Tommy Kahnle

P Worcester Red Sox

Age 37 RHP 6'1" / 230 lbs AAA

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	34	MIX3 levels	55	2	48	1.89	3.20	29.4%	9.3%	0.8	K 25.7% · BB 10.1% · FIP 3.64
2025	35	MLBDET	66	0	63	4.43	4.64	18.7%	11.6%	-0.1
2026	36	MIX2 levels	31	0	34	3.15	3.94	21.5%	12.1%	0.2	K 21.9% · BB 10.2% · FIP 3.11
2027PROJ	37	MLBBOS	59	0	62	4.27	4.57	21.3%	10.9%	-0.0
2028PROJ	38	MLBBOS	23	0	24	4.71	5.00	20.2%	11.6%	-0.1
2029PROJ	39	MLBBOS	9	0	9	5.15	5.44	19.1%	12.3%	-0.1

Last 3 seasons WAR 0.9 · Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

FB Velo36

xwOBA vs36

K%26

CompsKeiichi Yabu (age 36) · Juan Sandoval (age 36) · Rudy Gonzalez (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 119111vR 102

ERA4.27

FIP4.57

K%21.3%

BB%10.9%

K-BB%10.5%

Proj IP62

Value2027 · WAR

Runs saved vs league avg · 62 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL3535

CMD4040

K†5050

Statcast Percentiles

FB Velo

p3693.5

Whiff%

p5125.4%

p2618.7%

xwOBA vs

p360.328

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)
2014	24	MLB MLE	Colorado Rockies	54	0	69	285	63	29	7	0%	1.3	22.1%	10.2%	11.9%	2.2%	4.19	3.72	+0.7
2014	24	MLB RAW	Colorado Rockies	54	0	69	285	63	29	7	0%	1.3	22.1%	10.2%	11.9%	2.5%	4.19	3.86	+0.6
2015	25	MIX MLE	2 levels	57	0	60	267	67	39	6	0%	1.1	23.8%	14.8%	9.0%	2.1%	4.77	4.13	+0.3
2015	25	MIX RAW	2 levels	57	0	60	267	67	39	6	0%	1.1	25.1%	14.6%	10.5%	2.2%	4.77	4.11	+0.3
2016	26	MIX MLE	2 levels	52	0	54	228	61	27	2	0%	1.0	24.9%	12.1%	12.8%	0.9%	2.82	3.00	+1.0
2016	26	MIX RAW	2 levels	52	0	54	228	61	27	2	0%	1.0	26.8%	11.8%	14.9%	0.9%	2.82	2.82	+1.1
2017	27	MLB MLE	New York Yankees	69	0	63	256	96	16	4	0%	0.9	37.5%	6.2%	31.2%	1.5%	2.59	1.53	+2.3
2017	27	MLB RAW	New York Yankees	69	0	63	256	96	16	4	0%	0.9	37.5%	6.2%	31.2%	1.6%	2.59	1.63	+2.2
2018	28	MIX MLE	3 levels	50	1	50	222	69	26	6	2%	1.0	29.2%	12.4%	16.8%	2.3%	5.22	3.46	+0.6
2018	28	MIX RAW	3 levels	50	1	50	222	69	26	6	2%	1.0	31.1%	11.7%	19.4%	2.7%	5.22	3.46	+0.6
2019	29	MLB MLE	New York Yankees	72	0	61	248	88	20	9	0%	0.9	35.5%	8.1%	27.4%	3.4%	3.67	2.99	+1.1
2019	29	MLB RAW	New York Yankees	72	0	61	248	88	20	9	0%	0.9	35.5%	8.1%	27.4%	3.6%	3.67	3.12	+1.0
2020	30	MLB MLE	New York Yankees	1	0	1	6	3	0	0	0%	1.0	50.0%	0.0%	50.0%	0.0%	0.00	-1.12	+0.1
2020	30	MLB RAW	New York Yankees	1	0	1	6	3	0	0	0%	1.0	50.0%	0.0%	50.0%	0.0%	0.00	-2.90	+0.1
2022	32	MIX MLE	3 levels	28	3	26	102	28	6	4	11%	0.9	27.5%	5.9%	21.6%	3.9%	3.12	3.68	+0.3
2022	32	MIX RAW	3 levels	28	3	26	102	28	6	4	11%	0.9	27.5%	5.9%	21.6%	3.9%	3.12	3.64	+0.3
2023	33	MIX MLE	4 levels	47	2	46	184	54	22	5	4%	1.0	29.1%	11.5%	17.6%	2.8%	2.56	3.66	+0.5
2023	33	MIX RAW	4 levels	47	2	46	184	54	22	5	4%	1.0	29.3%	12.0%	17.4%	2.7%	2.56	3.60	+0.5
2024	34	MIX MLE	3 levels	55	2	48	194	57	18	5	4%	0.9	25.7%	10.1%	15.6%	2.6%	1.89	3.64	+0.5
2024	34	MIX MLE	3 levels	55	2	48	194	57	18	5	4%	0.9	25.7%	10.1%	15.6%	2.6%	1.89	3.64	+0.5
2024	34	MIX RAW	3 levels	55	2	48	194	57	18	5	4%	0.9	29.4%	9.3%	20.1%	2.6%	1.89	3.20	+0.8
2025	35	MLB MLE	Detroit Tigers	66	0	63	267	50	31	8	0%	1.0	18.7%	11.6%	7.1%	3.1%	4.43	4.71	-0.1
2025	35	MLB MLE	Detroit Tigers	66	0	63	267	50	31	8	0%	1.0	18.7%	11.6%	7.1%	3.1%	4.43	4.71	-0.1
2025	35	MLB RAW	Detroit Tigers	66	0	63	267	50	31	8	0%	1.0	18.7%	11.6%	7.1%	3.0%	4.43	4.64	-0.1
2026	36	MIX MLE	2 levels	31	0	34	149	32	18	3	0%	1.1	21.9%	10.2%	11.7%	1.0%	3.15	3.11	+0.6
2026	36	MIX MLE	2 levels	31	0	34	149	32	18	3	0%	1.1	21.9%	10.2%	11.7%	1.0%	3.15	3.11	+0.6
2026	36	MIX RAW	2 levels	31	0	34	149	32	18	3	0%	1.1	21.5%	12.1%	9.4%	2.0%	3.15	3.94	+0.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			464	0	446	1876	507	201	50	0%	1.0	27.0%	10.7%	16.3%	2.7%	3.70	3.64	+4.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	37	MLB	BOS	59	0	62	262	56	28	7	0%	1.1	21.3%	10.9%	10.5%	2.8%	4.27	4.57	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	38	MLB MLE	BOS	23	0	24	101	20	12	3	0%	1.0	20.2%	11.6%	8.6%	3.2%	4.71	5.00	-0.1
2029	39	MLB MLE	BOS	8	0	9	38	7	5	1	0%	1.1	19.1%	12.3%	6.8%	3.7%	5.15	5.44	-0.1
2030	40	MLB MLE	BOS	3	0	3	14	3	2	1	0%	1.1	17.9%	13.0%	4.9%	4.1%	5.58	5.87	-0.0
2028	38	MLB CTX	BOS	23	0	24	101	20	12	3	0%	1.0	20.2%	11.6%	8.6%	3.2%	4.71	5.00	-0.1
2029	39	MLB CTX	BOS	9	0	9	38	7	5	1	0%	1.0	19.1%	12.3%	6.8%	3.7%	5.15	5.44	-0.1
2030	40	MLB CTX	BOS	3	0	3	13	2	2	1	0%	1.0	17.9%	13.0%	4.9%	4.1%	5.58	5.87	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		498	0	482	2029	537	219	55	0%	1.0	26.5%	10.8%	15.7%	2.7%	3.79	3.73	+4.7
/ 200 IP	—	RAW		207	0	200	842	223	91	23	0%	1.0	26.5%	10.8%	15.7%	2.7%	3.79	3.73	+2.0
Career	—	MLE		582	8	575	2408	649	253	55	1%	1.0	27.0%	10.5%	16.5%	2.3%	3.57	3.41	+4.6
/ 200 IP	—	MLE		202	3	200	838	226	88	19	1%	1.0	27.0%	10.5%	16.5%	2.3%	3.57	3.41	+1.9

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.

2026 AAAWorcester Red Sox · 25 IPFIP 2.90 → 3.11 (+0.21)K% 25.5% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.47%	—	×0.860	21.91%
BB% (unintentional) /BF	11.32%	—	×0.900	10.19%
HR / BF /BF	0.94%	÷1.000	×1.100	1.04%

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 2.90 → 3.11, 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 AAAOklahoma City Dodgers · 10 IPFIP 4.68 → 5.27 (+0.59)K% 25.0% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.880	22.00%
BB% (unintentional) /BF	7.50%	—	×1.050	7.88%
HR / BF /BF	5.00%	÷0.997	×1.100	5.52%

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 99).

Result: FIP 4.68 → 5.27, 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 AAAScranton/Wilkes-Barre RailRiders · 25 IPFIP 2.51 → 2.98 (+0.47)K% 34.6% → 30.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.58%	—	×0.880	30.43%
BB% (unintentional) /BF	10.28%	—	×1.050	10.79%
HR / BF /BF	1.87%	÷1.046	×1.100	1.97%

Result: FIP 2.51 → 2.98, 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 AAACharlotte Knights · 27 IPFIP 1.47 → 1.87 (+0.39)K% 33.0% → 29.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.03%	—	×0.880	29.06%
BB% (unintentional) /BF	9.17%	—	×1.050	9.63%
HR / BF /BF	0.00%	÷0.950	×1.100	0.00%

Result: FIP 1.47 → 1.87, 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).

2015 AAAAlbuquerque Isotopes · 27 IPFIP 3.82 → 4.13 (+0.31)K% 25.0% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.880	22.00%
BB% (unintentional) /BF	10.71%	—	×1.050	11.25%
HR / BF /BF	2.68%	÷1.105	×1.100	2.67%

Result: FIP 3.82 → 4.13, 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
Tommy Kahnle (OGILVIE)	—	—	4.57	21.3%	10.9%	2.8%	—	5.00	20.2%	8.6%	24	+0.43	—	—	—
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	74
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
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	70
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	69
Matt Bush R 2023 · 3.79 FIP	37	SU	3.79	26.2%	8.7%	3.3%	.225	—	—	—	—	—	2.87	249	68
Pete Walker R 2006 · 4.45 FIP	37	SU	4.45	19.6%	8.0%	3.6%	.337	—	—	—	—	—	4.45	327	68
Scott Atchison R 2013 · 3.80 FIP	37	SU	3.80	14.4%	6.2%	2.1%	.273	3.60	16.7%	12.9%	92	-0.20	2.52	340	67
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	63
Hector Navarro R 2017 · 3.53 FIP	37	SU	3.53	11.5%	5.6%	1.3%	.283	—	—	—	—	—	2.30	126	61
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	59
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	58
Seunghwan Oh R 2019 · 5.75 FIP	37	SU	5.75	18.2%	6.8%	6.0%	.383	—	—	—	—	—	1.87	226	57
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	55
Tony Pena Jr. R 2017 · 3.43 FIP	36	SU	3.43	10.9%	4.3%	1.3%	.312	—	—	—	—	—	2.80	128	54
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	52
Heriberto Ruelas L 2017 · 3.42 FIP	35	SU	3.42	17.2%	6.7%	1.7%	.294	—	—	—	—	—	2.95	117	37
Brian Shouse L 2006 · 4.19 FIP	37	SU	4.19	13.2%	7.5%	2.3%	.269	3.37	15.3%	11.0%	127	-0.82	2.32	339	36
Jonathan Castellanos R 2019 · 5.24 FIP	37	SW	5.24	11.0%	9.0%	3.5%	.348	—	—	—	—	—	3.40	394	35
Hisanori Takahashi L 2012 · 4.04 FIP	37	SU	4.04	24.5%	6.1%	4.1%	.295	—	—	—	—	—	3.50	240	34
Logan Duran R 2019 · 3.66 FIP	37	SP-	3.66	15.5%	8.9%	1.4%	.427	—	—	—	—	—	3.42	187	33

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	2.7	42	25.9%	11.2%	1.06	3.84
Rudy Seanez ('08)	2.8	43	23.1%	11.4%	0.93	4.11
Brad Boxberger ('23)	2.4	43	28.7%	11.5%	1.19	3.50
Michael Wuertz ('11)	2.1	52	25.3%	11.3%	0.96	3.71
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*
Tony Fossas ('88-'99, 2 WAR*)	2.1	42	21.9%*	9.3%*	0.99*	3.88*
Mike Fetters ('89-'04, 6 WAR*)	3.6	55	20.3%*	10.4%*	0.83*	3.70*