Nicolo Pinazzi

P Rancho Cucamonga Quakes

Age 27 LHP 6'2" / 195 lbs A peak 0.2 · 2027

Overview

APROSPECT

ETA2029MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	22	MIX2 levels	12	5	43	4.85	2.26	38.0%	14.6%	—	K 25.7% · BB 18.4% · FIP 4.15
2023	23	MIX2 levels	25	2	36	7.75	5.54	27.2%	22.3%	—	K 19.3% · BB 26.9% · FIP 6.79
2026	26	MIX4 levels	48	0	64	0.84	1.63	38.9%	12.5%	—	K 30.1% · BB 13.8% · FIP 2.58
2027PROJ	27	MLBLAA	—	—	0	4.13	4.13	30.6%	14.5%	—
2028PROJ36% MLB	28	MLBLAA	21	0	22	4.20	4.23	29.7%	14.1%	0.1
2029PROJ55% MLB	29	MLBLAA	28	0	29	4.30	4.37	29.0%	14.0%	0.1

Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 -0.1 / P90 2.4, comp-based) · star 0% · didn't stick 90%

CompsMichael Gonzalez (age 27) · Justin King (age 26) · Riley Martin (age 26)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 95104vR 112

ERA4.13

FIP4.13

K%30.6%

BB%14.5%

K-BB%16.1%

Proj IP70

Value2027 · WAR

Runs saved vs league avg · 70 IP

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)
2021	21	ROK MLE	ACL Reds	14	0	17	82	31	14	1	0%	1.2	25.7%	21.3%	4.4%	1.7%	2.60	4.55	—
2021	21	ROK RAW	ACL Reds	14	0	17	82	31	14	1	0%	1.2	37.8%	17.1%	20.7%	1.2%	2.60	2.70	—
2022	22	MIX MLE	2 levels	12	5	43	192	73	28	2	42%	3.6	25.7%	18.4%	7.4%	1.6%	4.85	4.15	—
2022	22	MIX RAW	2 levels	12	5	43	192	73	28	2	42%	3.6	38.0%	14.6%	23.4%	1.0%	4.85	2.26	—
2023	23	MIX MLE	2 levels	25	2	36	184	50	41	5	8%	1.4	19.3%	26.9%	-7.6%	3.5%	7.75	6.79	—
2023	23	MIX RAW	2 levels	25	2	36	184	50	41	5	8%	1.4	27.2%	22.3%	4.9%	2.7%	7.75	5.54	—
2026	26	MIX MLE	4 levels	48	0	64	265	103	33	1	0%	1.3	30.1%	13.8%	16.3%	0.5%	0.84	2.58	—
2026	26	MIX MLE	4 levels	48	0	64	265	103	33	1	0%	1.3	30.1%	13.8%	16.3%	0.5%	0.84	2.58	—
2026	26	MIX RAW	4 levels	48	0	64	265	103	33	1	0%	1.3	38.9%	12.5%	26.4%	0.4%	0.84	1.63	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	LAA	—	—	0	—	—	—	—	—	—	30.6%	14.5%	16.1%	2.5%	4.13	4.13	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	LAA	56	0	59	250	74	35	7	0%	1.1	29.7%	14.1%	15.6%	2.7%	4.20	4.23	+0.2
2029	29	MLB MLE	LAA	50	0	52	221	64	31	6	0%	1.0	29.0%	14.0%	15.0%	2.9%	4.30	4.37	+0.1
2028	28	MLB CTX	LAA	21	0	22	93	28	13	2	0%	1.0	29.7%	14.1%	15.6%	2.7%	4.20	4.23	+0.1
2029	29	MLB CTX	LAA	28	0	29	122	36	17	3	0%	1.0	29.0%	14.0%	15.0%	2.9%	4.30	4.37	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		48	0	50	212	62	30	6	0%	1.0	29.3%	14.1%	15.3%	2.8%	4.26	3.93	+0.4
/ 200 IP	—	RAW		190	0	200	844	247	119	24	0%	1.1	29.3%	14.1%	15.3%	2.8%	4.26	3.93	+1.5
Career	—	MLE		99	7	160	723	186	139	12	7%	1.6	25.7%	19.2%	6.5%	1.7%	3.66	4.29	+0.3
/ 200 IP	—	MLE		124	9	200	904	232	173	15	7%	1.6	25.7%	19.2%	6.5%	1.7%	3.66	4.29	+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.

2026 ARancho Cucamonga Quakes · 29 IPFIP 1.50 → 2.70 (+1.20)K% 38.7% → 28.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.71%	—	×0.740	28.65%
BB% (unintentional) /BF	9.68%	—	×1.180	11.42%
HR / BF /BF	0.81%	÷1.000	×1.300	1.05%

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 100).

Result: FIP 1.50 → 2.70, 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).

2026 AARocket City Trash Pandas · 17 IPFIP 2.35 → 3.14 (+0.79)K% 35.3% → 28.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.29%	—	×0.810	28.59%
BB% (unintentional) /BF	17.65%	—	×1.100	19.41%
HR / BF /BF	0.00%	÷1.000	×1.200	0.00%

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 2.35 → 3.14, 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).

2026 A+Tri-City Dust Devils · 16 IPFIP 0.70 → 1.77 (+1.07)K% 44.6% → 34.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	44.62%	—	×0.770	34.35%
BB% (unintentional) /BF	10.77%	—	×1.150	12.38%
HR / BF /BF	0.00%	÷1.000	×1.250	0.00%

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 100).

Result: FIP 0.70 → 1.77, 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 ADaytona Tortugas · 18 IPFIP 5.31 → 6.66 (+1.35)K% 24.3% → 18.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.27%	—	×0.740	17.96%
BB% (unintentional) /BF	25.24%	—	×1.180	29.79%
HR / BF /BF	1.94%	÷1.060	×1.300	2.38%

Result: FIP 5.31 → 6.66, 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 ROKACL Reds · 18 IPFIP 4.87 → 6.95 (+2.07)K% 30.9% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.86%	—	×0.680	20.99%
BB% (unintentional) /BF	18.52%	—	×1.250	23.15%
HR / BF /BF	3.70%	÷1.059	×1.400	4.90%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 112).

Result: FIP 4.87 → 6.95, 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 ROKACL Reds · 40 IPFIP 2.38 → 4.15 (+1.76)K% 37.9% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.85%	—	×0.680	25.74%
BB% (unintentional) /BF	14.69%	—	×1.250	18.36%
HR / BF /BF	1.13%	÷0.970	×1.400	1.63%

Result: FIP 2.38 → 4.15, 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 ROKACL Reds · 17 IPFIP 2.74 → 4.55 (+1.81)K% 37.8% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.80%	—	×0.680	25.71%
BB% (unintentional) /BF	17.07%	—	×1.250	21.34%
HR / BF /BF	1.22%	÷1.026	×1.400	1.66%

Result: FIP 2.74 → 4.55, 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
Nicolo Pinazzi (OGILVIE)	—	—	—	—	—	2.5%	—	4.30	29.4%	15.3%	112	+0.17	—	—	—
Michael Gonzalez L 2005 · 3.04 FIP	27	SU	3.04	27.4%	13.7%	0.9%	.268	3.04	27.1%	16.4%	105	-0.00	1.45	454	80
Justin King L 2024 · 3.26 FIP	26	SU	3.26	29.5%	11.8%	2.1%	.346	4.16	22.4%	3.6%	65	+0.90	3.26	133	79
Riley Martin L 2024 · 3.73 FIP	26	SU	3.73	28.2%	14.2%	2.2%	.336	3.23	26.4%	14.3%	64	-0.50	3.23	266	78
Conor Lillis-White L 2018 · 3.48 FIP	26	SU	3.48	26.8%	11.1%	2.3%	.322	—	—	—	—	—	1.80	182	77
Ryan O'Rourke L 2015 · 3.85 FIP	27	SU	3.85	27.4%	12.9%	2.6%	.284	3.05	23.4%	16.7%	53	-0.80	3.05	145	77
Hayden Harris L 2024 · 3.79 FIP	25	SU	3.79	30.0%	12.7%	3.0%	.398	3.09	29.1%	17.2%	102	-0.70	1.77	196	75
Jack Leathersich L 2017 · 3.16 FIP	27	SU	3.16	33.5%	14.4%	1.9%	.250	4.55	21.7%	1.9%	32	+1.39	3.16	101	75
Aaron Brown L 2019 · 4.04 FIP	27	SU	4.04	22.9%	13.5%	2.1%	.317	4.77	19.4%	6.7%	129	+0.73	4.04	269	75
Brendan Cellucci L 2024 · 3.98 FIP	26	SU	3.98	26.3%	16.6%	1.8%	.288	4.38	21.7%	9.6%	79	+0.40	3.98	288	75
Matt Purke L 2017 · 3.01 FIP	27	SU	3.01	25.1%	10.1%	1.4%	.335	4.29	16.1%	0.1%	47	+1.28	3.01	226	74
Dale Stanavich L 2024 · 3.99 FIP	25	SU	3.99	27.2%	12.7%	2.9%	.268	5.49	23.2%	7.9%	97	+1.50	3.09	184	73
C.J. Widger L 2024 · 4.21 FIP	25	SU	4.21	26.0%	16.0%	2.3%	.258	5.75	18.1%	-0.7%	57	+1.54	3.83	105	73
Scott Sauerbeck L 2000 · 3.64 FIP	28	SU	3.64	23.8%	15.2%	1.1%	.344	3.44	25.1%	13.3%	182	-0.20	2.79	386	72
Edward Paredes L 2016 · 3.47 FIP	29	SU	3.47	24.1%	13.0%	1.4%	.225	3.55	23.7%	14.0%	86	+0.08	2.36	130	72
Yuki Matsui L 2024 · 3.63 FIP	28	SU	3.63	26.8%	8.9%	3.0%	.242	4.25	23.9%	12.6%	140	+0.62	3.63	203	72
Reagan Todd L 2022 · 3.82 FIP	26	SU	3.82	25.6%	12.0%	2.5%	.325	—	—	—	—	—	2.89	122	72
Jacob Latz L 2023 · 3.06 FIP	27	SU	3.06	28.1%	9.8%	2.0%	.366	3.36	25.0%	15.5%	216	+0.30	2.39	480	71
Keylan Killgore L 2024 · 3.73 FIP	27	SU	3.73	24.6%	14.1%	1.7%	.331	—	—	—	—	—	3.73	161	71
Amir Garrett L 2019 · 3.62 FIP	27	SU	3.62	31.7%	13.8%	2.6%	.291	3.77	28.5%	15.3%	111	+0.15	3.06	712	71
Fabian Williamson L 2016 · 3.39 FIP	27	SU	3.39	23.8%	9.0%	2.1%	.259	5.26	16.5%	-3.3%	54	+1.87	3.39	154	71

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	0.9	61	29.8%	14.2%	1.03	4.20
Kyle Barraclough ('23)	1.4	56	28.2%	13.9%	0.99	3.86
Chad Fox ('09)	1.1	57	25.8%	13.2%	0.87	3.79
Matt Mantei ('05)	1.6	54	28.0%	14.1%	0.84	4.07
Rod Scurry ('80-'88, 5 WAR*)	2.3	58	29.6%*	13.6%*	0.91*	3.67*
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Dwayne Henry ('84-'95, -1 WAR*)	1.7	67	23.8%*	14.2%*	0.90*	5.02*