Hunter Harvey

P Chicago Cubs

Age 32 RHP 6'3" / 233 lbs MLB peak 0.2 · 2027 ILInjured 60-Day (D60)

Overview

MLBRPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MLBKC	49	0	51	4.44	3.04	25.1%	6.4%	0.9
2025	30	MIX3 levels	15	1	14	0.00	0.98	31.4%	2.0%	0.6	K 31.4% · BB 2.0% · FIP 0.70
2026	31	MLBCHC	4	0	4	6.75	8.35	22.2%	5.6%	-0.2
2027PROJ	32	MLBCHC	39	0	41	4.09	3.99	25.9%	8.1%	0.3
2028PROJ95% MLB	33	MLBCHC	50	0	53	4.20	4.27	25.0%	8.5%	0.2
2029PROJ90% MLB	34	MLBCHC	40	0	42	4.42	4.57	24.1%	9.0%	-0.0

Last 3 seasons WAR 1.3 · Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff55Control55Command60FB velo65

CompsHirokazu Sawamura (age 33) · Sergio Santos (age 32) · Mike Broadway (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 109101vR 93

ERA4.09

FIP3.99

K%25.9%

BB%8.1%

K-BB%17.8%

Proj IP41

Value2027 · WAR

Runs saved vs league avg · 41 IP

Grades · Contract

Scouting Grades

STUF5555

CTRL5555

CMD6060

K†6060

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)
2016	21	ROK MLE	GCL Orioles	2	2	5	18	11	0	0	100%	2.5	61.1%	0.0%	61.1%	0.0%	0.00	-2.06	—
2016	21	ROK RAW	GCL Orioles	2	2	5	18	11	0	0	100%	2.5	61.1%	0.0%	61.1%	0.0%	0.00	-1.30	—
2017	22	MIX MLE	2 levels	6	6	14	54	20	3	0	100%	2.3	37.0%	5.6%	31.5%	0.0%	1.32	0.68	—
2017	22	MIX RAW	2 levels	6	6	14	54	20	3	0	100%	2.3	37.0%	5.6%	31.5%	0.0%	1.32	0.83	—
2018	23	AA MLE	Bowie Baysox	9	9	32	138	30	9	3	100%	3.6	17.6%	7.2%	10.4%	2.5%	5.57	3.87	—
2018	23	AA RAW	Bowie Baysox	9	9	32	138	30	9	3	100%	3.6	21.7%	6.5%	15.2%	2.2%	5.57	3.29	—
2019	24	MIX MLE	3 levels	33	11	82	353	94	30	17	33%	2.5	21.0%	8.7%	12.4%	6.0%	4.72	5.69	-1.1
2019	24	MIX RAW	3 levels	33	11	82	353	94	30	17	33%	2.5	26.6%	8.5%	18.1%	4.8%	4.72	4.60	-0.0
2020	25	MLB MLE	Baltimore Orioles	10	0	9	37	6	2	2	0%	0.9	16.2%	5.4%	10.8%	5.4%	4.15	5.38	-0.1
2020	25	MLB RAW	Baltimore Orioles	10	0	9	37	6	2	2	0%	0.9	16.2%	5.4%	10.8%	5.4%	4.15	5.41	-0.1
2021	26	MIX MLE	2 levels	17	1	19	87	13	4	3	6%	1.1	12.1%	2.1%	10.0%	4.3%	6.27	4.69	-0.0
2021	26	MIX RAW	2 levels	17	1	19	87	13	4	3	6%	1.1	14.9%	4.6%	10.3%	3.4%	6.27	4.44	+0.0
2022	27	MIX MLE	3 levels	44	2	45	179	53	14	1	5%	1.0	28.7%	7.6%	21.0%	0.6%	2.20	2.00	+1.4
2022	27	MIX RAW	3 levels	44	2	45	179	53	14	1	5%	1.0	29.6%	7.8%	21.8%	0.6%	2.20	1.97	+1.4
2023	28	MLB MLE	Washington Nationals	57	0	61	235	67	12	7	0%	1.1	28.5%	5.1%	23.4%	3.0%	2.82	2.97	+1.1
2023	28	MLB RAW	Washington Nationals	57	0	61	235	67	12	7	0%	1.1	28.5%	5.1%	23.4%	3.0%	2.82	2.98	+1.1
2024	29	MLB MLE	Kansas City Royals	49	0	51	219	55	14	5	0%	1.0	25.1%	6.4%	18.7%	2.5%	4.44	3.14	+0.8
2024	29	MLB MLE	Kansas City Royals	49	0	51	219	55	14	5	0%	1.0	25.1%	6.4%	18.7%	2.5%	4.44	3.14	+0.8
2024	29	MLB RAW	Kansas City Royals	49	0	51	219	55	14	5	0%	1.0	25.1%	6.4%	18.7%	2.3%	4.44	3.04	+0.9
2025	30	MIX MLE	3 levels	15	1	14	51	16	1	0	7%	0.9	31.4%	2.0%	29.4%	0.0%	0.00	0.70	+0.6
2025	30	MIX MLE	3 levels	15	1	14	51	16	1	0	7%	0.9	31.4%	2.0%	29.4%	0.0%	0.00	0.70	+0.6
2025	30	MIX RAW	3 levels	15	1	14	51	16	1	0	7%	0.9	31.4%	2.0%	29.4%	0.0%	0.00	0.98	+0.6
2026	31	MLB MLE	Chicago Cubs	4	0	4	18	4	1	2	0%	1.0	22.2%	5.6%	16.7%	11.1%	6.75	8.03	-0.2
2026	31	MLB MLE	Chicago Cubs	4	0	4	18	4	1	2	0%	1.0	22.2%	5.6%	16.7%	11.1%	6.75	8.03	-0.2
2026	31	MLB RAW	Chicago Cubs	4	0	4	18	4	1	2	0%	1.0	22.2%	5.6%	16.7%	11.1%	6.75	8.35	-0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			186	0	189	767	205	49	19	0%	1.0	26.7%	6.4%	20.3%	2.5%	3.19	3.02	+3.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	CHC	39	0	41	173	45	14	5	0%	1.1	25.9%	8.1%	17.8%	3.0%	4.09	3.99	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	CHC	54	0	56	237	59	20	8	0%	1.0	25.0%	8.5%	16.5%	3.3%	4.20	4.27	+0.2
2029	34	MLB MLE	CHC	44	0	46	195	47	18	7	0%	1.1	24.1%	9.0%	15.1%	3.6%	4.42	4.57	-0.0
2028	33	MLB CTX	CHC	50	0	53	224	56	19	7	0%	1.1	25.0%	8.5%	16.5%	3.3%	4.20	4.27	+0.2
2029	34	MLB CTX	CHC	40	0	42	177	43	16	6	0%	1.1	24.1%	9.0%	15.1%	3.6%	4.42	4.57	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		277	0	284	1169	304	84	33	0%	1.0	26.0%	7.2%	18.8%	2.8%	3.56	3.36	+4.0
/ 200 IP	—	RAW		195	0	200	823	214	59	23	0%	1.0	26.0%	7.2%	18.8%	2.8%	3.56	3.36	+2.8
Career	—	MLE		246	32	334	1389	339	89	46	13%	1.4	24.4%	6.4%	18.0%	3.3%	3.77	3.65	+3.6
/ 200 IP	—	MLE		147	19	200	831	203	53	27	13%	1.4	24.4%	6.4%	18.0%	3.3%	3.77	3.65	+2.5

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.

2021 AAANorfolk Tides · 10 IPFIP 4.34 → 4.69 (+0.35)K% 13.7% → 12.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.73%	—	×0.880	12.08%
BB% (unintentional) /BF	1.96%	—	×1.050	2.06%
HR / BF /BF	3.92%	÷1.009	×1.100	4.28%

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 102).

Result: FIP 4.34 → 4.69, 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).

2019 AABowie Baysox · 59 IPFIP 5.12 → 6.25 (+1.13)K% 23.7% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.74%	—	×0.810	19.23%
BB% (unintentional) /BF	8.17%	—	×1.100	8.99%
HR / BF /BF	5.45%	÷0.987	×1.200	6.62%

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 97).

Result: FIP 5.12 → 6.25, 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 AAANorfolk Tides · 17 IPFIP 2.92 → 3.66 (+0.74)K% 31.4% → 27.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.43%	—	×0.880	27.66%
BB% (unintentional) /BF	7.14%	—	×1.050	7.50%
HR / BF /BF	2.86%	÷0.889	×1.100	3.54%

Result: FIP 2.92 → 3.66, 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).

2018 AABowie Baysox · 32 IPFIP 3.28 → 3.87 (+0.59)K% 21.7% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.810	17.61%
BB% (unintentional) /BF	6.52%	—	×1.100	7.17%
HR / BF /BF	2.17%	÷1.060	×1.200	2.46%

Result: FIP 3.28 → 3.87, 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).

2017 ADelmarva Shorebirds · 9 IPFIP 0.74 → 1.85 (+1.11)K% 41.2% → 30.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	41.18%	—	×0.740	30.47%
BB% (unintentional) /BF	8.82%	—	×1.180	10.41%
HR / BF /BF	0.00%	÷0.811	×1.300	0.00%

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 62).

Result: FIP 0.74 → 1.85, 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).

2017 ROKGCL Orioles · 5 IPFIP 0.57 → 1.38 (+0.81)K% 30.0% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.680	20.40%
BB% (unintentional) /BF	0.00%	—	×1.250	0.00%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

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 100).

Result: FIP 0.57 → 1.38, 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
Hunter Harvey (OGILVIE)	—	—	3.99	25.9%	8.1%	3.0%	—	4.41	24.6%	15.9%	102	+0.42	—	—	—
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	73
Sergio Santos R 2015 · 4.17 FIP	32	SU	4.17	24.7%	8.2%	3.9%	.283	—	—	—	—	—	1.36	178	73
Mike Broadway R 2018 · 4.65 FIP	31	SU	4.65	23.0%	10.6%	3.9%	.282	—	—	—	—	—	1.74	195	72
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	72
Jumbo Diaz R 2016 · 3.85 FIP	32	SU	3.85	22.3%	9.2%	2.7%	.247	3.28	23.0%	13.8%	141	-0.57	2.80	319	71
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	70
Ryan Brasier R 2020 · 2.97 FIP	32	SU	2.97	27.3%	9.1%	1.9%	.324	3.30	23.4%	17.1%	134	+0.33	2.31	426	69
Mark Serrano R 2017 · 3.56 FIP	31	SU	3.56	21.9%	6.4%	2.7%	.240	5.25	20.2%	12.3%	42	+1.69	3.16	120	69
Adrian Ramirez R 2019 · 4.29 FIP	31	SU	4.29	21.7%	6.3%	4.0%	.327	—	—	—	—	—	3.04	137	68
Josh Fields R 2018 · 3.48 FIP	32	SU	3.48	21.4%	6.5%	2.5%	.224	7.56	18.9%	9.5%	25	+4.08	1.80	330	67
Chris Hatcher R 2017 · 4.31 FIP	32	SU	4.31	24.5%	7.4%	4.3%	.291	5.32	18.0%	8.4%	36	+1.01	2.51	246	67
Kazuhiro Sasaki R 2000 · 3.93 FIP	32	SU	3.93	29.4%	9.8%	3.8%	.212	2.88	24.9%	19.3%	161	-1.05	2.76	223	67
Gregory Infante R 2018 · 4.07 FIP	31	SU	4.07	22.0%	4.6%	4.1%	.291	—	—	—	—	—	2.97	230	67
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	66
Dale Thayer R 2013 · 3.62 FIP	32	SU	3.62	23.7%	7.4%	2.9%	.287	3.85	20.6%	15.0%	103	+0.23	2.75	239	65
Jose Manuel Lopez R 2017 · 3.61 FIP	32	SU	3.61	20.4%	10.0%	1.8%	.331	6.14	17.9%	3.7%	41	+2.53	3.50	170	65
James Hoyt R 2019 · 3.45 FIP	32	SU	3.45	22.2%	9.9%	1.8%	.355	5.63	22.1%	10.4%	50	+2.18	1.61	312	65
Jose Veras R 2013 · 3.51 FIP	32	SU	3.51	23.6%	8.3%	2.5%	.234	4.28	21.9%	10.2%	65	+0.77	3.37	422	65
Jeff Fulchino R 2011 · 4.44 FIP	31	SU	4.44	20.4%	11.1%	3.0%	.302	—	—	—	—	—	3.36	178	64
Jared Burton R 2013 · 3.20 FIP	32	SU	3.20	21.7%	6.0%	2.1%	.279	3.92	18.4%	10.0%	78	+0.72	3.09	367	64

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	1.7	56	25.9%	7.6%	1.11	3.65
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Jason Motte ('17)	2.0	50	23.0%	7.7%	1.11	3.30
Todd Worrell ('97)	1.7	55	23.4%	7.5%	0.95	3.58
Andrew Bailey ('17)	1.4	55	24.7%	8.2%	0.95	3.12
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Carlos Diaz ('82-'86, 2 WAR*)	1.3	52	25.1%*	8.7%*	0.91*	3.84*