Shawn Armstrong

P Cleveland Guardians

Age 36 RHP 6'2" / 225 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	33	MLBCHC	57	7	67	4.86	3.42	22.4%	8.5%	0.9
2025	34	MLBTEX	71	2	74	2.31	2.79	26.1%	7.0%	1.6
2026	35	MIX3 levels	47	1	41	4.83	3.71	23.6%	10.7%	0.4	K 22.1% · BB 11.7% · FIP 4.15
2027PROJ	36	MLBCLE	35	0	37	3.99	4.10	23.2%	9.3%	0.2
2028PROJ	37	MLBCLE	19	0	20	4.30	4.51	22.1%	10.0%	0.0

Last 3 seasons WAR 2.9 · Next 2 projected WAR 0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%50

xwOBA vs47

FB Velo36

CompsKazuhiro Sasaki (age 35) · Keiichi Yabu (age 36) · Juan Sandoval (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 110102vR 94

ERA3.99

FIP4.10

K%23.2%

BB%9.3%

K-BB%13.9%

Proj IP37

Value2027 · WAR

Runs saved vs league avg · 37 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p3693.5

Whiff%

p3923.7%

p5022.1%

xwOBA vs

p470.315

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	24	MIX MLE	2 levels	54	0	58	240	91	26	1	0%	1.1	33.5%	12.0%	21.5%	0.0%	2.34	1.79	+1.9
2015	24	MIX RAW	2 levels	54	0	58	240	91	26	1	0%	1.1	37.9%	10.8%	27.1%	0.4%	2.34	1.52	+2.1
2016	25	MIX MLE	3 levels	59	0	62	255	84	31	1	0%	1.0	31.1%	14.4%	16.6%	0.0%	1.90	2.30	+1.7
2016	25	MIX RAW	3 levels	59	0	62	255	84	31	1	0%	1.0	32.9%	12.2%	20.8%	0.4%	1.90	2.09	+1.8
2017	26	MIX MLE	2 levels	49	0	54	234	56	21	8	0%	1.1	22.1%	9.2%	12.9%	3.3%	3.67	4.21	+0.2
2017	26	MIX RAW	2 levels	49	0	54	234	56	21	8	0%	1.1	23.9%	9.0%	15.0%	3.4%	3.67	4.12	+0.3
2018	27	MIX MLE	2 levels	63	0	71	287	97	28	4	0%	1.1	30.4%	10.2%	20.2%	1.6%	1.66	2.71	+1.6
2018	27	MIX RAW	2 levels	63	0	71	287	97	28	4	0%	1.1	33.8%	9.8%	24.0%	1.4%	1.66	2.28	+1.9
2019	28	MIX MLE	2 levels	57	1	60	279	66	28	8	2%	1.1	23.2%	10.3%	12.9%	2.7%	5.55	3.91	+0.5
2019	28	MIX RAW	2 levels	57	1	60	279	66	28	8	2%	1.1	23.7%	10.0%	13.6%	2.9%	5.55	4.03	+0.4
2020	29	MLB MLE	Baltimore Orioles	14	0	15	57	14	3	1	0%	1.1	24.6%	5.3%	19.3%	1.6%	1.80	2.56	+0.4
2020	29	MLB RAW	Baltimore Orioles	14	0	15	57	14	3	1	0%	1.1	24.6%	5.3%	19.3%	1.8%	1.80	2.70	+0.3
2021	30	MIX MLE	2 levels	60	0	67	297	85	23	14	0%	1.1	27.0%	7.9%	19.1%	4.9%	4.97	4.49	+0.0
2021	30	MIX RAW	2 levels	60	0	67	297	85	23	14	0%	1.1	28.6%	7.7%	20.9%	4.7%	4.97	4.31	+0.2
2022	31	MIX MLE	2 levels	57	3	69	296	76	17	7	5%	1.2	24.5%	5.6%	19.0%	2.7%	4.19	3.20	+1.1
2022	31	MIX RAW	2 levels	57	3	69	296	76	17	7	5%	1.2	25.7%	5.7%	19.9%	2.4%	4.19	2.95	+1.3
2023	32	MIX MLE	2 levels	46	6	61	238	62	13	3	13%	1.3	26.1%	5.3%	20.8%	1.0%	1.48	2.12	+1.8
2023	32	MIX RAW	2 levels	46	6	61	238	62	13	3	13%	1.3	26.1%	5.5%	20.6%	1.3%	1.48	2.35	+1.6
2024	33	MLB MLE	Chicago Cubs	57	7	67	295	66	25	6	12%	1.2	22.4%	8.5%	13.9%	2.1%	4.86	3.44	+0.9
2024	33	MLB MLE	Chicago Cubs	57	7	67	295	66	25	6	12%	1.2	22.4%	8.5%	13.9%	2.1%	4.86	3.44	+0.9
2024	33	MLB RAW	Chicago Cubs	57	7	67	295	66	25	6	12%	1.2	22.4%	8.5%	13.9%	2.0%	4.86	3.42	+0.9
2025	34	MLB MLE	Texas Rangers	71	2	74	284	74	20	5	3%	1.0	26.1%	7.0%	19.0%	1.6%	2.31	2.69	+1.6
2025	34	MLB MLE	Texas Rangers	71	2	74	284	74	20	5	3%	1.0	26.1%	7.0%	19.0%	1.6%	2.31	2.69	+1.6
2025	34	MLB RAW	Texas Rangers	71	2	74	284	74	20	5	3%	1.0	26.1%	7.0%	19.0%	1.8%	2.31	2.79	+1.6
2026	35	MIX MLE	3 levels	47	1	41	178	42	19	4	2%	0.9	22.1%	11.7%	10.4%	2.6%	4.83	4.15	+0.2
2026	35	MIX MLE	3 levels	47	1	41	178	42	19	4	2%	0.9	22.1%	11.7%	10.4%	2.6%	4.83	4.15	+0.2
2026	35	MIX RAW	3 levels	47	1	41	178	42	19	4	2%	0.9	23.6%	10.7%	12.9%	2.2%	4.83	3.71	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			411	18	457	1941	468	152	51	4%	1.1	24.1%	7.8%	16.3%	2.6%	3.88	3.50	+5.7
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	CLE	35	0	37	156	36	15	4	0%	1.1	23.2%	9.3%	13.9%	2.6%	3.99	4.10	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	CLE	19	0	20	83	18	8	2	0%	1.0	22.1%	10.0%	12.2%	3.0%	4.30	4.51	+0.0
2028	37	MLB CTX	CLE	19	0	20	84	19	8	3	0%	1.1	22.1%	10.0%	12.2%	3.0%	4.30	4.51	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		430	18	476	2024	486	160	53	4%	1.1	24.0%	7.9%	16.1%	2.6%	3.90	3.52	+5.8
/ 200 IP	—	RAW		180	8	200	850	204	67	22	4%	1.1	24.0%	7.9%	16.1%	2.6%	3.90	3.52	+2.4
Career	—	MLE		634	20	697	2940	771	267	61	3%	1.1	26.2%	9.1%	17.2%	2.1%	3.34	3.17	+5.7
/ 200 IP	—	MLE		182	6	200	843	221	76	17	3%	1.1	26.2%	9.1%	17.2%	2.1%	3.34	3.17	+2.4

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.

2023 AAADurham Bulls · 9 IPFIP 3.51 → 3.67 (+0.17)K% 25.8% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.81%	—	×0.860	22.19%
BB% (unintentional) /BF	6.45%	—	×0.900	5.81%
HR / BF /BF	3.23%	÷1.137	×1.100	3.12%

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 127).

Result: FIP 3.51 → 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).

2022 AAADurham Bulls · 7 IPFIP 0.91 → 1.33 (+0.42)K% 37.0% → 32.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.04%	—	×0.880	32.59%
BB% (unintentional) /BF	7.41%	—	×1.050	7.78%
HR / BF /BF	0.00%	÷1.034	×1.100	0.00%

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 0.91 → 1.33, 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 AAADurham Bulls · 31 IPFIP 2.91 → 3.33 (+0.42)K% 31.1% → 27.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.06%	—	×0.880	27.33%
BB% (unintentional) /BF	6.06%	—	×1.050	6.36%
HR / BF /BF	3.03%	÷1.059	×1.100	3.15%

Result: FIP 2.91 → 3.33, 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 AAATacoma Rainiers · 56 IPFIP 2.24 → 2.81 (+0.57)K% 35.7% → 31.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.65%	—	×0.880	31.37%
BB% (unintentional) /BF	11.30%	—	×1.050	11.87%
HR / BF /BF	1.30%	÷0.918	×1.100	1.56%

Result: FIP 2.24 → 2.81, 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 AAAColumbus Clippers · 29 IPFIP 3.10 → 3.31 (+0.21)K% 28.6% → 25.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.880	25.14%
BB% (unintentional) /BF	8.73%	—	×1.050	9.17%
HR / BF /BF	2.38%	÷1.231	×1.100	2.13%

Result: FIP 3.10 → 3.31, 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 AAAColumbus Clippers · 49 IPFIP 1.86 → 2.30 (+0.44)K% 35.3% → 31.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.29%	—	×0.880	31.06%
BB% (unintentional) /BF	13.73%	—	×1.050	14.41%
HR / BF /BF	0.00%	÷1.113	×1.100	0.00%

Result: FIP 1.86 → 2.30, 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 AAAColumbus Clippers · 50 IPFIP 1.33 → 1.79 (+0.46)K% 38.1% → 33.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.10%	—	×0.880	33.52%
BB% (unintentional) /BF	11.43%	—	×1.050	12.00%
HR / BF /BF	0.00%	÷1.217	×1.100	0.00%

Result: FIP 1.33 → 1.79, 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).

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
Shawn Armstrong (OGILVIE)	—	—	—	—	—	2.6%	—	4.51	22.1%	12.2%	20	+0.41	—	—	—
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	75
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	72
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	71
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	70
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	68
Peter Moylan R 2015 · 3.11 FIP	36	SU	3.11	18.4%	8.2%	0.9%	.259	3.84	18.2%	9.1%	145	+0.73	2.54	431	67
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	65
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	63
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	63
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	61
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	61
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	60
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	58
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	57
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	56
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	55
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	53
Pete Walker R 2005 · 4.79 FIP	36	SU	4.79	12.0%	9.2%	2.8%	.261	4.45	19.6%	11.6%	30	-0.34	4.45	327	50
Tony Pena Jr. R 2017 · 3.43 FIP	36	SU	3.43	10.9%	4.3%	1.3%	.312	—	—	—	—	—	2.80	128	50
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	49

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	2.6	51	24.0%	8.3%	1.02	3.91
Fernando Salas ('19)	2.4	54	23.1%	7.3%	1.05	3.91
Ryan Tepera ('23)	1.8	52	24.3%	8.8%	1.13	3.62
Héctor Rondón ('20)	2.2	54	24.2%	7.4%	1.03	3.49
Rob Murphy ('85-'95, 6 WAR*)	3.1	69	25.2%*	9.2%*	1.07*	3.94*
Tom Niedenfuer ('81-'90, 8 WAR*)	3.3	65	23.1%*	8.1%*	1.19*	3.80*
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*