Connor Noland

P Iowa Cubs

Age 28 RHP 6'2" / 215 lbs AAA peak 0.4 · 2027

Overview

AAAPROSPECT

ETA2029MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	25	MIX2 levels	26	25	134	3.49	3.53	20.5%	6.4%	—	K 17.1% · BB 6.5% · FIP 4.02
2025	26	AAACHC	27	22	133	4.07	3.88	19.9%	9.5%	—	K 17.1% · BB 8.5% · FIP 3.95
2026	27	AAACHC	19	15	74	5.38	5.03	16.2%	9.4%	—	K 14.0% · BB 8.4% · FIP 5.25
2027PROJ	28	MLBCHC	2	2	15	4.95	4.95	14.7%	7.8%	0.1
2028PROJ37% MLB	29	MLBCHC	7	7	43	4.95	5.01	15.2%	8.1%	0.2
2029PROJ55% MLB	30	MLBCHC	9	9	54	5.03	5.13	15.3%	8.3%	0.2

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

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 -0.4 / P90 0.0, comp-based) · star 0% · didn't stick 98%

CompsNate Long (age 29) · Shawn Haviland (age 29) · Marco Antonio Duarte (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 127119vR 111

ERA4.95

FIP4.95

K%14.7%

BB%7.8%

K-BB%6.8%

Proj IP154

Value2027 · WAR

Runs saved vs league avg · 154 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)
2023	24	A+ MLE	South Bend Cubs	24	23	108	447	86	22	11	96%	4.5	14.8%	5.7%	9.2%	2.6%	4.08	3.98	—
2023	24	A+ RAW	South Bend Cubs	24	23	108	447	86	22	11	96%	4.5	19.2%	4.9%	14.3%	2.5%	4.08	3.44	—
2024	25	MIX MLE	2 levels	26	25	134	565	116	36	14	96%	5.2	17.1%	6.5%	10.6%	2.8%	3.49	4.02	—
2024	25	MIX MLE	2 levels	26	25	134	565	116	36	14	96%	5.2	17.1%	6.5%	10.6%	2.8%	3.49	4.02	—
2024	25	MIX RAW	2 levels	26	25	134	565	116	36	14	96%	5.2	20.5%	6.4%	14.2%	2.5%	3.49	3.53	—
2025	26	AAA MLE	Iowa Cubs	27	22	133	579	115	55	13	81%	4.9	17.1%	8.5%	8.5%	2.2%	4.07	3.95	—
2025	26	AAA MLE	Iowa Cubs	27	22	133	579	115	55	13	81%	4.9	17.1%	8.5%	8.5%	2.2%	4.07	3.95	—
2025	26	AAA RAW	Iowa Cubs	27	22	133	579	115	55	13	81%	4.9	19.9%	9.5%	10.4%	2.2%	4.07	3.88	—
2026	27	AAA MLE	Iowa Cubs	19	15	74	320	52	30	12	79%	3.9	14.0%	8.4%	5.5%	4.1%	5.38	5.25	—
2026	27	AAA MLE	Iowa Cubs	19	15	74	320	52	30	12	79%	3.9	14.0%	8.4%	5.5%	4.1%	5.38	5.25	—
2026	27	AAA RAW	Iowa Cubs	19	15	74	320	52	30	12	79%	3.9	16.2%	9.4%	6.9%	3.8%	5.38	5.03	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	CHC	2	2	15	63	9	5	2	100%	7.5	14.7%	7.8%	6.8%	3.1%	4.95	4.95	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	CHC	20	20	117	495	75	40	16	100%	5.9	15.2%	8.1%	7.1%	3.3%	4.95	5.01	+0.5
2029	30	MLB MLE	CHC	16	16	98	415	64	35	14	100%	6.2	15.3%	8.3%	7.0%	3.4%	5.03	5.13	+0.3
2028	29	MLB CTX	CHC	7	7	43	182	28	15	6	100%	6.1	15.2%	8.1%	7.1%	3.3%	4.95	5.01	+0.2
2029	30	MLB CTX	CHC	9	9	54	228	35	19	8	100%	6.0	15.3%	8.3%	7.0%	3.4%	5.03	5.13	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		16	16	97	411	63	34	14	100%	6.1	15.2%	8.2%	7.0%	3.4%	4.99	4.69	+0.8
/ 200 IP	—	RAW		33	33	200	844	129	70	29	100%	6.0	15.2%	8.2%	7.0%	3.4%	4.99	4.69	+1.6
Career	—	MLE		96	85	448	1911	306	138	53	89%	4.7	16.0%	7.2%	8.8%	2.8%	4.12	4.19	+0.8
/ 200 IP	—	MLE		43	38	200	852	137	62	24	89%	4.7	16.0%	7.2%	8.8%	2.8%	4.12	4.19	+0.8

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 AAAIowa Cubs · 74 IPFIP 4.97 → 5.25 (+0.28)K% 16.2% → 14.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.25%	—	×0.860	13.98%
BB% (unintentional) /BF	9.38%	—	×0.900	8.44%
HR / BF /BF	3.75%	÷1.000	×1.100	4.12%

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 100).

Result: FIP 4.97 → 5.25, 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).

2025 AAAIowa Cubs · 133 IPFIP 3.86 → 3.95 (+0.09)K% 19.9% → 17.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.86%	—	×0.860	17.08%
BB% (unintentional) /BF	9.50%	—	×0.900	8.55%
HR / BF /BF	2.25%	÷1.125	×1.100	2.19%

Result: FIP 3.86 → 3.95, 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).

2024 AATennessee Smokies · 86 IPFIP 2.87 → 3.51 (+0.64)K% 19.1% → 15.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.14%	—	×0.810	15.51%
BB% (unintentional) /BF	6.00%	—	×1.100	6.60%
HR / BF /BF	1.14%	÷0.854	×1.200	1.61%

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 71).

Result: FIP 2.87 → 3.51, 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).

2024 AAAIowa Cubs · 48 IPFIP 4.61 → 4.84 (+0.23)K% 22.8% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.79%	—	×0.860	19.60%
BB% (unintentional) /BF	6.98%	—	×0.900	6.28%
HR / BF /BF	4.65%	÷1.079	×1.100	4.74%

Result: FIP 4.61 → 4.84, 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 A+South Bend Cubs · 108 IPFIP 3.45 → 3.98 (+0.53)K% 19.2% → 14.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.24%	—	×0.770	14.81%
BB% (unintentional) /BF	4.92%	—	×1.150	5.66%
HR / BF /BF	2.46%	÷1.197	×1.250	2.57%

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 139).

Result: FIP 3.45 → 3.98, 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
Connor Noland (OGILVIE)	—	—	—	—	—	3.1%	—	5.06	15.2%	7.0%	216	+0.11	—	—	—
Nate Long R 2015 · 4.67 FIP	29	SP	4.67	14.8%	10.6%	2.7%	.285	—	—	—	—	—	4.67	151	83
Shawn Haviland R 2015 · 3.95 FIP	29	SP-	3.95	14.6%	6.0%	2.4%	.303	4.67	16.1%	8.8%	169	+0.72	3.69	283	82
Marco Antonio Duarte R 2015 · 4.07 FIP	28	SP	4.07	15.5%	9.1%	2.0%	.306	4.73	9.0%	-1.2%	38	+0.66	4.07	203	81
Travis Driskill R 2002 · 4.95 FIP	30	SP-	4.95	13.2%	8.0%	3.6%	.291	4.26	15.3%	12.0%	48	-0.69	4.26	181	81
Greg Peavey R 2015 · 4.39 FIP	27	SP	4.39	11.2%	6.5%	2.6%	.289	—	—	—	—	—	4.39	147	80
Juan Carlos Sulbaran R 2016 · 4.65 FIP	26	SP	4.65	15.0%	8.9%	3.1%	.289	—	—	—	—	—	4.65	278	79
Patrick Johnson R 2016 · 3.82 FIP	27	SP-	3.82	17.3%	6.9%	2.4%	.286	4.14	15.7%	6.4%	120	+0.32	3.82	248	79
Hiram Burgos R 2016 · 4.08 FIP	28	SP	4.08	16.3%	9.1%	2.2%	.330	4.94	19.3%	11.8%	62	+0.86	3.40	384	79
Manuel Flores R 2015 · 4.03 FIP	29	SP	4.03	13.8%	7.1%	2.2%	.317	3.57	14.4%	9.2%	263	-0.46	3.46	515	79
Brannon Easterling R 2017 · 4.40 FIP	26	SP-	4.40	12.7%	8.2%	2.4%	.322	—	—	—	—	—	3.50	334	79
Andres Ivan Meza R 2015 · 4.45 FIP	28	SP	4.45	10.9%	5.6%	2.9%	.322	4.14	12.5%	4.0%	85	-0.31	4.14	275	79
Mario Gonzalez R 2015 · 4.32 FIP	28	SP	4.32	12.6%	6.0%	2.8%	.302	4.20	16.2%	10.8%	233	-0.12	3.94	368	78
Frank Batista R 2015 · 4.29 FIP	26	SP-	4.29	12.1%	8.2%	2.1%	.280	—	—	—	—	—	4.29	118	78
Luke Irvine R 2016 · 4.31 FIP	27	SP-	4.31	13.4%	9.8%	2.0%	.302	—	—	—	—	—	4.31	104	78
Matt Benedict R 2015 · 4.00 FIP	26	SP-	4.00	10.3%	4.8%	2.1%	.325	—	—	—	—	—	4.00	130	78
Caleb Clay R 2015 · 4.67 FIP	27	SP	4.67	9.9%	6.9%	2.8%	.324	—	—	—	—	—	4.67	111	78
Scott Carroll R 2014 · 4.35 FIP	29	SP-	4.35	11.2%	7.7%	2.2%	.296	4.52	11.9%	3.7%	248	+0.17	3.92	377	77
Cory Riordan R 2016 · 4.24 FIP	30	SP	4.24	14.3%	5.0%	3.1%	.341	—	—	—	—	—	4.24	154	77
Edgar Alberto Garcia R 2016 · 4.37 FIP	28	SP-	4.37	14.6%	9.8%	2.3%	.311	4.01	17.3%	6.4%	44	-0.36	4.01	272	76
Octavio Acosta R 2019 · 4.19 FIP	28	SP	4.19	14.9%	8.3%	2.4%	.364	—	—	—	—	—	3.88	333	76

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	1.8	123	15.0%	8.0%	1.26	4.97
Paul Wilson ('05)	4.7	135	14.9%	8.1%	1.20	4.86
Brandon Claussen ('06)	1.6	105	16.0%	8.5%	1.37	5.04
Brian Bannister ('10)	3.3	133	13.2%	7.7%	1.16	5.08
Albert Williams ('80-'84, 6 WAR*)	3.2	129	14.9%*	8.4%*	1.26*	4.30*
Al Nipper ('83-'90, 4 WAR*)	4.0	133	14.8%*	8.5%*	1.23*	4.56*
Ken Schrom ('80-'87, 3 WAR*)	4.5	150	13.4%*	8.1%*	1.41*	4.77*