Chris Devenski

P Syracuse Mets

Age 36 RHP 6'3" / 208 lbs AAA peak 0.3 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	33	MIX2 levels	45	0	56	4.85	4.70	26.4%	10.0%	-0.1	K 24.1% · BB 9.6% · FIP 4.85
2025	34	MIX2 levels	48	2	54	2.98	4.04	21.0%	5.9%	0.3	K 19.0% · BB 5.6% · FIP 4.11
2026	35	MIX4 levels	26	1	32	3.69	3.57	28.2%	6.1%	0.4	K 33.1% · BB 6.9% · FIP 3.04
2027PROJ	36	MLBNYM	131	0	138	4.31	4.26	23.5%	7.4%	0.5
2028PROJ	37	MLBNYM	93	0	98	4.52	4.66	22.4%	8.1%	-0.1
2029PROJ	38	MLBNYM	68	0	71	4.86	5.09	21.3%	8.8%	-0.4

Last 3 seasons WAR 0.6 · Next 3 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

xwOBA vs62

K%45

FB Velo21

CompsRafael Martín (age 35) · Keiichi Yabu (age 36) · Chris Nichting (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 114106vR 98

ERA4.31

FIP4.26

K%23.5%

BB%7.4%

K-BB%16.1%

Proj IP138

Value2027 · WAR

Runs saved vs league avg · 138 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL4040

CMD4040

K†5555

Statcast Percentiles

FB Velo

p2192.4

Whiff%

p2621.9%

p4521.5%

xwOBA vs

p620.300

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	24	AA MLE	Corpus Christi Hooks	24	17	120	501	104	33	12	71%	5.0	16.8%	7.2%	9.6%	2.7%	3.01	4.09	—
2015	24	AA RAW	Corpus Christi Hooks	24	17	120	501	104	33	12	71%	5.0	20.8%	6.6%	14.2%	2.4%	3.01	3.49	—
2016	25	MLB MLE	Houston Astros	48	5	108	408	104	20	4	10%	2.3	25.5%	4.9%	20.6%	1.0%	2.16	2.12	+3.2
2016	25	MLB RAW	Houston Astros	48	5	108	408	104	20	4	10%	2.3	25.5%	4.9%	20.6%	1.0%	2.16	2.21	+3.1
2017	26	MLB MLE	Houston Astros	62	0	81	316	100	23	11	0%	1.3	31.6%	7.3%	24.4%	3.6%	2.68	3.31	+1.2
2017	26	MLB RAW	Houston Astros	62	0	81	316	100	23	11	0%	1.3	31.6%	7.3%	24.4%	3.5%	2.68	3.25	+1.3
2018	27	MIX MLE	3 levels	55	3	52	221	61	13	11	5%	0.9	26.0%	6.1%	19.9%	4.7%	4.50	4.27	+0.2
2018	27	MIX RAW	3 levels	55	3	52	221	61	13	11	5%	0.9	27.6%	5.9%	21.7%	5.0%	4.50	4.25	+0.2
2019	28	MLB MLE	Houston Astros	61	1	69	298	72	21	13	2%	1.1	24.2%	7.0%	17.1%	4.5%	4.83	4.41	+0.1
2019	28	MLB RAW	Houston Astros	61	1	69	298	72	21	13	2%	1.1	24.2%	7.0%	17.1%	4.4%	4.83	4.38	+0.1
2020	29	MLB MLE	Houston Astros	4	0	4	21	5	3	1	0%	0.9	23.8%	14.3%	9.5%	4.8%	14.73	5.51	-0.0
2020	29	MLB RAW	Houston Astros	4	0	4	21	5	3	1	0%	0.9	23.8%	14.3%	9.5%	4.8%	14.73	6.37	-0.1
2021	30	MLB MLE	Arizona Diamondbacks	8	0	7	35	5	2	2	0%	0.9	14.3%	5.7%	8.6%	5.7%	8.59	5.75	-0.1
2021	30	MLB RAW	Arizona Diamondbacks	8	0	7	35	5	2	2	0%	0.9	14.3%	5.7%	8.6%	5.7%	8.59	6.10	-0.1
2022	31	MIX MLE	3 levels	31	3	34	141	35	6	7	10%	1.1	22.8%	4.8%	18.0%	5.4%	5.61	4.76	-0.1
2022	31	MIX RAW	3 levels	31	3	34	141	35	6	7	10%	1.1	24.8%	4.3%	20.6%	5.0%	5.61	4.26	+0.1
2023	32	MIX MLE	4 levels	53	6	60	248	67	13	7	11%	1.1	24.3%	5.8%	18.5%	3.6%	3.88	3.74	+0.6
2023	32	MIX RAW	4 levels	53	6	60	248	67	13	7	11%	1.1	27.0%	5.2%	21.8%	2.8%	3.88	3.03	+1.1
2024	33	MIX MLE	2 levels	45	0	56	239	63	24	11	0%	1.2	24.1%	9.6%	14.5%	4.7%	4.85	4.85	-0.2
2024	33	MIX MLE	2 levels	45	0	56	239	63	24	11	0%	1.2	24.1%	9.6%	14.5%	4.7%	4.85	4.85	-0.2
2024	33	MIX RAW	2 levels	45	0	56	239	63	24	11	0%	1.2	26.4%	10.0%	16.3%	4.6%	4.85	4.70	-0.1
2025	34	MIX MLE	2 levels	48	2	54	219	46	13	8	4%	1.1	19.0%	5.6%	13.4%	3.5%	2.98	4.11	+0.3
2025	34	MIX MLE	2 levels	48	2	54	219	46	13	8	4%	1.1	19.0%	5.6%	13.4%	3.5%	2.98	4.11	+0.3
2025	34	MIX RAW	2 levels	48	2	54	219	46	13	8	4%	1.1	21.0%	5.9%	15.1%	3.7%	2.98	4.04	+0.3
2026	35	MIX MLE	4 levels	26	1	32	131	37	8	5	4%	1.2	33.1%	6.9%	26.2%	3.4%	3.69	3.04	+0.6
2026	35	MIX MLE	4 levels	26	1	32	131	37	8	5	4%	1.2	33.1%	6.9%	26.2%	3.4%	3.69	3.04	+0.6
2026	35	MIX RAW	4 levels	26	1	32	131	37	8	5	4%	1.2	28.2%	6.1%	22.1%	3.8%	3.69	3.57	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			325	8	424	1739	434	112	62	2%	1.3	25.0%	6.4%	18.5%	3.6%	4.05	3.75	+4.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	NYM	131	0	138	583	137	43	19	0%	1.1	23.5%	7.4%	16.1%	3.3%	4.31	4.26	+0.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	NYM	93	0	98	413	93	33	15	0%	1.1	22.4%	8.1%	14.4%	3.7%	4.52	4.66	-0.1
2029	38	MLB MLE	NYM	67	0	71	299	64	26	13	0%	1.1	21.3%	8.8%	12.5%	4.2%	4.86	5.09	-0.4
2028	37	MLB CTX	NYM	93	0	98	414	93	33	15	0%	1.1	22.4%	8.1%	14.4%	3.7%	4.52	4.66	-0.1
2029	38	MLB CTX	NYM	68	0	71	300	64	26	13	0%	1.0	21.3%	8.8%	12.5%	4.2%	4.86	5.09	-0.4
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		486	8	593	2451	590	171	90	2%	1.2	24.1%	7.0%	17.1%	3.7%	4.23	3.97	+4.1
/ 200 IP	—	RAW		164	3	200	827	199	58	30	2%	1.2	24.1%	7.0%	17.1%	3.7%	4.23	3.97	+1.4
Career	—	MLE		465	38	676	2778	662	184	96	8%	1.5	23.8%	6.6%	17.2%	3.4%	3.65	3.81	+3.5
/ 200 IP	—	MLE		138	11	200	821	196	54	28	8%	1.5	23.8%	6.6%	17.2%	3.4%	3.65	3.81	+1.2

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.

2026 AAASyracuse Mets · 16 IPFIP 2.52 → 3.04 (+0.53)K% 38.5% → 33.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.46%	—	×0.860	33.08%
BB% (unintentional) /BF	7.69%	—	×0.900	6.92%
HR / BF /BF	3.08%	÷1.000	×1.100	3.38%

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 100).

Result: FIP 2.52 → 3.04, 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).

2026 ABradenton Marauders · 7 IPFIP 2.37 → 3.04 (+0.68)K% 21.7% → 16.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.740	16.09%
BB% (unintentional) /BF	8.70%	—	×1.180	10.26%
HR / BF /BF	0.00%	÷1.000	×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 100).

Result: FIP 2.37 → 3.04, 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).

2025 AAASyracuse Mets · 38 IPFIP 4.50 → 4.54 (+0.04)K% 20.8% → 17.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.78%	—	×0.860	17.87%
BB% (unintentional) /BF	5.19%	—	×0.900	4.68%
HR / BF /BF	4.55%	÷1.167	×1.100	4.29%

Result: FIP 4.50 → 4.54, 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 AAATacoma Rainiers · 29 IPFIP 2.31 → 2.56 (+0.25)K% 33.3% → 28.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.860	28.67%
BB% (unintentional) /BF	8.55%	—	×0.900	7.69%
HR / BF /BF	1.71%	÷1.142	×1.100	1.65%

Result: FIP 2.31 → 2.56, 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 AAADurham Bulls · 10 IPFIP 3.49 → 3.61 (+0.12)K% 20.9% → 18.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.93%	—	×0.860	18.00%
BB% (unintentional) /BF	6.98%	—	×0.900	6.28%
HR / BF /BF	2.33%	÷1.137	×1.100	2.25%

Result: FIP 3.49 → 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).

2023 ROKACL Angels · 7 IPFIP -1.27 → 0.13 (+1.40)K% 51.7% → 35.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	51.72%	—	×0.680	35.17%
BB% (unintentional) /BF	0.00%	—	×1.250	0.00%
HR / BF /BF	0.00%	÷0.910	×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 82).

Result: FIP -1.27 → 0.13, 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 AAALehigh Valley IronPigs · 16 IPFIP 4.91 → 5.49 (+0.58)K% 31.7% → 27.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.75%	—	×0.880	27.94%
BB% (unintentional) /BF	7.94%	—	×1.050	8.33%
HR / BF /BF	6.35%	÷1.039	×1.100	6.72%

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 108).

Result: FIP 4.91 → 5.49, 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 AACorpus Christi Hooks · 120 IPFIP 3.50 → 4.09 (+0.59)K% 20.8% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.76%	—	×0.810	16.81%
BB% (unintentional) /BF	6.59%	—	×1.100	7.25%
HR / BF /BF	2.40%	÷1.059	×1.200	2.71%

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 112).

Result: FIP 3.50 → 4.09, 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 Devenski (OGILVIE)	—	—	4.26	23.5%	7.4%	3.3%	—	4.84	21.9%	13.6%	169	+0.58	—	—	—
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	71
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	69
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	69
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	68
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	68
Peter Moylan R 2015 · 3.11 FIP	36	SU	3.11	18.4%	8.2%	0.9%	.259	3.84	18.2%	9.1%	145	+0.73	2.54	431	65
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	65
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	64
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	63
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	62
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	62
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	61
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	60
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	59
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	58
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	57
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	57
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	54
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	53
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	52

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	3.7	81	24.0%	7.2%	1.39	4.24
Bob Lacey ('77-'84, 2 WAR*)	2.3	75	20.5%*	7.3%*	1.35*	4.11*
Tyler Duffey ('24)	2.4	61	23.1%	7.0%	1.24	4.57
Mark Huismann ('83-'91, 1 WAR*)	1.5	74	23.8%*	6.5%*	1.30*	4.49*
Joe Beckwith ('79-'86, 3 WAR*)	2.1	70	24.9%*	8.2%*	1.13*	3.96*
Tyler Lyons ('20)	1.4	57	24.3%	7.6%	1.27	4.30
Brett Cecil ('18)	3.8	76	20.7%	8.3%	1.14	4.29