Jonathan Russell

P Bowling Green Hot Rods

Age 22 RHP 6'0" / 180 lbs A+ peak 0.3 · 2030

Overview

A+PROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	19	ROKTB	12	6	31	6.89	3.36	20.9%	12.8%	—	K 14.2% · BB 16.0% · FIP 4.45
2025	20	MIX2 levels	28	0	40	2.45	1.54	29.6%	6.8%	—	K 22.5% · BB 8.6% · FIP 2.29
2026	21	MIX2 levels	31	3	50	3.99	3.02	31.1%	8.2%	—	K 23.0% · BB 9.4% · FIP 4.33
2027PROJ	22	MLBTB	24	0	25	4.41	4.41	22.8%	9.7%	0.0
2028PROJ49% MLB	23	MLBTB	22	0	23	4.31	4.25	23.6%	9.1%	0.1
2029PROJ70% MLB	24	MLBTB	31	0	33	4.21	4.15	24.1%	8.8%	0.2

Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0 WAR median (P10 -0 / P90 2, comp-based) · star 0% · didn't stick 91%

CompsKyle Zurak (age 22) · Cody Palmquist (age 22) · Jonathan Papelbon (age 24)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.41

FIP4.41

K%22.8%

BB%9.7%

K-BB%13.1%

Proj IP25

Value2027 · WAR

Runs saved vs league avg · 25 IP

Grades · Contract

Scouting Grades

K†5560

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 industry-standard fWAR.

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)
2023	18	ROK MLE	DSL Tampa Bay	11	11	36	145	34	14	0	100%	3.3	15.9%	12.1%	3.9%	0.0%	1.00	3.28	—
2023	18	ROK RAW	DSL Tampa Bay	11	11	36	145	34	14	0	100%	3.3	23.4%	9.7%	13.8%	0.0%	1.00	2.38	—
2024	19	ROK MLE	FCL Rays	12	6	31	148	31	19	1	50%	2.6	14.2%	16.0%	-1.8%	0.9%	6.89	4.45	—
2024	19	ROK MLE	FCL Rays	12	6	31	148	31	19	1	50%	2.6	14.2%	16.0%	-1.8%	0.9%	6.89	4.45	—
2024	19	ROK RAW	FCL Rays	12	6	31	148	31	19	1	50%	2.6	20.9%	12.8%	8.1%	0.7%	6.89	3.36	—
2025	20	MIX MLE	2 levels	28	0	40	162	48	11	0	0%	1.4	22.5%	8.6%	13.9%	0.0%	2.45	2.29	—
2025	20	MIX MLE	2 levels	28	0	40	162	48	11	0	0%	1.4	22.5%	8.6%	13.9%	0.0%	2.45	2.29	—
2025	20	MIX RAW	2 levels	28	0	40	162	48	11	0	0%	1.4	29.6%	6.8%	22.8%	0.0%	2.45	1.54	—
2026	21	MIX MLE	2 levels	31	3	50	219	68	18	6	10%	1.6	23.0%	9.4%	13.6%	3.6%	3.99	4.33	—
2026	21	MIX MLE	2 levels	31	3	50	219	68	18	6	10%	1.6	23.0%	9.4%	13.6%	3.6%	3.99	4.33	—
2026	21	MIX RAW	2 levels	31	3	50	219	68	18	6	10%	1.6	31.1%	8.2%	22.8%	2.7%	3.99	3.02	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	22	MLB	TB	24	0	25	106	24	10	3	0%	1.0	22.8%	9.7%	13.1%	3.0%	4.41	4.41	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	23	MLB MLE	TB	44	0	46	196	46	18	6	0%	1.1	23.6%	9.1%	14.5%	2.9%	4.31	4.25	+0.2
2029	24	MLB MLE	TB	45	0	48	201	48	18	6	0%	1.1	24.1%	8.8%	15.3%	2.9%	4.21	4.15	+0.2
2030	25	MLB MLE	TB	46	0	48	204	50	18	6	0%	1.0	24.5%	8.6%	15.8%	2.9%	4.14	4.09	+0.3
2031	26	MLB MLE	TB	46	0	48	204	50	17	6	0%	1.0	24.6%	8.6%	16.1%	2.9%	4.11	4.08	+0.3
2032	27	MLB MLE	TB	45	0	48	201	49	17	6	0%	1.1	24.6%	8.7%	16.0%	2.9%	4.11	4.12	+0.2
2028	23	MLB CTX	TB	22	0	23	97	23	9	3	0%	1.0	23.6%	9.1%	14.5%	2.9%	4.31	4.25	+0.1
2029	24	MLB CTX	TB	31	0	33	139	34	12	4	0%	1.1	24.1%	8.8%	15.3%	2.9%	4.21	4.15	+0.2
2030	25	MLB CTX	TB	37	0	39	165	40	14	5	0%	1.1	24.5%	8.6%	15.8%	2.9%	4.14	4.09	+0.2
2031	26	MLB CTX	TB	40	0	42	177	44	15	5	0%	1.1	24.6%	8.6%	16.1%	2.9%	4.11	4.08	+0.2
2032	27	MLB CTX	TB	41	0	43	182	45	16	5	0%	1.0	24.6%	8.7%	16.0%	2.9%	4.11	4.12	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		172	0	180	762	186	66	22	0%	1.0	24.4%	8.7%	15.7%	2.9%	4.16	3.75	+1.7
/ 200 IP	—	RAW		190	0	200	844	206	73	24	0%	1.0	24.4%	8.7%	15.7%	2.9%	4.16	3.75	+1.9
Career	—	MLE		82	20	157	674	131	76	9	24%	1.9	19.4%	11.2%	8.2%	1.4%	3.49	3.64	+1.2
/ 200 IP	—	MLE		104	25	200	857	167	96	12	24%	1.9	19.4%	11.2%	8.2%	1.4%	3.49	3.64	+1.0

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
Jonathan Russell (OGILVIE)	—	—	4.41	22.8%	9.7%	3.0%	—	4.16	24.1%	15.2%	142	-0.25	—	—	—
Kyle Zurak R 2017 · 3.96 FIP	22	SU	3.96	21.2%	9.4%	2.6%	.302	3.42	16.7%	7.5%	121	-0.54	3.20	181	88
Cody Palmquist R 2016 · 4.31 FIP	22	SU	4.31	21.9%	9.0%	3.5%	.278	6.02	14.7%	7.1%	62	+1.71	4.31	104	88
Jonathan Papelbon R 2005 · 3.93 FIP	24	SW	3.93	23.0%	10.1%	2.7%	.305	1.90	31.3%	26.8%	196	-2.03	1.33	726	86
Parker Curry R 2017 · 3.73 FIP	23	SU	3.73	22.5%	8.2%	2.7%	.341	4.62	18.6%	11.6%	193	+0.89	3.73	256	84
Addison Russ R 2017 · 3.62 FIP	22	SU	3.62	22.2%	7.9%	2.5%	.324	3.09	25.6%	17.5%	121	-0.53	3.08	194	83
Heath Holder R 2016 · 4.16 FIP	23	SU	4.16	21.1%	10.1%	2.8%	.287	3.30	22.3%	13.1%	247	-0.86	2.94	367	83
Michael Gomez R 2019 · 3.32 FIP	22	SU	3.32	22.6%	9.2%	1.8%	.283	3.81	17.9%	8.3%	110	+0.49	3.27	286	83
Brendan Collins R 2022 · 3.67 FIP	22	SW	3.67	21.8%	10.3%	2.0%	.294	3.86	20.2%	5.1%	54	+0.19	3.67	122	83
Todd Peterson R 2021 · 5.11 FIP	23	SW	5.11	18.4%	10.3%	4.1%	.279	4.83	15.0%	4.3%	124	-0.28	4.45	175	83
Brandon Beckel R 2024 · 3.75 FIP	22	SU	3.75	19.4%	10.3%	1.8%	.250	4.23	19.5%	10.5%	147	+0.48	3.75	194	82
Tyler Mitzel R 2019 · 4.34 FIP	23	SU	4.34	24.1%	12.0%	3.2%	.245	4.10	22.4%	14.3%	96	-0.24	4.03	142	82
Zac Ryan R 2017 · 3.83 FIP	23	SU	3.83	20.5%	10.9%	2.0%	.233	3.93	21.6%	6.9%	124	+0.10	3.83	184	82
Ryan Conroy R 2021 · 4.01 FIP	24	SU	4.01	22.2%	9.8%	2.8%	.353	4.75	19.3%	6.7%	54	+0.74	4.01	106	82
Tyler Bray R 2015 · 4.07 FIP	23	SU	4.07	22.3%	9.1%	3.1%	.356	3.21	17.9%	7.6%	114	-0.86	2.82	152	82
Austin Hutchison R 2017 · 4.05 FIP	22	SW	4.05	19.7%	6.0%	3.4%	.340	4.55	18.7%	9.2%	213	+0.50	3.51	235	82
David Garner R 2015 · 3.81 FIP	22	SU	3.81	21.7%	10.0%	2.3%	.282	4.52	19.8%	5.2%	98	+0.71	3.81	154	82
Troy Bacon R 2019 · 4.31 FIP	22	SU	4.31	21.8%	10.0%	3.3%	.349	4.85	18.2%	5.7%	83	+0.54	3.34	203	81
Garrett Mock R 2008 · 3.57 FIP	25	SU	3.57	25.6%	11.1%	2.2%	.300	4.06	17.1%	7.4%	91	+0.49	3.57	132	81
Kyle Zimmer R 2015 · 3.74 FIP	23	SW	3.74	21.8%	8.6%	2.5%	.293	4.55	20.4%	9.0%	33	+0.81	2.08	276	81
Chris Gau R 2018 · 4.76 FIP	21	SU	4.76	20.0%	8.6%	4.1%	.290	3.19	29.5%	23.5%	37	-1.57	2.96	211	81

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 1,474 reliever careers (754 from 1990-2025 + 720 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	1.3	44	24.2%	8.8%	1.13	4.20
Charlie Furbush ('15)	1.3	52	24.5%	8.1%	1.04	3.97
Yency Almonte ('24)	1.1	45	22.5%	9.9%	1.13	4.44
Michael Tonkin ('25)	1.6	55	23.2%	8.1%	1.28	4.23
Dave Baldwin ('66-'73, 2 WAR*)	1.1	56	22.2%*	9.2%*	0.96*	3.97*
Barney Schultz ('55-'65, 2 WAR*)	1.7	58	24.6%*	8.3%*	1.28*	3.93*
Carlos Diaz ('82-'86, 2 WAR*)	1.3	52	25.1%*	8.7%*	0.91*	3.84*