Juan Morillo

P Arizona Diamondbacks

Age 28 RHP 6'1" / 150 lbs MLB peak 0.7 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	25	AALAD	41	51	4.76	4.37	22.5%	13.1%	—	K 18.2% · BB 14.4% · FIP 5.05
2025	26	MIX2 levels	63	55	3.58	3.35	23.1%	12.0%	0.8	K 22.0% · BB 11.6% · FIP 3.47
2026	27	MLBARI	70	64	2.24	2.43	28.0%	8.3%	1.6
2027PROJ	28	MLBARI	50	53	3.64	3.66	28.2%	11.2%	0.6
2028PROJ90% MLB	29	MLBARI	43	45	3.81	3.88	27.1%	11.2%	0.4
2029PROJ92% MLB	30	MLBARI	35	37	4.01	4.12	26.1%	11.3%	0.2

Last 3 seasons WAR 2.4 · Next 3 projected WAR 1.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs90

K%81

Form1.31 ERA (proj 3.64) · K 25.0% (proj 28.2%) · BB 8.0% (proj 11.2%) · last 90d · 21 IP through Oct 27

CompsShintaro Fujinami (age 29) · Daniel Robert (age 28) · Jake Cousins (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8779vR 71

ERA3.64

FIP3.66

K%28.2%

BB%11.2%

K-BB%17.0%

Proj IP60

Value2027 · WAR

Runs saved vs league avg · 60 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL2525

CMD4040

K†6565

Statcast Percentiles

FB Velo

p9098.6

Whiff%

p7629.4%

p8128.0%

xwOBA vs

p900.243

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)
2016	17	ROK MLE	DSL Dodgers 2	13	8	40	174	18	25	0	62%	3.1	7.0%	18.0%	-10.9%	0.0%	3.79	4.78	—
2016	17	ROK RAW	DSL Dodgers 2	13	8	40	174	18	25	0	62%	3.1	10.3%	14.4%	-4.0%	0.0%	3.79	4.07	—
2017	18	ROK MLE	DSL Dodgers 1	14	12	47	198	50	19	0	86%	3.4	17.2%	12.0%	5.2%	0.0%	3.42	3.17	—
2017	18	ROK RAW	DSL Dodgers 1	14	12	47	198	50	19	0	86%	3.4	25.3%	9.6%	15.7%	0.0%	3.42	2.19	—
2018	19	ROK MLE	AZL Dodgers	6	3	22	90	28	13	1	50%	3.7	21.2%	18.1%	3.1%	1.6%	3.22	4.45	—
2018	19	ROK RAW	AZL Dodgers	6	3	22	90	28	13	1	50%	3.7	31.1%	14.4%	16.7%	1.1%	3.22	2.92	—
2019	20	ROK MLE	AZL Dodgers Lasorda	13	1	46	191	58	13	4	8%	3.5	20.6%	8.5%	12.1%	2.9%	2.56	4.04	—
2019	20	ROK RAW	AZL Dodgers Lasorda	13	1	46	191	58	13	4	8%	3.5	30.4%	6.8%	23.6%	2.1%	2.56	2.55	—
2021	22	A+ MLE	Great Lakes Loons	1	1	2	7	4	0	0	100%	1.7	57.1%	0.0%	57.1%	0.0%	0.00	-1.73	—
2021	22	A+ RAW	Great Lakes Loons	1	1	2	7	4	0	0	100%	1.7	57.1%	0.0%	57.1%	0.0%	0.00	-1.70	—
2023	24	MIX MLE	2 levels	35	2	33	152	48	22	5	6%	1.0	22.9%	16.2%	6.7%	5.1%	4.86	6.03	—
2023	24	MIX RAW	2 levels	35	2	33	152	48	22	5	6%	1.0	31.6%	14.5%	17.1%	3.3%	4.86	4.15	—
2024	25	AA MLE	Tulsa Drillers	41	0	51	222	50	29	6	0%	1.2	18.2%	14.4%	3.9%	3.1%	4.76	5.05	—
2024	25	AA MLE	Tulsa Drillers	41	0	51	222	50	29	6	0%	1.2	18.2%	14.4%	3.9%	3.1%	4.76	5.05	—
2024	25	AA RAW	Tulsa Drillers	41	0	51	222	50	29	6	0%	1.2	22.5%	13.1%	9.5%	2.7%	4.76	4.37	—
2025	26	MIX MLE	2 levels	63	0	55	242	56	29	3	0%	0.9	22.0%	11.6%	10.4%	1.4%	3.58	3.47	+0.7
2025	26	MIX MLE	2 levels	63	0	55	242	56	29	3	0%	0.9	22.0%	11.6%	10.4%	1.4%	3.58	3.47	+0.7
2025	26	MIX RAW	2 levels	63	0	55	242	56	29	3	0%	0.9	23.1%	12.0%	11.2%	1.2%	3.58	3.35	+0.8
2026	27	MLB MLE	Arizona Diamondbacks	70	0	64	264	74	22	3	0%	0.9	28.0%	8.3%	19.7%	1.2%	2.24	2.45	+1.6
2026	27	MLB MLE	Arizona Diamondbacks	70	0	64	264	74	22	3	0%	0.9	28.0%	8.3%	19.7%	1.2%	2.24	2.45	+1.6
2026	27	MLB RAW	Arizona Diamondbacks	70	0	64	264	74	22	3	0%	0.9	28.0%	8.3%	19.7%	1.1%	2.24	2.43	+1.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			112	0	99	416	110	42	5	0%	0.9	26.4%	10.1%	16.3%	1.2%	2.92	2.81	+2.1
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	28	MLB	ARI	50	0	53	224	63	25	5	0%	1.1	28.2%	11.2%	17.0%	2.1%	3.64	3.66	+0.6
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	29	MLB MLE	ARI	47	0	50	209	57	23	5	0%	1.1	27.1%	11.2%	15.9%	2.3%	3.81	3.88	+0.4
2029	30	MLB MLE	ARI	38	0	40	168	44	19	4	0%	1.0	26.1%	11.3%	14.8%	2.6%	4.01	4.12	+0.2
2028	29	MLB CTX	ARI	43	0	45	190	52	21	4	0%	1.0	27.1%	11.2%	15.9%	2.3%	3.81	3.88	+0.4
2029	30	MLB CTX	ARI	35	0	37	156	41	18	4	0%	1.1	26.1%	11.3%	14.8%	2.6%	4.01	4.12	+0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		189	0	180	759	202	81	13	0%	1.0	26.5%	10.6%	15.9%	1.8%	3.36	3.17	+3.0
/ 200 IP	—	RAW		211	0	200	844	224	90	14	0%	1.0	26.5%	10.6%	15.9%	1.8%	3.36	3.17	+3.3
Career	—	MLE		256	27	361	1540	311	194	28	11%	1.4	20.2%	12.6%	7.6%	1.8%	3.46	3.99	+2.7
/ 200 IP	—	MLE		142	15	200	852	172	107	16	11%	1.4	20.2%	12.6%	7.6%	1.8%	3.46	3.99	+2.8

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.

2025 AAAReno Aces · 21 IPFIP 3.10 → 3.37 (+0.27)K% 22.2% → 19.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.22%	—	×0.860	19.11%
BB% (unintentional) /BF	10.00%	—	×0.900	9.00%
HR / BF /BF	1.11%	÷0.904	×1.100	1.35%

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 81).

Result: FIP 3.10 → 3.37, 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 AATulsa Drillers · 51 IPFIP 4.34 → 5.05 (+0.72)K% 22.5% → 18.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.52%	—	×0.810	18.24%
BB% (unintentional) /BF	13.06%	—	×1.100	14.37%
HR / BF /BF	2.70%	÷1.063	×1.200	3.05%

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 113).

Result: FIP 4.34 → 5.05, 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 A+Great Lakes Loons · 28 IPFIP 4.52 → 6.03 (+1.52)K% 29.7% → 22.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.69%	—	×0.770	22.86%
BB% (unintentional) /BF	14.06%	—	×1.150	16.17%
HR / BF /BF	3.91%	÷0.951	×1.250	5.13%

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 90).

Result: FIP 4.52 → 6.03, 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 ARancho Cucamonga Quakes · 5 IPFIP 1.69 → 2.99 (+1.29)K% 41.7% → 30.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	41.67%	—	×0.740	30.83%
BB% (unintentional) /BF	16.67%	—	×1.180	19.67%
HR / BF /BF	0.00%	÷1.025	×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 105).

Result: FIP 1.69 → 2.99, 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 Dodgers Lasorda · 46 IPFIP 2.55 → 4.04 (+1.50)K% 30.4% → 20.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.37%	—	×0.680	20.65%
BB% (unintentional) /BF	6.81%	—	×1.250	8.51%
HR / BF /BF	2.09%	÷1.000	×1.400	2.93%

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 2.55 → 4.04, 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 ROKAZL Dodgers · 22 IPFIP 2.91 → 4.45 (+1.54)K% 31.1% → 21.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.11%	—	×0.680	21.16%
BB% (unintentional) /BF	14.44%	—	×1.250	18.06%
HR / BF /BF	1.11%	÷1.000	×1.400	1.56%

Result: FIP 2.91 → 4.45, 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 ROKDSL Dodgers 1 · 47 IPFIP 2.18 → 3.17 (+0.99)K% 25.3% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.25%	—	×0.680	17.17%
BB% (unintentional) /BF	9.60%	—	×1.250	11.99%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Result: FIP 2.18 → 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).

2016 ROKDSL Dodgers 2 · 40 IPFIP 4.05 → 4.78 (+0.73)K% 10.3% → 7.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.34%	—	×0.680	7.03%
BB% (unintentional) /BF	14.37%	—	×1.250	17.96%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Result: FIP 4.05 → 4.78, 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
Juan Morillo (OGILVIE)	—	—	3.66	28.2%	11.2%	2.1%	—	3.99	26.7%	15.4%	89	+0.33	—	—	—
Shintaro Fujinami R 2023 · 4.09 FIP	29	SU	4.09	23.2%	12.3%	2.5%	.290	4.49	22.0%	0.2%	51	+0.40	4.09	130	77
Daniel Robert R 2023 · 3.45 FIP	28	SU	3.45	22.7%	10.7%	1.7%	.310	3.02	25.4%	17.1%	87	-0.43	2.32	186	75
Jake Cousins R 2022 · 3.15 FIP	28	SU	3.15	30.9%	11.9%	2.1%	.265	3.82	31.3%	19.4%	65	+0.67	1.11	160	74
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	74
Trevor May R 2018 · 3.74 FIP	28	SU	3.74	26.1%	9.8%	2.9%	.291	3.47	31.9%	23.6%	150	-0.27	3.05	477	74
Kaleb Ort R 2019 · 2.88 FIP	27	SU	2.88	30.1%	14.0%	1.0%	.369	3.12	25.5%	15.8%	114	+0.24	1.75	423	73
Jon Edwards R 2015 · 3.40 FIP	27	SU	3.40	31.7%	13.1%	2.4%	.264	2.40	30.3%	22.6%	30	-1.00	2.40	132	72
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	72
Robert Stock R 2018 · 2.91 FIP	28	SU	2.91	22.2%	8.5%	1.1%	.262	4.51	24.1%	11.9%	88	+1.60	2.74	378	72
Blake Treinen R 2016 · 3.32 FIP	28	SU	3.32	24.0%	9.5%	1.9%	.271	3.04	26.0%	17.7%	215	-0.28	1.53	601	72
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
Jason Motte R 2010 · 3.24 FIP	28	SU	3.24	26.0%	7.2%	2.6%	.269	2.69	27.3%	21.8%	140	-0.55	2.22	387	71
Spencer Patton R 2016 · 2.79 FIP	28	SU	2.79	30.3%	12.2%	1.3%	.290	—	—	—	—	—	2.79	239	71
Chad Fox R 2001 · 3.17 FIP	30	SU	3.17	27.9%	10.1%	2.1%	.221	3.70	23.2%	9.6%	43	+0.53	2.82	194	71
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	71
Hunter Strickland R 2017 · 3.34 FIP	28	SU	3.34	21.6%	9.3%	1.6%	.304	4.75	17.9%	9.1%	70	+1.41	2.30	523	70
Dayan Díaz R 2017 · 3.45 FIP	28	SU	3.45	25.1%	8.7%	2.5%	.358	3.96	29.4%	19.3%	16	+0.51	3.08	205	70
Mychal Givens R 2018 · 2.66 FIP	28	SU	2.66	24.9%	8.2%	1.1%	.274	4.13	29.1%	18.9%	136	+1.47	1.77	526	70
Matt Ramsey R 2018 · 3.78 FIP	28	SU	3.78	24.9%	8.8%	3.0%	.276	2.42	21.7%	12.6%	34	-1.36	2.42	125	70
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	70

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	1.2	50	26.9%	10.9%	0.72	3.45
Kiko Calero ('09)	1.5	50	26.0%	10.1%	0.71	3.24
Chaz Roe ('21)	1.1	43	26.4%	11.1%	0.79	3.89
Arodys Vizcaíno ('22)	1.0	50	25.7%	11.3%	0.85	3.10
Ray Searage ('81-'90, 4 WAR*)	1.4	41	21.0%*	10.8%*	0.84*	3.70*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Dave Geisel ('78-'85, 2 WAR*)	1.0	42	23.5%*	10.6%*	0.84*	4.12*