Tanner Rainey

P Toledo Mud Hens

Age 34 RHP 6'3" / 245 lbs AAA peak 0.3 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	31	MLBWSH	50	0	51	4.76	5.06	19.0%	12.1%	-0.3
2025	32	MIX2 levels	49	2	50	4.47	2.68	30.1%	14.8%	1.1	K 27.5% · BB 12.2% · FIP 2.74
2026	33	MIX2 levels	51	1	55	3.46	2.70	30.9%	15.7%	1.2	K 28.1% · BB 12.7% · FIP 2.63
2027PROJ	34	MLBDET	53	0	56	4.41	4.31	26.5%	13.6%	0.2
2028PROJ95% MLB	35	MLBDET	36	0	38	4.55	4.64	25.5%	14.1%	-0.0
2029PROJ86% MLB	36	MLBDET	25	0	26	4.83	5.01	24.5%	14.8%	-0.1

Last 3 seasons WAR 2.0 · Next 3 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff50Control30Command30FB velo50

CompsDustin Molleken (age 32) · Hirokazu Sawamura (age 34) · Matt Miller (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 115107vR 99

ERA4.41

FIP4.31

K%26.5%

BB%13.6%

K-BB%12.9%

Proj IP56

Value2027 · WAR

Runs saved vs league avg · 56 IP

Grades · Contract

Scouting Grades

STUF5050

CTRL3030

CMD3030

K†6060

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	22	ROK MLE	Billings Mustangs	15	15	59	262	57	28	2	100%	3.9	14.8%	13.4%	1.4%	1.1%	4.27	4.13	—
2015	22	ROK RAW	Billings Mustangs	15	15	59	262	57	28	2	100%	3.9	21.8%	10.7%	11.1%	0.8%	4.27	3.03	—
2016	23	A MLE	Dayton Dragons	29	20	103	480	113	66	9	69%	3.6	17.4%	16.2%	1.2%	2.1%	5.57	4.84	—
2016	23	A RAW	Dayton Dragons	29	20	103	480	113	66	9	69%	3.6	23.5%	13.8%	9.8%	1.9%	5.57	3.96	—
2017	24	MIX MLE	2 levels	53	0	62	251	104	33	6	0%	1.2	32.3%	14.9%	17.4%	2.8%	3.19	3.81	—
2017	24	MIX RAW	2 levels	53	0	62	251	104	33	6	0%	1.2	41.4%	13.1%	28.3%	2.4%	3.19	2.60	—
2018	25	MIX MLE	2 levels	52	0	58	252	72	45	6	0%	1.1	27.6%	17.2%	10.4%	1.1%	5.28	3.54	+0.7
2018	25	MIX RAW	2 levels	52	0	58	252	72	45	6	0%	1.1	28.6%	17.9%	10.7%	2.4%	5.28	4.29	+0.2
2019	26	MIX MLE	2 levels	68	0	66	296	106	48	7	0%	1.0	34.5%	16.4%	18.1%	2.4%	3.93	3.59	+0.8
2019	26	MIX RAW	2 levels	68	0	66	296	106	48	7	0%	1.0	35.8%	16.2%	19.6%	2.4%	3.93	3.45	+0.9
2020	27	MLB MLE	Washington Nationals	20	0	20	75	32	6	4	0%	1.0	42.7%	8.0%	34.7%	5.3%	2.66	3.44	+0.3
2020	27	MLB RAW	Washington Nationals	20	0	20	75	32	6	4	0%	1.0	42.7%	8.0%	34.7%	5.3%	2.66	3.40	+0.3
2021	28	MIX MLE	2 levels	46	1	39	182	57	29	7	2%	0.9	27.8%	15.9%	11.9%	4.0%	6.41	4.95	-0.2
2021	28	MIX RAW	2 levels	46	1	39	182	57	29	7	2%	0.9	31.3%	15.9%	15.4%	3.8%	6.41	4.73	-0.1
2022	29	MLB MLE	Washington Nationals	29	0	30	128	36	13	5	0%	1.0	28.1%	10.2%	18.0%	3.9%	3.30	4.16	+0.1
2022	29	MLB RAW	Washington Nationals	29	0	30	128	36	13	5	0%	1.0	28.1%	10.2%	18.0%	3.9%	3.30	4.17	+0.1
2023	30	MIX MLE	5 levels	14	3	13	56	14	7	1	21%	0.9	25.0%	12.5%	12.5%	1.8%	3.46	3.55	+0.2
2023	30	MIX RAW	5 levels	14	3	13	56	14	7	1	21%	0.9	25.0%	12.5%	12.5%	1.8%	3.46	3.56	+0.2
2024	31	MLB MLE	Washington Nationals	50	0	51	231	44	28	8	0%	1.0	19.0%	12.1%	6.9%	3.5%	4.76	4.93	-0.2
2024	31	MLB MLE	Washington Nationals	50	0	51	231	44	28	8	0%	1.0	19.0%	12.1%	6.9%	3.5%	4.76	4.93	-0.2
2024	31	MLB RAW	Washington Nationals	50	0	51	231	44	28	8	0%	1.0	19.0%	12.1%	6.9%	3.5%	4.76	5.06	-0.3
2025	32	MIX MLE	2 levels	49	2	50	216	65	32	1	4%	1.0	27.5%	12.2%	15.2%	0.7%	4.47	2.74	+1.1
2025	32	MIX MLE	2 levels	49	2	50	216	65	32	1	4%	1.0	27.5%	12.2%	15.2%	0.7%	4.47	2.74	+1.1
2025	32	MIX RAW	2 levels	49	2	50	216	65	32	1	4%	1.0	30.1%	14.8%	15.3%	0.5%	4.47	2.68	+1.1
2026	33	MIX MLE	2 levels	51	1	55	236	73	37	1	2%	1.1	28.1%	12.7%	15.4%	0.5%	3.46	2.63	+1.3
2026	33	MIX MLE	2 levels	51	1	55	236	73	37	1	2%	1.1	28.1%	12.7%	15.4%	0.5%	3.46	2.63	+1.3
2026	33	MIX RAW	2 levels	51	1	55	236	73	37	1	2%	1.1	30.9%	15.7%	15.3%	0.4%	3.46	2.70	+1.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			217	0	206	927	253	136	33	0%	0.9	27.3%	14.7%	12.6%	3.6%	5.46	4.71	-0.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	DET	53	0	56	236	63	32	6	0%	1.1	26.5%	13.6%	12.9%	2.4%	4.41	4.31	+0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	DET	38	0	40	171	44	24	5	0%	1.1	25.5%	14.1%	11.4%	2.8%	4.55	4.64	-0.0
2029	36	MLB MLE	DET	29	0	30	127	31	19	4	0%	1.0	24.5%	14.8%	9.8%	3.2%	4.83	5.01	-0.2
2028	35	MLB CTX	DET	36	0	38	160	41	23	4	0%	1.1	25.5%	14.1%	11.4%	2.8%	4.55	4.64	-0.0
2029	36	MLB CTX	DET	25	0	26	110	27	16	3	0%	1.0	24.5%	14.8%	9.8%	3.2%	4.83	5.01	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		278	0	270	1199	321	175	41	0%	1.0	26.8%	14.6%	12.2%	3.4%	5.27	4.56	-0.0
/ 200 IP	—	RAW		206	0	200	887	237	129	30	0%	1.0	26.8%	14.6%	12.2%	3.4%	5.27	4.56	-0.0
Career	—	MLE		476	42	607	2665	678	382	58	9%	1.3	25.4%	14.3%	11.1%	2.2%	4.45	3.96	-0.6
/ 200 IP	—	MLE		157	14	200	878	223	126	19	9%	1.3	25.4%	14.3%	11.1%	2.2%	4.45	3.96	-0.4

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.

2026 AAAToledo Mud Hens · 48 IPFIP 2.40 → 2.63 (+0.23)K% 32.7% → 28.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.68%	—	×0.860	28.11%
BB% (unintentional) /BF	14.15%	—	×0.900	12.73%
HR / BF /BF	0.49%	÷1.000	×1.100	0.54%

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 2.40 → 2.63, 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 · 41 IPFIP 2.45 → 2.74 (+0.29)K% 32.0% → 27.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.95%	—	×0.860	27.48%
BB% (unintentional) /BF	13.61%	—	×0.900	12.25%
HR / BF /BF	0.59%	÷0.872	×1.100	0.75%

Result: FIP 2.45 → 2.74, 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 AAARochester Red Wings · 8 IPFIP 4.29 → 4.49 (+0.21)K% 25.0% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.860	21.50%
BB% (unintentional) /BF	12.50%	—	×0.900	11.25%
HR / BF /BF	3.12%	÷1.058	×1.100	3.25%

Result: FIP 4.29 → 4.49, 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 AAARochester Red Wings · 8 IPFIP 2.83 → 3.66 (+0.83)K% 48.4% → 42.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	48.39%	—	×0.880	42.58%
BB% (unintentional) /BF	16.13%	—	×1.050	16.94%
HR / BF /BF	3.23%	÷0.968	×1.100	3.67%

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 94).

Result: FIP 2.83 → 3.66, 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 AAAFresno Grizzlies · 18 IPFIP 2.33 → 2.91 (+0.58)K% 39.0% → 34.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.02%	—	×0.880	34.34%
BB% (unintentional) /BF	14.63%	—	×1.050	15.37%
HR / BF /BF	1.22%	÷0.962	×1.100	1.39%

Result: FIP 2.33 → 2.91, 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 AAALouisville Bats · 51 IPFIP 3.06 → 3.54 (+0.48)K% 31.4% → 27.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.40%	—	×0.880	27.63%
BB% (unintentional) /BF	16.43%	—	×1.050	17.25%
HR / BF /BF	0.97%	÷0.988	×1.100	1.08%

Result: FIP 3.06 → 3.54, 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+Daytona Tortugas · 45 IPFIP 2.26 → 3.73 (+1.46)K% 42.3% → 32.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	42.31%	—	×0.770	32.58%
BB% (unintentional) /BF	12.09%	—	×1.150	13.90%
HR / BF /BF	2.20%	÷0.932	×1.250	2.95%

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 86).

Result: FIP 2.26 → 3.73, 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 AAPensacola Blue Wahoos · 17 IPFIP 3.41 → 4.01 (+0.60)K% 39.1% → 31.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.13%	—	×0.810	31.70%
BB% (unintentional) /BF	15.94%	—	×1.100	17.54%
HR / BF /BF	2.90%	÷1.399	×1.200	2.49%

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 180).

Result: FIP 3.41 → 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).

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
Tanner Rainey (OGILVIE)	—	—	4.31	26.5%	13.6%	2.4%	—	4.80	25.1%	10.7%	70	+0.49	—	—	—
Dustin Molleken R 2017 · 3.38 FIP	32	SU	3.38	24.3%	12.4%	1.4%	.311	—	—	—	—	—	3.38	129	73
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	69
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	69
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	68
Leo Rosales R 2015 · 3.99 FIP	34	SU	3.99	18.1%	10.8%	1.9%	.261	4.24	7.8%	-0.4%	22	+0.25	3.85	130	68
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	68
Diego Moreno R 2021 · 4.75 FIP	34	SU	4.75	18.6%	9.4%	3.7%	.232	—	—	—	—	—	2.29	187	67
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	65
Rafael Dolis R 2022 · 3.61 FIP	34	SU	3.61	19.9%	11.9%	1.2%	.275	—	—	—	—	—	2.80	200	64
Carlos Fisher R 2017 · 3.91 FIP	34	SU	3.91	18.1%	12.9%	1.3%	.333	—	—	—	—	—	2.48	245	64
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	63
Fernando Rodriguez Jr. R 2018 · 4.07 FIP	34	SW	4.07	23.7%	9.9%	3.1%	.253	5.11	25.0%	13.8%	32	+1.04	3.34	335	63
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
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	62
Scott McGough R 2024 · 4.56 FIP	34	SU	4.56	18.6%	12.2%	2.7%	.289	3.82	20.3%	13.1%	57	-0.74	3.55	429	62
Matt Bush R 2018 · 4.72 FIP	32	SU	4.72	17.6%	13.0%	2.7%	.278	—	—	—	—	—	2.87	249	61
Blake Parker R 2019 · 4.65 FIP	34	SU	4.65	25.5%	8.6%	4.8%	.258	4.26	24.9%	15.4%	87	-0.39	2.52	406	61
Josh Lueke R 2019 · 4.11 FIP	34	SU	4.11	20.4%	6.4%	3.5%	.311	—	—	—	—	—	2.29	191	60
Radhames Liz R 2018 · 4.45 FIP	34	SU	4.45	21.8%	13.9%	2.6%	.343	—	—	—	—	—	2.81	219	59
Jean Machi R 2016 · 4.36 FIP	34	SU	4.36	18.3%	6.6%	3.6%	.282	4.12	18.6%	12.0%	67	-0.24	2.16	306	59

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	1.7	42	26.7%	14.8%	1.26	5.12
Armando Almanza ('05)	1.1	54	24.4%	14.3%	1.43	4.82
Amir Garrett ('24)	1.7	55	26.7%	13.3%	1.52	4.95
Doug Creek ('05)	1.4	58	22.2%	15.1%	1.52	5.32
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*
Dwayne Henry ('84-'95, -1 WAR*)	1.7	67	23.8%*	14.2%*	0.90*	5.02*
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*