.958	×1.100	0.00%

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 1.54 → 1.88, 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 AAAReno Aces · 37 IPFIP 4.98 → 5.26 (+0.28)K% 17.1% → 15.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.07%	—	×0.880	15.02%
BB% (unintentional) /BF	10.37%	—	×1.050	10.88%
HR / BF /BF	3.66%	÷1.077	×1.100	3.74%

Result: FIP 4.98 → 5.26, 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+Visalia Rawhide · 6 IPFIP 1.83 → 2.12 (+0.29)K% 15.0% → 11.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.00%	—	×0.770	11.55%
BB% (unintentional) /BF	0.00%	—	×1.150	0.00%
HR / BF /BF	0.00%	÷1.095	×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 119).

Result: FIP 1.83 → 2.12, 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 AAAReno Aces · 152 IPFIP 3.33 → 3.63 (+0.30)K% 19.6% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.62%	—	×0.880	17.27%
BB% (unintentional) /BF	6.75%	—	×1.050	7.09%
HR / BF /BF	1.88%	÷1.044	×1.100	1.99%

Result: FIP 3.33 → 3.63, 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 AAJackson Generals · 111 IPFIP 3.04 → 3.71 (+0.67)K% 23.5% → 19.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.52%	—	×0.810	19.05%
BB% (unintentional) /BF	8.57%	—	×1.100	9.43%
HR / BF /BF	1.54%	÷0.985	×1.200	1.87%

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 97).

Result: FIP 3.04 → 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 AAAReno Aces · 34 IPFIP 5.47 → 5.91 (+0.44)K% 21.5% → 18.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.53%	—	×0.880	18.94%
BB% (unintentional) /BF	9.03%	—	×1.050	9.48%
HR / BF /BF	5.56%	÷1.044	×1.100	5.85%

Result: FIP 5.47 → 5.91, 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
Taylor Clarke (OGILVIE)	—	—	—	—	—	3.1%	—	4.85	16.5%	10.3%	44	+0.47	—	—	—
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	69
Chris Jakubauskas R 2011 · 4.42 FIP	32	SW	4.42	15.7%	7.9%	3.0%	.339	—	—	—	—	—	4.42	165	69
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	65
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	64
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	64
Juan Sandoval R 2015 · 3.82 FIP	34	SU	3.82	15.4%	6.0%	2.3%	.294	3.33	17.4%	8.8%	116	-0.49	3.24	225	64
Hector Navarro R 2015 · 4.35 FIP	35	SU	4.35	15.0%	5.4%	3.3%	.281	3.00	15.2%	9.0%	81	-1.35	2.30	126	64
Zack Segovia R 2016 · 3.33 FIP	33	SU	3.33	20.2%	5.9%	2.2%	.312	4.55	17.6%	4.0%	57	+1.22	3.33	144	63
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	63
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	63
Tiago Da Silva R 2019 · 3.95 FIP	34	SW	3.95	15.0%	7.2%	2.2%	.365	—	—	—	—	—	2.21	287	62
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	61
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	61
Rafael Martín R 2017 · 3.40 FIP	33	SU	3.40	20.4%	5.1%	2.5%	.305	4.71	22.5%	16.5%	47	+1.31	2.94	227	61
Josh Lueke R 2019 · 4.11 FIP	34	SU	4.11	20.4%	6.4%	3.5%	.311	—	—	—	—	—	2.29	191	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	60
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	60
Diego Moreno R 2021 · 4.75 FIP	34	SU	4.75	18.6%	9.4%	3.7%	.232	—	—	—	—	—	2.29	187	59
Burch Smith R 2024 · 3.50 FIP	34	SU	3.50	19.1%	5.0%	2.5%	.328	4.49	24.9%	14.5%	37	+0.99	3.50	418	58
Chris Nichting R 2001 · 4.11 FIP	35	SU	4.11	21.2%	3.7%	4.2%	.351	4.67	16.6%	13.3%	36	+0.56	4.11	103	58

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	2.6	46	19.7%	6.8%	1.49	4.42
Gabe White ('05)	2.9	57	19.0%	5.9%	1.51	4.51
Bob Patterson ('85-'98, 4 WAR*)	3.1	51	23.0%*	6.7%*	1.32*	4.33*
Joel Peralta ('16)	3.2	54	23.0%	7.0%	1.32	4.03
Justin Speier ('09)	3.4	56	20.4%	8.0%	1.44	4.11
Randy St. Claire ('84-'94, -0 WAR*)	1.3	50	19.0%*	8.4%*	1.39*	4.61*
Bob Lacey ('77-'84, 2 WAR*)	2.3	75	20.5%*	7.3%*	1.35*	4.11*