José Rodríguez

P Buffalo Bisons

Age 26 RHP 6'6" / 200 lbs AAA peak 0.1 · 2027

Overview

AAARP40-MANROOKIE

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	23	MIX2 levels	25	8	91	3.38	1.81	32.9%	8.4%	—	K 24.9% · BB 9.7% · FIP 2.75
2025	24	MIX2 levels	45	2	54	5.50	3.80	34.1%	13.8%	—	K 29.0% · BB 13.1% · FIP 4.39
2026	25	MIX3 levels	32	3	43	3.38	2.40	31.1%	12.8%	1.1	K 24.3% · BB 13.0% · FIP 3.11
2027PROJ	26	MLBTOR	38	0	40	4.28	4.28	27.7%	13.4%	0.1
2028PROJ78% MLB	27	MLBTOR	37	0	39	4.31	4.32	27.2%	13.1%	0.1
2029PROJ84% MLB	28	MLBTOR	35	0	37	4.37	4.41	26.8%	13.0%	0.1

Last 3 seasons WAR 1.1 · Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0.0 WAR median (P10 -0.3 / P90 1.7, comp-based) · star 0% · didn't stick 91%

CompsPeyton Gray (age 26) · Zach Franklin (age 25) · Brandon Cunniff (age 26)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 115106vR 98

ERA4.28

FIP4.28

K%27.7%

BB%13.4%

K-BB%14.2%

Proj IP60

Value2027 · WAR

Runs saved vs league avg · 60 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)
2021	20	ROK MLE	DSL LAD Bautista	15	0	18	86	18	8	2	0%	1.2	14.2%	11.6%	2.6%	2.7%	5.40	4.85	—
2021	20	ROK RAW	DSL LAD Bautista	15	0	18	86	18	8	2	0%	1.2	20.9%	9.3%	11.6%	2.3%	5.40	3.86	—
2022	21	ROK MLE	DSL LAD Mega	17	0	19	68	30	5	1	0%	1.1	30.0%	9.2%	20.8%	2.1%	0.47	2.89	—
2022	21	ROK RAW	DSL LAD Mega	17	0	19	68	30	5	1	0%	1.1	44.1%	7.4%	36.8%	1.5%	0.47	1.42	—
2023	22	MIX MLE	2 levels	20	0	25	106	33	3	2	0%	1.2	21.5%	3.8%	17.7%	1.4%	5.11	2.55	—
2023	22	MIX RAW	2 levels	20	0	25	106	33	3	2	0%	1.2	31.1%	2.8%	28.3%	1.9%	5.11	1.84	—
2024	23	MIX MLE	2 levels	25	8	91	383	126	32	3	32%	3.6	24.9%	9.7%	15.2%	0.9%	3.38	2.75	—
2024	23	MIX MLE	2 levels	25	8	91	383	126	32	3	32%	3.6	24.9%	9.7%	15.2%	0.9%	3.38	2.75	—
2024	23	MIX RAW	2 levels	25	8	91	383	126	32	3	32%	3.6	32.9%	8.4%	24.5%	0.8%	3.38	1.81	—
2025	24	MIX MLE	2 levels	45	2	54	246	84	34	8	4%	1.2	29.0%	13.1%	15.9%	3.8%	5.50	4.39	—
2025	24	MIX MLE	2 levels	45	2	54	246	84	34	8	4%	1.2	29.0%	13.1%	15.9%	3.8%	5.50	4.39	—
2025	24	MIX RAW	2 levels	45	2	54	246	84	34	8	4%	1.2	34.1%	13.8%	20.3%	3.3%	5.50	3.80	—
2026	25	MIX MLE	3 levels	32	3	43	180	56	23	1	9%	1.3	24.3%	13.0%	11.3%	0.8%	3.38	3.11	+0.7
2026	25	MIX MLE	3 levels	32	3	43	180	56	23	1	9%	1.3	24.3%	13.0%	11.3%	0.8%	3.38	3.11	+0.7
2026	25	MIX RAW	3 levels	32	3	43	180	56	23	1	9%	1.3	31.1%	12.8%	18.3%	0.6%	3.38	2.40	+1.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			4	0	5	16	5	0	0	0%	1.2	31.2%	0.0%	31.2%	0.0%	0.00	0.96	+0.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	26	MLB	TOR	38	0	40	169	47	23	4	0%	1.1	27.7%	13.4%	14.2%	2.6%	4.28	4.28	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	27	MLB MLE	TOR	47	0	50	210	57	27	6	0%	1.1	27.2%	13.1%	14.1%	2.7%	4.31	4.32	+0.1
2029	28	MLB MLE	TOR	42	0	44	187	50	24	5	0%	1.1	26.8%	13.0%	13.9%	2.8%	4.37	4.41	+0.1
2028	27	MLB CTX	TOR	37	0	39	165	45	22	4	0%	1.1	27.2%	13.1%	14.1%	2.7%	4.31	4.32	+0.1
2029	28	MLB CTX	TOR	35	0	37	156	42	20	4	0%	1.1	26.8%	13.0%	13.9%	2.8%	4.37	4.41	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		76	0	81	337	92	42	9	0%	1.1	27.2%	12.4%	14.8%	2.6%	4.09	3.82	+0.7
/ 200 IP	—	RAW		189	0	200	835	228	104	22	0%	1.1	27.2%	12.4%	14.8%	2.6%	4.09	3.82	+1.7
Career	—	MLE		154	13	249	1069	266	113	20	8%	1.6	24.9%	10.6%	14.3%	1.8%	3.93	3.35	+0.4
/ 200 IP	—	MLE		124	10	200	857	213	91	16	8%	1.6	24.9%	10.6%	14.3%	1.8%	3.93	3.35	+0.8

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 · 33 IPFIP 2.93 → 3.11 (+0.19)K% 28.3% → 24.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.28%	—	×0.860	24.32%
BB% (unintentional) /BF	14.48%	—	×0.900	13.03%
HR / BF /BF	0.69%	÷1.000	×1.100	0.76%

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 2.93 → 3.11, 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 AAAOklahoma City Comets · 42 IPFIP 4.01 → 4.54 (+0.53)K% 33.7% → 29.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.68%	—	×0.860	28.97%
BB% (unintentional) /BF	13.68%	—	×0.900	12.32%
HR / BF /BF	3.68%	÷0.956	×1.100	4.24%

Result: FIP 4.01 → 4.54, 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 AATulsa Drillers · 12 IPFIP 2.87 → 3.88 (+1.00)K% 35.7% → 28.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.71%	—	×0.810	28.93%
BB% (unintentional) /BF	14.29%	—	×1.100	15.71%
HR / BF /BF	1.79%	÷0.957	×1.200	2.24%

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 2.87 → 3.88, 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 A+Great Lakes Loons · 56 IPFIP 2.14 → 2.98 (+0.84)K% 29.8% → 22.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.78%	—	×0.770	22.93%
BB% (unintentional) /BF	8.44%	—	×1.150	9.71%
HR / BF /BF	0.89%	÷1.036	×1.250	1.07%

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 107).

Result: FIP 2.14 → 2.98, 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 ARancho Cucamonga Quakes · 35 IPFIP 1.34 → 2.42 (+1.08)K% 37.3% → 27.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.34%	—	×0.740	27.63%
BB% (unintentional) /BF	8.23%	—	×1.180	9.71%
HR / BF /BF	0.63%	÷1.072	×1.300	0.77%

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 114).

Result: FIP 1.34 → 2.42, 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 ROKACL Dodgers · 23 IPFIP 1.38 → 2.55 (+1.17)K% 31.6% → 21.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.63%	—	×0.680	21.51%
BB% (unintentional) /BF	3.06%	—	×1.250	3.83%
HR / BF /BF	1.02%	÷1.005	×1.400	1.42%

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 101).

Result: FIP 1.38 → 2.55, 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 ROKDSL LAD Mega · 19 IPFIP 1.11 → 2.89 (+1.78)K% 44.1% → 30.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	44.12%	—	×0.680	30.00%
BB% (unintentional) /BF	7.35%	—	×1.250	9.19%
HR / BF /BF	1.47%	÷0.975	×1.400	2.11%

Result: FIP 1.11 → 2.89, 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 ROKDSL LAD Bautista · 18 IPFIP 3.79 → 4.85 (+1.06)K% 20.9% → 14.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.93%	—	×0.680	14.23%
BB% (unintentional) /BF	9.30%	—	×1.250	11.63%
HR / BF /BF	2.33%	÷1.211	×1.400	2.69%

Result: FIP 3.79 → 4.85, 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 Rodríguez (OGILVIE)	—	—	—	—	—	2.6%	—	4.36	27.0%	14.0%	94	+0.08	—	—	—
Peyton Gray R 2021 · 4.27 FIP	26	SU	4.27	29.2%	14.1%	3.4%	.307	—	—	—	—	—	3.62	160	84
Zach Franklin R 2024 · 3.24 FIP	25	SU	3.24	26.4%	10.9%	1.8%	.301	4.15	27.5%	18.3%	117	+0.91	3.24	158	83
Brandon Cunniff R 2015 · 4.28 FIP	26	SU	4.28	24.5%	13.2%	2.9%	.256	4.32	20.3%	9.0%	115	+0.04	3.89	180	82
Michael Flynn R 2023 · 4.31 FIP	26	SU	4.31	26.5%	11.2%	3.7%	.299	4.36	21.4%	10.8%	92	+0.05	3.89	168	81
Chaz Roe R 2013 · 4.02 FIP	26	SU	4.02	25.3%	10.5%	3.1%	.259	2.81	24.6%	16.2%	133	-1.21	1.72	279	81
Josh Roenicke R 2009 · 3.25 FIP	26	SU	3.25	23.9%	10.9%	1.5%	.341	4.17	15.4%	4.7%	124	+0.92	3.25	500	81
Jason Bulger R 2008 · 4.60 FIP	29	SU	4.60	27.4%	12.3%	4.1%	.293	3.94	25.1%	13.3%	90	-0.66	3.78	106	80
Eric Reyzelman R 2024 · 3.81 FIP	23	SU	3.81	30.3%	15.6%	2.4%	.261	4.78	24.5%	8.6%	94	+0.97	3.81	117	80
Jesus Pirela R 2015 · 3.84 FIP	26	SU	3.84	23.7%	11.6%	2.3%	.305	3.92	17.1%	13.9%	45	+0.08	3.24	161	80
Grant Balfour R 2004 · 3.79 FIP	26	SU	3.79	24.4%	11.6%	2.3%	.292	4.01	24.8%	8.3%	25	+0.22	2.04	533	80
Brooks Kriske R 2019 · 3.47 FIP	25	SU	3.47	26.1%	12.2%	1.9%	.245	4.95	28.6%	16.5%	41	+1.48	3.09	246	79
Logan Boyer R 2024 · 3.71 FIP	26	SU	3.71	25.0%	12.3%	2.1%	.267	4.65	17.5%	3.5%	54	+0.94	3.71	121	79
Jose De La Torre R 2013 · 5.22 FIP	27	SU	5.22	28.3%	18.9%	3.9%	.308	3.87	17.6%	6.6%	110	-1.35	2.64	141	79
Manny Acosta R 2007 · 4.05 FIP	26	SU	4.05	23.7%	14.0%	2.1%	.196	4.23	19.0%	9.1%	130	+0.18	3.08	350	79
Fernando Rodriguez Jr. R 2011 · 3.85 FIP	27	SU	3.85	24.7%	10.8%	2.7%	.315	3.90	25.2%	16.5%	70	+0.05	3.34	335	79
Jason Stoffel R 2015 · 3.61 FIP	26	SU	3.61	23.4%	10.1%	2.2%	.267	3.49	27.2%	19.2%	59	-0.12	3.49	103	79
Ronald Peña R 2018 · 4.19 FIP	26	SU	4.19	23.4%	11.6%	2.9%	.273	4.52	18.7%	7.1%	88	+0.33	3.72	187	78
Sam Gardner R 2023 · 4.04 FIP	26	SU	4.04	23.8%	15.1%	1.9%	.298	3.23	26.7%	16.4%	76	-0.81	2.28	121	78
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	78
Jeff Ames R 2017 · 3.94 FIP	26	SU	3.94	25.1%	13.4%	2.3%	.310	4.65	25.4%	10.8%	79	+0.71	3.85	272	78

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	0.8	53	27.4%	12.9%	1.02	4.19
Chad Fox ('09)	1.1	57	25.8%	13.2%	0.87	3.79
Kyle Barraclough ('23)	1.4	56	28.2%	13.9%	0.99	3.86
Rex Brothers ('21)	1.6	54	27.6%	13.5%	0.98	4.08
Mac Scarce ('72-'78, 1 WAR*)	1.0	52	25.9%*	12.4%*	1.27*	4.17*
Rod Scurry ('80-'88, 5 WAR*)	2.3	58	29.6%*	13.6%*	0.91*	3.67*
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*