David Peterson

P Chicago Cubs

Age 31 LHP 6'6" / 240 lbs MLB Svc 6.0 · Ctrl thru 2027 peak 1.5 · 2027

Overview

MLBSP40-MANExt / FA-dealFA 2028

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	28	MIX4 levels	27	27	145	2.61	2.95	22.6%	8.1%	4.4	K 19.8% · BB 9.0% · FIP 3.43
2025	29	MLBNYM	30	30	169	4.22	3.31	20.7%	8.9%	4.4
2026	30	MLBCHC	32	23	148	4.79	3.88	20.7%	9.8%	2.7
2027PROJ	31	MLBCHC	17	17	104	4.17	4.19	20.2%	9.4%	1.5
2028PROJ	32	MLBCHC	13	13	76	4.29	4.40	19.4%	9.8%	0.8
2029PROJ	33	MLBCHC	9	9	53	4.51	4.67	18.5%	10.3%	0.4

Last 3 seasons WAR 11.5 · Next 3 projected WAR 2.7Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2027Service6.517First FA2028

2027Ext / FA-deal$12M

Skills · percentileStatcast 2026

K%40

xwOBA vs32

FB Velo22

Form3.24 ERA (proj 4.17) · K 26.2% (proj 20.2%) · BB 12.1% (proj 9.4%) ▲ last 60d · 25 IP through Oct 27

CompsChris Narveson (age 29) · Wandy Rodriguez (age 31) · Mark Hendrickson (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 99107vR 115

ERA4.17

FIP4.19

K%20.2%

BB%9.4%

K-BB%10.8%

Proj IP104

Value2027 · WAR

Runs saved vs league avg · 104 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2292.5

Whiff%

p3222.7%

p4020.7%

xwOBA vs

p320.333

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status FA

MLB Debut 2020

Service 6.517 yrs.days

Team Control Through 2027

First FA Year 2028

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Ext / FA-deal	$12M	+1.5	$12M	+$0.0M
Totals		$12M		$12M	+$0.0M

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)
2018	22	MIX MLE	2 levels	22	22	128	533	115	30	2	100%	5.8	16.3%	6.5%	9.8%	0.5%	3.16	2.81	—
2018	22	MIX RAW	2 levels	22	22	128	533	115	30	2	100%	5.8	21.6%	5.6%	15.9%	0.4%	3.16	2.21	—
2019	23	AA MLE	Binghamton Rumble Ponies	24	24	116	498	122	37	9	100%	4.8	19.8%	8.2%	11.7%	2.1%	4.19	3.64	—
2019	23	AA RAW	Binghamton Rumble Ponies	24	24	116	498	122	37	9	100%	4.8	24.5%	7.4%	17.1%	1.8%	4.19	2.96	—
2020	24	MLB MLE	New York Mets	10	9	50	205	40	24	5	90%	5.0	19.5%	11.7%	7.8%	2.4%	3.44	4.28	+0.7
2020	24	MLB RAW	New York Mets	10	9	50	205	40	24	5	90%	5.0	19.5%	11.7%	7.8%	2.4%	3.44	4.25	+0.7
2021	25	MLB MLE	New York Mets	15	15	67	287	69	29	11	100%	4.4	24.0%	10.1%	13.9%	3.8%	5.53	4.46	+0.7
2021	25	MLB RAW	New York Mets	15	15	67	287	69	29	11	100%	4.4	24.0%	10.1%	13.9%	3.8%	5.53	4.48	+0.7
2022	26	MIX MLE	2 levels	34	25	132	573	160	57	12	74%	3.9	27.2%	10.0%	17.2%	2.1%	4.03	3.23	+3.5
2022	26	MIX RAW	2 levels	34	25	132	573	160	57	12	74%	3.9	27.9%	9.9%	18.0%	2.1%	4.03	3.15	+3.6
2023	27	MIX MLE	2 levels	34	28	148	658	171	73	21	82%	4.4	25.1%	10.7%	14.3%	3.3%	4.99	4.14	+2.2
2023	27	MIX RAW	2 levels	34	28	148	658	171	73	21	82%	4.4	26.0%	11.1%	14.9%	3.2%	4.99	4.11	+2.3
2024	28	MIX MLE	4 levels	27	27	145	603	136	49	8	100%	5.4	19.8%	9.0%	10.8%	1.6%	2.61	3.43	+3.5
2024	28	MIX MLE	4 levels	27	27	145	603	136	49	8	100%	5.4	19.8%	9.0%	10.8%	1.6%	2.61	3.43	+3.5
2024	28	MIX RAW	4 levels	27	27	145	603	136	49	8	100%	5.4	22.6%	8.1%	14.4%	1.3%	2.61	2.95	+4.4
2025	29	MLB MLE	New York Mets	30	30	169	723	150	64	11	100%	5.6	20.7%	8.9%	11.9%	1.5%	4.22	3.31	+4.4
2025	29	MLB MLE	New York Mets	30	30	169	723	150	64	11	100%	5.6	20.7%	8.9%	11.9%	1.5%	4.22	3.31	+4.4
2025	29	MLB RAW	New York Mets	30	30	169	723	150	64	11	100%	5.6	20.7%	8.9%	11.9%	1.5%	4.22	3.31	+4.4
2026	30	MLB MLE	Chicago Cubs	32	23	148	663	137	65	15	72%	4.6	20.7%	9.8%	10.9%	2.4%	4.79	3.89	+2.7
2026	30	MLB MLE	Chicago Cubs	32	23	148	663	137	65	15	72%	4.6	20.7%	9.8%	10.9%	2.4%	4.79	3.89	+2.7
2026	30	MLB RAW	Chicago Cubs	32	23	148	663	137	65	15	72%	4.6	20.7%	9.8%	10.9%	2.3%	4.79	3.88	+2.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			163	138	771	3334	751	325	77	85%	4.7	22.5%	9.7%	12.8%	2.3%	4.25	3.71	+15.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	CHC	17	17	104	439	89	41	10	100%	6.1	20.2%	9.4%	10.8%	2.2%	4.17	4.19	+1.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	CHC	13	13	76	320	62	32	8	100%	5.8	19.4%	9.8%	9.5%	2.5%	4.34	4.45	+0.8
2029	33	MLB MLE	CHC	9	9	53	223	41	23	6	100%	5.9	18.5%	10.3%	8.2%	2.8%	4.57	4.73	+0.4
2030	34	MLB MLE	CHC	6	6	35	147	26	16	4	100%	5.8	17.6%	10.8%	6.9%	3.1%	4.85	5.03	+0.1
2028	32	MLB CTX	CHC	13	13	76	321	62	32	8	100%	5.8	19.4%	9.8%	9.5%	2.4%	4.29	4.40	+0.8
2029	33	MLB CTX	CHC	9	9	53	224	41	23	6	100%	5.9	18.5%	10.3%	8.2%	2.6%	4.51	4.67	+0.4
2030	34	MLB CTX	CHC	6	6	35	148	26	16	4	100%	5.8	17.6%	10.8%	6.9%	2.9%	4.79	4.97	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		190	165	935	4025	880	395	95	87%	4.9	21.9%	9.8%	12.1%	2.4%	4.29	3.79	+18.1
/ 200 IP	—	RAW		41	35	200	861	188	85	20	87%	4.9	21.9%	9.8%	12.1%	2.4%	4.29	3.79	+3.9
Career	—	MLE		228	203	1102	4743	1022	440	99	89%	4.8	21.5%	9.3%	12.3%	2.1%	4.08	3.60	+17.0
/ 200 IP	—	MLE		41	37	200	861	186	80	18	89%	4.8	21.5%	9.3%	12.3%	2.1%	4.08	3.60	+3.6

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 AAASyracuse Mets · 10 IPFIP 1.53 → 1.77 (+0.24)K% 25.6% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.58%	—	×0.860	22.00%
BB% (unintentional) /BF	4.65%	—	×0.900	4.19%
HR / BF /BF	0.00%	÷1.059	×1.100	0.00%

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 112).

Result: FIP 1.53 → 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).

2024 ASt. Lucie Mets · 9 IPFIP -0.98 → 0.27 (+1.25)K% 53.3% → 39.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	53.33%	—	×0.740	39.47%
BB% (unintentional) /BF	3.33%	—	×1.180	3.93%
HR / BF /BF	0.00%	÷0.826	×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 65).

Result: FIP -0.98 → 0.27, 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 AABinghamton Rumble Ponies · 5 IPFIP -0.28 → 0.36 (+0.64)K% 40.0% → 32.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.00%	—	×0.810	32.40%
BB% (unintentional) /BF	0.00%	—	×1.100	0.00%
HR / BF /BF	0.00%	÷0.834	×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 67).

Result: FIP -0.28 → 0.36, 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).

2023 AAASyracuse Mets · 37 IPFIP 4.32 → 4.61 (+0.29)K% 25.9% → 22.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.90%	—	×0.860	22.28%
BB% (unintentional) /BF	13.86%	—	×0.900	12.47%
HR / BF /BF	3.01%	÷1.003	×1.100	3.30%

Result: FIP 4.32 → 4.61, 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 AAASyracuse Mets · 26 IPFIP 2.21 → 2.59 (+0.37)K% 28.6% → 25.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.880	25.14%
BB% (unintentional) /BF	8.40%	—	×1.050	8.82%
HR / BF /BF	0.84%	÷1.033	×1.100	0.90%

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 107).

Result: FIP 2.21 → 2.59, 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 AABinghamton Rumble Ponies · 116 IPFIP 2.96 → 3.64 (+0.67)K% 24.5% → 19.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.50%	—	×0.810	19.84%
BB% (unintentional) /BF	7.43%	—	×1.100	8.17%
HR / BF /BF	1.81%	÷1.012	×1.200	2.14%

Result: FIP 2.96 → 3.64, 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 A+St. Lucie Mets · 69 IPFIP 2.45 → 2.98 (+0.52)K% 19.3% → 14.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.27%	—	×0.770	14.84%
BB% (unintentional) /BF	6.31%	—	×1.150	7.26%
HR / BF /BF	0.33%	÷1.074	×1.250	0.39%

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 115).

Result: FIP 2.45 → 2.98, 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).

2018 AColumbia Fireflies · 59 IPFIP 1.86 → 2.59 (+0.73)K% 24.6% → 18.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.57%	—	×0.740	18.18%
BB% (unintentional) /BF	4.74%	—	×1.180	5.59%
HR / BF /BF	0.43%	÷0.976	×1.300	0.57%

Result: FIP 1.86 → 2.59, 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
David Peterson (OGILVIE)	—	—	—	—	—	2.2%	—	4.66	18.7%	8.6%	164	+0.47	—	—	—
Chris Narveson L 2011 · 4.01 FIP	29	SP	4.01	18.0%	9.2%	2.3%	.291	—	—	—	—	—	4.00	625	67
Wandy Rodriguez L 2010 · 3.37 FIP	31	SP	3.37	21.7%	7.9%	2.0%	.297	4.06	18.1%	11.6%	459	+0.69	3.37	1,572	63
Mark Hendrickson L 2005 · 4.57 FIP	31	SP	4.57	11.2%	6.0%	3.0%	.320	4.19	14.8%	7.9%	421	-0.38	3.04	1,169	63
Kaz Ishii L 2005 · 5.23 FIP	31	SP	5.23	13.3%	11.5%	3.3%	.258	—	—	—	—	—	4.51	564	59
Wei-Yin Chen L 2016 · 4.51 FIP	31	SP	4.51	19.2%	4.6%	4.5%	.299	4.37	19.6%	13.1%	235	-0.14	3.63	1,065	57
Tyler Anderson L 2021 · 4.30 FIP	31	SP	4.30	19.1%	5.1%	3.9%	.283	3.96	19.0%	11.1%	499	-0.34	3.10	1,288	56
Brian Tallet L 2009 · 4.45 FIP	31	SP-	4.45	16.7%	9.8%	2.8%	.294	6.14	14.7%	5.2%	91	+1.69	2.59	443	54
Yohan Flande L 2017 · 3.18 FIP	31	SP	3.18	15.9%	6.4%	1.1%	.346	4.21	10.0%	-1.9%	23	+1.03	3.18	394	52
Jorge Castillo L 2015 · 4.50 FIP	33	SP	4.50	16.8%	9.2%	3.0%	.292	4.95	12.4%	7.2%	186	+0.45	4.50	408	49
Tomas Solis L 2015 · 4.20 FIP	32	SP	4.20	15.0%	6.2%	2.9%	.264	5.09	12.0%	3.7%	159	+0.89	4.20	327	47
Alejandro Astorga L 2017 · 4.01 FIP	30	SP	4.01	14.4%	8.2%	2.0%	.306	—	—	—	—	—	3.72	311	47
Oscar Rivera L 2015 · 3.68 FIP	34	SP	3.68	14.2%	6.9%	1.7%	.298	4.66	10.4%	3.0%	129	+0.98	3.68	237	46
Paul Oseguera L 2015 · 3.83 FIP	31	SP	3.83	17.6%	8.5%	2.1%	.297	5.62	5.6%	-6.9%	28	+1.79	3.83	162	46
Arturo Lopez L 2015 · 4.80 FIP	32	SP	4.80	13.7%	6.2%	3.8%	.301	4.80	10.9%	4.2%	217	+0.00	4.34	445	46
Everett Teaford L 2015 · 4.42 FIP	31	SP-	4.42	13.9%	7.6%	2.8%	.324	—	—	—	—	—	4.42	208	44
Daniel Rodriguez L 2016 · 4.46 FIP	31	SP	4.46	19.3%	11.1%	2.9%	.333	4.32	16.9%	8.2%	226	-0.14	2.89	391	44
Joey Lucchesi L 2024 · 4.73 FIP	31	SP-	4.73	14.9%	10.1%	2.9%	.293	3.65	22.6%	15.8%	102	-1.08	3.12	817	44
Roy Merritt L 2017 · 3.95 FIP	31	SP	3.95	16.2%	5.8%	2.7%	.339	—	—	—	—	—	3.93	129	43
Orlando Lara L 2016 · 3.85 FIP	31	SP	3.85	13.8%	7.4%	1.8%	.308	4.24	18.5%	11.3%	202	+0.39	3.85	426	43
Yusei Kikuchi L 2022 · 5.19 FIP	31	SP-	5.19	27.3%	12.8%	5.1%	.281	3.78	25.4%	18.1%	522	-1.41	3.23	1,056	43

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	5.2	94	21.7%	9.8%	0.91	4.28
Darren Dreifort ('04)	4.4	97	21.2%	10.3%	0.93	4.36
Don Aase ('77-'90, 12 WAR*)	5.5	92	20.7%*	9.6%*	0.94*	4.14*
Matt Young ('83-'93, 8 WAR*)	5.9	119	22.8%*	10.5%*	0.91*	4.53*
Brandon Morrow ('18)	4.3	78	24.0%	10.1%	0.92	3.96
Skip Lockwood ('69-'80, 11 WAR*)	6.2	103	22.5%*	9.1%*	1.06*	4.28*
Tom Gorzelanny ('16)	4.4	88	18.4%	10.0%	1.00	4.40