Tommy Nance

P Minnesota Twins

Age 36 RHP 6'6" / 235 lbs MLB peak 0.5 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	33	MIX2 levels	46	55	4.07	3.37	22.2%	6.2%	0.8	K 20.2% · BB 5.8% · FIP 3.51
2025	34	MIX2 levels	57	63	3.29	1.83	28.5%	5.4%	2.1	K 26.2% · BB 5.1% · FIP 2.08
2026	35	MIX2 levels	58	61	4.15	3.59	26.5%	8.3%	0.7	K 26.4% · BB 8.5% · FIP 3.65
2027PROJ	36	MLBMIN	54	57	3.81	3.81	25.4%	7.4%	0.5
2028PROJ91% MLB	37	MLBMIN	32	34	4.05	4.21	24.3%	8.1%	0.1
2029PROJ77% MLB	38	MLBMIN	15	16	4.41	4.64	23.1%	8.8%	-0.0

Last 3 seasons WAR 3.5 · Next 3 projected WAR 0.6Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%73

FB Velo51

xwOBA vs40

Form4.43 ERA (proj 3.81) · K 31.1% (proj 25.4%) · BB 7.8% (proj 7.4%) ▲ last 90d · 20 IP through Oct 27

CompsSeunghwan Oh (age 36) · Juan Sandoval (age 36) · Keiichi Yabu (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 108100vR 92

ERA3.81

FIP3.81

K%25.4%

BB%7.4%

K-BB%18.0%

Proj IP57

Value2027 · WAR

Runs saved vs league avg · 57 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL5555

CMD5050

K†6060

Statcast Percentiles

FB Velo

p5194.6

Whiff%

p5826.5%

p7326.4%

xwOBA vs

p400.323

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)
2016	25	MIX MLE	3 levels	18	0	32	134	29	12	1	0%	1.8	17.4%	11.0%	6.4%	0.0%	3.13	3.02	—
2016	25	MIX RAW	3 levels	18	0	32	134	29	12	1	0%	1.8	21.6%	9.0%	12.7%	0.7%	3.13	2.82	—
2018	27	AA MLE	Tennessee Smokies	15	0	21	87	20	11	1	0%	1.4	18.6%	13.9%	4.7%	1.5%	3.48	4.09	—
2018	27	AA RAW	Tennessee Smokies	15	0	21	87	20	11	1	0%	1.4	23.0%	12.6%	10.3%	1.1%	3.48	3.39	—
2019	28	MIX MLE	2 levels	31	0	49	208	56	17	2	0%	1.6	21.8%	9.3%	12.5%	1.0%	4.07	3.01	—
2019	28	MIX RAW	2 levels	31	0	49	208	56	17	2	0%	1.6	26.9%	8.2%	18.8%	1.0%	4.07	2.38	—
2021	30	MIX MLE	2 levels	37	0	44	184	48	16	6	0%	1.2	24.9%	8.8%	16.1%	3.4%	5.52	3.96	+0.3
2021	30	MIX RAW	2 levels	37	0	44	184	48	16	6	0%	1.2	26.1%	8.7%	17.4%	3.3%	5.52	3.78	+0.4
2022	31	MIX MLE	4 levels	43	5	55	236	69	21	7	12%	1.3	29.1%	9.2%	19.9%	2.7%	3.90	3.28	+0.8
2022	31	MIX RAW	4 levels	43	5	55	236	69	21	7	12%	1.3	29.2%	8.9%	20.3%	3.0%	3.90	3.39	+0.8
2023	32	MIX MLE	4 levels	13	3	17	64	16	5	0	23%	1.3	25.0%	7.8%	17.2%	0.0%	1.59	1.98	—
2023	32	MIX RAW	4 levels	13	3	17	64	16	5	0	23%	1.3	25.0%	7.8%	17.2%	0.0%	1.59	2.10	—
2024	33	MIX MLE	2 levels	46	0	55	243	54	15	6	0%	1.2	20.2%	5.8%	14.4%	2.5%	4.07	3.51	+0.7
2024	33	MIX MLE	2 levels	46	0	55	243	54	15	6	0%	1.2	20.2%	5.8%	14.4%	2.5%	4.07	3.51	+0.7
2024	33	MIX RAW	2 levels	46	0	55	243	54	15	6	0%	1.2	22.2%	6.2%	16.0%	2.5%	4.07	3.37	+0.8
2025	34	MIX MLE	2 levels	57	0	63	260	74	14	2	0%	1.1	26.2%	5.1%	21.1%	1.0%	3.29	2.08	+1.9
2025	34	MIX MLE	2 levels	57	0	63	260	74	14	2	0%	1.1	26.2%	5.1%	21.1%	1.0%	3.29	2.08	+1.9
2025	34	MIX RAW	2 levels	57	0	63	260	74	14	2	0%	1.1	28.5%	5.4%	23.1%	0.8%	3.29	1.83	+2.1
2026	35	MIX MLE	2 levels	58	0	61	264	70	22	8	0%	1.0	26.4%	8.5%	17.8%	3.1%	4.15	3.65	+0.6
2026	35	MIX MLE	2 levels	58	0	61	264	70	22	8	0%	1.0	26.4%	8.5%	17.8%	3.1%	4.15	3.65	+0.6
2026	35	MIX RAW	2 levels	58	0	61	264	70	22	8	0%	1.0	26.5%	8.3%	18.2%	3.0%	4.15	3.59	+0.7
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			169	2	186	800	206	67	20	1%	1.1	25.8%	8.4%	17.4%	2.5%	4.22	3.36	+2.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	MIN	54	0	57	241	61	18	7	0%	1.1	25.4%	7.4%	18.0%	2.8%	3.81	3.81	+0.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	MIN	35	0	37	156	38	13	5	0%	1.1	24.3%	8.1%	16.2%	3.2%	4.05	4.21	+0.1
2029	38	MLB MLE	MIN	19	0	20	86	20	8	3	0%	1.1	23.1%	8.8%	14.4%	3.7%	4.41	4.64	-0.0
2030	39	MLB MLE	MIN	9	0	10	41	9	4	2	0%	1.1	22.0%	9.4%	12.5%	4.1%	4.81	5.07	-0.1
2028	37	MLB CTX	MIN	32	0	34	144	35	12	5	0%	1.1	24.3%	8.1%	16.2%	3.2%	4.05	4.21	+0.1
2029	38	MLB CTX	MIN	15	0	16	68	16	6	2	0%	1.1	23.1%	8.8%	14.4%	3.7%	4.41	4.64	-0.0
2030	39	MLB CTX	MIN	6	0	6	25	6	2	1	0%	1.0	22.0%	9.4%	12.5%	4.1%	4.81	5.07	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		222	2	241	1034	261	87	28	1%	1.1	25.3%	8.4%	16.9%	2.7%	4.22	3.51	+3.0
/ 200 IP	—	RAW		184	2	200	858	216	72	23	1%	1.1	25.3%	8.4%	16.9%	2.7%	4.22	3.51	+2.5
Career	—	MLE		318	8	396	1680	402	139	33	3%	1.2	23.9%	8.3%	15.7%	2.0%	3.88	3.20	+2.7
/ 200 IP	—	MLE		160	4	200	848	203	70	17	3%	1.2	23.9%	8.3%	15.7%	2.0%	3.88	3.20	+2.1

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.

2025 AAABuffalo Bisons · 31 IPFIP 1.93 → 2.48 (+0.55)K% 31.3% → 27.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.34%	—	×0.860	26.96%
BB% (unintentional) /BF	5.22%	—	×0.900	4.70%
HR / BF /BF	1.49%	÷0.847	×1.100	1.94%

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 69).

Result: FIP 1.93 → 2.48, 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).

2024 AAAEl Paso Chihuahuas · 33 IPFIP 3.27 → 3.51 (+0.24)K% 23.3% → 20.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.33%	—	×0.860	20.07%
BB% (unintentional) /BF	5.33%	—	×0.900	4.80%
HR / BF /BF	2.67%	÷1.078	×1.100	2.72%

Result: FIP 3.27 → 3.51, 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 AAPensacola Blue Wahoos · 8 IPFIP 2.13 → 2.59 (+0.47)K% 23.1% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.08%	—	×0.810	18.69%
BB% (unintentional) /BF	7.69%	—	×1.100	8.46%
HR / BF /BF	0.00%	÷1.091	×1.200	0.00%

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 118).

Result: FIP 2.13 → 2.59, 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 AJupiter Hammerheads · 5 IPFIP 1.45 → 2.12 (+0.67)K% 26.1% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.09%	—	×0.740	19.30%
BB% (unintentional) /BF	4.35%	—	×1.180	5.13%
HR / BF /BF	0.00%	÷0.924	×1.300	0.00%

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 85).

Result: FIP 1.45 → 2.12, 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).

2022 AAAJacksonville Jumbo Shrimp · 6 IPFIP 4.58 → 5.13 (+0.55)K% 30.0% → 26.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.880	26.40%
BB% (unintentional) /BF	10.00%	—	×1.050	10.50%
HR / BF /BF	5.00%	÷1.032	×1.100	5.33%

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 106).

Result: FIP 4.58 → 5.13, 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).

2021 AAAIowa Cubs · 15 IPFIP 2.06 → 2.43 (+0.37)K% 31.6% → 27.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.58%	—	×0.880	27.79%
BB% (unintentional) /BF	5.26%	—	×1.050	5.53%
HR / BF /BF	1.75%	÷1.081	×1.100	1.79%

Result: FIP 2.06 → 2.43, 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 AATennessee Smokies · 47 IPFIP 2.45 → 3.01 (+0.57)K% 26.9% → 21.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.87%	—	×0.810	21.76%
BB% (unintentional) /BF	8.46%	—	×1.100	9.30%
HR / BF /BF	1.00%	÷1.142	×1.200	1.05%

Result: FIP 2.45 → 3.01, 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).

2018 AATennessee Smokies · 21 IPFIP 3.39 → 4.09 (+0.70)K% 23.0% → 18.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.99%	—	×0.810	18.62%
BB% (unintentional) /BF	12.64%	—	×1.100	13.91%
HR / BF /BF	1.15%	÷0.947	×1.200	1.46%

Result: FIP 3.39 → 4.09, 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 Nance (OGILVIE)	—	—	3.81	25.4%	7.4%	2.8%	—	4.36	23.9%	15.6%	57	+0.55	—	—	—
Seunghwan Oh R 2018 · 2.83 FIP	36	SU	2.83	28.8%	5.8%	2.6%	.257	5.75	18.2%	11.4%	18	+2.92	1.87	226	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	66
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	65
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	64
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	64
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	64
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	62
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	60
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	59
Roy Corcoran R 2015 · 2.87 FIP	35	SU	2.87	17.7%	8.2%	0.4%	.359	—	—	—	—	—	2.87	129	59
Peter Moylan R 2015 · 3.11 FIP	36	SU	3.11	18.4%	8.2%	0.9%	.259	3.84	18.2%	9.1%	145	+0.73	2.54	431	58
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	57
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	56
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	55
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	54
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	52
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	51
Kazuhisa Makita R 2019 · 2.81 FIP	34	SU	2.81	18.9%	3.7%	1.5%	.291	—	—	—	—	—	2.81	132	48
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	44
Tony Pena Jr. R 2017 · 3.43 FIP	36	SU	3.43	10.9%	4.3%	1.3%	.312	—	—	—	—	—	2.80	128	43

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	1.6	34	25.2%	8.7%	1.07	4.15
Trevor May ('23)	2.3	50	26.8%	8.5%	1.20	4.24
Charlie Furbush ('15)	1.3	52	24.5%	8.1%	1.04	3.97
Tony Fossas ('88-'99, 2 WAR*)	2.1	42	21.9%*	9.3%*	0.99*	3.88*
David Carpenter ('19)	1.1	43	23.5%	8.4%	0.92	3.69
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Carlos Diaz ('82-'86, 2 WAR*)	1.3	52	25.1%*	8.7%*	0.91*	3.84*