Paul Sewald

P Toronto Blue Jays

Age 37 RHP 6'3" / 219 lbs MLB

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	34	MIX3 levels	44	0	41	4.14	3.44	27.0%	5.7%	0.5	K 26.1% · BB 5.5% · FIP 3.70
2025	35	MIX4 levels	31	2	28	4.13	4.55	25.2%	6.1%	0.0	K 24.7% · BB 7.4% · FIP 4.78
2026	36	MLBTOR	62	0	53	4.72	4.96	24.2%	5.5%	-0.3
2027PROJ	37	MLBTOR	50	0	52	4.45	4.66	23.2%	6.0%	-0.1
2028PROJ	38	MLBTOR	30	0	31	4.84	5.10	22.1%	6.7%	-0.2
2029PROJ	39	MLBTOR	14	0	15	5.25	5.53	20.9%	7.4%	-0.2

Last 3 seasons WAR 0.3 · Next 3 projected WAR -0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%61

xwOBA vs50

FB Velo10

Form3.45 ERA (proj 4.45) · K 15.2% (proj 23.2%) · BB 6.1% (proj 6.0%) ▼▼ last 90d · 16 IP through Oct 27

CompsChris Nichting (age 36) · Matt Bush (age 37) · Scott Atchison (age 37)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 116108vR 100

ERA4.45

FIP4.66

K%23.2%

BB%6.0%

K-BB%17.2%

Proj IP52

Value2027 · WAR

Runs saved vs league avg · 52 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL6060

CMD6060

K†5555

Statcast Percentiles

FB Velo

p1091.4

Whiff%

p5626.2%

p6124.2%

xwOBA vs

p500.312

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)
2015	25	AA MLE	Binghamton Mets	44	0	51	195	56	9	3	0%	1.2	23.3%	5.1%	18.2%	1.7%	1.75	2.71	—
2015	25	AA RAW	Binghamton Mets	44	0	51	195	56	9	3	0%	1.2	28.7%	4.6%	24.1%	1.5%	1.75	2.20	—
2016	26	AAA MLE	Las Vegas 51s	56	0	66	278	80	19	9	0%	1.2	25.3%	7.2%	18.1%	3.3%	3.29	3.67	—
2016	26	AAA RAW	Las Vegas 51s	56	0	66	278	80	19	9	0%	1.2	28.8%	6.8%	21.9%	3.2%	3.29	3.31	—
2017	27	MIX MLE	2 levels	65	0	74	311	81	20	9	0%	1.1	25.1%	6.9%	18.2%	2.9%	4.26	3.46	+1.0
2017	27	MIX RAW	2 levels	65	0	74	311	81	20	9	0%	1.1	26.0%	6.4%	19.6%	2.9%	4.26	3.30	+1.1
2018	28	MIX MLE	2 levels	53	0	64	285	65	21	8	0%	1.2	22.9%	8.3%	14.6%	3.2%	5.46	3.96	+0.5
2018	28	MIX RAW	2 levels	53	0	64	285	65	21	8	0%	1.2	22.8%	7.4%	15.4%	2.8%	5.46	3.68	+0.7
2019	29	MIX MLE	2 levels	58	0	71	297	74	17	9	0%	1.2	22.8%	6.0%	16.9%	3.3%	3.69	3.75	+0.7
2019	29	MIX RAW	2 levels	58	0	71	297	74	17	9	0%	1.2	24.9%	5.7%	19.2%	3.0%	3.69	3.38	+1.0
2020	30	MLB MLE	New York Mets	5	0	6	35	2	4	1	0%	1.2	5.7%	11.4%	-5.7%	2.9%	13.50	5.63	-0.1
2020	30	MLB RAW	New York Mets	5	0	6	35	2	4	1	0%	1.2	5.7%	11.4%	-5.7%	2.9%	13.50	6.60	-0.1
2021	31	MIX MLE	2 levels	64	1	69	279	114	19	10	2%	1.1	39.4%	7.2%	32.2%	3.9%	2.87	2.82	+1.4
2021	31	MIX RAW	2 levels	64	1	69	279	114	19	10	2%	1.1	40.9%	6.8%	34.1%	3.6%	2.87	2.51	+1.7
2022	32	MLB MLE	Seattle Mariners	65	0	64	242	72	13	10	0%	1.0	29.8%	5.4%	24.4%	4.2%	2.67	3.59	+0.7
2022	32	MLB RAW	Seattle Mariners	65	0	64	242	72	13	10	0%	1.0	29.8%	5.4%	24.4%	4.1%	2.67	3.49	+0.8
2023	33	MLB MLE	Arizona Diamondbacks	65	0	61	249	80	23	8	0%	0.9	32.1%	9.2%	22.9%	3.4%	3.12	3.42	+0.8
2023	33	MLB RAW	Arizona Diamondbacks	65	0	61	249	80	23	8	0%	0.9	32.1%	9.2%	22.9%	3.2%	3.12	3.31	+0.9
2024	34	MIX MLE	3 levels	44	0	41	174	47	10	6	0%	0.9	26.1%	5.5%	20.6%	3.8%	4.14	3.70	+0.4
2024	34	MIX MLE	3 levels	44	0	41	174	47	10	6	0%	0.9	26.1%	5.5%	20.6%	3.8%	4.14	3.70	+0.4
2024	34	MIX RAW	3 levels	44	0	41	174	47	10	6	0%	0.9	27.0%	5.7%	21.3%	3.4%	4.14	3.44	+0.5
2025	35	MIX MLE	4 levels	31	2	28	115	29	7	6	6%	0.9	24.7%	7.4%	17.3%	5.2%	4.13	4.78	-0.1
2025	35	MIX MLE	4 levels	31	2	28	115	29	7	6	6%	0.9	24.7%	7.4%	17.3%	5.2%	4.13	4.78	-0.1
2025	35	MIX RAW	4 levels	31	2	28	115	29	7	6	6%	0.9	25.2%	6.1%	19.1%	5.2%	4.13	4.55	+0.0
2026	36	MLB MLE	Toronto Blue Jays	62	0	53	219	53	12	13	0%	0.9	24.2%	5.5%	18.7%	5.9%	4.72	5.01	-0.3
2026	36	MLB MLE	Toronto Blue Jays	62	0	53	219	53	12	13	0%	0.9	24.2%	5.5%	18.7%	5.9%	4.72	5.01	-0.3
2026	36	MLB RAW	Toronto Blue Jays	62	0	53	219	53	12	13	0%	0.9	24.2%	5.5%	18.7%	5.9%	4.72	4.96	-0.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			443	0	449	1863	523	129	71	0%	1.0	28.1%	6.9%	21.1%	3.8%	4.19	3.69	+4.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	37	MLB	TOR	50	0	52	220	51	13	10	0%	1.0	23.2%	6.0%	17.2%	4.3%	4.45	4.66	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	38	MLB MLE	TOR	29	0	31	129	28	9	6	0%	1.1	22.1%	6.7%	15.4%	4.8%	4.84	5.10	-0.2
2029	39	MLB MLE	TOR	15	0	15	65	14	5	3	0%	1.0	20.9%	7.4%	13.5%	5.2%	5.25	5.53	-0.2
2028	38	MLB CTX	TOR	30	0	31	131	29	9	6	0%	1.0	22.1%	6.7%	15.4%	4.8%	4.84	5.10	-0.2
2029	39	MLB CTX	TOR	14	0	15	63	13	5	3	0%	1.1	20.9%	7.4%	13.5%	5.2%	5.25	5.53	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		487	0	495	2057	565	142	81	0%	1.0	27.5%	6.9%	20.5%	3.9%	4.26	3.81	+4.4
/ 200 IP	—	RAW		197	0	200	831	228	57	33	0%	1.0	27.5%	6.9%	20.5%	3.9%	4.26	3.81	+1.8
Career	—	MLE		612	3	649	2679	718	183	97	0%	1.1	26.8%	6.8%	20.0%	3.6%	3.72	3.68	+4.2
/ 200 IP	—	MLE		189	1	200	826	221	56	30	0%	1.1	26.8%	6.8%	20.0%	3.6%	3.72	3.68	+1.7

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.

2019 AAASyracuse Mets · 51 IPFIP 3.41 → 3.92 (+0.51)K% 24.0% → 21.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.96%	—	×0.880	21.09%
BB% (unintentional) /BF	6.45%	—	×1.050	6.77%
HR / BF /BF	2.76%	÷0.960	×1.100	3.17%

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 92).

Result: FIP 3.41 → 3.92, 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 AAALas Vegas 51s · 8 IPFIP 1.25 → 1.47 (+0.22)K% 21.9% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.88%	—	×0.880	19.25%
BB% (unintentional) /BF	0.00%	—	×1.050	0.00%
HR / BF /BF	0.00%	÷1.009	×1.100	0.00%

Result: FIP 1.25 → 1.47, 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).

2017 AAALas Vegas 51s · 9 IPFIP 2.16 → 2.38 (+0.22)K% 33.3% → 29.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.880	29.33%
BB% (unintentional) /BF	2.78%	—	×1.050	2.92%
HR / BF /BF	2.78%	÷1.209	×1.100	2.53%

Result: FIP 2.16 → 2.38, 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).

2016 AAALas Vegas 51s · 66 IPFIP 3.31 → 3.67 (+0.35)K% 28.8% → 25.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.78%	—	×0.880	25.32%
BB% (unintentional) /BF	6.83%	—	×1.050	7.18%
HR / BF /BF	3.24%	÷1.088	×1.100	3.27%

Result: FIP 3.31 → 3.67, 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).

2015 AABinghamton Mets · 51 IPFIP 2.10 → 2.71 (+0.61)K% 28.7% → 23.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.72%	—	×0.810	23.26%
BB% (unintentional) /BF	4.62%	—	×1.100	5.08%
HR / BF /BF	1.54%	÷1.088	×1.200	1.70%

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 118).

Result: FIP 2.10 → 2.71, 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
Paul Sewald (OGILVIE)	—	—	4.66	23.2%	6.0%	4.3%	—	5.24	21.7%	14.7%	46	+0.58	—	—	—
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	71
Matt Bush R 2023 · 3.79 FIP	37	SU	3.79	26.2%	8.7%	3.3%	.225	—	—	—	—	—	2.87	249	64
Scott Atchison R 2013 · 3.80 FIP	37	SU	3.80	14.4%	6.2%	2.1%	.273	3.60	16.7%	12.9%	92	-0.20	2.52	340	63
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	62
Pete Walker R 2006 · 4.45 FIP	37	SU	4.45	19.6%	8.0%	3.6%	.337	—	—	—	—	—	4.45	327	62
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	62
Seunghwan Oh R 2019 · 5.75 FIP	37	SU	5.75	18.2%	6.8%	6.0%	.383	—	—	—	—	—	1.87	226	62
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	55
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	50
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	49
Hector Navarro R 2017 · 3.53 FIP	37	SU	3.53	11.5%	5.6%	1.3%	.283	—	—	—	—	—	2.30	126	49
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	48
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	41
Hisanori Takahashi L 2012 · 4.04 FIP	37	SU	4.04	24.5%	6.1%	4.1%	.295	—	—	—	—	—	3.50	240	39
Brian Shouse L 2006 · 4.19 FIP	37	SU	4.19	13.2%	7.5%	2.3%	.269	3.37	15.3%	11.0%	127	-0.82	2.32	339	28
Jonathan Castellanos R 2019 · 5.24 FIP	37	SW	5.24	11.0%	9.0%	3.5%	.348	—	—	—	—	—	3.40	394	28
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	23

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	2.7	55	27.0%	7.5%	1.53	4.28
Shawn Kelley ('19)	2.3	46	27.0%	7.3%	1.47	3.80
Erik Swanson ('25)	1.3	53	25.7%	6.8%	1.66	4.20
Joel Peralta ('16)	3.2	54	23.0%	7.0%	1.32	4.03
Bob Patterson ('85-'98, 4 WAR*)	3.1	51	23.0%*	6.7%*	1.32*	4.33*
Pete Ladd ('79-'86, 0 WAR*)	1.4	72	24.6%*	7.8%*	1.53*	4.42*
Cecilio Guante ('82-'90, 6 WAR*)	3.0	66	26.6%*	9.2%*	1.30*	3.85*