Ryan Helsley

P Baltimore Orioles

Age 33 RHP 6'2" / 235 lbs MLB peak 0.3 · 2027 ILInjured 60-Day (D60)

Overview

MLBRPIL-6040-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MLBSTL	65	66	2.04	2.21	29.7%	7.5%	1.9
2025	31	MLBNYM	58	56	4.50	3.89	25.0%	8.7%	0.4
2026	32	MIX2 levels	20	18	3.44	3.59	34.6%	10.3%	0.2	K 30.9% · BB 11.8% · FIP 4.30
2027PROJ	33	MLBBAL	29	30	3.81	3.81	29.9%	9.5%	0.3
2028PROJ95% MLB	34	MLBBAL	37	39	3.99	4.12	29.0%	10.0%	0.2
2029PROJ91% MLB	35	MLBBAL	27	28	4.27	4.45	28.0%	10.5%	0.0

Last 3 seasons WAR 2.5 · Next 3 projected WAR 0.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

K%90

xwOBA vs90

CompsHirokazu Sawamura (age 33) · Jim Henderson (age 33) · Joe Nelson (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10697vR 89

ERA3.81

FIP3.81

K%29.9%

BB%9.5%

K-BB%20.4%

Proj IP30

Value2027 · WAR

Runs saved vs league avg · 30 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p9098.7

Whiff%

p9037.9%

p9030.9%

xwOBA vs

p900.265

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	21	ROK MLE	Johnson City Cardinals	11	9	40	171	35	19	1	82%	3.7	13.9%	13.9%	0.0%	0.8%	2.01	4.13	—
2015	21	ROK RAW	Johnson City Cardinals	11	9	40	171	35	19	1	82%	3.7	20.5%	11.1%	9.4%	0.6%	2.01	3.10	—
2016	22	A MLE	Peoria Chiefs	17	17	95	378	109	19	3	100%	5.6	21.3%	5.9%	15.4%	1.0%	1.61	2.58	—
2016	22	A RAW	Peoria Chiefs	17	17	95	378	109	19	3	100%	5.6	28.8%	5.0%	23.8%	0.8%	1.61	1.82	—
2017	23	MIX MLE	3 levels	24	23	132	538	137	48	7	96%	5.5	20.1%	9.9%	10.2%	1.6%	2.72	3.53	—
2017	23	MIX RAW	3 levels	24	23	132	538	137	48	7	96%	5.5	25.5%	8.9%	16.5%	1.3%	2.72	2.81	—
2018	24	MIX MLE	3 levels	13	13	70	292	82	32	7	100%	5.4	23.4%	11.2%	12.2%	2.6%	3.97	3.99	—
2018	24	MIX RAW	3 levels	13	13	70	292	82	32	7	100%	5.4	28.1%	11.0%	17.1%	2.4%	3.97	3.43	—
2019	25	MIX MLE	2 levels	41	7	74	308	73	30	8	17%	1.8	22.1%	10.1%	12.0%	2.8%	3.77	4.07	+0.4
2019	25	MIX RAW	2 levels	41	7	74	308	73	30	8	17%	1.8	23.7%	9.7%	14.0%	2.6%	3.77	3.75	+0.7
2020	26	MLB MLE	St. Louis Cardinals	12	0	12	52	10	7	3	0%	1.0	19.2%	13.5%	5.8%	6.2%	5.25	6.59	-0.3
2020	26	MLB RAW	St. Louis Cardinals	12	0	12	52	10	7	3	0%	1.0	19.2%	13.5%	5.8%	5.8%	5.25	6.43	-0.3
2021	27	MLB MLE	St. Louis Cardinals	51	0	47	206	47	25	4	0%	0.9	22.8%	12.1%	10.7%	2.1%	4.56	3.86	+0.4
2021	27	MLB RAW	St. Louis Cardinals	51	0	47	206	47	25	4	0%	0.9	22.8%	12.1%	10.7%	1.9%	4.56	3.80	+0.4
2022	28	MLB MLE	St. Louis Cardinals	54	0	65	239	94	19	6	0%	1.2	39.3%	7.9%	31.4%	2.7%	1.25	2.27	+1.8
2022	28	MLB RAW	St. Louis Cardinals	54	0	65	239	94	19	6	0%	1.2	39.3%	7.9%	31.4%	2.5%	1.25	2.28	+1.8
2023	29	MIX MLE	3 levels	38	0	41	169	56	18	2	0%	1.1	35.6%	11.0%	24.7%	0.7%	3.07	1.88	+1.3
2023	29	MIX RAW	3 levels	38	0	41	169	56	18	2	0%	1.1	33.1%	10.7%	22.5%	1.2%	3.07	2.32	+1.1
2024	30	MLB MLE	St. Louis Cardinals	65	0	66	266	79	20	3	0%	1.0	29.7%	7.5%	22.2%	1.2%	2.04	2.21	+1.9
2024	30	MLB MLE	St. Louis Cardinals	65	0	66	266	79	20	3	0%	1.0	29.7%	7.5%	22.2%	1.2%	2.04	2.21	+1.9
2024	30	MLB RAW	St. Louis Cardinals	65	0	66	266	79	20	3	0%	1.0	29.7%	7.5%	22.2%	1.1%	2.04	2.21	+1.9
2025	31	MLB MLE	New York Mets	58	0	56	252	63	22	8	0%	1.0	25.0%	8.7%	16.3%	3.2%	4.50	3.84	+0.5
2025	31	MLB MLE	New York Mets	58	0	56	252	63	22	8	0%	1.0	25.0%	8.7%	16.3%	3.2%	4.50	3.84	+0.5
2025	31	MLB RAW	New York Mets	58	0	56	252	63	22	8	0%	1.0	25.0%	8.7%	16.3%	3.2%	4.50	3.89	+0.4
2026	32	MIX MLE	2 levels	20	0	18	78	27	8	3	0%	0.9	30.9%	11.8%	19.1%	4.2%	3.44	4.30	+0.1
2026	32	MIX MLE	2 levels	20	0	18	78	27	8	3	0%	0.9	30.9%	11.8%	19.1%	4.2%	3.44	4.30	+0.1
2026	32	MIX RAW	2 levels	20	0	18	78	27	8	3	0%	0.9	34.6%	10.3%	24.4%	3.8%	3.44	3.59	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			314	0	335	1382	398	127	33	0%	1.1	28.8%	9.2%	19.6%	2.4%	3.01	3.14	+5.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	BAL	29	0	30	127	38	12	4	0%	1.0	29.9%	9.5%	20.4%	3.0%	3.81	3.81	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	BAL	39	0	41	175	51	17	6	0%	1.1	29.0%	10.0%	19.0%	3.3%	3.99	4.12	+0.2
2029	35	MLB MLE	BAL	30	0	31	131	37	14	5	0%	1.0	28.0%	10.5%	17.5%	3.7%	4.27	4.45	+0.0
2028	34	MLB CTX	BAL	37	0	39	165	48	16	5	0%	1.1	29.0%	10.0%	19.0%	3.3%	3.99	4.12	+0.2
2029	35	MLB CTX	BAL	27	0	28	118	33	12	4	0%	1.0	28.0%	10.5%	17.5%	3.7%	4.27	4.45	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		378	0	403	1668	480	156	43	0%	1.1	28.8%	9.4%	19.4%	2.6%	3.19	3.27	+6.1
/ 200 IP	—	RAW		188	0	200	828	238	77	21	0%	1.1	28.8%	9.4%	19.4%	2.6%	3.19	3.27	+3.1
Career	—	MLE		404	69	718	2949	726	284	60	17%	1.8	24.6%	9.6%	15.0%	2.0%	2.91	3.35	+5.9
/ 200 IP	—	MLE		113	19	200	822	202	79	17	17%	1.8	24.6%	9.6%	15.0%	2.0%	2.91	3.35	+2.9

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 AASpringfield Cardinals · 4 IPFIP 4.79 → 5.48 (+0.69)K% 20.0% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.810	16.20%
BB% (unintentional) /BF	5.00%	—	×1.100	5.50%
HR / BF /BF	5.00%	÷1.080	×1.200	5.56%

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 116).

Result: FIP 4.79 → 5.48, 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).

2019 AAAMemphis Redbirds · 37 IPFIP 3.56 → 4.04 (+0.48)K% 26.5% → 23.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.45%	—	×0.880	23.28%
BB% (unintentional) /BF	12.90%	—	×1.050	13.55%
HR / BF /BF	1.94%	÷0.982	×1.100	2.17%

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 96).

Result: FIP 3.56 → 4.04, 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 AASpringfield Cardinals · 41 IPFIP 4.00 → 4.53 (+0.53)K% 25.3% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.29%	—	×0.810	20.48%
BB% (unintentional) /BF	11.49%	—	×1.100	12.64%
HR / BF /BF	2.87%	÷1.215	×1.200	2.84%

Result: FIP 4.00 → 4.53, 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 AAAMemphis Redbirds · 27 IPFIP 2.50 → 3.10 (+0.60)K% 32.1% → 28.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.08%	—	×0.880	28.23%
BB% (unintentional) /BF	8.49%	—	×1.050	8.92%
HR / BF /BF	1.89%	÷0.909	×1.100	2.28%

Result: FIP 2.50 → 3.10, 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 A+Palm Beach Cardinals · 94 IPFIP 2.50 → 3.35 (+0.85)K% 24.3% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.33%	—	×0.770	18.74%
BB% (unintentional) /BF	8.02%	—	×1.150	9.22%
HR / BF /BF	0.80%	÷0.824	×1.250	1.22%

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 65).

Result: FIP 2.50 → 3.35, 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 AASpringfield Cardinals · 34 IPFIP 3.55 → 4.01 (+0.47)K% 29.1% → 23.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.08%	—	×0.810	23.55%
BB% (unintentional) /BF	10.64%	—	×1.100	11.70%
HR / BF /BF	2.84%	÷1.315	×1.200	2.59%

Result: FIP 3.55 → 4.01, 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 AAAMemphis Redbirds · 5 IPFIP 2.92 → 3.22 (+0.30)K% 21.7% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.880	19.13%
BB% (unintentional) /BF	13.04%	—	×1.050	13.70%
HR / BF /BF	0.00%	÷0.980	×1.100	0.00%

Result: FIP 2.92 → 3.22, 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 APeoria Chiefs · 95 IPFIP 1.74 → 2.58 (+0.84)K% 28.8% → 21.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.84%	—	×0.740	21.34%
BB% (unintentional) /BF	5.03%	—	×1.180	5.93%
HR / BF /BF	0.79%	÷1.066	×1.300	0.97%

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 113).

Result: FIP 1.74 → 2.58, 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
Ryan Helsley (OGILVIE)	—	—	—	—	—	3.0%	—	4.26	28.6%	18.4%	72	+0.45	—	—	—
Hirokazu Sawamura R 2021 · 4.48 FIP	33	SU	4.48	26.2%	11.2%	4.0%	.263	4.02	18.1%	6.8%	51	-0.46	4.02	104	66
Jim Henderson R 2016 · 4.55 FIP	33	SU	4.55	25.8%	9.0%	4.5%	.287	—	—	—	—	—	1.86	167	65
Joe Nelson R 2008 · 3.08 FIP	33	SU	3.08	26.1%	7.8%	2.2%	.252	5.35	19.8%	5.5%	40	+2.27	3.08	139	64
Huascar Brazobán R 2023 · 3.52 FIP	33	SU	3.52	25.3%	11.3%	2.1%	.304	3.54	21.9%	11.8%	195	+0.02	3.08	370	64
Brendan Donnelly R 2004 · 3.05 FIP	33	SU	3.05	32.6%	8.7%	2.9%	.302	3.88	19.0%	11.8%	150	+0.83	2.23	385	62
Jon Edwards R 2019 · 4.43 FIP	31	SU	4.43	25.1%	11.6%	3.6%	.290	—	—	—	—	—	2.40	132	60
Tony Barnette R 2017 · 3.56 FIP	33	SU	3.56	22.6%	7.1%	2.7%	.335	2.61	25.0%	20.2%	26	-0.95	2.61	144	59
Jumbo Diaz R 2017 · 4.17 FIP	33	SU	4.17	20.0%	9.7%	2.8%	.302	2.90	24.2%	15.4%	98	-1.27	2.80	319	58
Arcenio Leon R 2019 · 3.61 FIP	32	SU	3.61	21.4%	8.1%	2.4%	.314	—	—	—	—	—	3.00	115	58
Marcos Mateo R 2015 · 3.12 FIP	31	SU	3.12	28.0%	8.0%	2.5%	.247	—	—	—	—	—	3.12	104	57
Spencer Patton R 2021 · 3.04 FIP	33	SU	3.04	27.9%	8.7%	2.2%	.305	4.97	21.2%	10.4%	88	+1.93	2.79	239	57
James Hoyt R 2020 · 2.74 FIP	33	SU	2.74	32.3%	11.3%	1.7%	.235	6.83	17.8%	6.0%	35	+4.09	1.61	312	56
Tim Dillard R 2016 · 3.13 FIP	33	SU	3.13	22.6%	8.2%	1.7%	.358	4.14	13.7%	6.9%	250	+1.01	2.92	432	56
Dustin Molleken R 2017 · 3.38 FIP	32	SU	3.38	24.3%	12.4%	1.4%	.311	—	—	—	—	—	3.38	129	55
Adrian Ramirez R 2019 · 4.29 FIP	31	SU	4.29	21.7%	6.3%	4.0%	.327	—	—	—	—	—	3.04	137	55
Zack Segovia R 2016 · 3.33 FIP	33	SU	3.33	20.2%	5.9%	2.2%	.312	4.55	17.6%	4.0%	57	+1.22	3.33	144	55
Scott McGough R 2023 · 4.57 FIP	33	SU	4.57	28.6%	9.3%	4.9%	.266	4.23	19.4%	9.4%	126	-0.34	3.55	429	55
Akinori Otsuka R 2005 · 3.05 FIP	33	SU	3.05	21.7%	9.4%	1.1%	.278	2.59	19.4%	14.2%	92	-0.46	2.39	232	55
Michael Kohn R 2019 · 3.34 FIP	33	SU	3.34	36.1%	8.4%	4.0%	.244	—	—	—	—	—	3.32	133	55
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	54

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	2.2	49	28.8%	10.1%	1.01	3.25
Mychal Givens ('23)	2.3	58	28.2%	10.0%	1.05	3.47
Mike Gonzalez ('03-'13, 7 WAR*)	2.2	49	29.1%*	10.7%*	0.94*	3.18*
Cody Allen ('19)	2.3	58	30.5%	9.9%	1.07	3.14
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Rob Murphy ('85-'95, 6 WAR*)	3.1	69	25.2%*	9.2%*	1.07*	3.94*
Randy Myers ('85-'98, 16 WAR*)	4.4	74	29.4%*	10.4%*	0.92*	3.47*