PJ Poulin

P Washington Nationals

Age 31 LHP 6'1" / 195 lbs MLB ILInjured 60-Day (D60)

Overview

MLBRPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	28	MIX2 levels	45	0	60	2.10	2.03	29.8%	9.4%	—	K 24.8% · BB 9.5% · FIP 2.52
2025	29	MIX2 levels	63	3	67	3.48	2.95	30.3%	10.2%	1.3	K 27.3% · BB 9.6% · FIP 3.33
2026	30	MIX2 levels	41	11	43	2.95	5.07	15.6%	14.0%	-0.3	K 16.3% · BB 13.1% · FIP 4.81
2027PROJ	31	MLBWSH	16	0	17	4.17	4.58	20.4%	10.2%	-0.0
2028PROJ85% MLB	32	MLBWSH	20	0	21	4.54	4.81	19.9%	10.5%	-0.1
2029PROJ88% MLB	33	MLBWSH	14	0	15	4.83	5.05	19.3%	10.8%	-0.1

Last 3 seasons WAR 1.0 · Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs15

FB Velo10

K%10

CompsTommy Layne (age 31) · Raul Valdes (age 32) · Adam Liberatore (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 108117vR 125

ERA4.17

FIP4.58

K%20.4%

BB%10.2%

K-BB%10.2%

Proj IP21

Value2027 · WAR

Runs saved vs league avg · 21 IP

Grades · Statcast · Contract

Scouting Grades

STUF6565

CTRL5050

CMD6565

K†5050

Statcast Percentiles

FB Velo

p1090.1

Whiff%

p1016.9%

p1016.3%

xwOBA vs

p150.353

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	23	A MLE	Asheville Tourists	54	0	59	258	67	20	1	0%	1.1	19.2%	9.1%	10.1%	0.4%	2.90	2.86	—
2019	23	A RAW	Asheville Tourists	54	0	59	258	67	20	1	0%	1.1	26.0%	7.8%	18.2%	0.4%	2.90	2.07	—
2021	25	MIX MLE	2 levels	44	0	45	176	59	15	4	0%	1.0	26.3%	9.5%	16.8%	2.6%	3.63	3.49	—
2021	25	MIX RAW	2 levels	44	0	45	176	59	15	4	0%	1.0	33.5%	8.5%	25.0%	2.3%	3.63	2.63	—
2022	26	MIX MLE	2 levels	55	0	61	267	79	23	8	0%	1.1	24.6%	9.3%	15.3%	3.4%	4.15	4.04	—
2022	26	MIX RAW	2 levels	55	0	61	267	79	23	8	0%	1.1	29.6%	8.6%	21.0%	3.0%	4.15	3.35	—
2023	27	AAA MLE	Albuquerque Isotopes	32	0	50	238	39	32	10	0%	1.6	14.1%	12.1%	2.0%	4.4%	5.44	5.83	—
2023	27	AAA RAW	Albuquerque Isotopes	32	0	50	238	39	32	10	0%	1.6	16.4%	13.4%	2.9%	4.2%	5.44	6.08	—
2024	28	MIX MLE	2 levels	45	0	60	245	73	23	1	0%	1.3	24.8%	9.5%	15.3%	0.6%	2.10	2.52	—
2024	28	MIX MLE	2 levels	45	0	60	245	73	23	1	0%	1.3	24.8%	9.5%	15.3%	0.6%	2.10	2.52	—
2024	28	MIX RAW	2 levels	45	0	60	245	73	23	1	0%	1.3	29.8%	9.4%	20.4%	0.4%	2.10	2.03	—
2025	29	MIX MLE	2 levels	63	3	67	294	89	30	6	5%	1.1	27.3%	9.6%	17.7%	2.4%	3.48	3.33	+1.0
2025	29	MIX MLE	2 levels	63	3	67	294	89	30	6	5%	1.1	27.3%	9.6%	17.7%	2.4%	3.48	3.33	+1.0
2025	29	MIX RAW	2 levels	63	3	67	294	89	30	6	5%	1.1	30.3%	10.2%	20.1%	2.0%	3.48	2.95	+1.3
2026	30	MIX MLE	2 levels	41	11	43	179	28	25	5	27%	1.0	16.3%	13.1%	3.3%	2.6%	2.95	4.81	-0.1
2026	30	MIX MLE	2 levels	41	11	43	179	28	25	5	27%	1.0	16.3%	13.1%	3.3%	2.6%	2.95	4.81	-0.1
2026	30	MIX RAW	2 levels	41	11	43	179	28	25	5	27%	1.0	15.6%	14.0%	1.7%	2.8%	2.95	5.07	-0.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			63	11	61	263	52	33	6	17%	1.0	19.8%	12.5%	7.2%	2.3%	3.08	4.29	+0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	WSH	16	0	17	72	15	7	2	0%	1.1	20.4%	10.2%	10.2%	2.8%	4.17	4.58	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	WSH	24	0	25	105	21	11	3	0%	1.0	19.9%	10.5%	9.4%	3.1%	4.54	4.81	-0.1
2029	33	MLB MLE	WSH	16	0	16	70	13	8	2	0%	1.0	19.3%	10.8%	8.5%	3.3%	4.83	5.05	-0.1
2030	34	MLB MLE	WSH	10	0	10	43	8	5	2	0%	1.0	18.6%	11.2%	7.3%	3.6%	5.11	5.32	-0.1
2028	32	MLB CTX	WSH	20	0	21	89	18	9	3	0%	1.1	19.9%	10.5%	9.4%	3.1%	4.54	4.81	-0.1
2029	33	MLB CTX	WSH	14	0	15	63	12	7	2	0%	1.1	19.3%	10.8%	8.5%	3.3%	4.83	5.05	-0.1
2030	34	MLB CTX	WSH	9	0	9	38	7	4	1	0%	1.0	18.6%	11.2%	7.3%	3.6%	5.11	5.32	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		105	11	105	449	88	53	12	10%	1.0	19.6%	11.8%	7.8%	2.7%	3.78	4.41	+0.2
/ 200 IP	—	RAW		199	21	200	852	167	101	23	10%	1.0	19.6%	11.8%	7.8%	2.7%	3.78	4.41	+0.3
Career	—	MLE		334	14	384	1657	365	169	38	4%	1.1	22.1%	10.2%	11.9%	2.3%	3.49	3.79	-0.1
/ 200 IP	—	MLE		174	7	200	863	190	88	20	4%	1.1	22.1%	10.2%	11.9%	2.3%	3.49	3.79	-0.1

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 AAARochester Red Wings · 6 IPFIP 6.67 → 6.78 (+0.10)K% 11.5% → 9.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	11.54%	—	×0.860	9.92%
BB% (unintentional) /BF	19.23%	—	×0.900	17.31%
HR / BF /BF	3.85%	÷1.000	×1.100	4.23%

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 6.67 → 6.78, 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).

2025 AAAToledo Mud Hens · 43 IPFIP 2.62 → 3.21 (+0.59)K% 33.7% → 29.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.70%	—	×0.860	28.98%
BB% (unintentional) /BF	9.24%	—	×0.900	8.32%
HR / BF /BF	2.17%	÷0.872	×1.100	2.74%

Result: FIP 2.62 → 3.21, 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 AAErie SeaWolves · 34 IPFIP 1.75 → 2.36 (+0.61)K% 30.6% → 24.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.60%	—	×0.810	24.78%
BB% (unintentional) /BF	9.70%	—	×1.100	10.67%
HR / BF /BF	0.00%	÷0.941	×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 88).

Result: FIP 1.75 → 2.36, 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).

2024 AAAToledo Mud Hens · 26 IPFIP 2.30 → 2.72 (+0.42)K% 28.8% → 24.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.83%	—	×0.860	24.79%
BB% (unintentional) /BF	9.01%	—	×0.900	8.11%
HR / BF /BF	0.90%	÷0.792	×1.100	1.25%

Result: FIP 2.30 → 2.72, 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 AAAAlbuquerque Isotopes · 50 IPFIP 5.73 → 5.83 (+0.11)K% 16.4% → 14.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.39%	—	×0.860	14.09%
BB% (unintentional) /BF	13.45%	—	×0.900	12.10%
HR / BF /BF	4.20%	÷1.062	×1.100	4.35%

Result: FIP 5.73 → 5.83, 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 AAHartford Yard Goats · 41 IPFIP 2.53 → 3.44 (+0.91)K% 33.5% → 27.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.54%	—	×0.810	27.16%
BB% (unintentional) /BF	7.32%	—	×1.100	8.05%
HR / BF /BF	2.44%	÷0.992	×1.200	2.95%

Result: FIP 2.53 → 3.44, 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 AAAAlbuquerque Isotopes · 20 IPFIP 4.62 → 5.00 (+0.38)K% 23.3% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.30%	—	×0.880	20.50%
BB% (unintentional) /BF	10.68%	—	×1.050	11.21%
HR / BF /BF	3.88%	÷1.061	×1.100	4.03%

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 112).

Result: FIP 4.62 → 5.00, 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 AAHartford Yard Goats · 22 IPFIP 3.52 → 4.29 (+0.77)K% 24.2% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.18%	—	×0.810	19.58%
BB% (unintentional) /BF	9.89%	—	×1.100	10.88%
HR / BF /BF	2.20%	÷0.990	×1.200	2.66%

Result: FIP 3.52 → 4.29, 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
PJ Poulin (OGILVIE)	—	—	4.58	20.4%	10.2%	2.8%	—	4.99	19.4%	8.7%	52	+0.41	—	—	—
Tommy Layne L 2016 · 3.43 FIP	31	SU	3.43	20.3%	9.6%	1.5%	.266	4.25	19.4%	10.0%	90	+0.82	0.55	218	82
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	81
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	80
Buddy Baumann L 2018 · 4.87 FIP	30	SU	4.87	20.5%	11.9%	3.6%	.258	—	—	—	—	—	2.92	155	79
Randy Flores L 2006 · 4.00 FIP	31	SU	4.00	20.4%	9.7%	2.5%	.333	3.30	18.1%	10.5%	93	-0.70	2.51	250	78
Edward Paredes L 2018 · 3.81 FIP	31	SU	3.81	22.7%	11.9%	2.0%	.351	5.10	21.5%	14.2%	18	+1.29	2.36	130	78
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	78
George Sherrill L 2008 · 3.86 FIP	31	SU	3.86	24.3%	11.3%	2.5%	.277	3.38	20.6%	12.4%	141	-0.48	2.74	323	75
Tyler Olson L 2021 · 3.96 FIP	31	SU	3.96	21.6%	8.7%	2.9%	.250	—	—	—	—	—	3.15	263	75
Jeremy Bleich L 2018 · 3.69 FIP	31	SU	3.69	20.3%	6.6%	2.7%	.296	4.41	20.1%	13.1%	33	+0.72	3.20	241	74
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	73
Buddy Boshers L 2019 · 3.73 FIP	31	SU	3.73	25.3%	10.4%	2.6%	.294	—	—	—	—	—	2.76	239	73
Nick Hagadone L 2017 · 3.74 FIP	31	SU	3.74	22.6%	9.3%	2.5%	.267	—	—	—	—	—	2.97	141	73
David Purcey L 2013 · 3.79 FIP	31	SU	3.79	20.4%	11.5%	1.7%	.227	—	—	—	—	—	3.60	206	73
Brian Shouse L 2002 · 5.54 FIP	33	SU	5.54	15.5%	11.3%	4.2%	.245	3.31	16.3%	10.7%	159	-2.23	2.32	339	72
Pedro Feliciano L 2008 · 3.90 FIP	31	SU	3.90	21.1%	7.6%	2.9%	.309	3.02	22.1%	14.9%	122	-0.88	2.69	378	71
Juan Pablo Perez L 2007 · 5.32 FIP	28	SU	5.32	17.5%	14.0%	3.5%	.324	—	—	—	—	—	1.75	104	71
Tommy Phelps L 2005 · 3.98 FIP	31	SU	3.98	13.2%	7.5%	1.9%	.280	—	—	—	—	—	3.39	120	70
Brian Fuentes L 2007 · 3.68 FIP	31	SU	3.68	22.0%	9.0%	2.4%	.235	3.19	25.4%	16.4%	166	-0.49	2.08	602	70
Yuki Matsui L 2024 · 3.63 FIP	28	SU	3.63	26.8%	8.9%	3.0%	.242	4.25	23.9%	12.6%	140	+0.62	3.63	203	70

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	0.7	23	19.7%	11.6%	1.06	3.90
Manny Acosta ('12)	1.2	41	20.5%	10.9%	1.09	3.99
José Cisnero ('24)	1.4	46	24.0%	11.3%	1.01	4.16
Kevin Hickey ('81-'91, 0 WAR*)	1.2	47	17.7%*	10.2%*	1.10*	4.27*
Louis Coleman ('18)	1.4	46	23.4%	10.8%	1.17	3.51
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*
Mark Dewey ('90-'96, 3 WAR*)	1.2	42	18.7%*	9.6%*	0.80*	3.89*