Nate Lavender

P Syracuse Mets

Age 27 LHP 6'2" / 215 lbs AAA peak 1.1 · 2027

Overview

AAARP40-MANROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	23	MIX2 levels	42	1	54	2.98	2.93	36.9%	10.3%	—	K 30.2% · BB 9.9% · FIP 4.01
2024	24	AAANYM	5	1	7	3.86	7.24	26.5%	20.6%	—	K 26.5% · BB 20.6% · FIP 6.70
2026	26	MIX4 levels	55	2	65	3.06	2.06	37.9%	7.9%	1.9	K 33.9% · BB 6.6% · FIP 2.48
2027PROJ	27	MLBNYM	26	0	27	3.44	3.14	32.5%	6.8%	0.5
2028PROJ80% MLB	28	MLBNYM	29	0	30	3.43	3.34	31.4%	7.2%	0.4

Last 3 seasons WAR 1.9 · Next 2 projected WAR 0.9Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%90

xwOBA vs90

FB Velo21

Form1.66 ERA (proj 3.44) · K 32.9% (proj 32.5%) · BB 5.1% (proj 6.8%) · last 90d · 22 IP through Oct 27

Career WAR0.0 WAR median (P10 -0.3 / P90 2.7, comp-based) · star 0% · didn't stick 88%

CompsNick Mondak (age 26) · Josh Edgin (age 27) · Jeff Beliveau (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 7785vR 93

ERA3.44

FIP3.14

K%32.5%

BB%6.8%

K-BB%25.6%

Proj IP38

Value2027 · WAR

Runs saved vs league avg · 38 IP

Grades · Statcast · Contract

Scouting Grades

K†7070

Statcast Percentiles

FB Velo

p2192.4

Whiff%

p5826.6%

p9032.9%

xwOBA vs

p900.228

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)
2021	21	ROK MLE	FCL Mets	4	0	7	25	12	2	0	0%	1.7	48.0%	8.0%	40.0%	0.0%	1.35	0.06	—
2021	21	ROK RAW	FCL Mets	4	0	7	25	12	2	0	0%	1.7	48.0%	8.0%	40.0%	0.0%	1.35	0.40	—
2022	22	MIX MLE	2 levels	26	0	48	199	67	25	1	0%	1.8	25.5%	14.6%	10.9%	0.7%	1.70	3.17	—
2022	22	MIX RAW	2 levels	26	0	48	199	67	25	1	0%	1.8	33.7%	12.6%	21.1%	0.5%	1.70	2.13	—
2023	23	MIX MLE	2 levels	42	1	54	233	86	24	7	2%	1.3	30.2%	9.9%	20.3%	4.0%	2.98	4.01	—
2023	23	MIX RAW	2 levels	42	1	54	233	86	24	7	2%	1.3	36.9%	10.3%	26.6%	3.0%	2.98	2.93	—
2024	24	AAA MLE	Syracuse Mets	5	1	7	34	9	7	2	20%	1.4	26.5%	20.6%	5.9%	5.9%	3.86	6.70	—
2024	24	AAA MLE	Syracuse Mets	5	1	7	34	9	7	2	20%	1.4	26.5%	20.6%	5.9%	5.9%	3.86	6.70	—
2024	24	AAA RAW	Syracuse Mets	5	1	7	34	9	7	2	20%	1.4	26.5%	20.6%	5.9%	5.9%	3.86	7.24	—
2026	26	MIX MLE	4 levels	55	2	65	253	96	20	5	4%	1.2	33.9%	6.6%	27.3%	2.6%	3.06	2.48	+1.6
2026	26	MIX MLE	4 levels	55	2	65	253	96	20	5	4%	1.2	33.9%	6.6%	27.3%	2.6%	3.06	2.48	+1.6
2026	26	MIX RAW	4 levels	55	2	65	253	96	20	5	4%	1.2	37.9%	7.9%	30.0%	2.0%	3.06	2.06	+1.9
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			21	0	22	79	26	4	1	0%	1.0	32.9%	5.1%	27.8%	1.3%	1.66	1.85	+0.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	NYM	26	0	27	114	37	8	3	0%	1.0	32.5%	6.8%	25.6%	2.8%	3.44	3.14	+0.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	NYM	35	0	37	157	49	11	5	0%	1.1	31.4%	7.2%	24.2%	2.9%	3.43	3.34	+0.6
2028	28	MLB CTX	NYM	29	0	30	127	40	9	4	0%	1.0	31.4%	7.2%	24.2%	2.9%	3.43	3.34	+0.4
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		49	0	51	205	66	13	5	0%	1.1	32.0%	6.4%	25.6%	2.3%	2.69	2.46	+1.3
/ 200 IP	—	RAW		192	0	200	796	256	51	19	0%	1.0	32.0%	6.4%	25.6%	2.3%	2.69	2.46	+5.0
Career	—	MLE		132	4	180	744	228	78	19	3%	1.4	30.6%	10.5%	20.2%	2.6%	2.65	3.25	+1.2
/ 200 IP	—	MLE		146	4	200	825	253	86	21	3%	1.4	30.6%	10.5%	20.2%	2.6%	2.65	3.25	+4.2

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 · 33 IPFIP 2.43 → 2.96 (+0.54)K% 40.2% → 34.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.15%	—	×0.860	34.53%
BB% (unintentional) /BF	8.33%	—	×0.900	7.50%
HR / BF /BF	3.03%	÷1.000	×1.100	3.33%

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 2.43 → 2.96, 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 ASt. Lucie Mets · 7 IPFIP 1.64 → 2.79 (+1.15)K% 37.9% → 28.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.93%	—	×0.740	28.07%
BB% (unintentional) /BF	13.79%	—	×1.180	16.28%
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 1.64 → 2.79, 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 AAASyracuse Mets · 7 IPFIP 6.70 → 6.88 (+0.18)K% 26.5% → 22.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.47%	—	×0.860	22.76%
BB% (unintentional) /BF	20.59%	—	×0.900	18.53%
HR / BF /BF	5.88%	÷1.059	×1.100	6.11%

Result: FIP 6.70 → 6.88, 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).

2023 AAASyracuse Mets · 44 IPFIP 3.54 → 4.01 (+0.47)K% 35.1% → 30.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.08%	—	×0.860	30.17%
BB% (unintentional) /BF	10.99%	—	×0.900	9.90%
HR / BF /BF	3.66%	÷1.003	×1.100	4.02%

Result: FIP 3.54 → 4.01, 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).

2023 AABinghamton Rumble Ponies · 10 IPFIP 0.18 → 1.00 (+0.82)K% 45.2% → 36.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	45.24%	—	×0.810	36.64%
BB% (unintentional) /BF	7.14%	—	×1.100	7.86%
HR / BF /BF	0.00%	÷0.925	×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 85).

Result: FIP 0.18 → 1.00, 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).

2022 A+Brooklyn Cyclones · 29 IPFIP 2.55 → 3.52 (+0.97)K% 30.1% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.08%	—	×0.770	23.16%
BB% (unintentional) /BF	12.20%	—	×1.150	14.02%
HR / BF /BF	0.81%	÷0.925	×1.250	1.10%

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 85).

Result: FIP 2.55 → 3.52, 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 ASt. Lucie Mets · 19 IPFIP 1.43 → 2.60 (+1.17)K% 39.5% → 29.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.47%	—	×0.740	29.21%
BB% (unintentional) /BF	13.16%	—	×1.180	15.53%
HR / BF /BF	0.00%	÷0.949	×1.300	0.00%

Result: FIP 1.43 → 2.60, 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 ROKFCL Mets · 7 IPFIP 0.06 → 1.61 (+1.55)K% 48.0% → 32.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	48.00%	—	×0.680	32.64%
BB% (unintentional) /BF	8.00%	—	×1.250	10.00%
HR / BF /BF	0.00%	÷0.958	×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 92).

Result: FIP 0.06 → 1.61, 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).

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
Nate Lavender (OGILVIE)	—	—	3.14	32.5%	6.8%	2.8%	—	3.34	31.4%	24.2%	37	+0.20	—	—	—
Nick Mondak L 2024 · 2.70 FIP	26	SU	2.70	30.9%	6.4%	2.5%	.286	—	—	—	—	—	2.70	153	84
Josh Edgin L 2014 · 2.62 FIP	27	SU	2.62	26.9%	5.8%	1.9%	.250	3.84	19.0%	7.4%	70	+1.22	2.62	195	78
Jeff Beliveau L 2014 · 2.06 FIP	27	SU	2.06	28.0%	6.0%	1.0%	.277	3.91	25.6%	13.4%	111	+1.85	2.06	179	76
Jack Dreyer L 2024 · 2.66 FIP	25	SU	2.66	26.8%	5.0%	2.2%	.295	2.78	25.9%	19.2%	141	+0.12	2.53	253	75
Damaso Marte L 2002 · 2.55 FIP	27	SU	2.55	30.0%	6.7%	2.1%	.265	3.46	25.2%	14.9%	199	+0.91	2.41	495	74
Darien Núñez L 2019 · 2.48 FIP	26	SU	2.48	32.5%	11.3%	1.3%	.333	2.65	35.0%	24.6%	52	+0.17	2.48	122	72
Alex Vesia L 2023 · 3.23 FIP	27	SU	3.23	30.5%	7.1%	3.3%	.338	3.11	32.7%	22.2%	184	-0.12	1.91	408	72
Yuki Matsui L 2024 · 3.63 FIP	28	SU	3.63	26.8%	8.9%	3.0%	.242	4.25	23.9%	12.6%	140	+0.62	3.63	203	70
Dario Alvarez L 2016 · 3.70 FIP	27	SU	3.70	33.3%	8.4%	4.3%	.385	3.60	23.2%	10.8%	71	-0.10	3.47	160	70
Phillip Diehl L 2021 · 3.71 FIP	27	SU	3.71	29.1%	5.9%	4.2%	.244	5.30	19.8%	9.0%	72	+1.59	2.57	365	70
Brennan Bernardino L 2019 · 3.05 FIP	27	SU	3.05	25.2%	6.2%	2.4%	.267	2.51	24.6%	16.2%	33	-0.54	2.51	470	69
Justin King L 2024 · 3.26 FIP	26	SU	3.26	29.5%	11.8%	2.1%	.346	4.16	22.4%	3.6%	65	+0.90	3.26	133	69
Tyler Webb L 2017 · 3.62 FIP	27	SU	3.62	26.4%	4.9%	3.9%	.356	3.75	22.4%	14.3%	139	+0.13	3.10	337	68
Steven Okert L 2018 · 2.88 FIP	27	SU	2.88	26.3%	5.8%	2.3%	.378	4.04	27.9%	21.1%	114	+1.16	2.49	605	67
Daniel Zamora L 2019 · 2.15 FIP	26	SU	2.15	27.1%	6.3%	1.0%	.342	4.24	23.1%	11.9%	107	+2.09	2.15	310	67
Charlie Furbush L 2013 · 2.91 FIP	27	SU	2.91	28.6%	9.6%	1.8%	.256	2.79	26.1%	21.4%	64	-0.12	2.40	261	67
Tyler Olson L 2017 · 3.15 FIP	27	SU	3.15	27.3%	7.3%	2.6%	.236	3.54	26.6%	17.3%	58	+0.39	3.15	263	66
Ben Wereski L 2023 · 3.73 FIP	25	SU	3.73	29.0%	7.4%	3.9%	.449	5.33	21.2%	11.6%	65	+1.60	3.73	142	66
Chasen Shreve L 2017 · 4.11 FIP	27	SU	4.11	29.3%	11.1%	3.8%	.245	4.38	26.8%	15.4%	138	+0.27	1.33	483	66
Josh Smoker L 2016 · 2.94 FIP	27	SU	2.94	30.3%	6.6%	2.9%	.399	4.28	22.8%	13.6%	116	+1.34	2.71	230	66

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	0.5	32	32.1%	6.6%	0.98	3.04
Giovanny Gallegos ('24)	1.7	56	29.6%	6.6%	1.18	3.49
Sean Doolittle ('22)	2.3	56	29.0%	5.7%	0.96	3.20
Josh Fields ('18)	1.4	46	27.5%	8.0%	0.98	3.71
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Bryan Harvey ('87-'94, 12 WAR*)	1.9	64	37.8%*	8.9%*	0.94*	2.59*
Tom Henke ('82-'95, 25 WAR*)	3.9	66	35.9%*	7.7%*	0.93*	2.72*