Franco Aleman

P Columbus Clippers

Age 27 RHP 6'6" / 235 lbs AAA peak 0.6 · 2027

Overview

AAARP40-MANROOKIE

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	AAACLE	24	23	1.99	2.00	36.6%	10.8%	—	K 31.4% · BB 9.7% · FIP 2.28
2025	25	AAACLE	37	37	7.85	3.29	32.6%	13.1%	—	K 28.0% · BB 11.8% · FIP 3.50
2026	26	MIX2 levels	54	56	2.56	2.50	33.5%	8.2%	1.4	K 30.5% · BB 7.8% · FIP 2.71
2027PROJ	27	MLBCLE	45	47	3.88	3.73	30.3%	10.1%	0.5
2028PROJ78% MLB	28	MLBCLE	44	46	3.90	3.87	29.5%	10.2%	0.4
2029PROJ81% MLB	29	MLBCLE	41	43	4.02	4.07	28.4%	10.3%	0.2

Last 3 seasons WAR 1.4 · Next 3 projected WAR 1.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%77

FB Velo72

xwOBA vs39

Career WAR0.0 WAR median (P10 -0.3 / P90 2.7, comp-based) · star 0% · didn't stick 87%

CompsKiko Calero (age 28) · Robert Coello (age 28) · Jon Edwards (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10496vR 88

ERA3.88

FIP3.73

K%30.3%

BB%10.1%

K-BB%20.2%

Proj IP66

Value2027 · WAR

Runs saved vs league avg · 66 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p7296.1

Whiff%

p5926.7%

p7727.2%

xwOBA vs

p390.324

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)
2022	22	A MLE	Lynchburg Hillcats	24	8	56	260	75	18	6	33%	2.3	21.3%	8.2%	13.2%	3.0%	6.31	3.95	—
2022	22	A RAW	Lynchburg Hillcats	24	8	56	260	75	18	6	33%	2.3	28.8%	6.9%	21.9%	2.3%	6.31	2.78	—
2023	23	MIX MLE	2 levels	39	0	55	233	84	18	4	0%	1.4	28.4%	8.8%	19.6%	2.0%	3.11	2.92	—
2023	23	MIX RAW	2 levels	39	0	55	233	84	18	4	0%	1.4	36.1%	7.7%	28.3%	1.7%	3.11	1.97	—
2024	24	AAA MLE	Columbus Clippers	24	0	23	93	34	10	1	0%	0.9	31.4%	9.7%	21.8%	1.1%	1.99	2.28	—
2024	24	AAA MLE	Columbus Clippers	24	0	23	93	34	10	1	0%	0.9	31.4%	9.7%	21.8%	1.1%	1.99	2.28	—
2024	24	AAA RAW	Columbus Clippers	24	0	23	93	34	10	1	0%	0.9	36.6%	10.8%	25.8%	1.1%	1.99	2.00	—
2025	25	AAA MLE	Columbus Clippers	37	0	37	175	57	23	4	0%	1.0	28.0%	11.8%	16.2%	2.3%	7.85	3.50	—
2025	25	AAA MLE	Columbus Clippers	37	0	37	175	57	23	4	0%	1.0	28.0%	11.8%	16.2%	2.3%	7.85	3.50	—
2025	25	AAA RAW	Columbus Clippers	37	0	37	175	57	23	4	0%	1.0	32.6%	13.1%	19.4%	2.3%	7.85	3.29	—
2026	26	MIX MLE	2 levels	54	0	56	233	78	19	5	0%	1.0	30.5%	7.8%	22.7%	2.2%	2.56	2.71	+1.2
2026	26	MIX MLE	2 levels	54	0	56	233	78	19	5	0%	1.0	30.5%	7.8%	22.7%	2.2%	2.56	2.71	+1.2
2026	26	MIX RAW	2 levels	54	0	56	233	78	19	5	0%	1.0	33.5%	8.2%	25.3%	2.1%	2.56	2.50	+1.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			21	0	22	103	28	10	4	0%	1.1	27.2%	9.7%	17.5%	3.9%	4.43	4.26	+0.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	CLE	45	0	47	198	60	20	6	0%	1.0	30.3%	10.1%	20.2%	2.8%	3.88	3.73	+0.5
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	CLE	56	0	59	250	74	25	7	0%	1.1	29.5%	10.2%	19.3%	2.9%	3.90	3.87	+0.5
2029	29	MLB MLE	CLE	51	0	53	224	64	23	7	0%	1.0	28.4%	10.3%	18.1%	3.1%	4.02	4.07	+0.3
2028	28	MLB CTX	CLE	44	0	46	194	57	20	6	0%	1.0	29.5%	10.2%	19.3%	2.9%	3.90	3.87	+0.4
2029	29	MLB CTX	CLE	41	0	43	182	52	19	6	0%	1.0	28.4%	10.3%	18.1%	3.1%	4.02	4.07	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		106	0	111	479	137	49	15	0%	1.1	28.6%	10.2%	18.5%	3.2%	4.05	3.71	+1.1
/ 200 IP	—	RAW		190	0	200	860	246	88	27	0%	1.1	28.6%	10.2%	18.5%	3.2%	4.05	3.71	+2.0
Career	—	MLE		178	8	226	994	271	89	23	4%	1.3	27.3%	9.0%	18.3%	2.3%	4.41	3.19	+0.9
/ 200 IP	—	MLE		157	7	200	878	239	79	20	4%	1.3	27.3%	9.0%	18.3%	2.3%	4.41	3.19	+1.3

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 AAAColumbus Clippers · 34 IPFIP 1.15 → 1.56 (+0.41)K% 38.5% → 33.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.46%	—	×0.860	33.08%
BB% (unintentional) /BF	6.92%	—	×0.900	6.23%
HR / BF /BF	0.77%	÷1.000	×1.100	0.85%

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 1.15 → 1.56, 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 AAAColumbus Clippers · 37 IPFIP 3.27 → 3.50 (+0.23)K% 32.6% → 28.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.57%	—	×0.860	28.01%
BB% (unintentional) /BF	13.14%	—	×0.900	11.83%
HR / BF /BF	2.29%	÷1.093	×1.100	2.30%

Result: FIP 3.27 → 3.50, 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).

2024 AAAColumbus Clippers · 23 IPFIP 1.96 → 2.28 (+0.32)K% 36.6% → 31.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.56%	—	×0.860	31.44%
BB% (unintentional) /BF	10.75%	—	×0.900	9.68%
HR / BF /BF	1.08%	÷1.060	×1.100	1.12%

Result: FIP 1.96 → 2.28, 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 A+Lake County Captains · 31 IPFIP 3.07 → 4.14 (+1.07)K% 32.2% → 24.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.17%	—	×0.770	24.77%
BB% (unintentional) /BF	9.09%	—	×1.150	10.45%
HR / BF /BF	2.80%	÷1.060	×1.250	3.30%

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 112).

Result: FIP 3.07 → 4.14, 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 AAAkron RubberDucks · 24 IPFIP 0.24 → 0.99 (+0.75)K% 42.2% → 34.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	42.22%	—	×0.810	34.20%
BB% (unintentional) /BF	5.56%	—	×1.100	6.11%
HR / BF /BF	0.00%	÷0.909	×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 82).

Result: FIP 0.24 → 0.99, 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 ALynchburg Hillcats · 56 IPFIP 2.81 → 3.95 (+1.15)K% 28.8% → 21.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.85%	—	×0.740	21.35%
BB% (unintentional) /BF	6.92%	—	×1.180	8.17%
HR / BF /BF	2.31%	÷1.015	×1.300	2.95%

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 103).

Result: FIP 2.81 → 3.95, 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
Franco Aleman (OGILVIE)	—	—	—	—	—	2.8%	—	3.96	29.0%	18.7%	112	+0.23	—	—	—
Kiko Calero R 2003 · 3.54 FIP	28	SU	3.54	31.5%	11.1%	3.1%	.273	3.11	26.1%	18.9%	159	-0.43	2.27	298	87
Robert Coello R 2013 · 2.56 FIP	28	SU	2.56	31.5%	11.0%	1.3%	.317	4.31	13.4%	2.8%	95	+1.75	2.56	112	84
Jon Edwards R 2015 · 3.40 FIP	27	SU	3.40	31.7%	13.1%	2.4%	.264	2.40	30.3%	22.6%	30	-1.00	2.40	132	82
Michael Flynn R 2024 · 3.99 FIP	27	SU	3.99	27.6%	8.3%	3.9%	.277	4.68	16.2%	3.7%	50	+0.69	3.89	168	81
Maikel Cleto R 2016 · 3.44 FIP	27	SU	3.44	27.9%	9.9%	2.6%	.301	3.61	23.6%	14.3%	70	+0.17	2.59	185	81
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	80
Zach Franklin R 2024 · 3.24 FIP	25	SU	3.24	26.4%	10.9%	1.8%	.301	4.15	27.5%	18.3%	117	+0.91	3.24	158	80
Jordan Jankowski R 2016 · 2.95 FIP	27	SU	2.95	30.3%	9.8%	2.1%	.296	4.23	23.4%	8.8%	62	+1.28	2.57	196	79
Leonel Campos R 2015 · 2.62 FIP	28	SU	2.62	30.1%	11.1%	1.2%	.259	3.63	25.2%	12.2%	118	+1.01	2.62	168	79
Alex Powers R 2019 · 2.88 FIP	27	SU	2.88	27.9%	9.1%	1.8%	.272	4.24	25.0%	14.7%	48	+1.36	2.82	264	79
Simon Castro R 2015 · 3.28 FIP	27	SU	3.28	26.4%	8.5%	2.4%	.320	3.58	26.0%	17.6%	128	+0.30	3.01	185	79
Jason Bulger R 2008 · 4.60 FIP	29	SU	4.60	27.4%	12.3%	4.1%	.293	3.94	25.1%	13.3%	90	-0.66	3.78	106	79
Ryan Brasier R 2016 · 3.68 FIP	28	SU	3.68	25.0%	7.7%	3.1%	.289	3.09	24.2%	17.9%	130	-0.59	2.31	426	78
Jose Veras R 2008 · 3.67 FIP	27	SU	3.67	24.9%	9.1%	2.8%	.281	4.10	23.8%	11.7%	169	+0.43	3.37	422	78
Jason Stoffel R 2016 · 3.49 FIP	27	SU	3.49	27.2%	8.0%	3.0%	.293	—	—	—	—	—	3.49	103	78
Anthony Varvaro R 2012 · 3.66 FIP	27	SU	3.66	27.6%	10.5%	2.8%	.311	3.30	17.7%	11.0%	139	-0.36	3.02	208	78
Spencer Patton R 2015 · 3.47 FIP	27	SU	3.47	26.8%	9.6%	2.6%	.295	2.79	30.3%	18.1%	57	-0.68	2.79	239	78
Fernando Rodriguez Jr. R 2011 · 3.85 FIP	27	SU	3.85	24.7%	10.8%	2.7%	.315	3.90	25.2%	16.5%	70	+0.05	3.34	335	77
Taylor Williams R 2018 · 3.52 FIP	27	SU	3.52	24.2%	8.9%	2.4%	.309	4.35	22.5%	12.7%	108	+0.83	2.51	309	77
Dan Winkler R 2017 · 2.70 FIP	27	SU	2.70	34.0%	11.3%	1.9%	.214	3.82	24.0%	12.5%	131	+1.12	2.40	231	77

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	1.0	50	29.2%	10.2%	1.17	3.98
Colin Poche ('25)	1.1	54	26.7%	10.2%	1.32	4.01
Danny Farquhar ('18)	1.4	54	26.9%	9.2%	0.99	3.93
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*
Ernesto Frieri ('17)	1.5	51	30.1%	11.1%	1.25	3.59
Buddy Schultz ('75-'79, 2 WAR*)	1.2	60	27.4%*	8.5%*	1.21*	4.14*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*