Jonathan Loáisiga

P Arizona Diamondbacks

Age 32 RHP 5'11" / 195 lbs MLB peak 0.5 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	29	MLBNYY	3	0	4	0.00	2.35	15.0%	5.0%	0.1
2025	30	MIX3 levels	37	1	38	3.55	4.28	21.3%	4.9%	0.1	K 18.5% · BB 5.2% · FIP 4.86
2026	31	MLBARI	67	0	67	2.42	2.70	18.4%	3.8%	1.5
2027PROJ	32	MLBARI	57	0	60	3.69	3.70	20.3%	4.7%	0.6
2028PROJ95% MLB	33	MLBARI	40	0	42	3.86	3.98	19.5%	5.1%	0.3
2029PROJ90% MLB	34	MLBARI	26	0	27	4.11	4.28	18.6%	5.6%	0.1

Last 3 seasons WAR 1.7 · Next 3 projected WAR 1.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo90

xwOBA vs90

K%24

Form2.16 ERA (proj 3.69) · K 17.9% (proj 20.3%) · BB 4.2% (proj 4.7%) · last 90d · 25 IP through Oct 27

CompsShawn Camp (age 32) · Jumbo Diaz (age 32) · Chris Martin (age 32)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9486vR 78

ERA3.69

FIP3.70

K%20.3%

BB%4.7%

K-BB%15.7%

Proj IP60

Value2027 · WAR

Runs saved vs league avg · 60 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL5555

CMD4040

K†5050

Statcast Percentiles

FB Velo

p9097.6

Whiff%

p3523.1%

p2418.4%

xwOBA vs

p900.269

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	21	A MLE	Charleston RiverDogs	1	1	2	10	2	1	1	100%	2.3	20.0%	10.0%	10.0%	10.0%	7.71	8.17	—
2016	21	A RAW	Charleston RiverDogs	1	1	2	10	2	1	1	100%	2.3	20.0%	10.0%	10.0%	10.0%	7.71	8.24	—
2017	22	ROK MLE	GCL Yankees East	7	7	16	59	15	2	1	100%	2.2	17.3%	4.2%	13.1%	2.4%	2.30	3.48	—
2017	22	ROK RAW	GCL Yankees East	7	7	16	59	15	2	1	100%	2.2	25.4%	3.4%	22.0%	1.7%	2.30	2.40	—
2018	23	MIX MLE	4 levels	23	18	81	337	100	20	9	78%	3.5	26.0%	6.0%	20.0%	3.0%	3.57	3.30	+2.1
2018	23	MIX RAW	4 levels	23	18	81	337	100	20	9	78%	3.5	29.7%	5.9%	23.7%	2.7%	3.57	2.81	+2.6
2019	24	MIX MLE	3 levels	21	9	49	214	59	21	9	43%	2.3	26.0%	10.3%	15.7%	4.2%	4.93	4.50	+0.0
2019	24	MIX RAW	3 levels	21	9	49	214	59	21	9	43%	2.3	27.6%	9.8%	17.8%	4.2%	4.93	4.36	+0.1
2020	25	MLB MLE	New York Yankees	12	3	23	100	22	6	3	25%	1.9	22.0%	6.0%	16.0%	2.8%	3.52	3.54	+0.3
2020	25	MLB RAW	New York Yankees	12	3	23	100	22	6	3	25%	1.9	22.0%	6.0%	16.0%	3.0%	3.52	3.67	+0.2
2021	26	MLB MLE	New York Yankees	57	0	71	283	69	16	3	0%	1.2	24.4%	5.7%	18.7%	1.0%	2.17	2.30	+1.9
2021	26	MLB RAW	New York Yankees	57	0	71	283	69	16	3	0%	1.2	24.4%	5.7%	18.7%	1.1%	2.17	2.38	+1.8
2022	27	MIX MLE	3 levels	52	1	50	212	40	18	3	2%	1.0	18.2%	8.9%	9.4%	1.4%	4.14	3.44	+0.7
2022	27	MIX RAW	3 levels	52	1	50	212	40	18	3	2%	1.0	18.9%	8.5%	10.4%	1.4%	4.14	3.36	+0.7
2023	28	MIX MLE	3 levels	20	0	22	85	11	0	2	0%	1.1	8.7%	0.0%	8.7%	2.7%	3.32	3.86	+0.2
2023	28	MIX RAW	3 levels	20	0	22	85	11	0	2	0%	1.1	12.9%	0.0%	12.9%	2.4%	3.32	3.28	+0.3
2024	29	MLB MLE	New York Yankees	3	0	4	20	3	1	0	0%	1.3	15.0%	5.0%	10.0%	0.0%	0.00	2.47	+0.1
2024	29	MLB MLE	New York Yankees	3	0	4	20	3	1	0	0%	1.3	15.0%	5.0%	10.0%	0.0%	0.00	2.47	+0.1
2024	29	MLB RAW	New York Yankees	3	0	4	20	3	1	0	0%	1.3	15.0%	5.0%	10.0%	0.0%	0.00	2.35	+0.1
2025	30	MIX MLE	3 levels	37	1	38	164	35	8	7	3%	1.0	18.5%	5.2%	13.3%	4.9%	3.55	4.86	-0.1
2025	30	MIX MLE	3 levels	37	1	38	164	35	8	7	3%	1.0	18.5%	5.2%	13.3%	4.9%	3.55	4.86	-0.1
2025	30	MIX RAW	3 levels	37	1	38	164	35	8	7	3%	1.0	21.3%	4.9%	16.5%	4.3%	3.55	4.28	+0.1
2026	31	MLB MLE	Arizona Diamondbacks	67	0	67	261	48	10	3	0%	1.0	18.4%	3.8%	14.6%	1.2%	2.42	2.70	+1.5
2026	31	MLB MLE	Arizona Diamondbacks	67	0	67	261	48	10	3	0%	1.0	18.4%	3.8%	14.6%	1.2%	2.42	2.70	+1.5
2026	31	MLB RAW	Arizona Diamondbacks	67	0	67	261	48	10	3	0%	1.0	18.4%	3.8%	14.6%	1.1%	2.42	2.70	+1.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			260	11	316	1318	280	86	30	4%	1.2	21.2%	6.5%	14.7%	2.3%	3.30	3.38	+4.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	32	MLB	ARI	57	0	60	253	52	12	6	0%	1.1	20.3%	4.7%	15.7%	2.4%	3.69	3.70	+0.6
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	33	MLB MLE	ARI	42	0	44	186	36	10	5	0%	1.1	19.5%	5.1%	14.4%	2.7%	3.86	3.98	+0.3
2029	34	MLB MLE	ARI	29	0	30	128	24	7	4	0%	1.0	18.6%	5.6%	13.0%	3.0%	4.11	4.28	+0.1
2028	33	MLB CTX	ARI	40	0	42	177	35	9	5	0%	1.1	19.5%	5.1%	14.4%	2.7%	3.86	3.98	+0.3
2029	34	MLB CTX	ARI	26	0	27	114	21	6	3	0%	1.0	18.6%	5.6%	13.0%	3.0%	4.11	4.28	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		326	11	386	1610	336	102	38	3%	1.2	20.9%	6.3%	14.6%	2.4%	3.42	3.44	+5.1
/ 200 IP	—	RAW		169	6	200	835	174	53	20	3%	1.2	20.9%	6.3%	14.6%	2.4%	3.42	3.44	+2.6
Career	—	MLE		300	40	422	1745	374	106	43	13%	1.4	21.4%	6.1%	15.3%	2.5%	3.30	3.43	+4.8
/ 200 IP	—	MLE		142	19	200	826	177	50	21	13%	1.4	21.4%	6.1%	15.3%	2.5%	3.30	3.43	+2.5

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.

2019 AAAScranton/Wilkes-Barre RailRiders · 16 IPFIP 4.09 → 4.45 (+0.36)K% 27.9% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.94%	—	×0.880	24.59%
BB% (unintentional) /BF	7.35%	—	×1.050	7.72%
HR / BF /BF	4.41%	÷1.087	×1.100	4.46%

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 117).

Result: FIP 4.09 → 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).

2018 AATrenton Thunder · 34 IPFIP 3.58 → 4.46 (+0.89)K% 28.4% → 23.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.37%	—	×0.810	22.98%
BB% (unintentional) /BF	4.26%	—	×1.100	4.68%
HR / BF /BF	4.26%	÷1.034	×1.200	4.94%

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 107).

Result: FIP 3.58 → 4.46, 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+Tampa Tarpons · 20 IPFIP 0.51 → 1.17 (+0.65)K% 32.5% → 25.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.50%	—	×0.770	25.03%
BB% (unintentional) /BF	1.25%	—	×1.150	1.44%
HR / BF /BF	0.00%	÷1.205	×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 141).

Result: FIP 0.51 → 1.17, 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).

2017 ROKGCL Yankees East · 16 IPFIP 2.31 → 3.48 (+1.17)K% 25.4% → 17.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.42%	—	×0.680	17.29%
BB% (unintentional) /BF	3.39%	—	×1.250	4.24%
HR / BF /BF	1.69%	÷1.000	×1.400	2.37%

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 2.31 → 3.48, 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
Jonathan Loáisiga (OGILVIE)	—	—	3.70	20.3%	4.7%	2.4%	—	4.10	19.1%	13.8%	74	+0.40	—	—	—
Shawn Camp R 2008 · 2.79 FIP	32	SU	2.79	18.7%	4.8%	1.2%	.304	3.63	14.8%	9.2%	218	+0.84	2.79	589	75
Jumbo Diaz R 2016 · 3.85 FIP	32	SU	3.85	22.3%	9.2%	2.7%	.247	3.28	23.0%	13.8%	141	-0.57	2.80	319	74
Chris Martin R 2018 · 3.04 FIP	32	SU	3.04	20.9%	1.7%	2.7%	.311	2.92	25.7%	23.1%	117	-0.12	1.76	431	74
Tony Barnette R 2016 · 3.05 FIP	32	SU	3.05	19.9%	6.1%	1.6%	.281	3.26	23.4%	17.0%	84	+0.21	2.61	144	74
Gregory Infante R 2018 · 4.07 FIP	31	SU	4.07	22.0%	4.6%	4.1%	.291	—	—	—	—	—	2.97	230	74
Jared Burton R 2013 · 3.20 FIP	32	SU	3.20	21.7%	6.0%	2.1%	.279	3.92	18.4%	10.0%	78	+0.72	3.09	367	74
Tony Pena Jr. R 2015 · 2.80 FIP	34	SU	2.80	18.8%	2.7%	1.7%	.294	3.02	18.7%	14.4%	82	+0.22	2.80	128	74
Jean Machi R 2014 · 3.33 FIP	32	SU	3.33	20.5%	6.0%	2.2%	.225	4.34	17.8%	10.6%	186	+1.01	2.16	306	73
Spencer Bivens R 2024 · 4.07 FIP	30	SU	4.07	18.0%	5.8%	3.2%	.265	3.88	18.2%	10.9%	157	-0.19	3.52	391	73
Matt Lindstrom R 2012 · 2.71 FIP	32	SU	2.71	20.0%	6.0%	1.0%	.295	3.34	15.4%	7.9%	117	+0.63	2.40	443	73
Mark Serrano R 2017 · 3.56 FIP	31	SU	3.56	21.9%	6.4%	2.7%	.240	5.25	20.2%	12.3%	42	+1.69	3.16	120	73
Josh Fields R 2018 · 3.48 FIP	32	SU	3.48	21.4%	6.5%	2.5%	.224	7.56	18.9%	9.5%	25	+4.08	1.80	330	72
Jim Miller R 2015 · 3.95 FIP	33	SU	3.95	18.8%	4.1%	3.5%	.275	5.43	20.4%	6.3%	17	+1.48	3.95	140	72
Kerry Ligtenberg R 2003 · 4.06 FIP	32	SU	4.06	19.0%	4.5%	3.6%	.283	3.65	18.6%	11.8%	55	-0.41	2.94	381	72
Santiago Casilla R 2012 · 3.98 FIP	32	SU	3.98	20.2%	6.6%	3.2%	.246	3.23	21.6%	13.7%	166	-0.75	2.76	647	72
Gerardo Sanchez R 2019 · 3.58 FIP	30	SU	3.58	20.5%	5.4%	2.8%	.300	—	—	—	—	—	2.65	137	71
Dale Thayer R 2013 · 3.62 FIP	32	SU	3.62	23.7%	7.4%	2.9%	.287	3.85	20.6%	15.0%	103	+0.23	2.75	239	70
Juan Sandoval R 2015 · 3.82 FIP	34	SU	3.82	15.4%	6.0%	2.3%	.294	3.33	17.4%	8.8%	116	-0.49	3.24	225	70
Hector Navarro R 2015 · 4.35 FIP	35	SU	4.35	15.0%	5.4%	3.3%	.281	3.00	15.2%	9.0%	81	-1.35	2.30	126	69
Jesus Pirela R 2019 · 3.24 FIP	30	SU	3.24	22.5%	5.3%	2.5%	.277	—	—	—	—	—	3.24	161	69

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	2.3	45	20.8%	6.6%	0.93	3.46
Nick Vincent ('21)	2.1	46	24.1%	6.2%	0.94	3.30
George Kontos ('18)	1.8	51	19.8%	7.1%	0.96	3.10
Tony Castillo ('88-'98, 4 WAR*)	2.6	59	18.5%*	7.3%*	0.98*	3.76*
Drew Storen ('17)	2.2	55	22.6%	7.2%	0.78	3.45
Donnie Moore ('75-'88, 7 WAR*)	3.3	60	20.7%*	6.6%*	1.00*	3.83*
Ramon Hernandez ('67-'77, 6 WAR*)	2.2	61	19.7%*	7.3%*	0.77*	3.65*