Todd Peterson

P Harrisburg Senators

Age 29 RHP 6'5" / 230 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	25	A+WSH	42	0	56	4.85	4.03	17.8%	10.1%	—	K 13.7% · BB 11.6% · FIP 4.94
2024	26	MIX2 levels	42	2	51	3.91	3.83	18.4%	10.1%	—	K 14.7% · BB 11.2% · FIP 4.45
2025	27	MIX2 levels	23	0	27	7.33	5.14	16.2%	14.7%	—	K 12.8% · BB 15.1% · FIP 5.37
2026	28	No MLB games played
2027PROJ	29	MLBWSH	—	—	0	5.12	5.12	17.5%	11.9%	—
2028PROJ31% MLB	30	MLBWSH	5	0	5	5.15	5.22	17.7%	11.9%	-0.0

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

Contract

No contract data on file.

CompsWirfin Obispo (age 30) · Thad Weber (age 28) · Jim Miller (age 30)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 130122vR 114

ERA5.12

FIP5.12

K%17.5%

BB%11.9%

K-BB%5.6%

Proj IP17

Value2027 · WAR

Runs saved vs league avg · 17 IP

Grades · Contract

Scouting Grades

K†4040

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)
2019	21	ROK MLE	GCL Nationals	1	0	2	10	2	0	0	0%	2.0	20.0%	0.0%	20.0%	0.0%	9.00	1.41	—
2019	21	ROK RAW	GCL Nationals	1	0	2	10	2	0	0	0%	2.0	20.0%	0.0%	20.0%	0.0%	9.00	1.10	—
2021	23	MIX MLE	2 levels	20	3	35	154	36	13	4	15%	1.8	18.4%	10.3%	8.1%	4.1%	3.82	5.11	—
2021	23	MIX RAW	2 levels	20	3	35	154	36	13	4	15%	1.8	23.4%	8.4%	14.9%	2.6%	3.82	3.64	—
2022	24	A MLE	Fredericksburg Nationals	14	0	18	79	21	4	4	0%	1.3	19.7%	6.0%	13.7%	6.1%	5.09	5.56	—
2022	24	A RAW	Fredericksburg Nationals	14	0	18	79	21	4	4	0%	1.3	26.6%	5.1%	21.5%	5.1%	5.09	4.35	—
2023	25	A+ MLE	Wilmington Blue Rocks	42	0	56	247	44	25	5	0%	1.3	13.7%	11.6%	2.1%	2.8%	4.85	4.94	—
2023	25	A+ RAW	Wilmington Blue Rocks	42	0	56	247	44	25	5	0%	1.3	17.8%	10.1%	7.7%	2.0%	4.85	4.03	—
2024	26	MIX MLE	2 levels	42	2	51	228	42	23	4	5%	1.2	14.7%	11.2%	3.5%	2.1%	3.91	4.45	—
2024	26	MIX MLE	2 levels	42	2	51	228	42	23	4	5%	1.2	14.7%	11.2%	3.5%	2.1%	3.91	4.45	—
2024	26	MIX RAW	2 levels	42	2	51	228	42	23	4	5%	1.2	18.4%	10.1%	8.3%	1.8%	3.91	3.83	—
2025	27	MIX MLE	2 levels	23	0	27	136	22	20	3	0%	1.2	12.8%	15.1%	-2.3%	2.6%	7.33	5.37	—
2025	27	MIX MLE	2 levels	23	0	27	136	22	20	3	0%	1.2	12.8%	15.1%	-2.3%	2.6%	7.33	5.37	—
2025	27	MIX RAW	2 levels	23	0	27	136	22	20	3	0%	1.2	16.2%	14.7%	1.5%	2.2%	7.33	5.14	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	WSH	—	—	0	—	—	—	—	—	—	17.5%	11.9%	5.6%	2.9%	5.12	5.12	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	WSH	15	0	16	66	12	8	2	0%	1.0	17.7%	11.9%	5.8%	3.1%	5.15	5.22	-0.1
2028	30	MLB CTX	WSH	5	0	5	21	4	3	1	0%	1.0	17.7%	11.9%	5.8%	3.1%	5.15	5.22	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		5	0	5	21	4	2	1	0%	1.0	17.7%	11.9%	5.8%	3.1%	5.15	4.84	-0.0
/ 200 IP	—	RAW		190	0	200	844	164	82	41	0%	1.1	17.7%	11.9%	5.8%	3.1%	5.15	4.84	-0.7
Career	—	MLE		142	5	188	854	131	95	26	4%	1.3	15.3%	11.2%	4.1%	3.1%	4.83	4.92	-0.1
/ 200 IP	—	MLE		151	5	200	907	139	101	28	4%	1.3	15.3%	11.2%	4.1%	3.1%	4.83	4.92	-1.6

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.

2025 AAHarrisburg Senators · 21 IPFIP 4.66 → 5.37 (+0.71)K% 15.8% → 12.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.79%	—	×0.810	12.79%
BB% (unintentional) /BF	13.68%	—	×1.100	15.05%
HR / BF /BF	2.11%	÷0.963	×1.200	2.62%

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 93).

Result: FIP 4.66 → 5.37, 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 AAARochester Red Wings · 6 IPFIP 5.16 → 5.34 (+0.18)K% 17.1% → 14.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.07%	—	×0.860	14.68%
BB% (unintentional) /BF	17.07%	—	×0.900	15.37%
HR / BF /BF	2.44%	÷0.960	×1.100	2.80%

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 92).

Result: FIP 5.16 → 5.34, 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 AAHarrisburg Senators · 38 IPFIP 3.82 → 4.42 (+0.60)K% 17.1% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.14%	—	×0.810	13.89%
BB% (unintentional) /BF	9.71%	—	×1.100	10.69%
HR / BF /BF	1.71%	÷0.990	×1.200	2.08%

Result: FIP 3.82 → 4.42, 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 A+Wilmington Blue Rocks · 12 IPFIP 3.66 → 4.55 (+0.89)K% 22.6% → 17.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.64%	—	×0.770	17.43%
BB% (unintentional) /BF	11.32%	—	×1.150	13.02%
HR / BF /BF	1.89%	÷1.016	×1.250	2.32%

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 103).

Result: FIP 3.66 → 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).

2023 A+Wilmington Blue Rocks · 56 IPFIP 3.99 → 4.94 (+0.95)K% 17.8% → 13.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.81%	—	×0.770	13.72%
BB% (unintentional) /BF	10.12%	—	×1.150	11.64%
HR / BF /BF	2.02%	÷0.913	×1.250	2.77%

Result: FIP 3.99 → 4.94, 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 AFredericksburg Nationals · 18 IPFIP 4.28 → 5.56 (+1.28)K% 26.6% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.58%	—	×0.740	19.67%
BB% (unintentional) /BF	5.06%	—	×1.180	5.97%
HR / BF /BF	5.06%	÷1.074	×1.300	6.13%

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 115).

Result: FIP 4.28 → 5.56, 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 A+Wilmington Blue Rocks · 30 IPFIP 3.86 → 5.11 (+1.26)K% 23.9% → 18.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.88%	—	×0.770	18.39%
BB% (unintentional) /BF	8.96%	—	×1.150	10.30%
HR / BF /BF	2.99%	÷0.906	×1.250	4.12%

Result: FIP 3.86 → 5.11, 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 ROKFCL Nationals · 5 IPFIP 2.04 → 2.74 (+0.70)K% 20.0% → 13.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.680	13.60%
BB% (unintentional) /BF	5.00%	—	×1.250	6.25%
HR / BF /BF	0.00%	÷1.017	×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 103).

Result: FIP 2.04 → 2.74, 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
Todd Peterson (OGILVIE)	—	—	5.12	17.5%	11.9%	2.9%	—	5.22	17.7%	5.8%	16	+0.10	—	—	—
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	92
Thad Weber R 2013 · 5.18 FIP	28	SU	5.18	16.4%	13.1%	3.3%	.244	4.52	12.2%	6.0%	308	-0.66	4.40	378	87
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	83
Carlos Fisher R 2011 · 4.33 FIP	28	SU	4.33	15.5%	9.1%	2.5%	.275	—	—	—	—	—	2.48	245	81
Jason Boyd R 2002 · 5.81 FIP	29	SU	5.81	13.7%	10.7%	4.6%	.290	4.74	15.0%	3.7%	65	-1.07	3.68	128	80
Ronald Peña R 2021 · 4.49 FIP	29	SU	4.49	16.5%	10.4%	2.7%	.288	—	—	—	—	—	3.72	187	80
Brian Sanches R 2008 · 4.74 FIP	29	SU	4.74	18.5%	9.3%	3.7%	.378	4.01	20.5%	10.8%	182	-0.73	3.39	229	80
Brandon Puffer R 2004 · 5.23 FIP	28	SU	5.23	13.5%	11.2%	3.4%	.328	—	—	—	—	—	3.55	108	80
Gregory Infante R 2016 · 4.47 FIP	29	SU	4.47	19.0%	12.3%	2.6%	.333	3.43	22.5%	15.8%	120	-1.04	2.97	230	80
Santiago Casilla R 2009 · 4.44 FIP	29	SU	4.44	15.0%	9.4%	2.6%	.324	3.29	22.1%	13.0%	170	-1.15	2.76	647	80
Joe Colón J R 2017 · 4.25 FIP	27	SU	4.25	20.4%	12.1%	2.4%	.315	—	—	—	—	—	3.27	113	79
Brandon Cunniff R 2017 · 4.79 FIP	28	SU	4.79	20.1%	11.6%	3.5%	.275	6.80	13.9%	-1.9%	30	+2.01	3.89	180	79
Eder Llamas R 2015 · 4.84 FIP	29	SU	4.84	12.3%	8.8%	3.0%	.261	3.64	11.3%	4.6%	120	-1.20	3.23	171	79
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	79
Beck Wheeler R 2017 · 5.40 FIP	28	SU	5.40	15.3%	12.2%	3.7%	.347	—	—	—	—	—	3.46	173	78
Wil Browning R 2017 · 4.44 FIP	28	SU	4.44	17.9%	13.1%	2.2%	.359	—	—	—	—	—	2.14	162	78
Tony Fiore R 2000 · 5.61 FIP	28	SU	5.61	10.8%	9.5%	4.0%	.321	4.76	14.3%	3.5%	127	-0.85	4.60	142	78
Cole McDonald R 2024 · 5.01 FIP	27	SU	5.01	20.9%	12.7%	3.8%	.281	—	—	—	—	—	2.81	153	78
Drew Hayes R 2016 · 3.99 FIP	28	SU	3.99	13.8%	10.1%	1.4%	.302	—	—	—	—	—	3.35	117	77
Matt Lindstrom R 2009 · 4.12 FIP	29	SU	4.12	17.8%	10.0%	2.3%	.320	3.16	17.7%	11.7%	154	-0.96	2.40	443	77