Chad Green

P Toronto Blue Jays

Age 36 RHP 6'3" / 215 lbs MLB

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	32	MIX3 levels	24	1	24	3.33	2.11	31.3%	4.0%	0.7	K 30.8% · BB 5.8% · FIP 2.29
2024	33	MIX2 levels	56	0	56	3.04	3.92	21.2%	5.4%	0.4	K 21.9% · BB 5.2% · FIP 4.00
2025	34	MLBTOR	45	0	44	5.56	6.42	18.4%	5.8%	-0.9
2026	35	No MLB games played
2027PROJ	36	MLBTOR	52	0	55	4.66	4.79	20.3%	6.2%	-0.2
2028PROJ	37	MLBTOR	27	0	28	4.98	5.19	19.2%	6.9%	-0.2
2029PROJ	38	MLBTOR	12	0	13	5.36	5.62	18.1%	7.6%	-0.2

Last 3 seasons WAR 0.2 · Next 3 projected WAR -0.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

FB Velo60

K%24

xwOBA vs10

CompsChris Nichting (age 36) · Hirokazu Sawamura (age 34) · Keiichi Yabu (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 131123vR 115

ERA4.66

FIP4.79

K%20.3%

BB%6.2%

K-BB%14.1%

Proj IP55

Value2027 · WAR

Runs saved vs league avg · 55 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6095.2

Whiff%

p3222.7%

p2418.4%

xwOBA vs

p100.370

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)
2015	24	AA MLE	Erie SeaWolves	27	27	149	655	137	41	9	100%	5.5	16.9%	6.9%	10.1%	1.5%	3.93	3.36	—
2015	24	AA RAW	Erie SeaWolves	27	27	149	655	137	41	9	100%	5.5	20.9%	6.3%	14.7%	1.4%	3.93	2.87	—
2016	25	MIX MLE	2 levels	28	24	140	563	152	36	15	86%	5.0	24.9%	6.6%	18.3%	2.6%	2.57	3.25	+3.7
2016	25	MIX RAW	2 levels	28	24	140	563	152	36	15	86%	5.0	27.0%	6.4%	20.6%	2.7%	2.57	3.09	+4.0
2017	26	MIX MLE	3 levels	46	6	100	389	141	28	6	13%	2.2	35.1%	7.6%	27.5%	1.2%	2.62	1.77	+3.4
2017	26	MIX RAW	3 levels	46	6	100	389	141	28	6	13%	2.2	36.2%	7.2%	29.0%	1.5%	2.62	1.90	+3.3
2018	27	MLB MLE	New York Yankees	63	0	76	298	94	13	9	0%	1.2	31.5%	4.4%	27.2%	2.8%	2.50	2.54	+1.8
2018	27	MLB RAW	New York Yankees	63	0	76	298	94	13	9	0%	1.2	31.5%	4.4%	27.2%	3.0%	2.50	2.68	+1.7
2019	28	MIX MLE	2 levels	57	18	76	324	112	21	10	32%	1.3	33.2%	6.4%	26.8%	3.2%	4.01	2.85	+1.6
2019	28	MIX RAW	2 levels	57	18	76	324	112	21	10	32%	1.3	34.6%	6.5%	28.1%	3.1%	4.01	2.69	+1.7
2020	29	MLB MLE	New York Yankees	22	0	26	100	32	6	5	0%	1.2	32.0%	6.0%	26.0%	4.7%	3.51	3.73	+0.3
2020	29	MLB RAW	New York Yankees	22	0	26	100	32	6	5	0%	1.2	32.0%	6.0%	26.0%	5.0%	3.51	3.84	+0.2
2021	30	MLB MLE	New York Yankees	67	0	84	315	99	15	14	0%	1.2	31.4%	4.8%	26.7%	4.2%	3.12	3.33	+1.2
2021	30	MLB RAW	New York Yankees	67	0	84	315	99	15	14	0%	1.2	31.4%	4.8%	26.7%	4.4%	3.12	3.45	+1.1
2022	31	MLB MLE	New York Yankees	14	0	15	62	16	5	1	0%	1.1	25.8%	8.1%	17.7%	1.5%	3.00	2.77	+0.3
2022	31	MLB RAW	New York Yankees	14	0	15	62	16	5	1	0%	1.1	25.8%	8.1%	17.7%	1.6%	3.00	2.83	+0.3
2023	32	MIX MLE	3 levels	24	1	24	99	31	4	2	4%	1.0	30.8%	5.8%	25.0%	1.9%	3.33	2.29	+0.7
2023	32	MIX RAW	3 levels	24	1	24	99	31	4	2	4%	1.0	31.3%	4.0%	27.3%	2.0%	3.33	2.11	+0.7
2024	33	MIX MLE	2 levels	56	0	56	222	47	12	8	0%	1.0	21.9%	5.2%	16.7%	3.8%	3.04	4.00	+0.4
2024	33	MIX MLE	2 levels	56	0	56	222	47	12	8	0%	1.0	21.9%	5.2%	16.7%	3.8%	3.04	4.00	+0.4
2024	33	MIX RAW	2 levels	56	0	56	222	47	12	8	0%	1.0	21.2%	5.4%	15.8%	3.6%	3.04	3.92	+0.4
2025	34	MLB MLE	Toronto Blue Jays	45	0	44	190	35	11	14	0%	1.0	18.4%	5.8%	12.6%	7.4%	5.56	6.32	-0.9
2025	34	MLB MLE	Toronto Blue Jays	45	0	44	190	35	11	14	0%	1.0	18.4%	5.8%	12.6%	7.4%	5.56	6.32	-0.9
2025	34	MLB RAW	Toronto Blue Jays	45	0	44	190	35	11	14	0%	1.0	18.4%	5.8%	12.6%	7.4%	5.56	6.42	-0.9
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			382	24	493	1973	591	115	78	6%	1.3	30.0%	5.8%	24.1%	4.0%	3.43	3.46	+6.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	TOR	52	0	55	232	47	14	9	0%	1.1	20.3%	6.2%	14.1%	4.1%	4.66	4.79	-0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	TOR	27	0	28	118	23	8	5	0%	1.0	19.2%	6.9%	12.4%	4.5%	4.98	5.19	-0.2
2029	38	MLB MLE	TOR	12	0	13	54	10	4	3	0%	1.1	18.1%	7.6%	10.5%	4.9%	5.36	5.62	-0.2
2028	37	MLB CTX	TOR	27	0	28	118	23	8	5	0%	1.0	19.2%	6.9%	12.4%	4.5%	4.98	5.19	-0.2
2029	38	MLB CTX	TOR	12	0	13	55	10	4	3	0%	1.1	18.1%	7.6%	10.5%	4.9%	5.36	5.62	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		421	24	533	2145	624	127	86	6%	1.3	29.1%	5.9%	23.1%	4.0%	3.56	3.60	+6.0
/ 200 IP	—	RAW		158	9	200	804	234	48	32	6%	1.3	29.1%	5.9%	23.1%	4.0%	3.56	3.60	+2.3
Career	—	MLE		449	76	789	3217	850	200	91	17%	1.8	26.4%	6.2%	20.2%	2.8%	3.28	3.21	+6.0
/ 200 IP	—	MLE		114	19	200	815	215	51	23	17%	1.8	26.4%	6.2%	20.2%	2.8%	3.28	3.21	+2.3

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.

2023 AAABuffalo Bisons · 9 IPFIP 2.40 → 3.03 (+0.63)K% 30.6% → 26.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.56%	—	×0.860	26.28%
BB% (unintentional) /BF	2.78%	—	×0.900	2.50%
HR / BF /BF	2.78%	÷0.916	×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 83).

Result: FIP 2.40 → 3.03, 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 AAAScranton/Wilkes-Barre RailRiders · 7 IPFIP -0.10 → 0.43 (+0.53)K% 48.3% → 42.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	48.28%	—	×0.880	42.48%
BB% (unintentional) /BF	6.90%	—	×1.050	7.24%
HR / BF /BF	0.00%	÷1.087	×1.100	0.00%

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 117).

Result: FIP -0.10 → 0.43, 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).

2017 AAAScranton/Wilkes-Barre RailRiders · 27 IPFIP 2.41 → 2.69 (+0.28)K% 26.8% → 23.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.83%	—	×0.880	23.61%
BB% (unintentional) /BF	8.94%	—	×1.050	9.39%
HR / BF /BF	0.81%	÷1.247	×1.100	0.72%

Result: FIP 2.41 → 2.69, 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).

2016 AAAScranton/Wilkes-Barre RailRiders · 95 IPFIP 1.97 → 2.33 (+0.36)K% 27.4% → 24.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.40%	—	×0.880	24.11%
BB% (unintentional) /BF	5.75%	—	×1.050	6.04%
HR / BF /BF	0.82%	÷0.996	×1.100	0.91%

Result: FIP 1.97 → 2.33, 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).

2015 AAErie SeaWolves · 149 IPFIP 2.88 → 3.36 (+0.48)K% 20.9% → 16.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.92%	—	×0.810	16.94%
BB% (unintentional) /BF	6.26%	—	×1.100	6.89%
HR / BF /BF	1.37%	÷1.100	×1.200	1.50%

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 120).

Result: FIP 2.88 → 3.36, 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
Chad Green (OGILVIE)	—	—	—	—	—	4.1%	—	5.32	18.9%	11.8%	41	+0.53	—	—	—
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	65
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	64
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	63
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	60
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	58
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	58
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	54
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	53
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	53
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	53
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	52
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	52
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	50
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	50
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	50
Pete Walker R 2005 · 4.79 FIP	36	SU	4.79	12.0%	9.2%	2.8%	.261	4.45	19.6%	11.6%	30	-0.34	4.45	327	48
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	47
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	46
Leo Rosales R 2017 · 4.24 FIP	36	SU	4.24	7.8%	8.2%	1.4%	.203	—	—	—	—	—	3.85	130	41
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	40

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	2.9	54	28.2%	6.5%	1.55	3.66
Shawn Kelley ('19)	2.3	46	27.0%	7.3%	1.47	3.80
Giovanny Gallegos ('24)	1.7	56	29.6%	6.6%	1.18	3.49
Blake Parker ('21)	1.7	58	26.7%	7.9%	1.30	3.47
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Luis DeLeon ('81-'89, 4 WAR*)	1.7	67	25.0%*	5.7%*	1.69*	3.69*
Joe Sambito ('76-'87, 8 WAR*)	3.1	70	26.8%*	7.5%*	1.06*	3.65*