Matt Carasiti

P Gwinnett Stripers

Age 36 RHP 6'2" / 205 lbs AAA peak 0.1 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	31	AAASF	23	0	22	8.31	4.58	25.5%	10.9%	—	K 22.4% · BB 11.5% · FIP 4.88
2023	32	MIX3 levels	43	0	53	4.73	3.64	19.3%	9.7%	0.6	K 16.8% · BB 9.1% · FIP 3.73
2024	33	MIX2 levels	38	0	43	6.07	4.15	25.9%	13.9%	0.2	K 24.5% · BB 14.2% · FIP 4.02
2025	34	No MLB games played
2026	35	No MLB games played
2027PROJ	36	MLBCOL	5	0	5	4.68	4.56	23.7%	11.5%	-0.0
2028PROJMLE	37	MLBCOL	—	—	0	4.83	4.94	22.6%	11.9%	—

Last 3 seasons WAR 0.8 · Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff51Control37Command33FB velo53

CompsKeiichi Yabu (age 36) · Hirokazu Sawamura (age 34) · Joe Nelson (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 120112vR 104

ERA4.68

FIP4.56

K%23.7%

BB%11.5%

K-BB%12.2%

Proj IP51

Value2027 · WAR

Runs saved vs league avg · 51 IP

Grades · Contract

Scouting Grades

STUF5050

CTRL3535

CMD3535

K†5555

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	A+ MLE	Modesto Nuts	49	0	57	246	57	21	3	0%	1.2	17.8%	9.8%	8.0%	1.8%	3.02	3.85	—
2015	24	A+ RAW	Modesto Nuts	49	0	57	246	57	21	3	0%	1.2	23.2%	8.5%	14.6%	1.2%	3.02	2.89	—
2016	25	MIX MLE	3 levels	63	0	62	258	65	19	6	0%	1.0	22.2%	7.6%	14.7%	2.7%	3.79	3.68	+0.6
2016	25	MIX RAW	3 levels	63	0	62	258	65	19	6	0%	1.0	25.2%	7.4%	17.8%	2.3%	3.79	3.18	+1.0
2017	26	AAA MLE	Iowa Cubs	46	0	50	217	68	23	2	0%	1.1	27.6%	11.1%	16.4%	1.1%	3.26	2.80	—
2017	26	AAA RAW	Iowa Cubs	46	0	50	217	68	23	2	0%	1.1	31.3%	10.6%	20.7%	0.9%	3.26	2.27	—
2019	28	MIX MLE	2 levels	42	5	53	232	50	23	6	12%	1.3	18.6%	10.0%	8.6%	2.2%	3.74	4.01	+0.3
2019	28	MIX RAW	2 levels	42	5	53	232	50	23	6	12%	1.3	21.6%	9.9%	11.6%	2.6%	3.74	3.99	+0.4
2022	31	AAA MLE	Sacramento River Cats	23	0	22	110	28	12	4	0%	0.9	22.4%	11.5%	10.9%	4.0%	8.31	4.88	—
2022	31	AAA RAW	Sacramento River Cats	23	0	22	110	28	12	4	0%	0.9	25.5%	10.9%	14.5%	3.6%	8.31	4.58	—
2023	32	MIX MLE	3 levels	43	0	53	238	46	23	4	0%	1.2	16.8%	9.1%	7.6%	1.6%	4.73	3.73	+0.5
2023	32	MIX RAW	3 levels	43	0	53	238	46	23	4	0%	1.2	19.3%	9.7%	9.7%	1.7%	4.73	3.64	+0.6
2024	33	MIX MLE	2 levels	38	0	43	201	52	28	5	0%	1.1	24.5%	14.2%	10.3%	2.2%	6.07	4.02	+0.3
2024	33	MIX MLE	2 levels	38	0	43	201	52	28	5	0%	1.1	24.5%	14.2%	10.3%	2.2%	6.07	4.02	+0.3
2024	33	MIX RAW	2 levels	38	0	43	201	52	28	5	0%	1.1	25.9%	13.9%	11.9%	2.5%	6.07	4.15	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			53	5	58	279	50	30	8	9%	1.1	17.9%	10.8%	7.2%	2.9%	7.41	4.71	-0.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	COL	5	0	5	21	5	2	1	0%	1.0	23.7%	11.5%	12.2%	3.0%	4.68	4.56	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	COL	32	0	34	144	33	17	5	0%	1.1	22.6%	11.9%	10.7%	3.4%	4.83	4.94	-0.2
2028	37	MLB CTX	COL	—	—	0	—	—	—	—	—	—	22.6%	11.9%	10.7%	3.4%	4.83	4.94	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		53	5	59	280	50	30	8	9%	1.1	17.9%	10.8%	7.2%	2.9%	7.39	4.52	+0.0
/ 200 IP	—	RAW		182	17	200	956	170	102	27	9%	1.1	17.9%	10.8%	7.2%	2.9%	7.39	4.52	+0.1
Career	—	MLE		304	5	339	1502	318	154	32	2%	1.1	21.2%	10.2%	10.9%	2.1%	4.30	3.77	-0.3
/ 200 IP	—	MLE		179	3	200	886	188	91	19	2%	1.1	21.2%	10.2%	10.9%	2.1%	4.30	3.77	-0.6

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.

2024 AAAGwinnett Stripers · 34 IPFIP 3.74 → 4.02 (+0.28)K% 28.5% → 24.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.48%	—	×0.860	24.49%
BB% (unintentional) /BF	15.82%	—	×0.900	14.24%
HR / BF /BF	1.90%	÷0.968	×1.100	2.16%

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 94).

Result: FIP 3.74 → 4.02, 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 AAAAlbuquerque Isotopes · 27 IPFIP 2.89 → 3.04 (+0.16)K% 21.8% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.85%	—	×0.860	18.79%
BB% (unintentional) /BF	9.24%	—	×0.900	8.32%
HR / BF /BF	0.84%	÷1.062	×1.100	0.87%

Result: FIP 2.89 → 3.04, 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 AAASacramento River Cats · 22 IPFIP 4.33 → 4.88 (+0.55)K% 25.5% → 22.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.45%	—	×0.880	22.40%
BB% (unintentional) /BF	10.91%	—	×1.050	11.45%
HR / BF /BF	3.64%	÷0.989	×1.100	4.05%

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 98).

Result: FIP 4.33 → 4.88, 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 AAATacoma Rainiers · 43 IPFIP 3.68 → 4.01 (+0.33)K% 21.2% → 18.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.16%	—	×0.880	18.62%
BB% (unintentional) /BF	9.52%	—	×1.050	10.00%
HR / BF /BF	2.12%	÷1.049	×1.100	2.22%

Result: FIP 3.68 → 4.01, 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 AAAIowa Cubs · 50 IPFIP 2.30 → 2.80 (+0.50)K% 31.3% → 27.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.34%	—	×0.880	27.58%
BB% (unintentional) /BF	10.60%	—	×1.050	11.13%
HR / BF /BF	0.92%	÷0.899	×1.100	1.13%

Result: FIP 2.30 → 2.80, 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 AAHartford Yard Goats · 39 IPFIP 3.02 → 3.70 (+0.69)K% 28.7% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.67%	—	×0.810	23.22%
BB% (unintentional) /BF	4.00%	—	×1.100	4.40%
HR / BF /BF	3.33%	÷1.094	×1.200	3.66%

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 119).

Result: FIP 3.02 → 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).

2016 AAAAlbuquerque Isotopes · 7 IPFIP 2.42 → 2.68 (+0.25)K% 20.0% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.880	17.60%
BB% (unintentional) /BF	8.00%	—	×1.050	8.40%
HR / BF /BF	0.00%	÷1.202	×1.100	0.00%

Result: FIP 2.42 → 2.68, 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 A+Modesto Nuts · 57 IPFIP 2.89 → 3.85 (+0.96)K% 23.2% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.17%	—	×0.770	17.84%
BB% (unintentional) /BF	8.54%	—	×1.150	9.82%
HR / BF /BF	1.22%	÷0.827	×1.250	1.84%

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 65).

Result: FIP 2.89 → 3.85, 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
Matt Carasiti (OGILVIE)	—	—	4.56	23.7%	11.5%	3.0%	—	4.94	22.6%	10.7%	34	+0.38	—	—	—
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	78
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	76
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	74
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	74
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	72
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	71
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	68
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	68
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	64
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	63
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	61
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	61
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	61
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	60
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	59
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	58
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	56
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	55
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	54
Leo Rosales R 2017 · 4.24 FIP	36	SU	4.24	7.8%	8.2%	1.4%	.203	—	—	—	—	—	3.85	130	49

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	0.7	48	21.0%	11.3%	1.17	5.82
Will Cunnane ('04)	1.4	46	18.9%	10.4%	1.25	5.26
Matt Perisho ('05)	1.4	69	15.2%	12.2%	1.37	6.39
Daryl Patterson ('68-'74, -1 WAR*)	1.2	46	18.3%*	11.2%*	1.27*	5.03*
Brian Stokes ('10)	1.0	52	14.5%	10.1%	1.22	5.27
Dennis Powell ('85-'93, -2 WAR*)	1.7	49	17.6%*	10.1%*	1.18*	5.31*
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*