Steven Matz

P Tampa Bay Rays

Age 36 LHP 6'2" / 205 lbs MLB peak 0.3 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	33	MIX3 levels	21	16	72	4.37	3.89	19.6%	6.5%	1.3	K 17.3% · BB 6.5% · FIP 4.32
2025	34	MLBBOS	53	2	77	3.05	3.35	19.1%	3.6%	1.1
2026	35	MIX2 levels	41	12	86	5.42	4.92	17.8%	7.3%	-0.4	K 17.8% · BB 7.3% · FIP 5.00
2027PROJ	36	MLBTB	8	8	46	4.63	4.80	18.0%	6.7%	0.3
2028PROJ	37	MLBTB	6	6	33	4.91	5.14	16.9%	7.4%	0.1
2029PROJ	38	MLBTB	3	3	18	5.30	5.56	15.8%	8.1%	-0.0

Last 3 seasons WAR 2.0 · 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 Velo37

xwOBA vs24

K%20

Form3.81 ERA (proj 4.63) · K 18.7% (proj 18.0%) · BB 5.6% (proj 6.7%) · last 90d · 26 IP through Oct 27

CompsJuan Antonio Pena (age 37) · Francisley Bueno (age 36) · Tsuyoshi Wada (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 109117vR 125

ERA4.63

FIP4.80

K%18.0%

BB%6.7%

K-BB%11.3%

Proj IP46

Value2027 · WAR

Runs saved vs league avg · 46 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p3793.6

Whiff%

p1820.8%

p2017.8%

xwOBA vs

p240.342

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	MIX MLE	4 levels	25	24	141	563	141	44	10	96%	5.6	23.0%	8.4%	14.6%	2.0%	2.11	3.30	+3.7
2015	24	MIX RAW	4 levels	25	24	141	563	141	44	10	96%	5.6	25.0%	7.8%	17.2%	1.8%	2.11	2.96	+4.3
2016	25	MLB MLE	New York Mets	22	22	132	547	129	29	14	100%	6.0	23.6%	5.3%	18.3%	2.6%	3.40	3.19	+3.6
2016	25	MLB RAW	New York Mets	22	22	132	547	129	29	14	100%	6.0	23.6%	5.3%	18.3%	2.6%	3.40	3.18	+3.7
2017	26	MIX MLE	3 levels	17	17	83	364	68	21	15	100%	4.9	17.9%	5.4%	12.5%	4.2%	6.07	4.58	+0.8
2017	26	MIX RAW	3 levels	17	17	83	364	68	21	15	100%	4.9	18.7%	5.8%	12.9%	4.1%	6.07	4.57	+0.8
2018	27	MLB MLE	New York Mets	30	30	154	654	152	56	25	100%	5.1	23.2%	8.6%	14.7%	3.8%	3.97	4.33	+1.9
2018	27	MLB RAW	New York Mets	30	30	154	654	152	56	25	100%	5.1	23.2%	8.6%	14.7%	3.8%	3.97	4.33	+1.9
2019	28	MLB MLE	New York Mets	32	30	160	691	153	45	27	94%	5.0	22.1%	6.5%	15.6%	3.9%	4.21	4.21	+2.3
2019	28	MLB RAW	New York Mets	32	30	160	691	153	45	27	94%	5.0	22.1%	6.5%	15.6%	3.9%	4.21	4.22	+2.2
2020	29	MLB MLE	New York Mets	9	6	31	142	36	10	14	67%	3.4	25.4%	7.0%	18.3%	9.9%	9.68	7.28	-0.6
2020	29	MLB RAW	New York Mets	9	6	31	142	36	10	14	67%	3.4	25.4%	7.0%	18.3%	9.9%	9.68	7.67	-0.8
2021	30	MLB MLE	Toronto Blue Jays	29	29	151	647	144	43	18	100%	5.2	22.3%	6.6%	15.6%	2.8%	3.82	3.59	+3.3
2021	30	MLB RAW	Toronto Blue Jays	29	29	151	647	144	43	18	100%	5.2	22.3%	6.6%	15.6%	2.8%	3.82	3.60	+3.3
2022	31	MIX MLE	3 levels	23	16	65	278	76	17	9	70%	2.8	26.9%	5.0%	21.9%	3.7%	4.57	3.48	+1.5
2022	31	MIX RAW	3 levels	23	16	65	278	76	17	9	70%	2.8	27.3%	6.1%	21.2%	3.2%	4.57	3.35	+1.6
2023	32	MLB MLE	St. Louis Cardinals	25	17	105	449	98	32	11	68%	4.2	21.8%	7.1%	14.7%	2.6%	3.86	3.61	+2.3
2023	32	MLB RAW	St. Louis Cardinals	25	17	105	449	98	32	11	68%	4.2	21.8%	7.1%	14.7%	2.4%	3.86	3.51	+2.4
2024	33	MIX MLE	3 levels	21	16	72	306	60	20	9	76%	3.4	17.3%	6.5%	10.8%	3.4%	4.37	4.32	+0.9
2024	33	MIX MLE	3 levels	21	16	72	306	60	20	9	76%	3.4	17.3%	6.5%	10.8%	3.4%	4.37	4.32	+0.9
2024	33	MIX RAW	3 levels	21	16	72	306	60	20	9	76%	3.4	19.6%	6.5%	13.1%	2.9%	4.37	3.89	+1.3
2025	34	MLB MLE	Boston Red Sox	53	2	77	309	59	11	8	4%	1.4	19.1%	3.6%	15.5%	2.7%	3.05	3.40	+1.1
2025	34	MLB MLE	Boston Red Sox	53	2	77	309	59	11	8	4%	1.4	19.1%	3.6%	15.5%	2.7%	3.05	3.40	+1.1
2025	34	MLB RAW	Boston Red Sox	53	2	77	309	59	11	8	4%	1.4	19.1%	3.6%	15.5%	2.6%	3.05	3.35	+1.1
2026	35	MIX MLE	2 levels	41	12	86	370	66	27	16	29%	2.1	17.8%	7.3%	10.5%	4.5%	5.42	5.00	-0.4
2026	35	MIX MLE	2 levels	41	12	86	370	66	27	16	29%	2.1	17.8%	7.3%	10.5%	4.5%	5.42	5.00	-0.4
2026	35	MIX RAW	2 levels	41	12	86	370	66	27	16	29%	2.1	17.8%	7.3%	10.5%	4.3%	5.42	4.92	-0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			282	183	1085	4625	1001	302	163	65%	3.8	21.6%	6.5%	15.1%	3.5%	4.29	4.04	+17.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	TB	8	8	46	194	35	13	7	100%	5.8	18.0%	6.7%	11.3%	3.6%	4.63	4.80	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	TB	6	6	33	140	24	10	6	100%	5.5	16.9%	7.4%	9.5%	4.0%	4.97	5.20	+0.1
2029	38	MLB MLE	TB	3	3	18	76	12	6	3	100%	6.0	15.8%	8.1%	7.7%	4.5%	5.37	5.63	-0.0
2030	39	MLB MLE	TB	1	1	8	36	5	3	2	100%	8.5	14.6%	8.8%	5.8%	4.9%	5.79	6.07	-0.1
2028	37	MLB CTX	TB	6	6	33	139	24	10	5	100%	5.5	16.9%	7.4%	9.5%	3.9%	4.91	5.14	+0.1
2029	38	MLB CTX	TB	3	3	18	76	12	6	3	100%	6.0	15.8%	8.1%	7.7%	4.4%	5.30	5.56	-0.0
2030	39	MLB CTX	TB	1	1	8	34	5	3	2	100%	8.0	14.6%	8.8%	5.8%	4.8%	5.72	6.00	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		292	193	1144	4877	1042	322	174	66%	3.9	21.4%	6.6%	14.8%	3.6%	4.33	4.08	+17.8
/ 200 IP	—	RAW		51	34	200	852	182	56	30	66%	3.9	21.4%	6.6%	14.8%	3.6%	4.33	4.08	+3.1
Career	—	MLE		327	221	1257	5320	1159	354	182	68%	3.8	21.8%	6.6%	15.1%	3.4%	4.08	3.98	+17.4
/ 200 IP	—	MLE		52	35	200	846	184	56	29	68%	3.8	21.8%	6.6%	15.1%	3.4%	4.08	3.98	+3.0

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 AAADurham Bulls · 6 IPFIP 4.46 → 4.77 (+0.32)K% 17.9% → 15.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.86%	—	×0.860	15.36%
BB% (unintentional) /BF	7.14%	—	×0.900	6.43%
HR / BF /BF	3.57%	÷1.000	×1.100	3.93%

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 4.46 → 4.77, 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 AAAMemphis Redbirds · 23 IPFIP 3.23 → 3.49 (+0.26)K% 19.8% → 17.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.79%	—	×0.860	17.02%
BB% (unintentional) /BF	5.21%	—	×0.900	4.69%
HR / BF /BF	2.08%	÷1.020	×1.100	2.25%

Result: FIP 3.23 → 3.49, 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).

2024 AASpringfield Cardinals · 5 IPFIP 0.36 → 1.06 (+0.71)K% 40.0% → 32.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.00%	—	×0.810	32.40%
BB% (unintentional) /BF	5.00%	—	×1.100	5.50%
HR / BF /BF	0.00%	÷1.386	×1.200	0.00%

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 177).

Result: FIP 0.36 → 1.06, 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 AAAMemphis Redbirds · 15 IPFIP 1.89 → 2.32 (+0.43)K% 33.9% → 29.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.93%	—	×0.880	29.86%
BB% (unintentional) /BF	5.36%	—	×1.050	5.62%
HR / BF /BF	1.79%	÷1.048	×1.100	1.87%

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 1.89 → 2.32, 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 AAALas Vegas 51s · 13 IPFIP 3.98 → 4.05 (+0.07)K% 32.1% → 28.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.08%	—	×0.880	28.23%
BB% (unintentional) /BF	3.77%	—	×1.050	3.96%
HR / BF /BF	5.66%	÷1.209	×1.100	5.15%

Result: FIP 3.98 → 4.05, 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).

2015 AAALas Vegas 51s · 90 IPFIP 2.90 → 3.22 (+0.32)K% 26.2% → 23.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.18%	—	×0.880	23.04%
BB% (unintentional) /BF	8.64%	—	×1.050	9.07%
HR / BF /BF	1.67%	÷1.099	×1.100	1.67%

Result: FIP 2.90 → 3.22, 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 AABinghamton Mets · 11 IPFIP 1.50 → 2.00 (+0.50)K% 27.0% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.03%	—	×0.810	21.89%
BB% (unintentional) /BF	5.41%	—	×1.100	5.95%
HR / BF /BF	0.00%	÷1.088	×1.200	0.00%

Result: FIP 1.50 → 2.00, 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
Steven Matz (OGILVIE)	—	—	—	—	—	3.6%	—	5.35	16.5%	8.9%	51	+0.55	—	—	—
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	61
Francisley Bueno L 2017 · 4.44 FIP	36	SP	4.44	11.7%	8.4%	2.3%	.351	—	—	—	—	—	2.43	188	55
Tsuyoshi Wada L 2015 · 3.66 FIP	34	SP	3.66	16.8%	7.1%	2.0%	.307	—	—	—	—	—	3.60	188	52
Chris Michalak L 2006 · 5.71 FIP	35	SP-	5.71	6.2%	8.6%	3.7%	.273	—	—	—	—	—	4.71	186	51
Arturo Lopez L 2019 · 5.46 FIP	36	SP	5.46	12.2%	6.8%	4.6%	.320	—	—	—	—	—	4.34	445	50
Mauricio Lara L 2015 · 3.62 FIP	36	SP	3.62	13.9%	6.9%	1.5%	.310	3.69	12.1%	5.1%	220	+0.07	3.62	444	49
Jorge Castillo L 2017 · 4.97 FIP	35	SP	4.97	11.2%	6.7%	3.6%	.337	4.91	19.6%	11.5%	119	-0.06	4.50	408	47
Marlon Arias L 2019 · 4.83 FIP	34	SP	4.83	19.8%	11.2%	3.6%	.273	—	—	—	—	—	3.73	154	46
Oscar Rivera L 2017 · 5.80 FIP	36	SP	5.80	9.8%	7.8%	4.6%	.252	—	—	—	—	—	3.68	237	45
Jesse Estrada R 2019 · 4.34 FIP	35	SP-	4.34	14.4%	6.6%	2.9%	.312	—	—	—	—	—	3.80	257	36
Jason Lane L 2015 · 5.56 FIP	38	SP	5.56	8.4%	6.2%	4.3%	.307	—	—	—	—	—	5.56	164	34
Austin Bibens-Dirkx R 2021 · 4.20 FIP	36	SP-	4.20	15.8%	5.6%	3.1%	.333	—	—	—	—	—	3.97	567	33
Kenshin Kawakami R 2010 · 4.06 FIP	35	SP	4.06	15.1%	5.6%	2.8%	.293	—	—	—	—	—	3.97	244	32
Jose Oyervides R 2017 · 5.38 FIP	35	SP	5.38	18.0%	6.3%	5.5%	.254	5.66	16.3%	5.1%	115	+0.28	3.01	407	27
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	26
Juan Delgadillo R 2017 · 4.47 FIP	34	SP	4.47	10.9%	6.2%	2.7%	.311	—	—	—	—	—	4.47	143	26
Frankie De La Cruz R 2019 · 4.38 FIP	35	SP-	4.38	16.2%	8.0%	3.0%	.322	—	—	—	—	—	2.87	262	26
David Buchanan R 2024 · 4.26 FIP	35	SP-	4.26	14.7%	6.5%	2.9%	.337	5.56	12.2%	4.1%	29	+1.30	3.88	560	26
Chris Smith R 2017 · 5.08 FIP	36	SP-	5.08	15.6%	7.5%	4.3%	.286	—	—	—	—	—	3.79	477	25
Jorge Martinez R 2019 · 3.71 FIP	34	SP	3.71	16.3%	5.7%	2.3%	.348	—	—	—	—	—	3.53	214	25

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	6.0	74	21.2%	6.9%	1.38	4.35
Jesse Chavez ('25)	5.7	82	21.4%	7.4%	1.32	4.27
Doug Bird ('73-'83, 10 WAR*)	6.1	110	20.3%*	5.8%*	1.41*	4.43*
Drew Smyly ('24)	5.7	104	23.1%	7.7%	1.49	4.20
Dan Schatzeder ('77-'91, 8 WAR*)	6.6	94	18.8%*	8.5%*	1.28*	4.25*
Scott Bankhead ('86-'95, 10 WAR*)	4.5	100	21.2%*	7.3%*	1.36*	4.16*
Carlos Villanueva ('16)	5.0	91	20.4%	7.6%	1.25	4.31