José Soriano

P Toronto Blue Jays

Age 28 RHP 6'3" / 220 lbs MLB Svc 4.0 · Ctrl thru 2028 peak 2.3 · 2027

Overview

MLBSP40-MANArb-2FA 2029

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR
2024	25	MLBLAA	22	20	113	3.42	3.47	20.7%	9.4%	2.7
2025	26	MLBLAA	31	31	169	4.26	3.61	21.0%	10.8%	3.7
2026	27	MLBTOR	32	32	177	3.91	3.73	22.7%	9.7%	3.6
2027PROJ	28	MLBTOR	22	22	134	4.08	4.04	23.5%	10.0%	2.2
2028PROJ	29	MLBTOR	17	17	104	4.15	4.20	22.9%	10.2%	1.5
2029PROJ	30	MLBTOR	14	14	82	4.30	4.41	22.2%	10.5%	0.9

Last 3 seasons WAR 9.9 · Next 3 projected WAR 4.6Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2028Service4.703First FA2029

2027Arb-2$7M2028Arb-3$8M

Skills · percentileStatcast 2026

FB Velo87

K%53

xwOBA vs40

Form6.00 ERA (proj 4.08) · K 18.3% (proj 23.5%) · BB 5.6% (proj 10.0%) · last 60d · 33 IP through Oct 27

CompsRodrigo Lopez (age 28) · Bud Norris (age 28) · Chase Anderson (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 111103vR 95

ERA4.08

FIP4.04

K%23.5%

BB%10.0%

K-BB%13.5%

Proj IP134

Value2027 · WAR

Runs saved vs league avg · 134 IP

━ projected WAR━ paid WAR ($/8M) surplus deficit━ post-control+$14M ctrl surplus

Statcast · Contract

Statcast Percentiles

FB Velo

p8797.1

Whiff%

p7128.6%

p5322.7%

xwOBA vs

p400.323

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status Arb-2

MLB Debut 2023

Service 4.703 yrs.days

Team Control Through 2028

First FA Year 2029

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Arb-2	$7M	+2.2	$18M	+$10M
2028	Arb-3	$8M	+1.5	$11M	+$4M
Totals		$15M		$29M	+$14M

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	17	ROK MLE	DSL Angels	14	14	57	236	45	30	2	100%	4.1	13.0%	15.9%	-2.9%	1.2%	1.58	4.67	—
2016	17	ROK RAW	DSL Angels	14	14	57	236	45	30	2	100%	4.1	19.1%	12.7%	6.4%	0.8%	1.58	3.56	—
2017	18	ROK MLE	Orem Owlz	13	11	52	218	39	18	2	85%	4.0	12.2%	10.3%	1.8%	1.3%	2.92	4.09	—
2017	18	ROK RAW	Orem Owlz	13	11	52	218	39	18	2	85%	4.0	17.9%	8.3%	9.6%	0.9%	2.92	3.14	—
2018	19	A MLE	Burlington Bees	14	14	46	198	42	35	1	100%	3.3	15.7%	20.9%	-5.2%	0.7%	4.47	4.81	—
2018	19	A RAW	Burlington Bees	14	14	46	198	42	35	1	100%	3.3	21.2%	17.7%	3.5%	0.5%	4.47	3.83	—
2019	20	MIX MLE	2 levels	20	18	82	346	92	51	5	90%	4.1	19.2%	17.5%	1.7%	2.1%	2.51	4.85	—
2019	20	MIX RAW	2 levels	20	18	82	346	92	51	5	90%	4.1	26.6%	14.7%	11.8%	1.4%	2.51	3.51	—
2021	22	A MLE	Bradenton Marauders	2	2	4	20	6	3	0	100%	1.8	30.0%	15.0%	15.0%	0.0%	14.73	2.47	—
2021	22	A RAW	Bradenton Marauders	2	2	4	20	6	3	0	100%	1.8	30.0%	15.0%	15.0%	0.0%	14.73	2.28	—
2022	23	MIX MLE	2 levels	7	7	13	52	17	4	0	100%	1.9	32.7%	7.7%	25.0%	0.0%	2.08	1.31	—
2022	23	MIX RAW	2 levels	7	7	13	52	17	4	0	100%	1.9	32.7%	7.7%	25.0%	0.0%	2.08	1.41	—
2023	24	MIX MLE	2 levels	55	2	65	289	87	37	5	4%	1.2	28.1%	13.4%	14.7%	1.8%	3.86	3.40	+0.9
2023	24	MIX RAW	2 levels	55	2	65	289	87	37	5	4%	1.2	30.1%	12.8%	17.3%	1.7%	3.86	3.13	+1.1
2024	25	MLB MLE	Los Angeles Angels	22	20	113	468	97	44	8	91%	5.1	20.7%	9.4%	11.3%	1.7%	3.42	3.46	+2.7
2024	25	MLB MLE	Los Angeles Angels	22	20	113	468	97	44	8	91%	5.1	20.7%	9.4%	11.3%	1.7%	3.42	3.46	+2.7
2024	25	MLB RAW	Los Angeles Angels	22	20	113	468	97	44	8	91%	5.1	20.7%	9.4%	11.3%	1.7%	3.42	3.47	+2.7
2025	26	MLB MLE	Los Angeles Angels	31	31	169	724	152	78	12	100%	5.5	21.0%	10.8%	10.2%	1.6%	4.26	3.58	+3.7
2025	26	MLB MLE	Los Angeles Angels	31	31	169	724	152	78	12	100%	5.5	21.0%	10.8%	10.2%	1.6%	4.26	3.58	+3.7
2025	26	MLB RAW	Los Angeles Angels	31	31	169	724	152	78	12	100%	5.5	21.0%	10.8%	10.2%	1.7%	4.26	3.61	+3.7
2026	27	MLB MLE	Toronto Blue Jays	32	32	177	745	169	72	18	100%	5.5	22.7%	9.7%	13.0%	2.4%	3.91	3.73	+3.6
2026	27	MLB MLE	Toronto Blue Jays	32	32	177	745	169	72	18	100%	5.5	22.7%	9.7%	13.0%	2.4%	3.91	3.73	+3.6
2026	27	MLB RAW	Toronto Blue Jays	32	32	177	745	169	72	18	100%	5.5	22.7%	9.7%	13.0%	2.4%	3.91	3.73	+3.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			123	83	501	2122	474	215	42	67%	4.1	22.3%	10.1%	12.2%	2.0%	3.90	3.58	+11.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	TOR	22	22	134	566	133	57	13	100%	6.1	23.5%	10.0%	13.5%	2.3%	4.08	4.04	+2.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	TOR	17	17	104	437	100	45	11	100%	6.1	22.9%	10.2%	12.7%	2.5%	4.15	4.20	+1.5
2029	30	MLB MLE	TOR	14	14	82	347	77	37	9	100%	5.9	22.2%	10.5%	11.7%	2.7%	4.30	4.41	+1.0
2030	31	MLB MLE	TOR	11	11	65	274	59	30	8	100%	5.9	21.5%	10.9%	10.6%	2.9%	4.50	4.64	+0.6
2031	32	MLB MLE	TOR	8	8	48	204	42	23	6	100%	6.0	20.7%	11.3%	9.4%	3.2%	4.74	4.89	+0.3
2032	33	MLB MLE	TOR	6	6	33	141	28	16	5	100%	5.5	19.8%	11.7%	8.1%	3.4%	5.00	5.17	+0.1
2028	29	MLB CTX	TOR	17	17	104	439	101	45	11	100%	6.1	22.9%	10.2%	12.7%	2.5%	4.15	4.20	+1.5
2029	30	MLB CTX	TOR	14	14	82	346	77	36	9	100%	5.9	22.2%	10.5%	11.7%	2.7%	4.30	4.41	+0.9
2030	31	MLB CTX	TOR	11	11	65	274	59	30	8	100%	5.9	21.5%	10.9%	10.6%	2.9%	4.50	4.64	+0.6
2031	32	MLB CTX	TOR	8	8	48	203	42	23	6	100%	6.0	20.7%	11.3%	9.4%	3.2%	4.74	4.89	+0.3
2032	33	MLB CTX	TOR	6	6	33	139	28	16	5	100%	5.5	19.8%	11.7%	8.1%	3.4%	5.00	5.17	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		178	138	833	3524	780	366	81	78%	4.7	22.1%	10.4%	11.8%	2.3%	4.11	3.81	+15.9
/ 200 IP	—	RAW		43	33	200	846	187	88	19	78%	4.7	22.1%	10.4%	11.8%	2.3%	4.11	3.81	+3.8
Career	—	MLE		210	151	779	3296	677	402	57	72%	3.7	20.5%	12.2%	8.4%	1.7%	3.58	3.86	+14.2
/ 200 IP	—	MLE		54	39	200	846	174	103	15	72%	3.7	20.5%	12.2%	8.4%	1.7%	3.58	3.86	+3.4

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.

2023 AARocket City Trash Pandas · 23 IPFIP 3.06 → 3.86 (+0.80)K% 29.8% → 24.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.81%	—	×0.810	24.14%
BB% (unintentional) /BF	15.38%	—	×1.100	16.92%
HR / BF /BF	0.96%	÷0.970	×1.200	1.19%

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 94).

Result: FIP 3.06 → 3.86, 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 ROKACL Angels · 8 IPFIP 0.77 → 2.01 (+1.24)K% 37.9% → 25.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.93%	—	×0.680	25.79%
BB% (unintentional) /BF	6.90%	—	×1.250	8.62%
HR / BF /BF	0.00%	÷0.943	×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 89).

Result: FIP 0.77 → 2.01, 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 AInland Empire 66ers · 5 IPFIP 2.00 → 2.77 (+0.77)K% 26.1% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.09%	—	×0.740	19.30%
BB% (unintentional) /BF	8.70%	—	×1.180	10.26%
HR / BF /BF	0.00%	÷0.889	×1.300	0.00%

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 78).

Result: FIP 2.00 → 2.77, 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).

2021 ABradenton Marauders · 4 IPFIP 2.47 → 3.47 (+1.00)K% 30.0% → 22.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.740	22.20%
BB% (unintentional) /BF	15.00%	—	×1.180	17.70%
HR / BF /BF	0.00%	÷1.302	×1.300	0.00%

Result: FIP 2.47 → 3.47, 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 ABurlington Bees · 78 IPFIP 3.64 → 4.85 (+1.21)K% 26.0% → 19.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.01%	—	×0.740	19.24%
BB% (unintentional) /BF	14.86%	—	×1.180	17.54%
HR / BF /BF	1.55%	÷0.960	×1.300	2.10%

Result: FIP 3.64 → 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).

2019 ROKAZL Angels · 5 IPFIP 1.81 → 3.17 (+1.35)K% 34.8% → 23.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.78%	—	×0.680	23.65%
BB% (unintentional) /BF	13.04%	—	×1.250	16.30%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Result: FIP 1.81 → 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).

2018 ABurlington Bees · 46 IPFIP 3.83 → 4.81 (+0.99)K% 21.2% → 15.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.21%	—	×0.740	15.70%
BB% (unintentional) /BF	17.68%	—	×1.180	20.86%
HR / BF /BF	0.51%	÷0.907	×1.300	0.72%

Result: FIP 3.83 → 4.81, 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).

2017 ROKOrem Owlz · 52 IPFIP 3.14 → 4.09 (+0.95)K% 17.9% → 12.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.89%	—	×0.680	12.17%
BB% (unintentional) /BF	8.26%	—	×1.250	10.32%
HR / BF /BF	0.92%	÷1.000	×1.400	1.28%

Result: FIP 3.14 → 4.09, 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 Soriano (OGILVIE)	—	—	—	—	—	2.3%	—	4.38	22.3%	11.8%	250	+0.34	—	—	—
Rodrigo Lopez R 2004 · 4.21 FIP	28	SP-	4.21	16.9%	7.3%	2.9%	.275	4.57	14.2%	7.8%	478	+0.36	4.06	1,344	58
Bud Norris R 2013 · 3.68 FIP	28	SP	3.68	19.0%	8.7%	2.0%	.330	4.05	20.0%	11.9%	361	+0.37	3.51	1,221	57
Chase Anderson R 2016 · 4.83 FIP	28	SP	4.83	18.5%	8.2%	4.1%	.285	4.25	21.3%	13.3%	438	-0.58	3.30	1,260	57
David Phelps R 2015 · 3.94 FIP	28	SP	3.94	16.0%	6.8%	2.4%	.299	2.82	29.9%	20.6%	142	-1.12	2.50	672	54
Jake Odorizzi R 2018 · 3.91 FIP	28	SP	3.91	22.8%	9.4%	2.8%	.284	3.73	24.3%	16.6%	277	-0.18	3.16	1,246	52
Sonny Gray R 2018 · 3.79 FIP	28	SP-	3.79	21.1%	9.8%	2.2%	.320	3.21	28.5%	19.1%	367	-0.58	2.55	2,087	52
Brad Peacock R 2016 · 3.97 FIP	28	SP-	3.97	20.9%	8.7%	2.8%	.302	3.45	29.4%	20.4%	289	-0.52	2.97	748	51
Luke Hochevar R 2012 · 4.50 FIP	28	SP	4.50	18.0%	7.2%	3.6%	.306	3.66	26.8%	19.3%	131	-0.84	3.04	940	50
Zack Godley R 2018 · 3.56 FIP	28	SP	3.56	23.4%	10.0%	2.1%	.315	5.07	17.6%	6.5%	219	+1.51	3.21	953	49
Claudio Vargas R 2006 · 4.54 FIP	28	SP	4.54	16.5%	6.7%	3.6%	.289	4.48	17.2%	8.9%	213	-0.06	3.66	765	49
Josh Fogg R 2005 · 4.85 FIP	28	SP-	4.85	11.5%	5.7%	3.6%	.287	4.77	12.4%	5.7%	416	-0.08	0.87	1,159	48
Hector Noesí R 2015 · 4.37 FIP	28	SP-	4.37	17.6%	8.1%	3.2%	.261	—	—	—	—	—	3.73	613	48
Jacob deGrom R 2016 · 3.22 FIP	28	SP	3.22	23.7%	6.0%	2.5%	.310	2.46	31.0%	25.4%	622	-0.76	0.74	1,692	48
Brandon Morrow R 2012 · 3.63 FIP	28	SP	3.63	21.4%	8.1%	2.4%	.251	4.35	18.4%	10.4%	121	+0.72	2.67	922	48
Taylor Jungmann R 2017 · 3.88 FIP	27	SP-	3.88	18.8%	11.1%	1.8%	.279	—	—	—	—	—	3.59	435	47
Jorge Sosa R 2005 · 4.23 FIP	28	SW	4.23	14.7%	9.7%	2.1%	.259	4.99	14.0%	6.8%	252	+0.76	4.06	773	47
Victor Santos R 2005 · 4.67 FIP	28	SP	4.67	13.9%	8.1%	3.1%	.278	4.90	16.0%	7.8%	179	+0.23	4.09	602	47
Jason Hammel R 2011 · 4.52 FIP	28	SP	4.52	12.7%	8.8%	2.5%	.275	3.89	20.3%	13.2%	434	-0.63	3.10	1,810	46
Mike Thurman R 2001 · 4.45 FIP	28	SP	4.45	14.6%	6.5%	3.2%	.303	3.46	15.1%	7.9%	33	-0.99	3.46	482	46
Jesse Hahn R 2017 · 3.66 FIP	28	SP	3.66	16.6%	9.7%	1.3%	.327	2.19	29.2%	16.9%	17	-1.47	2.19	499	46

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	4.8	97	22.3%	10.4%	0.90	4.10
Darren Dreifort ('04)	4.4	97	21.2%	10.3%	0.93	4.36
Brandon Morrow ('18)	4.3	78	24.0%	10.1%	0.92	3.96
Matt Young ('83-'93, 8 WAR*)	5.9	119	22.8%*	10.5%*	0.91*	4.53*
Scott Garrelts ('82-'91, 10 WAR*)	4.8	120	22.5%*	10.1%*	1.02*	3.96*
Don Aase ('77-'90, 12 WAR*)	5.5	92	20.7%*	9.6%*	0.94*	4.14*
Byung-Hyun Kim ('07)	4.2	105	21.9%	10.2%	1.01	4.42