Endrys Briceno

P Salt Lake Bees

Age 35 RHP 6'5" / 175 lbs AAA

Overview

AAAPROSPECT

MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2018	26	MIX2 levels	18	3	46	4.70	3.88	17.7%	11.4%	—	K 14.0% · BB 13.2% · FIP 4.76
2023	31	MIX2 levels	14	0	16	5.17	6.10	18.9%	16.2%	—	K 16.4% · BB 14.6% · FIP 6.19
2025	33	MIX2 levels	30	0	43	11.51	7.70	17.3%	17.7%	—	K 14.7% · BB 15.4% · FIP 7.30
2026	34	No MLB games played
2027PROJ	35	MLBLAA	2	0	2	5.72	5.72	16.9%	13.5%	-0.0
2028PROJMLE	36	MLBLAA	—	—	0	5.79	5.92	16.9%	13.7%	—

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsIvan Maldonado (age 35) · Daniel Guerrero (age 34) · Brian Schlitter (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 142134vR 126

ERA5.72

FIP5.72

K%16.9%

BB%13.5%

K-BB%3.5%

Proj IP20

Value2027 · WAR

Runs saved vs league avg · 20 IP

Grades · Contract

Scouting Grades

K†4040

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	13	11	47	210	37	14	2	85%	3.6	12.7%	7.7%	5.0%	1.2%	3.47	3.67	—
2015	23	MIX RAW	2 levels	13	11	47	210	37	14	2	85%	3.6	17.6%	6.7%	11.0%	1.0%	3.47	2.97	—
2016	24	A+ MLE	Lakeland Flying Tigers	25	17	103	484	81	56	10	68%	4.1	12.9%	13.3%	-0.4%	2.2%	5.94	4.92	—
2016	24	A+ RAW	Lakeland Flying Tigers	25	17	103	484	81	56	10	68%	4.1	16.7%	11.6%	5.2%	2.1%	5.94	4.42	—
2017	25	A+ MLE	Lakeland Flying Tigers	27	6	58	253	36	24	3	22%	2.1	11.0%	10.9%	0.0%	0.8%	3.59	4.02	—
2017	25	A+ RAW	Lakeland Flying Tigers	27	6	58	253	36	24	3	22%	2.1	14.2%	9.5%	4.7%	1.2%	3.59	3.78	—
2018	26	MIX MLE	2 levels	18	3	46	220	39	25	3	17%	2.6	14.0%	13.2%	0.7%	2.1%	4.70	4.76	—
2018	26	MIX RAW	2 levels	18	3	46	220	39	25	3	17%	2.6	17.7%	11.4%	6.4%	1.4%	4.70	3.88	—
2023	31	MIX MLE	2 levels	14	0	16	74	14	12	3	0%	1.1	16.4%	14.6%	1.9%	4.8%	5.17	6.19	—
2023	31	MIX RAW	2 levels	14	0	16	74	14	12	3	0%	1.1	18.9%	16.2%	2.7%	4.1%	5.17	6.10	—
2025	33	MIX MLE	2 levels	30	0	43	226	39	40	12	0%	1.4	14.7%	15.4%	-0.7%	6.4%	11.51	7.30	—
2025	33	MIX MLE	2 levels	30	0	43	226	39	40	12	0%	1.4	14.7%	15.4%	-0.7%	6.4%	11.51	7.30	—
2025	33	MIX RAW	2 levels	30	0	43	226	39	40	12	0%	1.4	17.3%	17.7%	-0.4%	5.3%	11.51	7.70	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	LAA	2	0	2	8	1	1	0	0%	1.0	16.9%	13.5%	3.5%	3.6%	5.72	5.72	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	LAA	14	0	14	61	10	8	2	0%	1.0	16.9%	13.7%	3.2%	3.9%	5.79	5.92	-0.2
2028	36	MLB CTX	LAA	—	—	0	—	—	—	—	—	—	16.9%	13.7%	3.2%	3.9%	5.79	5.92	—
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		0	0	0	1	0	0	0	—	—	16.9%	13.7%	3.2%	3.9%	5.79	5.54	-0.0
/ 200 IP	—	RAW		190	0	200	844	0	0	0	0%	1.1	16.9%	13.7%	3.2%	3.9%	5.79	5.54	-2.3
Career	—	MLE		127	37	312	1467	193	183	38	29%	2.5	13.1%	12.5%	0.7%	2.6%	5.68	4.99	-0.2
/ 200 IP	—	MLE		81	24	200	940	124	117	24	29%	2.5	13.1%	12.5%	0.7%	2.6%	5.68	4.99	-3.1

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 AAASalt Lake Bees · 39 IPFIP 6.96 → 7.30 (+0.34)K% 17.1% → 14.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.14%	—	×0.860	14.74%
BB% (unintentional) /BF	17.14%	—	×0.900	15.43%
HR / BF /BF	5.71%	÷0.989	×1.100	6.35%

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 98).

Result: FIP 6.96 → 7.30, 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 AAAReno Aces · 15 IPFIP 5.96 → 6.19 (+0.23)K% 19.1% → 16.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.12%	—	×0.860	16.44%
BB% (unintentional) /BF	16.18%	—	×0.900	14.56%
HR / BF /BF	4.41%	÷1.012	×1.100	4.80%

Result: FIP 5.96 → 6.19, 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).

2018 AWest Michigan Whitecaps · 42 IPFIP 3.77 → 4.76 (+0.99)K% 18.9% → 14.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.88%	—	×0.740	13.97%
BB% (unintentional) /BF	11.22%	—	×1.180	13.24%
HR / BF /BF	1.53%	÷0.942	×1.300	2.11%

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 88).

Result: FIP 3.77 → 4.76, 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 A+Lakeland Flying Tigers · 4 IPFIP 3.98 → 4.38 (+0.40)K% 8.3% → 6.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	8.33%	—	×0.770	6.42%
BB% (unintentional) /BF	12.50%	—	×1.150	14.37%
HR / BF /BF	0.00%	÷1.277	×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 155).

Result: FIP 3.98 → 4.38, 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 A+Lakeland Flying Tigers · 58 IPFIP 3.75 → 4.02 (+0.27)K% 14.2% → 11.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.23%	—	×0.770	10.96%
BB% (unintentional) /BF	9.49%	—	×1.150	10.91%
HR / BF /BF	1.19%	÷1.747	×1.250	0.85%

Result: FIP 3.75 → 4.02, 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 A+Lakeland Flying Tigers · 103 IPFIP 4.29 → 4.92 (+0.63)K% 16.7% → 12.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.74%	—	×0.770	12.89%
BB% (unintentional) /BF	11.57%	—	×1.150	13.31%
HR / BF /BF	2.07%	÷1.161	×1.250	2.23%

Result: FIP 4.29 → 4.92, 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+Lakeland Flying Tigers · 33 IPFIP 4.02 → 4.58 (+0.57)K% 10.5% → 8.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.53%	—	×0.770	8.11%
BB% (unintentional) /BF	8.55%	—	×1.150	9.84%
HR / BF /BF	1.32%	÷0.981	×1.250	1.68%

Result: FIP 4.02 → 4.58, 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 ROKGCL Tigers · 14 IPFIP 0.26 → 1.29 (+1.03)K% 36.2% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	36.21%	—	×0.680	24.62%
BB% (unintentional) /BF	1.72%	—	×1.250	2.16%
HR / BF /BF	0.00%	÷1.000	×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 100).

Result: FIP 0.26 → 1.29, 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
Endrys Briceno (OGILVIE)	—	—	5.72	16.9%	13.5%	3.6%	—	5.92	16.9%	3.2%	14	+0.20	—	—	—
Ivan Maldonado R 2015 · 4.60 FIP	35	SW	4.60	12.8%	8.7%	2.7%	.269	5.41	6.4%	-3.3%	104	+0.81	4.60	172	72
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	70
Brian Schlitter R 2021 · 4.70 FIP	35	SU	4.70	14.9%	9.1%	3.1%	.318	—	—	—	—	—	3.27	300	69
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	65
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	64
Jose Manuel Lopez R 2019 · 6.14 FIP	34	SU	6.14	17.9%	14.2%	5.0%	.299	—	—	—	—	—	3.50	170	63
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	63
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	62
Pete Walker R 2003 · 5.73 FIP	34	SW	5.73	12.0%	9.1%	4.5%	.267	4.70	14.0%	5.1%	114	-1.03	4.45	327	61
Ramon Ramirez R 2016 · 5.25 FIP	33	SW	5.25	9.2%	14.1%	2.1%	.343	—	—	—	—	—	3.65	182	59
Fernando Rodriguez Jr. R 2019 · 5.11 FIP	35	SW	5.11	25.0%	11.2%	4.9%	.312	—	—	—	—	—	3.34	335	58
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	57
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	55
Esmerling Vasquez R 2019 · 5.15 FIP	35	SP-	5.15	8.7%	16.8%	1.2%	.238	—	—	—	—	—	3.70	204	52
Giovanni Carrara R 2003 · 6.00 FIP	35	SU	6.00	9.5%	10.2%	4.4%	.327	3.53	19.3%	10.8%	157	-2.47	2.50	462	52
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	50
Todd Williams R 2006 · 4.79 FIP	35	SU	4.79	9.2%	6.2%	3.1%	.321	4.42	14.1%	7.9%	14	-0.37	3.51	223	45
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	44
Yohan Pino R 2017 · 5.33 FIP	33	SP-	5.33	15.2%	3.6%	5.6%	.296	—	—	—	—	—	3.84	193	44
Cesar Carrillo R 2019 · 6.22 FIP	35	SP-	6.22	11.0%	9.2%	5.3%	.392	—	—	—	—	—	4.14	336	44