Brandon Waddell

P Syracuse Mets

Age 33 LHP 6'3" / 180 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	28	AAASTL	23	0	30	3.30	2.33	25.8%	7.8%	—	K 22.7% · BB 8.2% · FIP 2.67
2025	31	MIX3 levels	31	17	109	4.45	4.33	19.2%	7.6%	1.4	K 16.4% · BB 7.2% · FIP 4.44
2026	32	AAANYM	4	2	11	7.15	5.04	19.6%	2.0%	—	K 16.9% · BB 1.8% · FIP 5.45
2027PROJ	33	MLBNYM	1	1	5	5.02	5.18	14.0%	7.9%	0.0
2028PROJ6% MLB	34	MLBNYM	1	1	1	5.21	5.40	14.1%	8.6%	0.0

Last 3 seasons WAR 1.4 · Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

FB Velo10

K%10

xwOBA vs10

Career WAR0.0 WAR median (P10 0.0 / P90 0.0, comp-based) · star 0% · didn't stick 100%

CompsEdgar Osuna (age 31) · Kwang Hyun Kim (age 33) · Everett Teaford (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 115123vR 132

ERA5.02

FIP5.18

K%14.0%

BB%7.9%

K-BB%6.1%

Proj IP49

Value2027 · WAR

Runs saved vs league avg · 49 IP

Grades · Statcast · Contract

Scouting Grades

STUF4040

CTRL5050

CMD3535

K†3030

Statcast Percentiles

FB Velo

p1090.7

Whiff%

p1720.6%

p1016.4%

xwOBA vs

p100.360

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	22	MIX MLE	2 levels	27	25	147	624	120	62	10	93%	5.4	15.4%	10.9%	4.5%	1.9%	3.49	4.22	—
2016	22	MIX RAW	2 levels	27	25	147	624	120	62	10	93%	5.4	19.2%	9.9%	9.3%	1.6%	3.49	3.62	—
2017	23	MIX MLE	2 levels	16	15	69	293	60	27	3	94%	4.3	16.0%	10.5%	5.5%	1.2%	3.39	3.75	—
2017	23	MIX RAW	2 levels	16	15	69	293	60	27	3	94%	4.3	20.5%	9.2%	11.3%	1.0%	3.39	3.10	—
2018	24	MIX MLE	2 levels	28	23	135	576	103	55	7	82%	4.8	15.2%	10.2%	5.0%	1.5%	3.59	3.94	—
2018	24	MIX RAW	2 levels	28	23	135	576	103	55	7	82%	4.8	17.9%	9.5%	8.3%	1.2%	3.59	3.47	—
2019	25	MIX MLE	2 levels	36	14	97	449	111	48	11	39%	2.7	21.1%	11.3%	9.8%	2.8%	6.29	4.30	—
2019	25	MIX RAW	2 levels	36	14	97	449	111	48	11	39%	2.7	24.7%	10.7%	14.0%	2.4%	6.29	3.77	—
2020	26	MLB MLE	Pittsburgh Pirates	2	0	3	14	2	2	0	0%	1.7	14.3%	14.3%	0.0%	0.0%	2.70	3.70	+0.0
2020	26	MLB RAW	Pittsburgh Pirates	2	0	3	14	2	2	0	0%	1.7	14.3%	14.3%	0.0%	0.0%	2.70	3.70	+0.0
2021	27	MIX MLE	2 levels	20	0	23	105	19	12	3	0%	1.1	16.7%	11.6%	5.2%	3.2%	3.97	4.88	-0.1
2021	27	MIX RAW	2 levels	20	0	23	105	19	12	3	0%	1.1	18.1%	11.4%	6.7%	2.9%	3.97	4.73	-0.0
2022	28	AAA MLE	Memphis Redbirds	23	0	30	128	33	10	1	0%	1.3	22.7%	8.2%	14.5%	0.8%	3.30	2.67	—
2022	28	AAA RAW	Memphis Redbirds	23	0	30	128	33	10	1	0%	1.3	25.8%	7.8%	18.0%	0.8%	3.30	2.33	—
2025	31	MIX MLE	3 levels	31	17	109	473	91	36	16	55%	3.5	16.4%	7.2%	9.2%	3.3%	4.45	4.44	+1.2
2025	31	MIX MLE	3 levels	31	17	109	473	91	36	16	55%	3.5	16.4%	7.2%	9.2%	3.3%	4.45	4.44	+1.2
2025	31	MIX RAW	3 levels	31	17	109	473	91	36	16	55%	3.5	19.2%	7.6%	11.6%	3.4%	4.45	4.33	+1.4
2026	32	AAA MLE	Syracuse Mets	4	2	11	51	10	1	3	50%	2.8	16.9%	1.8%	15.1%	6.5%	7.15	5.45	—
2026	32	AAA MLE	Syracuse Mets	4	2	11	51	10	1	3	50%	2.8	16.9%	1.8%	15.1%	6.5%	7.15	5.45	—
2026	32	AAA RAW	Syracuse Mets	4	2	11	51	10	1	3	50%	2.8	19.6%	2.0%	17.6%	5.9%	7.15	5.04	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			22	1	44	199	31	22	6	5%	2.0	15.6%	11.1%	4.5%	3.0%	4.09	4.96	-0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	NYM	1	1	5	21	3	2	1	100%	5.0	14.0%	7.9%	6.1%	3.4%	5.02	5.18	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	NYM	3	3	22	92	13	8	3	100%	7.3	14.1%	8.6%	5.5%	3.7%	5.21	5.40	0.0
2028	34	MLB CTX	NYM	1	1	1	4	1	0	0	100%	1.0	14.1%	8.6%	5.5%	3.7%	5.21	5.40	0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		22	1	45	204	32	22	6	5%	2.1	15.5%	11.0%	4.5%	3.0%	4.12	4.84	-0.2
/ 200 IP	—	RAW		98	5	200	903	142	97	27	5%	2.0	15.5%	11.0%	4.5%	3.0%	4.12	4.84	-0.7
Career	—	MLE		187	96	625	2713	460	268	60	51%	3.3	17.0%	9.9%	7.1%	2.2%	4.17	4.14	+0.2
/ 200 IP	—	MLE		60	31	200	868	147	86	19	51%	3.3	17.0%	9.9%	7.1%	2.2%	4.17	4.14	+0.7

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 · 11 IPFIP 4.92 → 5.45 (+0.53)K% 19.6% → 16.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.61%	—	×0.860	16.86%
BB% (unintentional) /BF	1.96%	—	×0.900	1.76%
HR / BF /BF	5.88%	÷1.000	×1.100	6.47%

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 4.92 → 5.45, 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 · 75 IPFIP 4.44 → 4.46 (+0.02)K% 19.0% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.03%	—	×0.860	16.37%
BB% (unintentional) /BF	7.55%	—	×0.900	6.80%
HR / BF /BF	3.63%	÷1.167	×1.100	3.42%

Result: FIP 4.44 → 4.46, 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 · 30 IPFIP 2.34 → 2.67 (+0.33)K% 25.8% → 22.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.78%	—	×0.880	22.69%
BB% (unintentional) /BF	7.81%	—	×1.050	8.20%
HR / BF /BF	0.78%	÷1.048	×1.100	0.82%

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 2.34 → 2.67, 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 AAAMemphis Redbirds · 13 IPFIP 2.94 → 3.37 (+0.42)K% 22.2% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.22%	—	×0.880	19.56%
BB% (unintentional) /BF	5.56%	—	×1.050	5.83%
HR / BF /BF	1.85%	÷0.949	×1.100	2.15%

Result: FIP 2.94 → 3.37, 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 AAAIndianapolis Indians · 61 IPFIP 4.81 → 5.43 (+0.62)K% 22.8% → 20.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.85%	—	×0.880	20.11%
BB% (unintentional) /BF	12.91%	—	×1.050	13.56%
HR / BF /BF	3.64%	÷0.953	×1.100	4.20%

Result: FIP 4.81 → 5.43, 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 AAAltoona Curve · 36 IPFIP 1.46 → 2.00 (+0.54)K% 28.6% → 23.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.810	23.14%
BB% (unintentional) /BF	6.12%	—	×1.100	6.73%
HR / BF /BF	0.00%	÷0.783	×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 57).

Result: FIP 1.46 → 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).

2018 AAAIndianapolis Indians · 82 IPFIP 3.41 → 3.67 (+0.26)K% 16.6% → 14.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.57%	—	×0.880	14.59%
BB% (unintentional) /BF	9.94%	—	×1.050	10.44%
HR / BF /BF	0.83%	÷1.041	×1.100	0.88%

Result: FIP 3.41 → 3.67, 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 AAAltoona Curve · 54 IPFIP 3.55 → 4.40 (+0.85)K% 20.1% → 16.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.09%	—	×0.810	16.28%
BB% (unintentional) /BF	8.88%	—	×1.100	9.77%
HR / BF /BF	1.87%	÷0.857	×1.200	2.62%

Result: FIP 3.55 → 4.40, 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
Brandon Waddell (OGILVIE)	—	—	5.18	14.0%	7.9%	3.4%	—	5.40	14.1%	5.5%	22	+0.22	—	—	—
Edgar Osuna L 2019 · 5.09 FIP	31	SW	5.09	10.0%	7.2%	3.5%	.373	—	—	—	—	—	5.05	175	67
Kwang Hyun Kim L 2021 · 4.21 FIP	33	SP-	4.21	17.7%	8.2%	2.9%	.266	—	—	—	—	—	3.76	146	62
Everett Teaford L 2015 · 4.42 FIP	31	SP-	4.42	13.9%	7.6%	2.8%	.324	—	—	—	—	—	4.42	208	61
Carlos Teller L 2019 · 4.90 FIP	32	SP-	4.90	13.1%	14.2%	2.0%	.365	—	—	—	—	—	3.75	333	59
Yohan Flande L 2019 · 4.21 FIP	33	SP-	4.21	10.0%	11.9%	0.8%	.386	—	—	—	—	—	3.18	394	58
Alejandro Soto L 2019 · 4.03 FIP	33	SW	4.03	17.1%	6.9%	2.7%	.376	—	—	—	—	—	3.96	280	55
Luke Westphal L 2022 · 4.35 FIP	33	SW	4.35	24.2%	13.4%	2.9%	.302	5.19	15.7%	-0.7%	26	+0.84	2.97	294	55
Orlando Lara L 2017 · 4.39 FIP	32	SP	4.39	14.7%	6.5%	3.1%	.346	4.09	22.0%	14.2%	104	-0.30	3.85	426	54
Micah Bowie L 2007 · 4.60 FIP	32	SW	4.60	16.9%	10.9%	2.8%	.279	—	—	—	—	—	3.78	140	53
Chris Narveson L 2015 · 4.79 FIP	33	SW	4.79	23.6%	8.0%	4.9%	.333	4.59	14.1%	4.5%	193	-0.20	4.00	625	51
Paul Oseguera L 2016 · 5.62 FIP	32	SP	5.62	5.6%	12.5%	2.6%	.320	—	—	—	—	—	3.83	162	51
Tomas Solis L 2016 · 4.86 FIP	33	SP	4.86	12.4%	8.5%	3.1%	.273	5.95	10.1%	2.4%	117	+1.09	4.20	327	51
Brandon Mann L 2017 · 4.24 FIP	33	SU	4.24	19.8%	11.3%	2.5%	.286	3.59	17.8%	6.4%	52	-0.65	3.59	190	48
Jorge Castillo L 2015 · 4.50 FIP	33	SP	4.50	16.8%	9.2%	3.0%	.292	4.95	12.4%	7.2%	186	+0.45	4.50	408	48
Arturo Lopez L 2016 · 5.26 FIP	33	SP	5.26	11.5%	7.9%	3.9%	.341	4.92	11.3%	5.2%	224	-0.34	4.34	445	47
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	41
Carlos Pulido L 2004 · 4.41 FIP	32	SU	4.41	16.4%	5.5%	3.6%	.341	—	—	—	—	—	2.91	111	40
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	40
Tommy Phelps L 2005 · 3.98 FIP	31	SU	3.98	13.2%	7.5%	1.9%	.280	—	—	—	—	—	3.39	120	39
Marco Quevedo R 2019 · 5.17 FIP	32	SP-	5.17	13.6%	11.4%	3.2%	.344	—	—	—	—	—	4.13	300	39

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	0.6	38	14.7%	9.3%	1.28	4.70
Brian Stokes ('10)	1.0	52	14.5%	10.1%	1.22	5.27
Sam Long ('25)	1.1	42	19.5%	9.7%	1.28	4.65
Jeff Manship ('16)	1.1	32	16.5%	9.3%	1.05	4.82
Kevin Hickey ('81-'91, 0 WAR*)	1.2	47	17.7%*	10.2%*	1.10*	4.27*
Bob Macdonald ('90-'96, 1 WAR*)	1.2	59	18.3%*	9.8%*	1.20*	4.32*
Greg Booker ('83-'90, -1 WAR*)	1.3	53	13.3%*	10.2%*	1.07*	4.51*