Roman Phansalkar

P Rocket City Trash Pandas

Age 29 RHP 6'1" / 203 lbs AA

Overview

AAPROSPECT

MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	26	MIX2 levels	42	0	59	5.22	4.33	20.7%	11.4%	—	K 15.8% · BB 13.2% · FIP 5.39
2025	27	MIX3 levels	29	0	37	2.89	3.07	25.5%	11.5%	—	K 18.2% · BB 14.5% · FIP 4.33
2026	28	AALAA	2	0	0	108.00	57.10	0.0%	60.0%	—	K 0.0% · BB 60.0% · FIP 10.70
2027PROJ	29	MLBLAA	—	—	0	5.05	5.05	19.5%	12.6%	—
2028PROJ31% MLB	30	MLBLAA	5	0	5	5.08	5.16	19.5%	12.5%	-0.0

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

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 0.0 / P90 0.0, comp-based) · star 0% · didn't stick 100%

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

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 129121vR 113

ERA5.05

FIP5.05

K%19.5%

BB%12.6%

K-BB%6.9%

Proj IP22

Value2027 · WAR

Runs saved vs league avg · 22 IP

Grades · Contract

Scouting Grades

K†4545

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)
2022	24	A MLE	Inland Empire 66ers	12	0	17	77	14	12	2	0%	1.4	13.5%	18.4%	-4.9%	3.8%	5.29	6.38	—
2022	24	A RAW	Inland Empire 66ers	12	0	17	77	14	12	2	0%	1.4	18.2%	15.6%	2.6%	2.6%	5.29	5.10	—
2023	25	MIX MLE	2 levels	36	0	58	240	59	27	4	0%	1.6	18.7%	13.1%	5.6%	2.2%	3.28	4.38	—
2023	25	MIX RAW	2 levels	36	0	58	240	59	27	4	0%	1.6	24.6%	11.2%	13.3%	1.7%	3.28	3.36	—
2024	26	MIX MLE	2 levels	42	0	59	271	56	31	7	0%	1.4	15.8%	13.2%	2.6%	3.6%	5.22	5.39	—
2024	26	MIX MLE	2 levels	42	0	59	271	56	31	7	0%	1.4	15.8%	13.2%	2.6%	3.6%	5.22	5.39	—
2024	26	MIX RAW	2 levels	42	0	59	271	56	31	7	0%	1.4	20.7%	11.4%	9.2%	2.6%	5.22	4.33	—
2025	27	MIX MLE	3 levels	29	0	37	165	42	19	2	0%	1.3	18.2%	14.5%	3.6%	1.7%	2.89	4.33	—
2025	27	MIX MLE	3 levels	29	0	37	165	42	19	2	0%	1.3	18.2%	14.5%	3.6%	1.7%	2.89	4.33	—
2025	27	MIX RAW	3 levels	29	0	37	165	42	19	2	0%	1.3	25.5%	11.5%	13.9%	1.2%	2.89	3.07	—
2026	28	AA MLE	Rocket City Trash Pandas	2	0	0	10	0	6	0	0%	0.2	0.0%	60.0%	-60.0%	0.0%	108.00	10.70	—
2026	28	AA MLE	Rocket City Trash Pandas	2	0	0	10	0	6	0	0%	0.2	0.0%	60.0%	-60.0%	0.0%	108.00	10.70	—
2026	28	AA RAW	Rocket City Trash Pandas	2	0	0	10	0	6	0	0%	0.2	0.0%	60.0%	-60.0%	0.0%	108.00	57.10	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	LAA	—	—	0	—	—	—	—	—	—	19.5%	12.6%	6.9%	3.0%	5.05	5.05	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	LAA	16	0	17	70	14	9	2	0%	1.0	19.5%	12.5%	7.0%	3.2%	5.08	5.16	-0.1
2028	30	MLB CTX	LAA	5	0	5	21	4	3	1	0%	1.0	19.5%	12.5%	7.0%	3.2%	5.08	5.16	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		5	0	5	22	4	3	1	0%	1.0	19.5%	12.5%	7.0%	3.2%	5.08	4.78	-0.0
/ 200 IP	—	RAW		190	0	200	844	154	115	38	0%	1.1	19.5%	12.5%	7.0%	3.2%	5.08	4.78	-0.5
Career	—	MLE		121	0	171	763	128	111	21	0%	1.4	16.8%	14.6%	2.2%	2.7%	4.26	5.01	-0.1
/ 200 IP	—	MLE		142	0	200	892	150	130	24	0%	1.4	16.8%	14.6%	2.2%	2.7%	4.26	5.01	-1.4

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 A+Tri-City Dust Devils · 18 IPFIP 1.56 → 2.24 (+0.68)K% 28.4% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.38%	—	×0.770	21.85%
BB% (unintentional) /BF	6.76%	—	×1.150	7.77%
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 1.56 → 2.24, 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 AARocket City Trash Pandas · 13 IPFIP 5.94 → 6.76 (+0.82)K% 17.2% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.19%	—	×0.810	13.92%
BB% (unintentional) /BF	20.31%	—	×1.100	22.34%
HR / BF /BF	3.12%	÷1.028	×1.200	3.65%

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 106).

Result: FIP 5.94 → 6.76, 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 ROKACL Angels · 6 IPFIP 0.44 → 1.56 (+1.12)K% 37.0% → 25.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.04%	—	×0.680	25.19%
BB% (unintentional) /BF	3.70%	—	×1.250	4.63%
HR / BF /BF	0.00%	÷0.873	×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 75).

Result: FIP 0.44 → 1.56, 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).

2024 A+Tri-City Dust Devils · 44 IPFIP 4.55 → 5.79 (+1.24)K% 21.6% → 16.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.60%	—	×0.770	16.63%
BB% (unintentional) /BF	13.62%	—	×1.150	15.66%
HR / BF /BF	2.82%	÷0.916	×1.250	3.84%

Result: FIP 4.55 → 5.79, 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 AInland Empire 66ers · 15 IPFIP 3.03 → 3.93 (+0.91)K% 17.2% → 12.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.24%	—	×0.740	12.76%
BB% (unintentional) /BF	3.45%	—	×1.180	4.07%
HR / BF /BF	1.72%	÷0.882	×1.300	2.54%

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 76).

Result: FIP 3.03 → 3.93, 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+Tri-City Dust Devils · 44 IPFIP 3.38 → 4.24 (+0.85)K% 21.9% → 16.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.91%	—	×0.770	16.87%
BB% (unintentional) /BF	9.55%	—	×1.150	10.98%
HR / BF /BF	1.69%	÷0.990	×1.250	2.13%

Result: FIP 3.38 → 4.24, 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 AInland Empire 66ers · 14 IPFIP 3.30 → 4.78 (+1.47)K% 32.3% → 23.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.26%	—	×0.740	23.87%
BB% (unintentional) /BF	16.13%	—	×1.180	19.03%
HR / BF /BF	1.61%	÷0.897	×1.300	2.34%

Result: FIP 3.30 → 4.78, 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 AInland Empire 66ers · 17 IPFIP 4.96 → 6.38 (+1.41)K% 18.2% → 13.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.18%	—	×0.740	13.45%
BB% (unintentional) /BF	15.58%	—	×1.180	18.39%
HR / BF /BF	2.60%	÷0.889	×1.300	3.80%

Result: FIP 4.96 → 6.38, 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
Roman Phansalkar (OGILVIE)	—	—	5.05	19.5%	12.6%	3.0%	—	5.16	19.5%	7.0%	17	+0.11	—	—	—
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	93
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	87
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	86
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	81
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	81
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	81
Joe Colón J R 2017 · 4.25 FIP	27	SU	4.25	20.4%	12.1%	2.4%	.315	—	—	—	—	—	3.27	113	81
Cole McDonald R 2024 · 5.01 FIP	27	SU	5.01	20.9%	12.7%	3.8%	.281	—	—	—	—	—	2.81	153	80
Wil Browning R 2017 · 4.44 FIP	28	SU	4.44	17.9%	13.1%	2.2%	.359	—	—	—	—	—	2.14	162	79
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	79
Carlos Fisher R 2011 · 4.33 FIP	28	SU	4.33	15.5%	9.1%	2.5%	.275	—	—	—	—	—	2.48	245	79
Ronald Peña R 2021 · 4.49 FIP	29	SU	4.49	16.5%	10.4%	2.7%	.288	—	—	—	—	—	3.72	187	79
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	78
Tyler Sturdevant R 2015 · 4.37 FIP	29	SU	4.37	21.3%	8.5%	3.6%	.241	3.97	18.5%	11.9%	118	-0.40	3.44	149	77
Dan Winkler R 2019 · 4.99 FIP	29	SU	4.99	20.6%	15.2%	3.1%	.237	4.44	23.0%	8.4%	104	-0.55	2.40	231	77
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	77
Beck Wheeler R 2017 · 5.40 FIP	28	SU	5.40	15.3%	12.2%	3.7%	.347	—	—	—	—	—	3.46	173	77
Scott Dohmann R 2007 · 4.28 FIP	29	SU	4.28	19.1%	12.5%	2.2%	.289	4.38	18.2%	9.1%	15	+0.10	4.28	173	77
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	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