Zach Messinger

P Scranton/Wilkes-Barre RailRiders

Age 27 RHP 6'6" / 225 lbs AAA

Overview

AAAPROSPECT

ETA2028MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	AANYY	27	25	150	3.06	3.11	22.0%	8.4%	—	K 17.8% · BB 9.2% · FIP 3.70
2025	25	MIX2 levels	33	7	73	5.55	4.32	21.0%	11.2%	—	K 18.1% · BB 10.5% · FIP 4.18
2026	26	AAAScranton/Wilk…	42	1	73	3.34	3.83	23.0%	11.1%	—	K 19.7% · BB 10.0% · FIP 4.09
2027PROJ	27	MLBNYY	7	0	7	4.82	4.82	18.5%	11.3%	-0.0
2028PROJ43% MLB	28	MLBNYY	21	0	22	4.82	4.85	18.9%	11.3%	-0.1
2029PROJ59% MLB	29	MLBNYY	25	0	26	4.88	4.94	19.0%	11.3%	-0.1

Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff45Control40Command40

Career WAR0.0 WAR median (P10 -0.2 / P90 0.8, comp-based) · star 0% · didn't stick 95%

CompsRoberto Gómez (age 27) · Tyler Yates (age 26) · Cameron Foster (age 25)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 125117vR 108

ERA4.82

FIP4.82

K%18.5%

BB%11.3%

K-BB%7.2%

Proj IP70

Value2027 · WAR

Runs saved vs league avg · 70 IP

Grades · Contract

Scouting Grades

STUF4545

CTRL4040

CMD4040

K†4545

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)
2022	22	A MLE	Tampa Tarpons	31	16	84	361	112	45	5	52%	2.7	23.0%	14.7%	8.2%	1.8%	4.30	3.99	—
2022	22	A RAW	Tampa Tarpons	31	16	84	361	112	45	5	52%	2.7	31.0%	12.5%	18.6%	1.4%	4.30	2.81	—
2023	23	MIX MLE	2 levels	22	21	101	446	120	54	14	95%	4.6	20.4%	14.3%	6.1%	3.8%	4.35	5.26	—
2023	23	MIX RAW	2 levels	22	21	101	446	120	54	14	95%	4.6	26.9%	12.1%	14.8%	3.1%	4.35	4.13	—
2024	24	AA MLE	Somerset Patriots	27	25	150	619	136	52	9	93%	5.6	17.8%	9.2%	8.6%	1.7%	3.06	3.70	—
2024	24	AA MLE	Somerset Patriots	27	25	150	619	136	52	9	93%	5.6	17.8%	9.2%	8.6%	1.7%	3.06	3.70	—
2024	24	AA RAW	Somerset Patriots	27	25	150	619	136	52	9	93%	5.6	22.0%	8.4%	13.6%	1.5%	3.06	3.11	—
2025	25	MIX MLE	2 levels	33	7	73	338	71	38	9	21%	2.2	18.1%	10.5%	7.6%	2.3%	5.55	4.18	—
2025	25	MIX MLE	2 levels	33	7	73	338	71	38	9	21%	2.2	18.1%	10.5%	7.6%	2.3%	5.55	4.18	—
2025	25	MIX RAW	2 levels	33	7	73	338	71	38	9	21%	2.2	21.0%	11.2%	9.8%	2.7%	5.55	4.32	—
2026	26	AAA MLE	Scranton/Wilkes-Barre RailRiders	42	1	73	305	70	34	7	2%	1.7	19.7%	10.0%	9.7%	2.5%	3.34	4.09	—
2026	26	AAA MLE	Scranton/Wilkes-Barre RailRiders	42	1	73	305	70	34	7	2%	1.7	19.7%	10.0%	9.7%	2.5%	3.34	4.09	—
2026	26	AAA RAW	Scranton/Wilkes-Barre RailRiders	42	1	73	305	70	34	7	2%	1.7	23.0%	11.1%	11.8%	2.3%	3.34	3.83	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	NYY	7	0	7	30	5	3	1	0%	1.0	18.5%	11.3%	7.2%	2.7%	4.82	4.82	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	NYY	50	0	52	221	42	25	6	0%	1.0	18.9%	11.3%	7.6%	2.8%	4.82	4.85	-0.2
2029	29	MLB MLE	NYY	42	0	44	186	35	21	6	0%	1.0	19.0%	11.3%	7.7%	3.0%	4.88	4.94	-0.2
2028	28	MLB CTX	NYY	21	0	22	93	18	10	3	0%	1.0	18.9%	11.3%	7.6%	2.8%	4.82	4.85	-0.1
2029	29	MLB CTX	NYY	25	0	26	110	21	12	3	0%	1.0	19.0%	11.3%	7.7%	3.0%	4.88	4.94	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		46	0	48	204	39	23	6	0%	1.0	19.0%	11.3%	7.7%	2.9%	4.85	4.52	+0.0
/ 200 IP	—	RAW		190	0	200	844	162	95	25	0%	1.1	19.0%	11.3%	7.7%	2.9%	4.85	4.52	+0.1
Career	—	MLE		155	70	481	2069	405	240	49	45%	3.1	19.6%	11.6%	8.0%	2.4%	3.97	4.22	-0.4
/ 200 IP	—	MLE		64	29	200	861	169	100	21	45%	3.1	19.6%	11.6%	8.0%	2.4%	3.97	4.22	-0.8

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 AAAScranton/Wilkes-Barre RailRiders · 73 IPFIP 3.83 → 4.09 (+0.26)K% 23.0% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.95%	—	×0.860	19.74%
BB% (unintentional) /BF	11.15%	—	×0.900	10.03%
HR / BF /BF	2.30%	÷1.000	×1.100	2.52%

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 3.83 → 4.09, 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 AAAScranton/Wilkes-Barre RailRiders · 67 IPFIP 4.05 → 4.18 (+0.14)K% 21.1% → 18.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.10%	—	×0.860	18.15%
BB% (unintentional) /BF	11.69%	—	×0.900	10.52%
HR / BF /BF	2.27%	÷1.070	×1.100	2.34%

Result: FIP 4.05 → 4.18, 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 AASomerset Patriots · 6 IPFIP 5.91 → 6.92 (+1.01)K% 20.0% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.810	16.20%
BB% (unintentional) /BF	6.67%	—	×1.100	7.33%
HR / BF /BF	6.67%	÷1.030	×1.200	7.77%

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 106).

Result: FIP 5.91 → 6.92, 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 AASomerset Patriots · 150 IPFIP 3.11 → 3.70 (+0.59)K% 22.0% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.97%	—	×0.810	17.80%
BB% (unintentional) /BF	8.40%	—	×1.100	9.24%
HR / BF /BF	1.45%	÷1.027	×1.200	1.70%

Result: FIP 3.11 → 3.70, 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 A+Hudson Valley Renegades · 97 IPFIP 4.11 → 5.26 (+1.15)K% 26.5% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.46%	—	×0.770	20.38%
BB% (unintentional) /BF	12.41%	—	×1.150	14.27%
HR / BF /BF	3.04%	÷1.006	×1.250	3.78%

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 101).

Result: FIP 4.11 → 5.26, 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 ATampa Tarpons · 84 IPFIP 2.82 → 3.99 (+1.17)K% 31.0% → 23.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.02%	—	×0.740	22.96%
BB% (unintentional) /BF	12.47%	—	×1.180	14.71%
HR / BF /BF	1.39%	÷1.026	×1.300	1.76%

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 105).

Result: FIP 2.82 → 3.99, 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
Zach Messinger (OGILVIE)	—	—	4.82	18.5%	11.3%	2.7%	—	4.89	18.9%	7.7%	96	+0.07	—	—	—
Roberto Gómez R 2017 · 3.95 FIP	27	SW	3.95	18.4%	8.9%	2.3%	.315	5.25	13.8%	4.9%	18	+1.30	3.95	115	86
Tyler Yates R 2004 · 4.47 FIP	26	SW	4.47	15.4%	9.6%	2.6%	.333	3.64	22.5%	13.5%	116	-0.83	3.23	248	84
Cameron Foster R 2024 · 4.83 FIP	25	SW	4.83	17.4%	11.5%	3.2%	.321	3.09	25.5%	17.5%	60	-1.74	2.50	213	83
Jorge Reyes R 2015 · 4.58 FIP	27	SW	4.58	15.9%	8.5%	3.2%	.307	3.49	14.2%	6.2%	129	-1.09	3.39	227	83
Josh Roeder R 2019 · 4.71 FIP	26	SW	4.71	15.1%	8.7%	3.3%	.264	—	—	—	—	—	4.71	175	82
Dakota Hawkins R 2024 · 4.31 FIP	24	SW	4.31	16.3%	8.9%	2.7%	.277	4.32	19.4%	10.4%	102	+0.01	3.50	186	82
Logan Bawcom R 2016 · 3.73 FIP	27	SW	3.73	16.6%	9.2%	1.6%	.251	4.85	16.0%	6.7%	234	+1.12	3.73	399	82
Dennis O'Grady R 2015 · 4.95 FIP	26	SW	4.95	13.5%	11.0%	2.9%	.339	—	—	—	—	—	3.62	157	82
Aaron Northcraft R 2016 · 4.19 FIP	26	SW	4.19	16.1%	8.3%	2.5%	.309	3.32	24.1%	13.4%	34	-0.87	3.32	252	81
Danny Kirwin R 2024 · 4.19 FIP	24	SW	4.19	17.9%	9.9%	2.4%	.262	5.33	16.8%	4.6%	91	+1.14	4.16	161	81
Taylor Grover R 2017 · 4.93 FIP	26	SW	4.93	20.0%	10.9%	3.9%	.320	4.89	16.2%	8.9%	14	-0.04	3.68	239	81
Emmanuel Chapman R 2024 · 4.08 FIP	25	SW	4.08	19.7%	10.6%	2.4%	.256	4.52	17.8%	1.3%	117	+0.44	3.81	198	80
Grant Black R 2021 · 4.46 FIP	27	SW	4.46	19.0%	13.9%	2.2%	.327	5.35	15.0%	3.8%	156	+0.89	4.46	215	79
Andres Santiago R 2017 · 4.16 FIP	27	SW	4.16	16.3%	9.1%	2.3%	.384	5.26	13.1%	3.2%	159	+1.10	3.76	291	79
Lisalverto Bonilla R 2017 · 4.61 FIP	27	SW	4.61	18.2%	10.5%	3.1%	.304	4.70	15.8%	3.2%	11	+0.09	3.22	242	78
Colby Morris R 2022 · 5.76 FIP	25	SW	5.76	16.2%	11.8%	4.6%	.268	—	—	—	—	—	4.11	105	77
Jose Pina R 2015 · 4.90 FIP	29	SW	4.90	16.9%	9.2%	3.8%	.231	3.66	13.6%	6.9%	246	-1.24	3.63	424	77
Noah Davis R 2024 · 3.88 FIP	27	SW	3.88	18.5%	8.4%	2.3%	.368	5.10	18.3%	7.9%	183	+1.22	3.76	596	77
Jarret Whorff R 2024 · 4.04 FIP	25	SU	4.04	18.9%	10.2%	2.3%	.271	4.52	19.6%	12.0%	123	+0.48	3.88	257	76
Nolan Sanburn R 2017 · 4.55 FIP	26	SW	4.55	15.0%	10.4%	2.5%	.310	—	—	—	—	—	3.82	169	76

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	55	18.8%	11.3%	1.08	4.84
Mike Matthews ('05)	1.2	61	16.3%	11.3%	1.07	4.54
Tyler Yates ('09)	1.2	62	19.7%	12.0%	0.94	5.12
Bill Wilson ('69-'73, -0 WAR*)	1.3	52	20.3%*	11.5%*	1.13*	4.84*
Valerio De Los Santos ('08)	1.3	51	18.7%	11.5%	1.30	4.57
Fred Lasher ('63-'71, -1 WAR*)	1.0	50	21.5%*	11.9%*	1.05*	4.68*
Bob Macdonald ('90-'96, 1 WAR*)	1.2	59	18.3%*	9.8%*	1.20*	4.32*