Matt Seelinger

P Syracuse Mets

Age 32 RHP 6'3" / 190 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	AADET	20	0	27	1.65	2.15	42.2%	8.3%	—	K 34.2% · BB 9.1% · FIP 3.29
2025	30	MIX2 levels	47	0	66	2.74	3.15	28.4%	9.5%	—	K 24.1% · BB 8.8% · FIP 3.73
2026	31	MIX2 levels	43	1	63	5.14	5.13	27.7%	13.5%	-0.4	K 24.6% · BB 11.5% · FIP 5.28
2027PROJ	32	MLBNYM	41	0	43	4.61	4.61	25.3%	10.2%	-0.0
2028PROJ75% MLB	33	MLBNYM	36	0	38	4.73	4.83	24.6%	10.5%	-0.1
2029PROJ71% MLB	34	MLBNYM	28	0	29	4.92	5.07	24.0%	10.9%	-0.2

Last 3 seasons WAR -0.4 · Next 3 projected WAR -0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff55Control45Command55

Career WAR0.0 WAR median (P10 0.0 / P90 0.1, comp-based) · star 0% · didn't stick 98%

CompsSergio Santos (age 32) · Joe Nelson (age 31) · Arcenio Leon (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 121113vR 105

ERA4.61

FIP4.61

K%25.3%

BB%10.2%

K-BB%15.1%

Proj IP64

Value2027 · WAR

Runs saved vs league avg · 64 IP

Grades · Contract

Scouting Grades

STUF5555

CTRL4545

CMD5555

K†6060

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)
2018	23	A MLE	Bowling Green Hot Rods	36	0	45	192	67	15	7	0%	1.2	25.8%	9.2%	16.6%	4.6%	2.80	4.60	—
2018	23	A RAW	Bowling Green Hot Rods	36	0	45	192	67	15	7	0%	1.2	34.9%	7.8%	27.1%	3.6%	2.80	3.14	—
2019	24	MIX MLE	2 levels	30	0	51	225	76	20	4	0%	1.7	27.9%	10.3%	17.6%	2.0%	5.86	3.13	—
2019	24	MIX RAW	2 levels	30	0	51	225	76	20	4	0%	1.7	33.8%	8.9%	24.9%	1.8%	5.86	2.31	—
2021	26	AA MLE	Richmond Flying Squirrels	36	0	41	180	64	30	6	0%	1.1	28.8%	18.3%	10.5%	4.3%	3.07	5.37	—
2021	26	AA RAW	Richmond Flying Squirrels	36	0	41	180	64	30	6	0%	1.1	35.6%	16.7%	18.9%	3.3%	3.07	4.08	—
2022	27	MIX MLE	2 levels	47	2	64	280	79	36	6	4%	1.4	22.3%	14.2%	8.2%	2.5%	3.38	4.39	—
2022	27	MIX RAW	2 levels	47	2	64	280	79	36	6	4%	1.4	28.2%	12.9%	15.4%	2.1%	3.38	3.54	—
2023	28	AA MLE	Reading Fightin Phils	21	0	27	109	28	12	6	0%	1.3	20.8%	12.1%	8.7%	6.3%	3.71	6.33	—
2023	28	AA RAW	Reading Fightin Phils	21	0	27	109	28	12	6	0%	1.3	25.7%	11.0%	14.7%	5.5%	3.71	5.28	—
2024	29	AA MLE	Erie SeaWolves	20	0	27	109	46	9	3	0%	1.4	34.2%	9.1%	25.1%	3.5%	1.65	3.29	—
2024	29	AA MLE	Erie SeaWolves	20	0	27	109	46	9	3	0%	1.4	34.2%	9.1%	25.1%	3.5%	1.65	3.29	—
2024	29	AA RAW	Erie SeaWolves	20	0	27	109	46	9	3	0%	1.4	42.2%	8.3%	33.9%	2.8%	1.65	2.15	—
2025	30	MIX MLE	2 levels	47	0	66	264	75	25	6	0%	1.4	24.1%	8.8%	15.3%	2.8%	2.74	3.73	—
2025	30	MIX MLE	2 levels	47	0	66	264	75	25	6	0%	1.4	24.1%	8.8%	15.3%	2.8%	2.74	3.73	—
2025	30	MIX RAW	2 levels	47	0	66	264	75	25	6	0%	1.4	28.4%	9.5%	18.9%	2.3%	2.74	3.15	—
2026	31	MIX MLE	2 levels	43	1	63	274	76	37	13	2%	1.5	24.6%	11.5%	13.1%	5.1%	5.14	5.28	-0.5
2026	31	MIX MLE	2 levels	43	1	63	274	76	37	13	2%	1.5	24.6%	11.5%	13.1%	5.1%	5.14	5.28	-0.5
2026	31	MIX RAW	2 levels	43	1	63	274	76	37	13	2%	1.5	27.7%	13.5%	14.2%	4.7%	5.14	5.13	-0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			1	0	2	15	2	4	1	0%	2.0	13.3%	26.7%	-13.3%	6.7%	31.50	13.60	-0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	NYM	41	0	43	182	46	18	7	0%	1.0	25.3%	10.2%	15.1%	3.6%	4.61	4.61	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	NYM	49	0	51	215	53	23	8	0%	1.0	24.6%	10.5%	14.1%	3.8%	4.73	4.83	-0.2
2029	34	MLB MLE	NYM	39	0	41	175	42	19	7	0%	1.1	24.0%	10.9%	13.1%	4.1%	4.92	5.07	-0.2
2028	33	MLB CTX	NYM	36	0	38	160	40	17	6	0%	1.1	24.6%	10.5%	14.1%	3.8%	4.73	4.83	-0.1
2029	34	MLB CTX	NYM	28	0	29	122	29	13	5	0%	1.0	24.0%	10.9%	13.1%	4.1%	4.92	5.07	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		66	0	70	301	72	35	12	0%	1.1	23.8%	11.5%	12.3%	4.1%	5.58	4.78	-0.2
/ 200 IP	—	RAW		188	0	200	863	206	100	34	0%	1.1	23.8%	11.5%	12.3%	4.1%	5.58	4.78	-0.5
Career	—	MLE		280	3	383	1633	418	191	60	1%	1.4	25.6%	11.7%	13.9%	3.7%	3.69	4.45	-0.6
/ 200 IP	—	MLE		146	2	200	852	218	100	31	1%	1.4	25.6%	11.7%	13.9%	3.7%	3.69	4.45	-1.3

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 · 61 IPFIP 4.84 → 5.28 (+0.43)K% 28.6% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.860	24.57%
BB% (unintentional) /BF	12.74%	—	×0.900	11.47%
HR / BF /BF	4.63%	÷1.000	×1.100	5.10%

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 4.84 → 5.28, 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 AAAToledo Mud Hens · 46 IPFIP 3.56 → 4.13 (+0.57)K% 28.6% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.65%	—	×0.860	24.64%
BB% (unintentional) /BF	11.46%	—	×0.900	10.31%
HR / BF /BF	2.60%	÷0.872	×1.100	3.29%

Result: FIP 3.56 → 4.13, 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).

2025 AAErie SeaWolves · 19 IPFIP 2.04 → 2.65 (+0.60)K% 27.8% → 22.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.78%	—	×0.810	22.50%
BB% (unintentional) /BF	4.17%	—	×1.100	4.58%
HR / BF /BF	1.39%	÷1.056	×1.200	1.58%

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 111).

Result: FIP 2.04 → 2.65, 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 AAErie SeaWolves · 27 IPFIP 2.09 → 3.29 (+1.20)K% 42.2% → 34.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	42.20%	—	×0.810	34.18%
BB% (unintentional) /BF	8.26%	—	×1.100	9.08%
HR / BF /BF	2.75%	÷0.941	×1.200	3.51%

Result: FIP 2.09 → 3.29, 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 AAReading Fightin Phils · 27 IPFIP 5.35 → 6.33 (+0.98)K% 25.7% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.69%	—	×0.810	20.81%
BB% (unintentional) /BF	11.01%	—	×1.100	12.11%
HR / BF /BF	5.50%	÷1.050	×1.200	6.29%

Result: FIP 5.35 → 6.33, 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 AAReading Fightin Phils · 62 IPFIP 3.61 → 4.39 (+0.78)K% 27.6% → 22.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.57%	—	×0.810	22.33%
BB% (unintentional) /BF	12.87%	—	×1.100	14.15%
HR / BF /BF	2.21%	÷1.051	×1.200	2.52%

Result: FIP 3.61 → 4.39, 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 AARichmond Flying Squirrels · 41 IPFIP 4.04 → 5.37 (+1.33)K% 35.6% → 28.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.56%	—	×0.810	28.80%
BB% (unintentional) /BF	16.67%	—	×1.100	18.33%
HR / BF /BF	3.33%	÷0.923	×1.200	4.33%

Result: FIP 4.04 → 5.37, 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 AAugusta GreenJackets · 42 IPFIP 1.92 → 3.13 (+1.21)K% 37.7% → 27.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.70%	—	×0.740	27.90%
BB% (unintentional) /BF	8.74%	—	×1.180	10.32%
HR / BF /BF	1.64%	÷1.082	×1.300	1.97%

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 116).

Result: FIP 1.92 → 3.13, 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).

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
Matt Seelinger (OGILVIE)	—	—	4.61	25.3%	10.2%	3.6%	—	4.94	24.3%	13.6%	92	+0.33	—	—	—
Sergio Santos R 2015 · 4.17 FIP	32	SU	4.17	24.7%	8.2%	3.9%	.283	—	—	—	—	—	1.36	178	83
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	81
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	81
Mike Broadway R 2018 · 4.65 FIP	31	SU	4.65	23.0%	10.6%	3.9%	.282	—	—	—	—	—	1.74	195	79
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	76
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	75
Jose Manuel Lopez R 2017 · 3.61 FIP	32	SU	3.61	20.4%	10.0%	1.8%	.331	6.14	17.9%	3.7%	41	+2.53	3.50	170	74
Scott Atchison R 2007 · 4.55 FIP	31	SU	4.55	19.1%	7.6%	3.8%	.297	4.59	16.2%	9.5%	60	+0.04	2.52	340	74
Adrian Ramirez R 2019 · 4.29 FIP	31	SU	4.29	21.7%	6.3%	4.0%	.327	—	—	—	—	—	3.04	137	73
Kazuhiro Sasaki R 2000 · 3.93 FIP	32	SU	3.93	29.4%	9.8%	3.8%	.212	2.88	24.9%	19.3%	161	-1.05	2.76	223	73
Mark Serrano R 2017 · 3.56 FIP	31	SU	3.56	21.9%	6.4%	2.7%	.240	5.25	20.2%	12.3%	42	+1.69	3.16	120	73
Dale Thayer R 2013 · 3.62 FIP	32	SU	3.62	23.7%	7.4%	2.9%	.287	3.85	20.6%	15.0%	103	+0.23	2.75	239	72
Jeff Fulchino R 2011 · 4.44 FIP	31	SU	4.44	20.4%	11.1%	3.0%	.302	—	—	—	—	—	3.36	178	72
James Hoyt R 2019 · 3.45 FIP	32	SU	3.45	22.2%	9.9%	1.8%	.355	5.63	22.1%	10.4%	50	+2.18	1.61	312	71
Leonel Campos R 2017 · 3.70 FIP	30	SU	3.70	25.7%	11.8%	2.3%	.241	—	—	—	—	—	2.62	168	71
Neil Wagner R 2016 · 4.05 FIP	32	SU	4.05	19.8%	12.3%	1.9%	.360	3.11	20.7%	12.1%	54	-0.94	3.11	135	70
Dan Winkler R 2022 · 3.87 FIP	32	SU	3.87	24.4%	14.0%	1.9%	.281	—	—	—	—	—	2.40	231	70
Fernando Nieve R 2015 · 4.27 FIP	33	SU	4.27	22.0%	10.5%	3.1%	.290	2.43	26.9%	22.6%	89	-1.84	1.96	287	69
Jumbo Diaz R 2016 · 3.85 FIP	32	SU	3.85	22.3%	9.2%	2.7%	.247	3.28	23.0%	13.8%	141	-0.57	2.80	319	69
Jason Bulger R 2010 · 4.36 FIP	31	SU	4.36	22.5%	13.5%	2.6%	.324	—	—	—	—	—	3.78	106	68

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	0.8	53	24.5%	10.8%	1.49	5.07
José Ruiz ('25)	1.4	57	22.3%	10.4%	1.44	4.62
Josh Lindblom ('21)	1.0	52	22.1%	9.6%	1.25	4.78
J.J. Hoover ('18)	1.4	57	23.8%	11.4%	1.42	4.17
Phil Hennigan ('69-'73, -0 WAR*)	1.4	70	20.9%*	10.4%*	1.50*	4.95*
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*