Tanner Tully

P Scranton/Wilkes-Barre RailRiders

Age 32 LHP 6'2" / 205 lbs AAA peak 0.4 · 2027

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	27	MIX2 levels	27	20	128	4.78	3.58	17.8%	5.5%	2.8	K 16.2% · BB 5.1% · FIP 3.74
2023	28	AAAScranton/Wilk…	19	19	91	5.64	4.26	18.6%	6.3%	—	K 16.0% · BB 5.7% · FIP 4.48
2024	29	AAAScranton/Wilk…	25	22	100	6.41	5.41	12.9%	6.3%	—	K 11.1% · BB 5.7% · FIP 5.50
2025	30	No MLB games played
2026	31	No MLB games played
2027PROJ	32	MLBNYY	3	3	16	4.87	4.87	15.1%	7.1%	0.1
2028PROJ37% MLB	33	MLBNYY	8	8	45	4.96	5.06	15.3%	7.6%	0.2
2029PROJ32% MLB	34	MLBNYY	5	5	31	5.12	5.27	15.2%	8.1%	0.0

Last 3 seasons WAR 2.8 · Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsArturo Lopez (age 32) · Tomas Solis (age 32) · Jorge Castillo (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 109118vR 126

ERA4.87

FIP4.87

K%15.1%

BB%7.1%

K-BB%8.0%

Proj IP165

Value2027 · WAR

Runs saved vs league avg · 165 IP

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)
2017	22	MIX MLE	4 levels	25	15	124	523	95	22	14	60%	5.0	13.9%	4.9%	9.0%	3.4%	3.69	4.40	—
2017	22	MIX RAW	4 levels	25	15	124	523	95	22	14	60%	5.0	18.2%	4.2%	14.0%	2.7%	3.69	3.57	—
2018	23	A+ MLE	Lynchburg Hillcats	26	26	147	628	109	19	17	100%	5.7	13.4%	3.5%	9.9%	3.5%	4.47	4.33	—
2018	23	A+ RAW	Lynchburg Hillcats	26	26	147	628	109	19	17	100%	5.7	17.4%	3.0%	14.3%	2.7%	4.47	3.51	—
2019	24	MIX MLE	2 levels	26	26	144	621	89	28	11	100%	5.5	11.8%	4.9%	6.8%	2.4%	4.44	4.04	—
2019	24	MIX RAW	2 levels	26	26	144	621	89	28	11	100%	5.5	14.3%	4.5%	9.8%	1.8%	4.44	3.44	—
2021	26	MIX MLE	2 levels	26	18	113	491	97	32	10	69%	4.3	16.5%	7.0%	9.4%	2.5%	3.50	3.98	—
2021	26	MIX RAW	2 levels	26	18	113	491	97	32	10	69%	4.3	19.8%	6.5%	13.2%	2.0%	3.50	3.38	—
2022	27	MIX MLE	2 levels	27	20	128	561	100	31	13	74%	4.7	16.2%	5.1%	11.1%	2.5%	4.78	3.74	+2.6
2022	27	MIX RAW	2 levels	27	20	128	561	100	31	13	74%	4.7	17.8%	5.5%	12.3%	2.3%	4.78	3.58	+2.8
2023	28	AAA MLE	Scranton/Wilkes-Barre RailRiders	19	19	91	413	77	26	14	100%	4.8	16.0%	5.7%	10.4%	3.7%	5.64	4.48	—
2023	28	AAA RAW	Scranton/Wilkes-Barre RailRiders	19	19	91	413	77	26	14	100%	4.8	18.6%	6.3%	12.3%	3.4%	5.64	4.26	—
2024	29	AAA MLE	Scranton/Wilkes-Barre RailRiders	25	22	100	443	57	28	20	88%	4.0	11.1%	5.7%	5.4%	4.8%	6.41	5.50	—
2024	29	AAA MLE	Scranton/Wilkes-Barre RailRiders	25	22	100	443	57	28	20	88%	4.0	11.1%	5.7%	5.4%	4.8%	6.41	5.50	—
2024	29	AAA RAW	Scranton/Wilkes-Barre RailRiders	25	22	100	443	57	28	20	88%	4.0	12.9%	6.3%	6.5%	4.5%	6.41	5.41	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			3	0	6	30	2	5	1	0%	2.0	6.7%	16.7%	-10.0%	3.3%	6.00	7.10	-0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	NYY	3	3	16	68	10	5	2	100%	5.3	15.1%	7.1%	8.0%	3.2%	4.87	4.87	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	NYY	20	20	122	513	78	39	18	100%	6.1	15.3%	7.6%	7.7%	3.5%	4.96	5.06	+0.5
2029	34	MLB MLE	NYY	16	16	97	409	62	33	15	100%	6.1	15.2%	8.1%	7.1%	3.7%	5.12	5.27	+0.1
2028	33	MLB CTX	NYY	8	8	45	190	29	14	7	100%	5.6	15.3%	7.6%	7.7%	3.5%	4.96	5.06	+0.2
2029	34	MLB CTX	NYY	5	5	31	131	20	11	5	100%	6.2	15.2%	8.1%	7.1%	3.7%	5.12	5.27	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		16	13	82	351	51	30	13	81%	5.1	14.5%	8.5%	6.0%	3.6%	5.10	4.91	+0.5
/ 200 IP	—	RAW		38	31	200	856	124	73	32	81%	5.2	14.5%	8.5%	6.0%	3.6%	5.10	4.91	+1.1
Career	—	MLE		174	146	847	3680	517	190	117	84%	4.9	14.0%	5.2%	8.9%	3.2%	4.62	4.31	+0.5
/ 200 IP	—	MLE		41	34	200	869	122	45	28	84%	4.9	14.0%	5.2%	8.9%	3.2%	4.62	4.31	+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.

2024 AAAScranton/Wilkes-Barre RailRiders · 100 IPFIP 5.29 → 5.50 (+0.20)K% 12.9% → 11.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	12.87%	—	×0.860	11.07%
BB% (unintentional) /BF	6.32%	—	×0.900	5.69%
HR / BF /BF	4.51%	÷1.045	×1.100	4.75%

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 109).

Result: FIP 5.29 → 5.50, 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 AAAScranton/Wilkes-Barre RailRiders · 91 IPFIP 4.18 → 4.48 (+0.29)K% 18.6% → 16.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.64%	—	×0.860	16.03%
BB% (unintentional) /BF	6.30%	—	×0.900	5.67%
HR / BF /BF	3.39%	÷1.017	×1.100	3.67%

Result: FIP 4.18 → 4.48, 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).

2022 AAAColumbus Clippers · 122 IPFIP 3.40 → 3.74 (+0.34)K% 18.5% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.46%	—	×0.880	16.24%
BB% (unintentional) /BF	4.90%	—	×1.050	5.14%
HR / BF /BF	2.26%	÷1.001	×1.100	2.48%

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 100).

Result: FIP 3.40 → 3.74, 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).

2021 AAAkron RubberDucks · 74 IPFIP 3.15 → 3.90 (+0.75)K% 20.0% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.810	16.20%
BB% (unintentional) /BF	5.62%	—	×1.100	6.19%
HR / BF /BF	1.88%	÷0.893	×1.200	2.52%

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 79).

Result: FIP 3.15 → 3.90, 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 AAAColumbus Clippers · 39 IPFIP 3.79 → 4.12 (+0.33)K% 19.3% → 17.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.30%	—	×0.880	16.98%
BB% (unintentional) /BF	8.19%	—	×1.050	8.60%
HR / BF /BF	2.34%	÷1.034	×1.100	2.49%

Result: FIP 3.79 → 4.12, 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 AAAkron RubberDucks · 129 IPFIP 3.44 → 4.05 (+0.62)K% 13.3% → 10.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.35%	—	×0.810	10.81%
BB% (unintentional) /BF	4.63%	—	×1.100	5.09%
HR / BF /BF	1.60%	÷0.863	×1.200	2.23%

Result: FIP 3.44 → 4.05, 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 AAAColumbus Clippers · 15 IPFIP 3.39 → 3.87 (+0.48)K% 23.7% → 20.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.73%	—	×0.880	20.88%
BB% (unintentional) /BF	3.39%	—	×1.050	3.56%
HR / BF /BF	3.39%	÷0.985	×1.100	3.79%

Result: FIP 3.39 → 3.87, 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 A+Lynchburg Hillcats · 147 IPFIP 3.50 → 4.33 (+0.83)K% 17.4% → 13.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.36%	—	×0.770	13.36%
BB% (unintentional) /BF	3.03%	—	×1.150	3.48%
HR / BF /BF	2.71%	÷0.967	×1.250	3.50%

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 93).

Result: FIP 3.50 → 4.33, 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).

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 Tully (OGILVIE)	—	—	—	—	—	3.2%	—	5.15	15.3%	7.5%	218	+0.28	—	—	—
Arturo Lopez L 2015 · 4.80 FIP	32	SP	4.80	13.7%	6.2%	3.8%	.301	4.80	10.9%	4.2%	217	+0.00	4.34	445	78
Tomas Solis L 2015 · 4.20 FIP	32	SP	4.20	15.0%	6.2%	2.9%	.264	5.09	12.0%	3.7%	159	+0.89	4.20	327	78
Jorge Castillo L 2015 · 4.50 FIP	33	SP	4.50	16.8%	9.2%	3.0%	.292	4.95	12.4%	7.2%	186	+0.45	4.50	408	74
Oscar Rivera L 2015 · 3.68 FIP	34	SP	3.68	14.2%	6.9%	1.7%	.298	4.66	10.4%	3.0%	129	+0.98	3.68	237	74
Alejandro Astorga L 2017 · 4.01 FIP	30	SP	4.01	14.4%	8.2%	2.0%	.306	—	—	—	—	—	3.72	311	73
Roy Merritt L 2017 · 3.95 FIP	31	SP	3.95	16.2%	5.8%	2.7%	.339	—	—	—	—	—	3.93	129	68
Orlando Lara L 2017 · 4.39 FIP	32	SP	4.39	14.7%	6.5%	3.1%	.346	4.09	22.0%	14.2%	104	-0.30	3.85	426	67
Daniel Rodriguez L 2017 · 3.87 FIP	32	SP	3.87	13.1%	9.7%	1.2%	.335	4.77	20.8%	13.0%	112	+0.90	2.89	391	66
Everett Teaford L 2015 · 4.42 FIP	31	SP-	4.42	13.9%	7.6%	2.8%	.324	—	—	—	—	—	4.42	208	63
Brandon Mann L 2016 · 4.72 FIP	32	SP	4.72	19.4%	7.9%	4.1%	.320	3.97	19.0%	7.6%	128	-0.75	3.59	190	58
Kelvin Marte L 2019 · 5.25 FIP	31	SP	5.25	12.0%	10.8%	3.3%	.340	—	—	—	—	—	3.44	360	57
Eric Stults L 2012 · 3.71 FIP	32	SP-	3.71	13.3%	6.5%	1.7%	.262	4.01	14.8%	9.6%	517	+0.30	3.50	773	56
Enrique Lechuga L 2017 · 3.78 FIP	31	SP	3.78	10.5%	6.1%	1.5%	.390	—	—	—	—	—	3.78	137	53
Santiago Gutierrez L 2017 · 3.95 FIP	31	SP-	3.95	15.0%	8.2%	2.0%	.366	6.54	17.1%	3.7%	46	+2.59	3.90	341	53
Matt Dermody L 2022 · 4.22 FIP	32	SP-	4.22	17.9%	5.5%	3.5%	.302	3.87	20.7%	16.1%	49	-0.35	2.38	348	52
Shota Imanaga L 2024 · 3.71 FIP	30	SP	3.71	25.1%	4.0%	4.0%	.262	4.76	22.8%	17.9%	319	+1.05	3.71	492	50
Kwang Hyun Kim L 2020 · 3.76 FIP	32	SP	3.76	15.6%	6.5%	2.1%	.214	4.21	17.7%	9.5%	107	+0.45	3.76	146	48
Darrell May L 2004 · 5.17 FIP	32	SP	5.17	14.4%	6.1%	4.6%	.315	5.59	11.6%	4.3%	66	+0.42	4.24	657	46
Anthony Vasquez L 2019 · 6.00 FIP	32	SW	6.00	14.6%	7.3%	5.9%	.383	—	—	—	—	—	4.02	671	45
Sean Nolin L 2021 · 4.54 FIP	31	SP-	4.54	20.4%	8.9%	3.7%	.316	5.87	19.3%	12.9%	23	+1.33	3.79	309	43

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	1.9	130	15.0%	7.7%	1.33	4.98
Brandon Claussen ('06)	1.6	105	16.0%	8.5%	1.37	5.04
Don August ('88-'91, 1 WAR*)	2.2	147	12.7%*	8.1%*	1.26*	5.01*
Jason Dickson ('00)	2.0	99	12.2%	7.0%	1.36	4.99
Chad Ogea ('99)	3.2	127	13.4%	7.8%	1.32	4.88
Tommy Boggs ('76-'85, 3 WAR*)	2.9	97	16.4%*	8.1%*	1.24*	4.77*
Ken Schrom ('80-'87, 3 WAR*)	4.5	150	13.4%*	8.1%*	1.41*	4.77*