Joey Gerber — OGILVIE

Joey Gerber P Syracuse Mets

Age 29 RHP 6'4" / 212 lbs AAA peak 0.2 · 2026

2026 ROS PROJECTION36 act + 5 ip · rates below = ROS only

showingROS

wRC+ AGAINSTvL 10597vR 89

ERA3.80

FIP3.80

K%22.9%

BB%7.2%

K-BB%15.8%

ROS IP5

Value2026 · WAR

Runs saved vs league avg · 41 IP

Grades · Arsenal · Contract

Scouting Grades

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FB (54%)

CT (42%)

CH (2%)

SL (2%)

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 industry-standard fWAR.

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 + 2026 so far)
2018	21	A MLE	Clinton LumberKings	9	0	12	47	22	5	0	0%	1.3	46.8%	10.6%	36.2%	0.0%	2.31	0.49	—
2018	21	A RAW	Clinton LumberKings	9	0	12	47	22	5	0	0%	1.3	46.8%	10.6%	36.2%	0.0%	2.31	0.61	—
2019	22	MIX MLE	2 levels	44	0	49	204	69	19	2	0%	1.1	26.6%	10.5%	16.1%	1.5%	2.59	3.01	—
2019	22	MIX RAW	2 levels	44	0	49	204	69	19	2	0%	1.1	33.8%	9.3%	24.5%	1.0%	2.59	1.97	—
2020	23	MLB MLE	Seattle Mariners	17	0	16	62	6	5	1	0%	0.9	9.7%	8.1%	1.6%	1.7%	4.02	4.21	+0.1
2020	23	MLB RAW	Seattle Mariners	17	0	16	62	6	5	1	0%	0.9	9.7%	8.1%	1.6%	1.6%	4.02	4.12	+0.1
2022	25	ROK MLE	ACL Mariners	1	0	1	5	0	0	0	0%	1.0	0.0%	0.0%	0.0%	0.0%	0.00	3.10	—
2022	25	ROK RAW	ACL Mariners	1	0	1	5	0	0	0	0%	1.0	0.0%	0.0%	0.0%	0.0%	0.00	3.10	—
2024	27	MIX MLE	4 levels	26	1	33	142	40	15	3	4%	1.3	21.1%	11.1%	10.0%	2.6%	2.43	4.13	—
2024	27	MIX MLE	4 levels	26	1	33	142	40	15	3	4%	1.3	21.1%	11.1%	10.0%	2.6%	2.43	4.13	—
2024	27	MIX RAW	4 levels	26	1	33	142	40	15	3	4%	1.3	28.2%	10.6%	17.6%	2.1%	2.43	3.22	—
2025	28	MIX MLE	3 levels	37	10	49	212	59	15	10	27%	1.3	23.2%	7.0%	16.2%	3.7%	5.73	4.06	+0.3
2025	28	MIX MLE	3 levels	37	10	49	212	59	15	10	27%	1.3	23.2%	7.0%	16.2%	3.7%	5.73	4.06	+0.3
2025	28	MIX RAW	3 levels	37	10	49	212	59	15	10	27%	1.3	27.8%	7.1%	20.8%	4.7%	5.73	4.27	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			26	0	28	111	18	9	3	0%	1.1	16.2%	8.1%	8.1%	2.7%	3.81	4.16	+0.1
▸ 2026 season
YTD	29	MLB MLE	New York Mets	31	1	36	162	35	15	4	3%	1.2	17.9%	7.9%	10.0%	2.6%	7.00	4.04	—
YTD	29	MLB MLE	New York Mets	31	1	36	162	35	15	4	3%	1.2	17.9%	7.9%	10.0%	2.6%	7.00	4.04	—
▸ 2026 season — so far · projected rest · full
2026	29	A RAW	NYM	1	0	1	3	0	0	0	0%	1.0	0.0%	0.0%	0.0%	0.0%	0.00	3.10	—
2026	29	AAA RAW	NYM	23	1	27	125	27	11	3	4%	1.2	21.6%	8.8%	12.8%	2.4%	8.10	3.71	—
2026	29	MLB RAW	NYM	7	0	8	34	8	4	1	0%	1.2	23.5%	11.8%	11.8%	2.9%	4.32	4.22	—
SO FAR	29	MLB RAW	NYM	7	0	8	34	8	4	1	0%	1.2	23.5%	11.8%	11.8%	2.9%	4.32	4.22	—
ROS	29	MLB MLE	NYM	5	0	5	22	5	2	1	0%	1.1	22.9%	7.2%	15.8%	3.2%	3.80	3.80	+0.0
ROS	29	MLB CTX	NYM	5	0	5	21	5	2	1	0%	1.0	22.9%	7.2%	15.8%	3.2%	3.80	3.80	+0.0
2026 TOT	29	MLB MLE	NYM	—	—	41	184	40	17	5	—	—	21.8%	9.0%	12.8%	2.6%	6.59	4.01	+0.3
FULL	29	MLB A+C	NYM	—	—	14	58	40	17	5	—	—	69.8%	28.9%	40.9%	8.2%	19.97	5.36	+0.1
▸ Career projection (2027+) — MLB context, MLB playing time (IP × MLB%)
2027	30	MLB MLE	NYM	52	0	55	231	53	18	8	0%	1.1	23.0%	8.0%	15.1%	3.3%	4.29	4.37	+0.1
2028	31	MLB MLE	NYM	49	0	51	216	48	18	8	0%	1.0	22.3%	8.4%	13.9%	3.5%	4.48	4.60	-0.0
2027	30	MLB CTX	NYM	50	0	52	220	51	17	7	0%	1.0	23.0%	8.0%	15.1%	3.3%	4.29	4.37	+0.1
2028	31	MLB CTX	NYM	47	0	49	207	46	17	7	0%	1.0	22.3%	8.4%	13.9%	3.5%	4.48	4.60	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		127	0	134	558	120	45	18	0%	1.1	21.4%	8.1%	13.3%	3.3%	4.24	4.12	+0.7
/ 200 IP	—	RAW		189	0	200	832	179	67	27	0%	1.1	21.4%	8.1%	13.3%	3.3%	4.24	4.12	+1.0
Career	—	MLE		165	12	195	834	191	75	20	7%	1.2	22.9%	9.0%	13.9%	2.4%	4.25	3.62	+0.3
/ 200 IP	—	MLE		169	12	200	855	195	77	20	7%	1.2	22.9%	9.0%	13.9%	2.4%	4.25	3.62	+0.4

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
Joey Gerber (OGILVIE)	—	—	4.10	23.8%	7.1%	3.2%	—	4.48	22.7%	14.5%	106	+0.38	—	—	—
David Hernandez R 2015 · 4.40 FIP	30	SU	4.40	22.9%	7.6%	4.1%	.287	3.17	24.9%	18.4%	192	-1.23	2.60	268	85
Tyler Sturdevant R 2015 · 4.37 FIP	29	SU	4.37	21.3%	8.5%	3.6%	.241	3.97	18.5%	11.9%	118	-0.40	3.44	149	82
Cory Gearrin R 2015 · 3.52 FIP	29	SU	3.52	22.1%	7.5%	2.5%	.283	3.54	22.2%	13.6%	174	+0.02	3.15	272	81
Kaleb Bowman R 2023 · 3.88 FIP	26	SU	3.88	22.0%	7.5%	3.1%	.333	4.55	17.5%	6.5%	168	+0.67	3.88	202	81
Cody Hall R 2017 · 4.03 FIP	29	SU	4.03	26.8%	10.0%	3.5%	.299	3.92	25.9%	8.4%	19	-0.11	3.51	134	81
Ryan Brasier R 2016 · 3.68 FIP	28	SU	3.68	25.0%	7.7%	3.1%	.289	3.09	24.2%	17.9%	130	-0.59	2.31	426	79
Chris Hatcher R 2015 · 2.92 FIP	30	SU	2.92	27.1%	6.6%	2.3%	.292	4.67	22.6%	14.0%	137	+1.75	2.92	176	79
Gerardo Sanchez R 2017 · 3.47 FIP	28	SU	3.47	21.6%	8.4%	2.1%	.355	3.58	20.5%	15.1%	56	+0.11	2.65	137	79
Jaye Chapman R 2016 · 3.25 FIP	29	SU	3.25	24.6%	6.4%	2.6%	.274	5.51	13.0%	-0.4%	31	+2.26	3.25	150	79
Ross Ohlendorf R 2015 · 3.30 FIP	32	SU	3.30	23.7%	7.2%	2.3%	.342	4.93	23.4%	12.7%	66	+1.63	3.30	122	78
Jonathan Broxton R 2015 · 3.67 FIP	31	SU	3.67	24.5%	8.2%	2.9%	.325	3.89	21.7%	13.5%	76	+0.22	3.67	137	78
Andrew Robinson R 2017 · 4.32 FIP	29	SU	4.32	23.4%	8.8%	3.8%	.245	—	—	—	—	—	3.19	160	77
Diego Moreno R 2016 · 3.75 FIP	29	SU	3.75	21.0%	8.2%	2.5%	.347	3.13	17.8%	11.9%	54	-0.62	2.29	187	77
Joakim Soria R 2015 · 3.70 FIP	31	SU	3.70	23.5%	6.6%	3.2%	.258	2.91	26.6%	18.9%	183	-0.79	2.01	380	76
Erik Davis R 2016 · 3.05 FIP	29	SU	3.05	24.4%	9.5%	1.5%	.329	3.88	19.8%	9.8%	129	+0.83	3.05	227	76
Matt Albers R 2015 · 3.19 FIP	32	SU	3.19	18.8%	4.7%	2.0%	.252	4.45	20.4%	13.3%	147	+1.26	3.15	244	76
Kaleb Fleck R 2018 · 3.47 FIP	29	SU	3.47	24.6%	5.9%	3.1%	.346	—	—	—	—	—	2.68	177	75
Rob Wooten R 2015 · 4.86 FIP	30	SU	4.86	19.3%	5.5%	4.9%	.299	3.87	17.7%	14.4%	111	-0.99	3.19	191	75
Mark Serrano R 2016 · 3.16 FIP	30	SU	3.16	17.7%	6.5%	1.3%	.290	4.36	21.1%	14.0%	88	+1.20	3.16	120	75
Tommy Hunter R 2015 · 3.72 FIP	29	SU	3.72	18.9%	4.8%	2.9%	.295	3.14	21.2%	15.9%	172	-0.58	2.78	316	75

Career Comps

Retired MLB careers whose totals most resemble what we're projecting for this player. Pool: 133 completed pitcher careers (2015-2024).

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	0.8	55	23.1%	7.8%	1.29	4.28
Noé Ramirez ('22)	1.3	52	24.8%	9.0%	1.33	4.19
Mike Mayers ('23)	1.2	47	24.3%	7.8%	1.50	4.47
Eli Morgan ('24)	1.3	66	23.8%	6.4%	1.46	3.97
Jarlín García ('22)	1.5	61	19.6%	7.8%	1.33	3.79
Adbert Alzolay ('23)	1.1	70	26.1%	7.0%	1.32	3.84
Félix Peña ('20)	1.2	62	23.6%	8.3%	1.37	4.46