Max Green

P Binghamton Rumble Ponies

Age 31 LHP 6'1" / 175 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2021	25	AADET	16	1	26	7.27	4.33	21.8%	5.6%	—	K 17.6% · BB 6.2% · FIP 4.91
2023	27	MIX2 levels	14	3	15	11.40	5.10	20.5%	15.4%	—	K 16.1% · BB 18.8% · FIP 4.97
2026	30	AANYM	10	5	35	4.89	3.67	18.8%	5.4%	—	K 15.2% · BB 5.9% · FIP 4.33
2027PROJ	31	MLBNYM	—	—	0	4.52	4.52	17.4%	6.7%	—
2028PROJ31% MLB	32	MLBNYM	5	0	5	4.64	4.73	17.6%	7.3%	-0.0

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

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

CompsRaul Valdes (age 32) · Jeremy Bleich (age 31) · Andrew Barbosa (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 103111vR 119

ERA4.52

FIP4.52

K%17.4%

BB%6.7%

K-BB%10.7%

Proj IP19

Value2027 · WAR

Runs saved vs league avg · 19 IP

Grades · Contract

Scouting Grades

K†4040

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	22	A MLE	West Michigan Whitecaps	17	0	35	160	34	18	3	0%	2.1	15.7%	13.3%	2.4%	2.6%	4.11	4.87	—
2018	22	A RAW	West Michigan Whitecaps	17	0	35	160	34	18	3	0%	2.1	21.2%	11.2%	10.0%	1.9%	4.11	3.81	—
2019	23	A+ MLE	Lakeland Flying Tigers	36	0	60	251	40	17	4	0%	1.7	12.3%	7.8%	4.5%	2.1%	2.83	4.19	—
2019	23	A+ RAW	Lakeland Flying Tigers	36	0	60	251	40	17	4	0%	1.7	15.9%	6.8%	9.2%	1.6%	2.83	3.48	—
2021	25	AA MLE	Erie SeaWolves	16	1	26	124	27	7	5	6%	1.6	17.6%	6.2%	11.4%	4.6%	7.27	4.91	—
2021	25	AA RAW	Erie SeaWolves	16	1	26	124	27	7	5	6%	1.6	21.8%	5.6%	16.1%	4.0%	7.27	4.33	—
2023	27	MIX MLE	2 levels	14	3	15	78	16	12	2	21%	1.1	16.1%	18.8%	-2.7%	1.5%	11.40	4.97	—
2023	27	MIX RAW	2 levels	14	3	15	78	16	12	2	21%	1.1	20.5%	15.4%	5.1%	2.6%	11.40	5.10	—
2026	30	AA MLE	Binghamton Rumble Ponies	10	5	35	149	28	8	4	50%	3.5	15.2%	5.9%	9.3%	3.2%	4.89	4.33	—
2026	30	AA MLE	Binghamton Rumble Ponies	10	5	35	149	28	8	4	50%	3.5	15.2%	5.9%	9.3%	3.2%	4.89	4.33	—
2026	30	AA RAW	Binghamton Rumble Ponies	10	5	35	149	28	8	4	50%	3.5	18.8%	5.4%	13.4%	2.7%	4.89	3.67	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	NYM	—	—	0	—	—	—	—	—	—	17.4%	6.7%	10.7%	3.0%	4.52	4.52	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	NYM	16	0	17	73	13	5	2	0%	1.1	17.6%	7.3%	10.2%	3.3%	4.64	4.73	-0.0
2028	32	MLB CTX	NYM	5	0	5	21	4	2	1	0%	1.0	17.6%	7.3%	10.2%	3.3%	4.64	4.73	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		5	0	5	23	4	2	1	0%	1.1	17.6%	7.3%	10.2%	3.3%	4.64	4.35	+0.0
/ 200 IP	—	RAW		190	0	200	844	149	74	37	0%	1.1	17.6%	7.3%	10.2%	3.3%	4.64	4.35	+0.5
Career	—	MLE		93	9	171	762	113	72	21	10%	1.8	14.8%	9.4%	5.4%	2.8%	4.94	4.56	-0.0
/ 200 IP	—	MLE		109	11	200	889	132	84	25	10%	1.8	14.8%	9.4%	5.4%	2.8%	4.94	4.56	-0.4

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 AABinghamton Rumble Ponies · 35 IPFIP 3.67 → 4.33 (+0.66)K% 18.8% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.79%	—	×0.810	15.22%
BB% (unintentional) /BF	5.37%	—	×1.100	5.91%
HR / BF /BF	2.68%	÷1.000	×1.200	3.22%

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 100).

Result: FIP 3.67 → 4.33, 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).

2023 ALakeland Flying Tigers · 13 IPFIP 4.08 → 4.97 (+0.90)K% 21.7% → 16.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.740	16.09%
BB% (unintentional) /BF	15.94%	—	×1.180	18.81%
HR / BF /BF	1.45%	÷1.216	×1.300	1.55%

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 143).

Result: FIP 4.08 → 4.97, 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 AAErie SeaWolves · 26 IPFIP 4.19 → 4.91 (+0.73)K% 21.8% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.77%	—	×0.810	17.64%
BB% (unintentional) /BF	5.65%	—	×1.100	6.21%
HR / BF /BF	4.03%	÷1.055	×1.200	4.59%

Result: FIP 4.19 → 4.91, 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 A+Lakeland Flying Tigers · 60 IPFIP 3.49 → 4.19 (+0.70)K% 15.9% → 12.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.94%	—	×0.770	12.27%
BB% (unintentional) /BF	6.77%	—	×1.150	7.79%
HR / BF /BF	1.59%	÷0.963	×1.250	2.07%

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 93).

Result: FIP 3.49 → 4.19, 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 AWest Michigan Whitecaps · 35 IPFIP 3.76 → 4.87 (+1.11)K% 21.2% → 15.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.25%	—	×0.740	15.72%
BB% (unintentional) /BF	11.25%	—	×1.180	13.28%
HR / BF /BF	1.88%	÷0.942	×1.300	2.59%

Result: FIP 3.76 → 4.87, 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
Max Green (OGILVIE)	—	—	4.52	17.4%	6.7%	3.0%	—	4.73	17.6%	10.2%	17	+0.21	—	—	—
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	79
Jeremy Bleich L 2018 · 3.69 FIP	31	SU	3.69	20.3%	6.6%	2.7%	.296	4.41	20.1%	13.1%	33	+0.72	3.20	241	78
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	76
Jason Gurka L 2019 · 5.13 FIP	31	SW	5.13	15.5%	7.6%	4.3%	.382	—	—	—	—	—	2.82	183	75
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	73
Kraig Sitton L 2017 · 3.87 FIP	29	SU	3.87	14.7%	6.1%	2.2%	.312	—	—	—	—	—	2.81	166	71
Nick Hagadone L 2017 · 3.74 FIP	31	SU	3.74	22.6%	9.3%	2.5%	.267	—	—	—	—	—	2.97	141	71
Tommy Phelps L 2005 · 3.98 FIP	31	SU	3.98	13.2%	7.5%	1.9%	.280	—	—	—	—	—	3.39	120	71
Randy Flores L 2006 · 4.00 FIP	31	SU	4.00	20.4%	9.7%	2.5%	.333	3.30	18.1%	10.5%	93	-0.70	2.51	250	71
Darin Downs L 2015 · 4.06 FIP	30	SU	4.06	12.9%	4.2%	2.8%	.288	—	—	—	—	—	2.83	107	70
Colt Hynes L 2016 · 3.55 FIP	31	SU	3.55	22.3%	4.8%	3.1%	.353	3.75	14.1%	9.5%	56	+0.20	3.42	169	70
Edward Paredes L 2018 · 3.81 FIP	31	SU	3.81	22.7%	11.9%	2.0%	.351	5.10	21.5%	14.2%	18	+1.29	2.36	130	70
Tom Martin L 2001 · 5.68 FIP	31	SU	5.68	14.1%	9.4%	4.7%	.311	4.25	19.8%	11.1%	96	-1.43	3.40	303	69
Brian Shouse L 2002 · 5.54 FIP	33	SU	5.54	15.5%	11.3%	4.2%	.245	3.31	16.3%	10.7%	159	-2.23	2.32	339	69
Vic Darensbourg L 2002 · 5.46 FIP	31	SU	5.46	14.2%	9.4%	4.3%	.304	4.11	9.4%	4.2%	22	-1.35	2.94	287	68
Chad Zerbe L 2003 · 3.89 FIP	31	SU	3.89	7.8%	5.5%	1.4%	.306	—	—	—	—	—	3.75	145	67
Doug Slaten L 2011 · 4.66 FIP	31	SU	4.66	15.5%	7.1%	3.6%	.371	5.10	10.9%	-3.6%	13	+0.44	3.22	139	67
David Purcey L 2013 · 3.79 FIP	31	SU	3.79	20.4%	11.5%	1.7%	.227	—	—	—	—	—	3.60	206	66
Pedro Feliciano L 2008 · 3.90 FIP	31	SU	3.90	21.1%	7.6%	2.9%	.309	3.02	22.1%	14.9%	122	-0.88	2.69	378	66
Erasmo Ramirez L 2005 · 4.68 FIP	29	SU	4.68	6.2%	3.1%	3.1%	.250	—	—	—	—	—	3.59	108	66