José Berríos

P Buffalo Bisons

Age 33 RHP 6'0" / 205 lbs AAA peak 0.8 · 2027 ILInjured 60-Day (D60)

Overview

AAASPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MLBTOR	32	32	192	3.60	4.43	19.5%	6.7%	2.2
2025	31	MLBTOR	31	30	166	4.17	4.45	19.8%	7.7%	1.8
2026	32	MIX2 levels	4	4	14	10.67	8.47	16.2%	10.3%	—	K 16.2% · BB 10.3% · FIP 7.88
2027PROJ	33	MLBTOR	9	9	52	4.48	4.71	19.8%	7.5%	0.4
2028PROJ	34	MLBTOR	16	16	95	4.80	5.01	18.9%	8.0%	0.4
2029PROJ	35	MLBTOR	10	10	59	5.13	5.35	17.9%	8.6%	0.0

Last 3 seasons WAR 4.0 · Next 3 projected WAR 0.9Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2025

K%34

FB Velo29

xwOBA vs29

CompsMiguel González (age 33) · Masato Yoshii (age 35) · Chuck Smith (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 123114vR 106

ERA4.48

FIP4.71

K%19.8%

BB%7.5%

K-BB%12.2%

Proj IP94

Value2027 · WAR

Runs saved vs league avg · 94 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2993.0

Whiff%

p2722.0%

p3419.8%

xwOBA vs

p290.337

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	21	MIX MLE	2 levels	27	27	166	667	175	38	12	100%	6.2	22.1%	6.2%	16.0%	1.9%	2.87	3.06	—
2015	21	MIX RAW	2 levels	27	27	166	667	175	38	12	100%	6.2	26.2%	5.7%	20.5%	1.8%	2.87	2.62	—
2016	22	MIX MLE	2 levels	31	31	170	713	174	71	20	100%	5.5	22.3%	10.2%	12.1%	3.0%	4.40	4.14	+2.5
2016	22	MIX RAW	2 levels	31	31	170	713	174	71	20	100%	5.5	24.4%	10.0%	14.4%	2.8%	4.40	3.84	+3.2
2017	23	MIX MLE	2 levels	32	31	185	768	178	56	17	97%	5.8	22.6%	7.3%	15.2%	2.2%	3.30	3.36	+4.7
2017	23	MIX RAW	2 levels	32	31	185	768	178	56	17	97%	5.8	23.2%	7.3%	15.9%	2.2%	3.30	3.28	+4.9
2018	24	MLB MLE	Minnesota Twins	32	32	192	797	202	60	25	100%	6.0	25.3%	7.5%	17.8%	3.1%	3.84	3.63	+4.1
2018	24	MLB RAW	Minnesota Twins	32	32	192	797	202	60	25	100%	6.0	25.3%	7.5%	17.8%	3.1%	3.84	3.63	+4.2
2019	25	MLB MLE	Minnesota Twins	32	32	200	842	195	51	26	100%	6.3	23.2%	6.1%	17.1%	3.1%	3.68	3.60	+4.4
2019	25	MLB RAW	Minnesota Twins	32	32	200	842	195	51	26	100%	6.3	23.2%	6.1%	17.1%	3.1%	3.68	3.60	+4.4
2020	26	MLB MLE	Minnesota Twins	12	12	63	271	68	26	8	100%	5.2	25.1%	9.6%	15.5%	2.9%	4.00	3.81	+1.2
2020	26	MLB RAW	Minnesota Twins	12	12	63	271	68	26	8	100%	5.2	25.1%	9.6%	15.5%	3.0%	4.00	3.83	+1.2
2021	27	MLB MLE	Toronto Blue Jays	32	32	192	781	204	45	22	100%	6.0	26.1%	5.8%	20.4%	2.8%	3.52	3.17	+5.3
2021	27	MLB RAW	Toronto Blue Jays	32	32	192	781	204	45	22	100%	6.0	26.1%	5.8%	20.4%	2.8%	3.52	3.17	+5.3
2022	28	MLB MLE	Toronto Blue Jays	32	32	172	753	149	45	29	100%	5.4	19.8%	6.0%	13.8%	3.9%	5.23	4.30	+2.2
2022	28	MLB RAW	Toronto Blue Jays	32	32	172	753	149	45	29	100%	5.4	19.8%	6.0%	13.8%	3.9%	5.23	4.34	+2.1
2023	29	MLB MLE	Toronto Blue Jays	32	32	190	782	184	49	25	100%	5.9	23.5%	6.3%	17.3%	3.2%	3.65	3.66	+4.0
2023	29	MLB RAW	Toronto Blue Jays	32	32	190	782	184	49	25	100%	5.9	23.5%	6.3%	17.3%	3.2%	3.65	3.65	+4.0
2024	30	MLB MLE	Toronto Blue Jays	32	32	192	786	153	53	31	100%	6.0	19.5%	6.7%	12.7%	3.9%	3.60	4.48	+2.1
2024	30	MLB MLE	Toronto Blue Jays	32	32	192	786	153	53	31	100%	6.0	19.5%	6.7%	12.7%	3.9%	3.60	4.48	+2.1
2024	30	MLB RAW	Toronto Blue Jays	32	32	192	786	153	53	31	100%	6.0	19.5%	6.7%	12.7%	3.9%	3.60	4.43	+2.2
2025	31	MLB MLE	Toronto Blue Jays	31	30	166	698	138	54	26	97%	5.4	19.8%	7.7%	12.0%	3.7%	4.17	4.45	+1.8
2025	31	MLB MLE	Toronto Blue Jays	31	30	166	698	138	54	26	97%	5.4	19.8%	7.7%	12.0%	3.7%	4.17	4.45	+1.8
2025	31	MLB RAW	Toronto Blue Jays	31	30	166	698	138	54	26	97%	5.4	19.8%	7.7%	12.0%	3.7%	4.17	4.45	+1.8
2026	32	MIX MLE	2 levels	4	4	14	68	11	7	6	100%	3.6	16.2%	10.3%	5.9%	8.8%	10.67	7.88	—
2026	32	MIX MLE	2 levels	4	4	14	68	11	7	6	100%	3.6	16.2%	10.3%	5.9%	8.8%	10.67	7.88	—
2026	32	MIX RAW	2 levels	4	4	14	68	11	7	6	100%	3.6	16.2%	10.3%	5.9%	8.8%	10.67	8.47	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			275	273	1572	6607	1481	466	219	99%	5.7	22.4%	7.1%	15.4%	3.3%	4.08	3.92	+27.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	TOR	9	9	52	220	43	17	8	100%	5.8	19.8%	7.5%	12.2%	3.5%	4.48	4.71	+0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	TOR	16	16	95	402	76	32	15	100%	6.0	18.9%	8.0%	10.8%	3.8%	4.80	5.01	+0.4
2029	35	MLB MLE	TOR	10	10	59	248	44	21	10	100%	5.9	17.9%	8.6%	9.3%	4.2%	5.13	5.35	+0.0
2028	34	MLB CTX	TOR	16	16	95	401	76	32	15	100%	5.9	18.9%	8.0%	10.8%	3.8%	4.80	5.01	+0.4
2029	35	MLB CTX	TOR	10	10	59	249	45	21	10	100%	5.9	17.9%	8.6%	9.3%	4.2%	5.13	5.35	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		301	299	1726	7257	1601	520	245	99%	5.7	22.1%	7.2%	14.9%	3.4%	4.16	4.00	+28.9
/ 200 IP	—	RAW		35	35	200	841	186	60	28	99%	5.7	22.1%	7.2%	14.9%	3.4%	4.16	4.00	+3.3
Career	—	MLE		329	327	1903	7926	1784	560	249	99%	5.8	22.5%	7.1%	15.4%	3.1%	3.87	3.82	+28.2
/ 200 IP	—	MLE		35	34	200	833	187	59	26	99%	5.8	22.5%	7.1%	15.4%	3.1%	3.87	3.82	+3.3

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 AAABuffalo Bisons · 8 IPFIP 11.23 → 11.81 (+0.58)K% 7.5% → 6.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	7.50%	—	×0.860	6.45%
BB% (unintentional) /BF	15.00%	—	×0.900	13.50%
HR / BF /BF	12.50%	÷1.000	×1.100	13.75%

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 11.23 → 11.81, 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).

2026 ADunedin Blue Jays · 7 IPFIP 3.10 → 4.40 (+1.30)K% 28.6% → 21.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.740	21.14%
BB% (unintentional) /BF	3.57%	—	×1.180	4.21%
HR / BF /BF	3.57%	÷1.000	×1.300	4.64%

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 100).

Result: FIP 3.10 → 4.40, 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 AAARochester Red Wings · 40 IPFIP 2.32 → 2.72 (+0.40)K% 25.7% → 22.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.66%	—	×0.880	22.58%
BB% (unintentional) /BF	5.26%	—	×1.050	5.53%
HR / BF /BF	1.32%	÷0.956	×1.100	1.51%

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 91).

Result: FIP 2.32 → 2.72, 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 AAARochester Red Wings · 111 IPFIP 2.73 → 3.23 (+0.50)K% 28.9% → 25.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.94%	—	×0.880	25.46%
BB% (unintentional) /BF	8.33%	—	×1.050	8.75%
HR / BF /BF	1.85%	÷0.951	×1.100	2.14%

Result: FIP 2.73 → 3.23, 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 AAChattanooga Lookouts · 91 IPFIP 2.71 → 3.17 (+0.46)K% 25.1% → 20.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.07%	—	×0.810	20.31%
BB% (unintentional) /BF	6.54%	—	×1.100	7.19%
HR / BF /BF	1.63%	÷1.232	×1.200	1.59%

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 146).

Result: FIP 2.71 → 3.17, 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 AAARochester Red Wings · 76 IPFIP 2.45 → 2.93 (+0.47)K% 27.7% → 24.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.67%	—	×0.880	24.35%
BB% (unintentional) /BF	4.67%	—	×1.050	4.90%
HR / BF /BF	2.00%	÷0.956	×1.100	2.30%

Result: FIP 2.45 → 2.93, 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
José Berríos (OGILVIE)	—	—	—	—	—	3.5%	—	5.14	18.5%	10.2%	154	+0.43	—	—	—
Miguel González R 2017 · 4.54 FIP	33	SP	4.54	14.6%	7.7%	3.1%	.285	6.88	7.6%	-1.5%	12	+2.34	3.72	905	70
Masato Yoshii R 2000 · 5.32 FIP	35	SP	5.32	12.1%	6.5%	4.4%	.302	4.38	13.1%	8.0%	244	-0.94	4.15	757	66
Chuck Smith R 2001 · 3.99 FIP	31	SP	3.99	18.4%	8.1%	2.6%	.289	—	—	—	—	—	3.08	211	64
Kenta Maeda R 2021 · 3.79 FIP	33	SP	3.79	24.9%	6.8%	3.5%	.307	4.26	23.6%	17.1%	230	+0.47	2.87	1,092	63
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	59
Steve Parris R 2001 · 5.31 FIP	33	SP	5.31	10.4%	7.9%	3.8%	.294	5.65	11.3%	3.5%	119	+0.34	3.60	753	59
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	58
Merrill Kelly R 2022 · 3.69 FIP	33	SP	3.69	22.0%	7.3%	2.8%	.267	3.76	23.5%	15.9%	435	+0.07	3.60	1,170	58
Jarrett Grube R 2015 · 3.75 FIP	33	SP	3.75	18.7%	4.2%	3.1%	.285	4.57	17.3%	9.6%	253	+0.82	3.75	393	55
Alfredo Simon R 2014 · 3.91 FIP	33	SP	3.91	15.5%	6.0%	2.5%	.256	4.88	13.7%	5.1%	261	+0.97	2.83	784	55
Jose Contreras R 2005 · 4.13 FIP	33	SP	4.13	18.0%	8.5%	2.7%	.254	4.04	14.4%	7.8%	506	-0.09	2.43	1,279	54
Cory Riordan R 2016 · 4.24 FIP	30	SP	4.24	14.3%	5.0%	3.1%	.341	—	—	—	—	—	4.24	154	53
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	53
Junior Guerra R 2018 · 3.99 FIP	33	SP	3.99	22.3%	9.0%	3.0%	.309	4.12	21.2%	9.1%	173	+0.13	3.18	646	52
Hector Ambriz R 2016 · 3.63 FIP	32	SP	3.63	16.0%	6.4%	2.0%	.273	6.40	10.4%	2.9%	91	+2.77	2.24	259	52
Scott Carroll R 2017 · 5.71 FIP	32	SP	5.71	14.4%	8.6%	5.0%	.300	—	—	—	—	—	3.92	377	51
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	51
Jonathan Castellanos R 2015 · 3.63 FIP	33	SP	3.63	12.2%	6.5%	1.3%	.313	4.15	11.8%	3.5%	216	+0.52	3.40	394	51
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	50
Jason Simontacchi R 2007 · 4.96 FIP	33	SP	4.96	13.0%	5.9%	4.0%	.331	—	—	—	—	—	4.79	356	50

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	9.2	141	21.9%	7.3%	1.31	4.18
Mike Minor ('22)	7.0	128	21.6%	7.0%	1.30	4.25
Jake Odorizzi ('22)	6.3	139	22.2%	8.1%	1.23	3.99
John Montefusco ('74-'86, 20 WAR*)	8.3	150	22.5%*	7.3%*	1.20*	4.16*
Ted Lilly ('13)	9.9	142	20.0%	7.9%	1.33	4.14
Mark Portugal ('85-'99, 18 WAR*)	9.1	130	18.0%*	7.7%*	1.27*	4.25*
Lynn McGlothen ('72-'82, 13 WAR*)	7.5	150	21.2%*	8.7%*	1.26*	4.50*