Willian Suarez

P Tri-City Dust Devils

Age 29 RHP 6'3" / 209 lbs A+

Overview

A+PROSPECT

MLB%0%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	24	MIX2 levels	26	0	47	4.02	2.14	44.2%	11.7%	—	K 31.1% · BB 14.2% · FIP 3.99
2023	25	MIX2 levels	40	0	52	3.78	5.14	24.6%	16.8%	—	K 19.5% · BB 19.6% · FIP 6.17
2024	26	A+LAA	24	0	31	6.46	3.62	34.1%	10.9%	—	K 26.2% · BB 12.5% · FIP 5.18
2025	27	No MLB games played
2026	28	No MLB games played
2027PROJ	29	MLBLAA	—	—	0	4.42	4.42	24.0%	10.3%	—
2028PROJ30% MLB	30	MLBLAA	6	0	6	4.52	4.59	23.6%	10.5%	-0.0

Next 2 projected WAR -0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsJoe Nelson (age 31) · Jason Bulger (age 29) · Neil Wagner (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 117109vR 101

ERA4.42

FIP4.42

K%24.0%

BB%10.3%

K-BB%13.7%

Proj IP18

Value2027 · WAR

Runs saved vs league avg · 18 IP

Grades · Contract

Scouting Grades

K†6060

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	18	ROK MLE	DSL Giants	4	0	5	29	3	4	0	0%	1.3	10.3%	13.8%	-3.4%	0.0%	3.38	3.97	—
2016	18	ROK RAW	DSL Giants	4	0	5	29	3	4	0	0%	1.3	10.3%	13.8%	-3.4%	0.0%	3.38	4.22	—
2017	19	ROK MLE	DSL Giants	4	0	2	16	4	1	0	0%	0.5	25.0%	6.2%	18.8%	0.0%	13.50	1.78	—
2017	19	ROK RAW	DSL Giants	4	0	2	16	4	1	0	0%	0.5	25.0%	6.2%	18.8%	0.0%	13.50	0.60	—
2018	20	ROK MLE	DSL Giants	17	0	29	127	26	15	4	0%	1.7	13.9%	14.8%	-0.8%	4.4%	5.02	6.21	—
2018	20	ROK RAW	DSL Giants	17	0	29	127	26	15	4	0%	1.7	20.5%	11.8%	8.7%	3.1%	5.02	4.67	—
2019	21	ROK MLE	DSL Giants	22	1	34	168	39	22	1	5%	1.5	15.8%	16.4%	-0.6%	0.8%	6.68	4.30	—
2019	21	ROK RAW	DSL Giants	22	1	34	168	39	22	1	5%	1.5	23.2%	13.1%	10.1%	0.6%	6.68	3.13	—
2021	23	ROK MLE	ACL Giants Orange	15	0	29	130	39	18	3	0%	1.9	20.4%	17.3%	3.1%	3.0%	4.40	5.23	—
2021	23	ROK RAW	ACL Giants Orange	15	0	29	130	39	18	3	0%	1.9	30.0%	13.8%	16.2%	2.3%	4.40	3.62	—
2022	24	MIX MLE	2 levels	26	0	47	206	91	24	5	0%	1.8	31.1%	14.2%	16.9%	3.1%	4.02	3.99	—
2022	24	MIX RAW	2 levels	26	0	47	206	91	24	5	0%	1.8	44.2%	11.7%	32.5%	2.4%	4.02	2.14	—
2023	25	MIX MLE	2 levels	40	0	52	232	57	39	8	0%	1.3	19.5%	19.6%	-0.1%	4.1%	3.78	6.17	—
2023	25	MIX RAW	2 levels	40	0	52	232	57	39	8	0%	1.3	24.6%	16.8%	7.8%	3.4%	3.78	5.14	—
2024	26	A+ MLE	Tri-City Dust Devils	24	0	31	138	47	15	5	0%	1.3	26.2%	12.5%	13.7%	4.9%	6.46	5.18	—
2024	26	A+ MLE	Tri-City Dust Devils	24	0	31	138	47	15	5	0%	1.3	26.2%	12.5%	13.7%	4.9%	6.46	5.18	—
2024	26	A+ RAW	Tri-City Dust Devils	24	0	31	138	47	15	5	0%	1.3	34.1%	10.9%	23.2%	3.6%	6.46	3.62	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	LAA	—	—	0	—	—	—	—	—	—	24.0%	10.3%	13.7%	3.0%	4.42	4.42	—
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	LAA	18	0	18	78	18	8	3	0%	1.0	23.6%	10.5%	13.1%	3.2%	4.52	4.59	-0.0
2028	30	MLB CTX	LAA	6	0	6	25	6	3	1	0%	1.0	23.6%	10.5%	13.1%	3.2%	4.52	4.59	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		5	0	6	24	6	2	1	0%	1.1	23.6%	10.5%	13.1%	3.2%	4.52	4.21	+0.0
/ 200 IP	—	RAW		190	0	200	844	215	72	36	0%	1.1	23.6%	10.5%	13.1%	3.2%	4.52	4.21	+0.8
Career	—	MLE		152	1	228	1046	223	166	34	1%	1.5	21.3%	15.8%	5.5%	3.2%	4.93	5.07	-0.0
/ 200 IP	—	MLE		133	1	200	916	195	145	29	1%	1.5	21.3%	15.8%	5.5%	3.2%	4.93	5.07	-0.1

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.

2024 A+Tri-City Dust Devils · 31 IPFIP 3.59 → 5.18 (+1.59)K% 34.1% → 26.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	34.06%	—	×0.770	26.22%
BB% (unintentional) /BF	10.87%	—	×1.150	12.50%
HR / BF /BF	3.62%	÷0.916	×1.250	4.94%

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 83).

Result: FIP 3.59 → 5.18, 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+Tri-City Dust Devils · 50 IPFIP 4.89 → 6.17 (+1.28)K% 25.3% → 19.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.35%	—	×0.770	19.52%
BB% (unintentional) /BF	17.05%	—	×1.150	19.61%
HR / BF /BF	3.23%	÷0.990	×1.250	4.07%

Result: FIP 4.89 → 6.17, 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).

2022 ROKACL Giants Black · 30 IPFIP 2.30 → 4.33 (+2.03)K% 44.4% → 30.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	44.44%	—	×0.680	30.22%
BB% (unintentional) /BF	9.52%	—	×1.250	11.90%
HR / BF /BF	3.17%	÷1.073	×1.400	4.14%

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 115).

Result: FIP 2.30 → 4.33, 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 ASan Jose Giants · 17 IPFIP 1.99 → 3.45 (+1.46)K% 43.8% → 32.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	43.75%	—	×0.740	32.38%
BB% (unintentional) /BF	15.00%	—	×1.180	17.70%
HR / BF /BF	1.25%	÷1.063	×1.300	1.53%

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 113).

Result: FIP 1.99 → 3.45, 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).

2021 ROKACL Giants Orange · 29 IPFIP 3.59 → 5.23 (+1.65)K% 30.0% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.00%	—	×0.680	20.40%
BB% (unintentional) /BF	13.85%	—	×1.250	17.31%
HR / BF /BF	2.31%	÷1.065	×1.400	3.03%

Result: FIP 3.59 → 5.23, 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).

2019 ROKDSL Giants · 34 IPFIP 3.13 → 4.30 (+1.17)K% 23.2% → 15.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.21%	—	×0.680	15.79%
BB% (unintentional) /BF	13.10%	—	×1.250	16.37%
HR / BF /BF	0.60%	÷1.000	×1.400	0.83%

Result: FIP 3.13 → 4.30, 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 ROKDSL Giants · 29 IPFIP 4.60 → 6.21 (+1.62)K% 20.5% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.47%	—	×0.680	13.92%
BB% (unintentional) /BF	11.81%	—	×1.250	14.76%
HR / BF /BF	3.15%	÷1.000	×1.400	4.41%

Result: FIP 4.60 → 6.21, 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 Giants · 5 IPFIP 3.97 → 4.69 (+0.72)K% 10.3% → 7.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	10.34%	—	×0.680	7.03%
BB% (unintentional) /BF	13.79%	—	×1.250	17.24%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

Result: FIP 3.97 → 4.69, 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
Willian Suarez (OGILVIE)	—	—	4.42	24.0%	10.3%	3.0%	—	4.59	23.6%	13.1%	18	+0.17	—	—	—
Joe Nelson R 2006 · 3.91 FIP	31	SU	3.91	22.8%	10.4%	2.6%	.258	4.05	23.4%	12.8%	94	+0.14	3.08	139	86
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	86
Neil Wagner R 2013 · 4.02 FIP	29	SU	4.02	20.5%	7.5%	3.1%	.306	4.05	19.8%	7.5%	43	+0.03	3.11	135	84
Cody Hall R 2017 · 4.03 FIP	29	SU	4.03	26.8%	10.0%	3.5%	.299	3.92	25.9%	8.4%	19	-0.11	3.51	134	84
Tyler Sturdevant R 2015 · 4.37 FIP	29	SU	4.37	21.3%	8.5%	3.6%	.241	3.97	18.5%	11.9%	118	-0.40	3.44	149	83
Coleman Huntley III R 2022 · 4.05 FIP	29	SU	4.05	24.4%	10.9%	3.0%	.311	2.38	20.9%	12.6%	25	-1.67	2.38	160	83
Jim Miller R 2012 · 4.56 FIP	30	SU	4.56	20.9%	11.8%	3.1%	.243	3.95	18.8%	14.7%	74	-0.61	3.95	140	82
Diego Moreno R 2016 · 3.75 FIP	29	SU	3.75	21.0%	8.2%	2.5%	.347	3.13	17.8%	11.9%	54	-0.62	2.29	187	81
Gerardo Sanchez R 2017 · 3.47 FIP	28	SU	3.47	21.6%	8.4%	2.1%	.355	3.58	20.5%	15.1%	56	+0.11	2.65	137	81
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	80
Scott Atchison R 2004 · 3.61 FIP	28	SU	3.61	27.1%	9.0%	3.0%	.309	4.55	19.1%	11.5%	31	+0.94	2.52	340	80
Marcos Mateo R 2011 · 3.40 FIP	27	SU	3.40	25.5%	10.2%	2.1%	.361	—	—	—	—	—	3.12	104	80
Wirfin Obispo R 2015 · 4.80 FIP	30	SU	4.80	18.6%	12.2%	3.1%	.292	1.31	28.6%	24.5%	126	-3.49	1.30	218	79
Dustin Molleken R 2015 · 3.74 FIP	30	SU	3.74	20.4%	11.7%	1.6%	.287	3.90	20.6%	8.4%	77	+0.16	3.38	129	79
Andrew Robinson R 2017 · 4.32 FIP	29	SU	4.32	23.4%	8.8%	3.8%	.245	—	—	—	—	—	3.19	160	78
Michael Dimock R 2018 · 3.37 FIP	28	SU	3.37	23.5%	10.7%	1.6%	.346	—	—	—	—	—	3.08	166	78
Manny Acosta R 2010 · 3.61 FIP	29	SU	3.61	26.8%	10.8%	2.5%	.277	4.12	21.9%	12.6%	94	+0.51	3.08	350	78
Brandon Cunniff R 2017 · 4.79 FIP	28	SU	4.79	20.1%	11.6%	3.5%	.275	6.80	13.9%	-1.9%	30	+2.01	3.89	180	77
Tyler Walker R 2005 · 4.19 FIP	29	SU	4.19	19.4%	7.5%	3.2%	.303	3.61	19.7%	11.3%	93	-0.58	2.38	289	76
Alex Powers R 2021 · 4.24 FIP	29	SU	4.24	25.0%	10.3%	3.5%	.312	—	—	—	—	—	2.82	264	76