Alan Rangel

P Lehigh Valley IronPigs

Age 29 RHP 6'2" / 170 lbs AAA

Overview

AAARP40-MANROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	26	MIX4 levels	18	13	57	3.77	3.03	27.3%	6.2%	—	K 21.1% · BB 6.4% · FIP 4.05
2025	27	MIX2 levels	30	25	136	4.38	4.94	24.1%	8.0%	0.7	K 21.3% · BB 6.8% · FIP 5.65
2026	28	MIX2 levels	29	22	134	5.64	4.79	22.8%	9.7%	0.9	K 20.3% · BB 9.0% · FIP 4.99
2027PROJ	29	MLBPHI	19	19	116	4.69	4.94	20.1%	7.8%	0.6
2028PROJ81% MLB	30	MLBPHI	12	12	75	4.89	5.07	19.6%	8.1%	0.3
2029PROJ85% MLB	31	MLBPHI	9	9	53	5.08	5.24	19.1%	8.4%	0.1

Last 3 seasons WAR 1.6 · Next 3 projected WAR 1.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

K%58

xwOBA vs45

FB Velo27

Career WAR0.0 WAR median (P10 -0.4 / P90 0.1, comp-based) · star 0% · didn't stick 100%

CompsZack Segovia (age 32) · Travis Smith (age 29) · Fabio Castillo (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 127119vR 111

ERA4.69

FIP4.94

K%20.1%

BB%7.8%

K-BB%12.4%

Proj IP156

Value2027 · WAR

Runs saved vs league avg · 156 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2792.9

Whiff%

p4424.4%

p5823.6%

xwOBA vs

p450.317

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	17	ROK MLE	GCL Braves	14	1	26	116	23	9	3	7%	1.8	13.5%	9.7%	3.8%	3.6%	6.31	5.18	—
2015	17	ROK RAW	GCL Braves	14	1	26	116	23	9	3	7%	1.8	19.8%	7.8%	12.1%	2.6%	6.31	3.88	—
2016	18	ROK MLE	GCL Braves	12	9	47	189	47	10	2	75%	3.9	16.9%	6.6%	10.3%	1.5%	3.28	3.32	—
2016	18	ROK RAW	GCL Braves	12	9	47	189	47	10	2	75%	3.9	24.9%	5.3%	19.6%	1.1%	3.28	2.29	—
2017	19	A MLE	Rome Braves	15	13	71	299	48	14	5	87%	4.7	11.9%	5.5%	6.4%	2.9%	4.71	4.38	—
2017	19	A RAW	Rome Braves	15	13	71	299	48	14	5	87%	4.7	16.1%	4.7%	11.4%	1.7%	4.71	3.26	—
2018	20	A MLE	Rome Braves	25	22	125	533	105	31	12	88%	5.0	14.6%	6.9%	7.7%	3.3%	4.09	4.56	—
2018	20	A RAW	Rome Braves	25	22	125	533	105	31	12	88%	5.0	19.7%	5.8%	13.9%	2.3%	4.09	3.41	—
2019	21	A MLE	Rome Braves	28	24	132	581	121	46	10	86%	4.7	15.4%	9.3%	6.1%	2.6%	4.51	4.43	—
2019	21	A RAW	Rome Braves	28	24	132	581	121	46	10	86%	4.7	20.8%	7.9%	12.9%	1.7%	4.51	3.30	—
2021	23	MIX MLE	2 levels	22	21	105	425	136	26	11	95%	4.8	25.0%	7.0%	18.0%	3.7%	3.87	3.90	—
2021	23	MIX RAW	2 levels	22	21	105	425	136	26	11	95%	4.8	32.0%	6.1%	25.9%	2.6%	3.87	2.61	—
2022	24	MIX MLE	2 levels	27	27	118	526	144	52	15	100%	4.4	22.0%	10.8%	11.3%	3.8%	5.28	4.69	—
2022	24	MIX RAW	2 levels	27	27	118	526	144	52	15	100%	4.4	27.4%	9.9%	17.5%	2.9%	5.28	3.64	—
2023	25	MIX MLE	2 levels	26	25	127	548	140	50	18	96%	4.9	20.8%	9.7%	11.1%	4.8%	4.61	5.22	—
2023	25	MIX RAW	2 levels	26	25	127	548	140	50	18	96%	4.9	25.5%	9.1%	16.4%	3.3%	4.61	3.92	—
2024	26	MIX MLE	4 levels	18	13	57	227	62	14	6	72%	3.2	21.1%	6.4%	14.7%	3.5%	3.77	4.05	—
2024	26	MIX MLE	4 levels	18	13	57	227	62	14	6	72%	3.2	21.1%	6.4%	14.7%	3.5%	3.77	4.05	—
2024	26	MIX RAW	4 levels	18	13	57	227	62	14	6	72%	3.2	27.3%	6.2%	21.1%	2.6%	3.77	3.03	—
2025	27	MIX MLE	2 levels	30	25	136	576	139	46	30	83%	4.5	21.3%	6.8%	14.5%	6.4%	4.38	5.65	-0.4
2025	27	MIX MLE	2 levels	30	25	136	576	139	46	30	83%	4.5	21.3%	6.8%	14.5%	6.4%	4.38	5.65	-0.4
2025	27	MIX RAW	2 levels	30	25	136	576	139	46	30	83%	4.5	24.1%	8.0%	16.1%	5.2%	4.38	4.94	+0.7
2026	28	MIX MLE	2 levels	29	22	134	597	136	58	25	76%	4.6	20.3%	9.0%	11.3%	4.5%	5.64	4.99	+0.6
2026	28	MIX MLE	2 levels	29	22	134	597	136	58	25	76%	4.6	20.3%	9.0%	11.3%	4.5%	5.64	4.99	+0.6
2026	28	MIX RAW	2 levels	29	22	134	597	136	58	25	76%	4.6	22.8%	9.7%	13.1%	4.2%	5.64	4.79	+0.9
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			12	3	37	169	37	19	5	25%	3.1	21.9%	11.2%	10.7%	3.0%	4.86	4.40	+0.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	PHI	19	19	116	490	99	38	19	100%	6.1	20.1%	7.8%	12.4%	4.0%	4.69	4.94	+0.6
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	PHI	16	16	93	393	77	32	16	100%	5.8	19.6%	8.1%	11.6%	4.1%	4.89	5.07	+0.3
2029	31	MLB MLE	PHI	10	10	62	262	50	22	11	100%	6.2	19.1%	8.4%	10.7%	4.2%	5.08	5.24	+0.1
2028	30	MLB CTX	PHI	12	12	75	317	62	26	13	100%	6.2	19.6%	8.1%	11.6%	4.1%	4.89	5.07	+0.3
2029	31	MLB CTX	PHI	9	9	53	224	43	19	9	100%	5.9	19.1%	8.4%	10.7%	4.2%	5.08	5.24	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		33	24	165	710	142	63	27	73%	5.0	20.0%	8.9%	11.1%	3.8%	4.95	4.65	+1.4
/ 200 IP	—	RAW		40	29	200	860	172	76	33	73%	5.0	20.0%	8.9%	11.1%	3.8%	4.95	4.65	+1.7
Career	—	MLE		246	202	1076	4617	878	378	182	82%	4.4	19.0%	8.2%	10.8%	3.9%	4.58	4.70	+0.9
/ 200 IP	—	MLE		46	38	200	858	163	70	34	82%	4.4	19.0%	8.2%	10.8%	3.9%	4.58	4.70	+0.9

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 AAALehigh Valley IronPigs · 108 IPFIP 4.83 → 5.22 (+0.39)K% 22.6% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.57%	—	×0.860	19.41%
BB% (unintentional) /BF	9.49%	—	×0.900	8.54%
HR / BF /BF	4.43%	÷1.000	×1.100	4.87%

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 4.83 → 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).

2025 AAALehigh Valley IronPigs · 125 IPFIP 4.97 → 5.65 (+0.68)K% 24.7% → 21.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.72%	—	×0.860	21.26%
BB% (unintentional) /BF	7.55%	—	×0.900	6.79%
HR / BF /BF	5.47%	÷0.947	×1.100	6.35%

Result: FIP 4.97 → 5.65, 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).

2024 AAALehigh Valley IronPigs · 29 IPFIP 4.05 → 4.43 (+0.38)K% 21.6% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.55%	—	×0.860	18.53%
BB% (unintentional) /BF	6.90%	—	×0.900	6.21%
HR / BF /BF	3.45%	÷0.989	×1.100	3.83%

Result: FIP 4.05 → 4.43, 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).

2024 AARocket City Trash Pandas · 21 IPFIP 2.64 → 3.51 (+0.87)K% 30.5% → 24.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.49%	—	×0.810	24.70%
BB% (unintentional) /BF	6.10%	—	×1.100	6.71%
HR / BF /BF	2.44%	÷0.976	×1.200	3.00%

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 95).

Result: FIP 2.64 → 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).

2024 ROKACL Angels · 5 IPFIP 0.61 → 1.73 (+1.13)K% 36.4% → 24.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.36%	—	×0.680	24.73%
BB% (unintentional) /BF	4.55%	—	×1.250	5.68%
HR / BF /BF	0.00%	÷0.895	×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 79).

Result: FIP 0.61 → 1.73, 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 AAMississippi Braves · 112 IPFIP 3.76 → 5.22 (+1.47)K% 26.6% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.56%	—	×0.810	21.51%
BB% (unintentional) /BF	8.51%	—	×1.100	9.36%
HR / BF /BF	3.32%	÷0.793	×1.200	5.02%

Result: FIP 3.76 → 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).

2023 AAAGwinnett Stripers · 15 IPFIP 4.96 → 5.19 (+0.24)K% 18.2% → 15.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.18%	—	×0.860	15.64%
BB% (unintentional) /BF	13.64%	—	×0.900	12.27%
HR / BF /BF	3.03%	÷0.984	×1.100	3.39%

Result: FIP 4.96 → 5.19, 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 AAMississippi Braves · 115 IPFIP 3.65 → 4.69 (+1.03)K% 27.2% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.20%	—	×0.810	22.03%
BB% (unintentional) /BF	9.78%	—	×1.100	10.76%
HR / BF /BF	2.94%	÷0.927	×1.200	3.80%

Result: FIP 3.65 → 4.69, 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
Alan Rangel (OGILVIE)	—	—	—	—	—	4.0%	—	5.14	19.4%	11.2%	155	+0.20	—	—	—
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	77
Travis Smith R 2002 · 5.28 FIP	29	SP	5.28	13.1%	8.2%	4.1%	.324	5.71	15.0%	9.0%	51	+0.43	3.67	105	74
Fabio Castillo R 2017 · 3.98 FIP	28	SP-	3.98	20.9%	8.8%	2.8%	.291	—	—	—	—	—	3.13	195	72
Ariel Peña R 2017 · 3.99 FIP	28	SP	3.99	22.5%	12.0%	2.3%	.304	6.07	19.9%	-5.7%	21	+2.08	3.74	223	70
Yohan Pino R 2014 · 3.84 FIP	30	SP	3.84	19.4%	5.4%	3.1%	.312	4.45	17.1%	11.9%	133	+0.61	3.84	193	69
Reinier Roibal R 2019 · 4.91 FIP	30	SP	4.91	17.5%	11.8%	3.3%	.426	—	—	—	—	—	3.11	133	69
Rolando Valdez R 2015 · 4.07 FIP	29	SP	4.07	13.3%	5.9%	2.4%	.301	3.78	13.2%	7.9%	176	-0.29	3.06	258	68
Andrew Triggs R 2018 · 4.68 FIP	29	SP	4.68	23.6%	9.9%	4.2%	.263	—	—	—	—	—	1.66	242	67
Seth Greisinger R 2004 · 5.38 FIP	29	SP-	5.38	15.5%	6.0%	5.1%	.327	—	—	—	—	—	4.53	219	66
Stan Spencer R 2000 · 4.42 FIP	30	SP	4.42	19.2%	8.7%	3.4%	.259	—	—	—	—	—	3.61	119	66
Hiram Burgos R 2017 · 4.94 FIP	29	SP	4.94	19.3%	7.5%	4.6%	.351	—	—	—	—	—	3.40	384	65
Cody Anderson R 2019 · 4.33 FIP	28	SP	4.33	18.9%	7.5%	3.4%	.328	—	—	—	—	—	3.69	279	65
Erik Hiljus R 2002 · 6.07 FIP	29	SP	6.07	14.1%	9.7%	5.3%	.281	—	—	—	—	—	3.35	112	65
Tyler Herron R 2016 · 4.40 FIP	29	SP	4.40	13.4%	6.9%	2.9%	.389	4.02	16.4%	7.7%	60	-0.38	4.02	117	65
Marco Quevedo R 2016 · 4.57 FIP	29	SP	4.57	13.6%	6.2%	3.3%	.258	5.02	11.5%	3.2%	107	+0.45	4.13	300	64
Austin Bibens-Dirkx R 2015 · 3.97 FIP	30	SP	3.97	17.4%	7.1%	2.6%	.315	4.94	15.9%	8.7%	305	+0.97	3.97	567	64
Kohei Arihara R 2022 · 4.58 FIP	29	SP-	4.58	16.4%	7.2%	3.5%	.333	—	—	—	—	—	4.58	135	64
Zach Stewart R 2015 · 4.43 FIP	28	SP-	4.43	14.4%	8.3%	2.7%	.322	5.17	11.7%	2.1%	51	+0.74	4.27	194	63
David Griffin R 2023 · 5.83 FIP	27	SP-	5.83	14.2%	9.2%	5.0%	.306	—	—	—	—	—	4.43	239	63
Juan Delgadillo R 2015 · 4.54 FIP	32	SP	4.54	15.5%	7.2%	3.3%	.316	4.47	10.9%	4.7%	39	-0.07	4.47	143	63

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	1.7	87	20.0%	8.4%	1.52	4.83
Andrew Suárez ('23)	1.2	77	18.0%	8.1%	1.48	4.96
Alec Mills ('23)	1.3	86	19.3%	7.2%	1.50	5.00
Jharel Cotton ('22)	1.2	58	19.8%	9.2%	1.59	4.50
Mike Gardiner ('90-'95, -1 WAR*)	2.0	98	18.3%*	8.8%*	1.37*	5.10*
John Boozer ('62-'69, -2 WAR*)	2.0	66	21.1%*	9.0%*	1.46*	5.15*
John Strohmayer ('70-'74, -1 WAR*)	1.6	78	19.6%*	9.2%*	1.42*	5.14*