Daniel Palencia

P Chicago Cubs

Age 27 RHP 5'11" / 160 lbs MLB peak 0.6 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	24	MIX2 levels	43	1	54	5.20	2.60	34.9%	14.1%	1.3	K 30.9% · BB 13.2% · FIP 2.86
2025	25	MIX2 levels	60	0	57	2.83	2.68	29.7%	7.2%	1.3	K 28.4% · BB 7.0% · FIP 2.91
2026	26	MIX3 levels	38	0	35	4.41	3.85	25.0%	10.8%	0.3	K 24.4% · BB 8.1% · FIP 3.92
2027PROJ	27	MLBCHC	57	0	60	3.73	3.58	29.2%	8.3%	0.7
2028PROJ95% MLB	28	MLBCHC	50	0	53	3.69	3.67	29.1%	8.4%	0.6
2029PROJ95% MLB	29	MLBCHC	44	0	46	3.77	3.83	28.5%	8.6%	0.4

Last 3 seasons WAR 2.8 · Next 3 projected WAR 1.7Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs63

K%62

CompsJumbo Diaz (age 30) · Kyle Finnegan (age 27) · Raisel Iglesias (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 10193vR 85

ERA3.73

FIP3.58

K%29.2%

BB%8.3%

K-BB%20.9%

Proj IP64

Value2027 · WAR

Runs saved vs league avg · 64 IP

Grades · Statcast · Contract

Scouting Grades

STUF5555

CTRL4545

CMD5555

K†6565

Statcast Percentiles

FB Velo

p9099.2

Whiff%

p6127.0%

p6224.4%

xwOBA vs

p630.299

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)
2021	21	A MLE	Myrtle Beach Pelicans	13	13	41	180	52	24	5	100%	3.2	21.4%	15.7%	5.6%	3.7%	4.79	5.32	—
2021	21	A RAW	Myrtle Beach Pelicans	13	13	41	180	52	24	5	100%	3.2	28.9%	13.3%	15.6%	2.8%	4.79	3.90	—
2022	22	A+ MLE	South Bend Cubs	21	20	75	315	98	35	7	95%	3.6	24.0%	12.8%	11.2%	3.0%	3.94	4.32	—
2022	22	A+ RAW	South Bend Cubs	21	20	75	315	98	35	7	95%	3.6	31.1%	11.1%	20.0%	2.2%	3.94	3.10	—
2023	23	MIX MLE	3 levels	45	5	57	251	69	30	7	11%	1.3	25.1%	12.0%	13.1%	3.0%	5.65	4.16	+0.3
2023	23	MIX RAW	3 levels	45	5	57	251	69	30	7	11%	1.3	27.5%	12.0%	15.5%	2.8%	5.65	3.85	+0.5
2024	24	MIX MLE	2 levels	43	1	54	241	84	34	3	2%	1.2	30.9%	13.2%	17.7%	1.3%	5.20	2.86	+1.1
2024	24	MIX MLE	2 levels	43	1	54	241	84	34	3	2%	1.2	30.9%	13.2%	17.7%	1.3%	5.20	2.86	+1.1
2024	24	MIX RAW	2 levels	43	1	54	241	84	34	3	2%	1.2	34.9%	14.1%	20.7%	1.2%	5.20	2.60	+1.3
2025	25	MIX MLE	2 levels	60	0	57	236	70	17	5	0%	1.0	28.4%	7.0%	21.4%	2.4%	2.83	2.91	+1.1
2025	25	MIX MLE	2 levels	60	0	57	236	70	17	5	0%	1.0	28.4%	7.0%	21.4%	2.4%	2.83	2.91	+1.1
2025	25	MIX RAW	2 levels	60	0	57	236	70	17	5	0%	1.0	29.7%	7.2%	22.5%	2.1%	2.83	2.68	+1.3
2026	26	MIX MLE	3 levels	38	0	35	148	37	16	4	0%	0.9	24.4%	8.1%	16.3%	3.4%	4.41	3.92	+0.3
2026	26	MIX MLE	3 levels	38	0	35	148	37	16	4	0%	0.9	24.4%	8.1%	16.3%	3.4%	4.41	3.92	+0.3
2026	26	MIX RAW	3 levels	38	0	35	148	37	16	4	0%	0.9	25.0%	10.8%	14.2%	2.7%	4.41	3.85	+0.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			123	0	125	526	140	51	12	0%	1.0	26.6%	9.7%	16.9%	2.3%	3.81	3.33	+1.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	CHC	57	0	60	253	74	21	7	0%	1.1	29.2%	8.3%	20.9%	2.8%	3.73	3.58	+0.7
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	CHC	53	0	55	233	68	20	7	0%	1.0	29.1%	8.4%	20.7%	2.9%	3.69	3.67	+0.6
2029	29	MLB MLE	CHC	46	0	48	202	58	17	6	0%	1.0	28.5%	8.6%	19.9%	3.0%	3.77	3.83	+0.4
2028	28	MLB CTX	CHC	50	0	53	224	65	19	6	0%	1.1	29.1%	8.4%	20.7%	2.9%	3.69	3.67	+0.6
2029	29	MLB CTX	CHC	44	0	46	194	55	17	6	0%	1.0	28.5%	8.6%	19.9%	3.0%	3.77	3.83	+0.4
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		216	0	223	940	259	86	24	0%	1.0	27.6%	9.2%	18.4%	2.6%	3.77	3.35	+3.2
/ 200 IP	—	RAW		194	0	200	842	232	77	21	0%	1.0	27.6%	9.2%	18.4%	2.6%	3.77	3.35	+2.9
Career	—	MLE		220	39	320	1371	355	159	37	18%	1.5	25.9%	11.6%	14.3%	2.7%	4.42	3.88	+2.8
/ 200 IP	—	MLE		138	24	200	858	222	100	23	18%	1.5	25.9%	11.6%	14.3%	2.7%	4.42	3.88	+2.5

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 AAAIowa Cubs · 5 IPFIP 0.69 → 1.07 (+0.39)K% 42.9% → 36.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	42.86%	—	×0.860	36.86%
BB% (unintentional) /BF	9.52%	—	×0.900	8.57%
HR / BF /BF	0.00%	÷1.125	×1.100	0.00%

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 125).

Result: FIP 0.69 → 1.07, 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 AAAIowa Cubs · 39 IPFIP 2.34 → 2.66 (+0.32)K% 39.5% → 34.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.53%	—	×0.860	34.00%
BB% (unintentional) /BF	12.79%	—	×0.900	11.51%
HR / BF /BF	1.74%	÷1.079	×1.100	1.78%

Result: FIP 2.34 → 2.66, 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 AAAIowa Cubs · 14 IPFIP 2.97 → 3.28 (+0.30)K% 27.3% → 23.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.27%	—	×0.860	23.45%
BB% (unintentional) /BF	10.61%	—	×0.900	9.55%
HR / BF /BF	1.52%	÷0.965	×1.100	1.73%

Result: FIP 2.97 → 3.28, 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 AATennessee Smokies · 15 IPFIP 5.02 → 5.90 (+0.88)K% 27.3% → 22.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.27%	—	×0.810	22.09%
BB% (unintentional) /BF	13.64%	—	×1.100	15.00%
HR / BF /BF	4.55%	÷1.082	×1.200	5.04%

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 116).

Result: FIP 5.02 → 5.90, 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).

2022 A+South Bend Cubs · 75 IPFIP 3.10 → 4.32 (+1.22)K% 31.1% → 24.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.11%	—	×0.770	23.96%
BB% (unintentional) /BF	11.11%	—	×1.150	12.78%
HR / BF /BF	2.22%	÷0.938	×1.250	2.96%

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 88).

Result: FIP 3.10 → 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).

2021 AMyrtle Beach Pelicans · 41 IPFIP 3.87 → 5.32 (+1.44)K% 28.9% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.89%	—	×0.740	21.38%
BB% (unintentional) /BF	13.33%	—	×1.180	15.73%
HR / BF /BF	2.78%	÷0.977	×1.300	3.69%

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 95).

Result: FIP 3.87 → 5.32, 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).

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 Palencia (OGILVIE)	—	—	3.58	29.2%	8.3%	2.8%	—	3.74	28.8%	20.3%	103	+0.16	—	—	—
Jumbo Diaz R 2014 · 2.87 FIP	30	SU	2.87	26.1%	7.0%	2.0%	.283	3.64	23.0%	15.0%	186	+0.77	2.80	319	72
Kyle Finnegan R 2019 · 2.57 FIP	27	SU	2.57	29.5%	9.3%	1.4%	.319	3.77	24.7%	15.9%	157	+1.20	2.57	753	71
Raisel Iglesias R 2017 · 2.47 FIP	27	SU	2.47	30.1%	8.5%	1.5%	.284	3.30	30.3%	23.6%	162	+0.83	1.34	825	70
Nate Pearson R 2024 · 4.24 FIP	27	SU	4.24	26.0%	7.4%	4.4%	.309	3.47	21.1%	9.0%	105	-0.77	3.37	413	69
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	69
Arquimedes Caminero R 2013 · 4.10 FIP	26	SU	4.10	23.1%	5.8%	4.0%	.229	3.95	20.7%	10.9%	135	-0.15	3.54	186	69
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	68
Nate Jones R 2013 · 2.58 FIP	27	SU	2.58	28.3%	7.9%	1.6%	.327	3.03	31.0%	25.7%	90	+0.45	2.57	307	68
Drew Steckenrider R 2018 · 3.31 FIP	27	SU	3.31	27.2%	8.1%	2.7%	.284	3.92	22.1%	16.0%	82	+0.61	2.61	392	68
Will Vest R 2022 · 3.43 FIP	27	SU	3.43	23.2%	8.1%	2.3%	.309	2.91	25.4%	18.5%	200	-0.52	2.56	448	68
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	68
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	67
Connor Brogdon R 2022 · 3.23 FIP	27	SU	3.23	26.6%	5.9%	3.0%	.314	4.84	23.5%	14.7%	139	+1.61	2.98	402	67
Matt Barnes R 2017 · 3.27 FIP	27	SU	3.27	28.9%	9.8%	2.5%	.296	3.03	36.3%	24.3%	149	-0.24	2.54	495	67
Kyle Keller R 2019 · 3.83 FIP	26	SU	3.83	28.7%	9.4%	3.6%	.293	4.88	28.0%	17.8%	52	+1.05	2.72	296	67
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	67
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	67
JT Chargois R 2018 · 3.36 FIP	27	SU	3.36	24.4%	9.9%	2.0%	.282	3.48	25.9%	17.5%	108	+0.12	2.58	362	66
Ben Heller R 2017 · 2.86 FIP	25	SU	2.86	32.4%	9.4%	2.4%	.243	—	—	—	—	—	2.49	246	66
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	66

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	1.5	49	28.0%	9.1%	1.02	3.76
Danny Farquhar ('18)	1.4	54	26.9%	9.2%	0.99	3.93
Josh Fields ('18)	1.4	46	27.5%	8.0%	0.98	3.71
Diego Castillo ('24)	1.4	56	27.2%	9.6%	1.07	3.20
Alberto Reyes ('95-'08, 5 WAR*)	2.1	54	27.1%*	10.2%*	1.18*	3.66*
Carlos Diaz ('82-'86, 2 WAR*)	1.3	52	25.1%*	8.7%*	0.91*	3.84*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*