Daniel Camarena

P El Paso Chihuahuas

Age 34 LHP 6'0" / 210 lbs AAA

Overview

AAARPROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2021	28	MIX2 levels	28	19	93	5.24	4.44	16.7%	7.5%	1.0	K 14.9% · BB 8.3% · FIP 4.47
2023	30	AASD	25	17	70	6.20	3.92	17.3%	5.1%	—	K 14.0% · BB 5.6% · FIP 4.94
2024	31	MIX5 levels	32	9	59	7.74	7.30	14.7%	11.5%	—	K 13.2% · BB 10.8% · FIP 6.81
2025	32	No MLB games played
2026	33	No MLB games played
2027PROJ	34	MLBSD	3	3	16	4.86	4.86	19.4%	9.7%	0.1
2028PROJ6% MLB	35	MLBSD	1	1	6	4.98	5.10	19.0%	10.1%	0.0
2029PROJMLE	36	MLBSD	—	—	0	5.19	5.38	18.6%	10.5%	—

Last 3 seasons WAR 1.0 · Next 3 projected WAR 0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsJuan Antonio Pena (age 37) · Daniel Rodriguez (age 34) · Orlando Lara (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 109117vR 125

ERA4.86

FIP4.86

K%19.4%

BB%9.7%

K-BB%9.7%

Proj IP162

Value2027 · WAR

Runs saved vs league avg · 162 IP

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	23	MIX MLE	3 levels	29	22	142	575	114	24	14	76%	4.9	16.3%	4.4%	11.9%	2.7%	3.68	3.76	—
2016	23	MIX RAW	3 levels	29	22	142	575	114	24	14	76%	4.9	19.8%	4.2%	15.7%	2.4%	3.68	3.28	—
2017	24	MIX MLE	2 levels	22	22	118	497	78	31	8	100%	5.4	13.1%	6.8%	6.4%	1.8%	3.65	3.83	—
2017	24	MIX RAW	2 levels	22	22	118	497	78	31	8	100%	5.4	15.7%	6.2%	9.5%	1.6%	3.65	3.45	—
2018	25	MIX MLE	2 levels	25	24	126	570	100	51	13	96%	5.0	15.9%	9.7%	6.2%	2.4%	5.30	4.32	—
2018	25	MIX RAW	2 levels	25	24	126	570	100	51	13	96%	5.0	17.5%	8.9%	8.6%	2.3%	5.30	4.07	—
2019	26	AAA MLE	Scranton/Wilkes-Barre RailRiders	18	16	103	447	94	21	22	89%	5.7	18.5%	4.9%	13.6%	5.0%	6.27	4.90	—
2019	26	AAA RAW	Scranton/Wilkes-Barre RailRiders	18	16	103	447	94	21	22	89%	5.7	21.0%	4.7%	16.3%	4.9%	6.27	4.66	—
2021	28	MIX MLE	2 levels	28	19	93	413	69	31	13	68%	3.3	14.9%	8.3%	6.6%	2.9%	5.24	4.47	+1.0
2021	28	MIX RAW	2 levels	28	19	93	413	69	31	13	68%	3.3	16.7%	7.5%	9.2%	3.1%	5.24	4.44	+1.0
2023	30	AA MLE	San Antonio Missions	25	17	70	313	54	16	9	68%	2.8	14.0%	5.6%	8.4%	4.2%	6.20	4.94	—
2023	30	AA RAW	San Antonio Missions	25	17	70	313	54	16	9	68%	2.8	17.3%	5.1%	12.1%	2.9%	6.20	3.92	—
2024	31	MIX MLE	5 levels	32	9	59	286	42	33	18	28%	1.9	13.2%	10.8%	2.4%	6.3%	7.74	6.81	—
2024	31	MIX MLE	5 levels	32	9	59	286	42	33	18	28%	1.9	13.2%	10.8%	2.4%	6.3%	7.74	6.81	—
2024	31	MIX RAW	5 levels	32	9	59	286	42	33	18	28%	1.9	14.7%	11.5%	3.1%	6.3%	7.74	7.30	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			6	0	9	47	7	2	2	0%	1.6	14.9%	4.3%	10.6%	4.3%	9.64	5.03	-0.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	SD	3	3	16	68	13	7	2	100%	5.3	19.4%	9.7%	9.7%	3.3%	4.86	4.86	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	SD	19	19	112	473	90	48	17	100%	5.9	19.0%	10.1%	9.0%	3.6%	4.98	5.10	+0.4
2029	36	MLB MLE	SD	14	14	84	356	66	37	14	100%	6.0	18.6%	10.5%	8.1%	3.9%	5.19	5.38	+0.0
2028	35	MLB CTX	SD	1	1	6	25	5	3	1	100%	6.0	19.0%	10.1%	9.0%	3.6%	4.98	5.10	+0.0
2029	36	MLB CTX	SD	—	—	0	—	—	—	—	—	—	18.6%	10.5%	8.1%	3.9%	5.19	5.38	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		7	1	15	73	12	5	3	14%	2.2	16.4%	6.3%	10.1%	4.0%	7.79	4.72	-0.0
/ 200 IP	—	RAW		91	13	200	942	155	65	39	15%	2.2	16.4%	6.3%	10.1%	4.0%	7.79	4.72	-0.4
Career	—	MLE		179	129	711	3101	476	219	104	72%	4.0	15.3%	7.1%	8.3%	3.3%	5.13	4.53	+0.4
/ 200 IP	—	MLE		50	36	200	873	134	62	29	72%	4.0	15.3%	7.1%	8.3%	3.3%	5.13	4.53	+0.4

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 AAAEl Paso Chihuahuas · 36 IPFIP 7.35 → 7.45 (+0.10)K% 15.9% → 13.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.93%	—	×0.860	13.70%
BB% (unintentional) /BF	13.19%	—	×0.900	11.87%
HR / BF /BF	7.14%	÷1.078	×1.100	7.29%

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 116).

Result: FIP 7.35 → 7.45, 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).

2024 AASan Antonio Missions · 15 IPFIP 4.39 → 5.12 (+0.74)K% 14.5% → 11.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.49%	—	×0.810	11.74%
BB% (unintentional) /BF	7.25%	—	×1.100	7.97%
HR / BF /BF	2.90%	÷0.953	×1.200	3.65%

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 91).

Result: FIP 4.39 → 5.12, 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).

2024 ALake Elsinore Storm · 5 IPFIP 7.32 → 8.77 (+1.45)K% 5.0% → 3.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	5.00%	—	×0.740	3.70%
BB% (unintentional) /BF	15.00%	—	×1.180	17.70%
HR / BF /BF	5.00%	÷0.953	×1.300	6.82%

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 91).

Result: FIP 7.32 → 8.77, 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 AASan Antonio Missions · 70 IPFIP 3.87 → 4.94 (+1.07)K% 17.3% → 14.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.25%	—	×0.810	13.97%
BB% (unintentional) /BF	5.11%	—	×1.100	5.62%
HR / BF /BF	2.88%	÷0.820	×1.200	4.21%

Result: FIP 3.87 → 4.94, 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 AAAEl Paso Chihuahuas · 83 IPFIP 4.32 → 4.47 (+0.14)K% 16.9% → 14.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.94%	—	×0.880	14.91%
BB% (unintentional) /BF	7.92%	—	×1.050	8.32%
HR / BF /BF	3.01%	÷1.154	×1.100	2.86%

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 131).

Result: FIP 4.32 → 4.47, 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 · 103 IPFIP 4.62 → 4.90 (+0.27)K% 21.0% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.03%	—	×0.880	18.51%
BB% (unintentional) /BF	4.70%	—	×1.050	4.93%
HR / BF /BF	4.92%	÷1.087	×1.100	4.98%

Result: FIP 4.62 → 4.90, 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 AAASacramento River Cats · 119 IPFIP 3.96 → 4.32 (+0.37)K% 18.1% → 15.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.08%	—	×0.880	15.91%
BB% (unintentional) /BF	9.23%	—	×1.050	9.69%
HR / BF /BF	2.21%	÷0.998	×1.100	2.44%

Result: FIP 3.96 → 4.32, 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 AARichmond Flying Squirrels · 7 IPFIP 4.91 → 5.82 (+0.91)K% 7.1% → 5.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	7.14%	—	×0.810	5.79%
BB% (unintentional) /BF	3.57%	—	×1.100	3.93%
HR / BF /BF	3.57%	÷0.869	×1.200	4.93%

Result: FIP 4.91 → 5.82, 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
Daniel Camarena (OGILVIE)	—	—	—	—	—	3.3%	—	5.22	18.8%	8.6%	196	+0.36	—	—	—
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	72
Daniel Rodriguez L 2019 · 4.77 FIP	34	SP	4.77	20.8%	7.8%	4.5%	.329	—	—	—	—	—	2.89	391	67
Orlando Lara L 2019 · 4.09 FIP	34	SP	4.09	22.0%	7.8%	3.4%	.329	—	—	—	—	—	3.85	426	66
Kwang Hyun Kim L 2021 · 4.21 FIP	33	SP-	4.21	17.7%	8.2%	2.9%	.266	—	—	—	—	—	3.76	146	63
Oscar Rivera L 2015 · 3.68 FIP	34	SP	3.68	14.2%	6.9%	1.7%	.298	4.66	10.4%	3.0%	129	+0.98	3.68	237	61
Chris Narveson L 2016 · 4.59 FIP	34	SP-	4.59	16.2%	8.5%	3.2%	.253	4.59	12.2%	1.8%	103	+0.00	4.00	625	58
Arturo Lopez L 2017 · 4.34 FIP	34	SP	4.34	10.3%	5.4%	2.6%	.342	5.46	12.2%	5.4%	117	+1.12	4.34	445	57
Marlon Arias L 2019 · 4.83 FIP	34	SP	4.83	19.8%	11.2%	3.6%	.273	—	—	—	—	—	3.73	154	52
Jorge Castillo L 2016 · 4.94 FIP	34	SP	4.94	13.0%	4.6%	4.3%	.246	4.93	16.8%	9.2%	179	-0.01	4.50	408	51
Tomas Solis L 2017 · 5.48 FIP	34	SP	5.48	11.3%	7.9%	4.2%	.322	6.44	8.9%	1.4%	57	+0.96	4.20	327	50
Luke Westphal L 2022 · 4.35 FIP	33	SW	4.35	24.2%	13.4%	2.9%	.302	5.19	15.7%	-0.7%	26	+0.84	2.97	294	48
Chris Michalak L 2006 · 5.71 FIP	35	SP-	5.71	6.2%	8.6%	3.7%	.273	—	—	—	—	—	4.71	186	41
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	40
Francisley Bueno L 2015 · 3.77 FIP	34	SP	3.77	14.8%	7.1%	1.9%	.315	3.99	13.3%	6.6%	110	+0.22	2.43	188	39
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	38
Paul Oseguera L 2016 · 5.62 FIP	32	SP	5.62	5.6%	12.5%	2.6%	.320	—	—	—	—	—	3.83	162	34
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	33
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	33
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	31
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	31

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	1.8	123	19.0%	9.9%	1.36	5.09
Kevin Foster ('01)	2.5	127	18.9%	10.0%	1.55	4.86
Jeff Robinson ('87-'92, 0 WAR*)	3.5	118	18.3%*	10.9%*	1.38*	5.14*
James Kaprielian ('23)	1.6	106	20.5%	9.7%	1.30	4.61
Dave Stenhouse ('62-'64, 2 WAR*)	1.9	124	17.9%*	10.5%*	1.33*	4.51*
Chris Knapp ('75-'80, -1 WAR*)	3.0	121	21.3%*	9.3%*	1.41*	5.48*
Brandon Claussen ('06)	1.6	105	16.0%	8.5%	1.37	5.04