Ben Heller

P Indianapolis Indians

Age 35 RHP 6'3" / 210 lbs AAA peak 0.2 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	30	MIX2 levels	7	2	9	9.35	7.48	18.4%	20.4%	—	K 18.4% · BB 20.4% · FIP 6.37
2023	31	MIX2 levels	51	0	63	3.45	2.80	26.2%	9.0%	1.3	K 23.3% · BB 8.5% · FIP 3.04
2024	32	MIX3 levels	41	0	49	5.14	2.81	36.3%	7.9%	1.0	K 31.0% · BB 7.9% · FIP 3.76
2025	33	No MLB games played
2026	34	No MLB games played
2027PROJ	35	MLBPIT	5	0	5	4.38	4.21	26.6%	10.3%	0.0
2028PROJMLE	36	MLBPIT	—	—	0	4.51	4.59	25.3%	10.8%	—

Last 3 seasons WAR 2.3 · Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2024

FB Velo77

K%53

xwOBA vs13

CompsNeil Wagner (age 33) · Huascar Brazobán (age 34) · Mark Serrano (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 113105vR 97

ERA4.38

FIP4.21

K%26.6%

BB%10.3%

K-BB%16.3%

Proj IP50

Value2027 · WAR

Runs saved vs league avg · 50 IP

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL3535

CMD4040

K†6060

Statcast Percentiles

FB Velo

p7796.4

Whiff%

p7729.6%

p5322.7%

xwOBA vs

p130.356

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	23	MIX MLE	2 levels	41	0	40	174	58	14	0	0%	1.0	22.4%	10.1%	12.3%	0.0%	4.02	2.49	—
2015	23	MIX RAW	2 levels	41	0	40	174	58	14	0	0%	1.0	33.3%	8.0%	25.3%	0.0%	4.02	1.27	—
2016	24	MIX MLE	3 levels	59	0	55	229	61	15	5	0%	0.9	24.8%	6.8%	18.0%	1.3%	2.29	2.56	+1.3
2016	24	MIX RAW	3 levels	59	0	55	229	61	15	5	0%	0.9	26.6%	6.6%	20.1%	2.2%	2.29	2.88	+1.1
2017	25	MIX MLE	2 levels	50	0	67	266	91	26	6	0%	1.3	32.4%	9.4%	22.9%	2.4%	2.54	2.86	+1.4
2017	25	MIX RAW	2 levels	50	0	67	266	91	26	6	0%	1.3	34.2%	9.8%	24.4%	2.3%	2.54	2.71	+1.5
2019	27	MIX MLE	3 levels	17	6	21	79	24	6	1	35%	1.2	30.4%	7.6%	22.8%	1.3%	0.86	2.19	+0.6
2019	27	MIX RAW	3 levels	17	6	21	79	24	6	1	35%	1.2	30.4%	7.6%	22.8%	1.3%	0.86	2.29	+0.6
2020	28	MLB MLE	New York Yankees	6	0	6	27	6	2	2	0%	1.0	22.2%	7.4%	14.8%	7.4%	3.00	6.23	-0.1
2020	28	MLB RAW	New York Yankees	6	0	6	27	6	2	2	0%	1.0	22.2%	7.4%	14.8%	7.4%	3.00	6.43	-0.1
2022	30	MIX MLE	2 levels	7	2	9	49	9	10	2	29%	1.2	18.4%	20.4%	-2.0%	4.1%	9.35	6.37	—
2022	30	MIX RAW	2 levels	7	2	9	49	9	10	2	29%	1.2	18.4%	20.4%	-2.0%	4.1%	9.35	7.48	—
2023	31	MIX MLE	2 levels	51	0	63	279	73	25	4	0%	1.2	23.3%	8.5%	14.8%	1.5%	3.45	3.04	+1.1
2023	31	MIX RAW	2 levels	51	0	63	279	73	25	4	0%	1.2	26.2%	9.0%	17.2%	1.4%	3.45	2.80	+1.3
2024	32	MIX MLE	3 levels	41	0	49	215	78	17	7	0%	1.2	31.0%	7.9%	23.1%	4.2%	5.14	3.76	+0.5
2024	32	MIX MLE	3 levels	41	0	49	215	78	17	7	0%	1.2	31.0%	7.9%	23.1%	4.2%	5.14	3.76	+0.5
2024	32	MIX RAW	3 levels	41	0	49	215	78	17	7	0%	1.2	36.3%	7.9%	28.4%	3.3%	5.14	2.81	+1.0
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			58	0	62	286	61	29	9	0%	1.1	21.3%	10.1%	11.2%	3.1%	4.65	4.42	+0.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	PIT	5	0	5	21	6	2	1	0%	1.0	26.6%	10.3%	16.3%	3.0%	4.38	4.21	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	PIT	36	0	38	159	40	17	5	0%	1.0	25.3%	10.8%	14.5%	3.4%	4.51	4.59	-0.0
2028	36	MLB CTX	PIT	—	—	0	—	—	—	—	—	—	25.3%	10.8%	14.5%	3.4%	4.51	4.59	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		58	0	62	288	61	29	9	0%	1.1	21.4%	10.1%	11.2%	3.1%	4.64	4.31	+0.2
/ 200 IP	—	RAW		187	0	200	922	196	93	29	0%	1.1	21.4%	10.1%	11.2%	3.1%	4.64	4.31	+0.6
Career	—	MLE		272	8	310	1318	352	117	27	3%	1.1	26.7%	8.9%	17.9%	2.1%	3.37	3.10	+0.1
/ 200 IP	—	MLE		175	5	200	850	227	75	18	3%	1.1	26.7%	8.9%	17.9%	2.1%	3.37	3.10	+0.2

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 AAAIndianapolis Indians · 33 IPFIP 3.23 → 4.11 (+0.88)K% 40.7% → 35.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.74%	—	×0.860	35.04%
BB% (unintentional) /BF	8.89%	—	×0.900	8.00%
HR / BF /BF	4.44%	÷0.909	×1.100	5.38%

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 82).

Result: FIP 3.23 → 4.11, 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).

2023 AAAGwinnett Stripers · 44 IPFIP 2.11 → 2.43 (+0.32)K% 28.9% → 24.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.93%	—	×0.860	24.88%
BB% (unintentional) /BF	7.11%	—	×0.900	6.40%
HR / BF /BF	1.02%	÷0.984	×1.100	1.13%

Result: FIP 2.11 → 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).

2022 ROKFCL Twins · 7 IPFIP 4.16 → 5.22 (+1.07)K% 16.7% → 11.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.680	11.33%
BB% (unintentional) /BF	19.44%	—	×1.250	24.31%
HR / BF /BF	0.00%	÷1.282	×1.400	0.00%

Factors used: pitcher ROK level factors — K ×0.68, BB ×1.25, HR ×1.40. Pitcher translation is level-wide (no per-league rows, no age adjustment); HR/BF is park-adjusted at half strength (park HR factor 156).

Result: FIP 4.16 → 5.22, 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 AAAScranton/Wilkes-Barre RailRiders · 11 IPFIP 1.26 → 1.65 (+0.39)K% 33.3% → 29.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.880	29.33%
BB% (unintentional) /BF	7.69%	—	×1.050	8.08%
HR / BF /BF	0.00%	÷1.087	×1.100	0.00%

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 117).

Result: FIP 1.26 → 1.65, 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 AAAScranton/Wilkes-Barre RailRiders · 56 IPFIP 2.61 → 2.86 (+0.26)K% 36.8% → 32.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.77%	—	×0.880	32.36%
BB% (unintentional) /BF	8.97%	—	×1.050	9.42%
HR / BF /BF	2.69%	÷1.247	×1.100	2.37%

Result: FIP 2.61 → 2.86, 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 AAAScranton/Wilkes-Barre RailRiders · 32 IPFIP 2.12 → 2.45 (+0.33)K% 24.8% → 21.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.81%	—	×0.880	21.83%
BB% (unintentional) /BF	5.43%	—	×1.050	5.70%
HR / BF /BF	0.78%	÷0.996	×1.100	0.86%

Result: FIP 2.12 → 2.45, 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 AAAkron RubberDucks · 16 IPFIP 1.83 → 2.80 (+0.96)K% 38.3% → 31.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.33%	—	×0.810	31.05%
BB% (unintentional) /BF	8.33%	—	×1.100	9.17%
HR / BF /BF	1.67%	÷0.949	×1.200	2.11%

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 90).

Result: FIP 1.83 → 2.80, 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 A+Lynchburg Hillcats · 34 IPFIP 1.76 → 2.49 (+0.73)K% 29.1% → 22.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.05%	—	×0.770	22.37%
BB% (unintentional) /BF	8.78%	—	×1.150	10.10%
HR / BF /BF	0.00%	÷1.129	×1.250	0.00%

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 126).

Result: FIP 1.76 → 2.49, 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).

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
Ben Heller (OGILVIE)	—	—	4.21	26.6%	10.3%	3.0%	—	4.59	25.3%	14.5%	38	+0.38	—	—	—
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	74
Huascar Brazobán R 2024 · 3.08 FIP	34	SU	3.08	23.5%	11.5%	0.9%	.259	3.71	21.3%	11.7%	144	+0.63	3.08	370	73
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	73
Toby Borland R 2004 · 4.28 FIP	35	SU	4.28	20.9%	8.1%	3.5%	.259	—	—	—	—	—	2.92	248	70
Brendan Donnelly R 2006 · 4.21 FIP	35	SU	4.21	19.1%	9.0%	2.9%	.260	3.19	18.6%	10.1%	60	-1.02	2.23	385	70
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	69
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	68
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	67
Juan Sandoval R 2016 · 3.24 FIP	35	SU	3.24	18.2%	10.2%	0.7%	.288	4.40	16.3%	9.1%	116	+1.16	3.24	225	67
Fernando Rodriguez Jr. R 2019 · 5.11 FIP	35	SW	5.11	25.0%	11.2%	4.9%	.312	—	—	—	—	—	3.34	335	66
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	66
Yoshihisa Hirano R 2019 · 3.72 FIP	35	SU	3.72	26.2%	8.6%	3.2%	.303	5.02	17.5%	4.8%	12	+1.30	3.45	132	66
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	65
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
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	62
Spencer Patton R 2023 · 4.33 FIP	35	SU	4.33	18.9%	9.7%	2.9%	.295	—	—	—	—	—	2.79	239	62
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	62
Hector Navarro R 2015 · 4.35 FIP	35	SU	4.35	15.0%	5.4%	3.3%	.281	3.00	15.2%	9.0%	81	-1.35	2.30	126	62
Rodolfo Aguirre R 2019 · 4.50 FIP	34	SU	4.50	21.3%	5.1%	4.7%	.244	—	—	—	—	—	3.39	212	62
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	62

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	0.7	75	24.0%	10.8%	1.23	4.52
Justin Grimm ('22)	1.9	75	23.0%	10.1%	1.17	5.10
J.J. Hoover ('18)	1.4	57	23.8%	11.4%	1.42	4.17
José Ruiz ('25)	1.4	57	22.3%	10.4%	1.44	4.62
Bill Scherrer ('82-'88, 1 WAR*)	1.6	62	20.7%*	10.2%*	1.19*	4.44*
Fred Lasher ('63-'71, -1 WAR*)	1.0	50	21.5%*	11.9%*	1.05*	4.68*
Bill Kelso ('64-'68, 1 WAR*)	1.0	67	23.1%*	11.6%*	1.09*	4.19*