Adrian Houser

P San Francisco Giants

Age 34 RHP 6'4" / 227 lbs MLB peak 0.4 · 2027 ILInjured 60-Day (D60)

Overview

MLBSPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	31	MIX2 levels	30	14	105	6.02	4.39	15.1%	9.5%	0.2	K 14.3% · BB 9.2% · FIP 4.40
2025	32	MIX2 levels	30	29	164	3.72	3.61	19.0%	7.8%	3.6	K 18.2% · BB 7.6% · FIP 3.69
2026	33	MLBSF	26	16	110	4.02	4.44	16.0%	7.2%	1.2
2027PROJ	34	MLBSF	7	7	42	4.50	4.61	17.1%	7.7%	0.4
2028PROJ	35	MLBSF	8	8	46	4.64	4.82	16.1%	8.3%	0.2
2029PROJ	36	MLBSF	6	6	37	4.79	4.90	17.1%	8.1%	0.1

Last 3 seasons WAR 5.0 · Next 3 projected WAR 0.8Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo67

xwOBA vs27

K%10

Form0.79 ERA (proj 4.50) · K 19.3% (proj 17.1%) · BB 5.7% (proj 7.7%) ▲ last 90d · 23 IP through Oct 27

CompsKenshin Kawakami (age 34) · Jarrett Grube (age 34) · Hiroki Kuroda (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 122114vR 106

ERA4.50

FIP4.61

K%17.1%

BB%7.7%

K-BB%9.3%

Proj IP42

Value2027 · WAR

Runs saved vs league avg · 42 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p6795.7

Whiff%

p1018.1%

p1016.0%

xwOBA vs

p270.339

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)
2015	22	MIX MLE	3 levels	28	20	122	531	110	43	13	71%	4.4	16.6%	8.8%	7.8%	2.9%	4.35	4.38	+1.5
2015	22	MIX RAW	3 levels	28	20	122	531	110	43	13	71%	4.4	20.7%	8.1%	12.6%	2.4%	4.35	3.74	+2.4
2016	23	AA MLE	Biloxi Shuckers	13	13	70	304	56	22	5	100%	5.4	14.9%	8.0%	7.0%	1.6%	5.25	3.72	—
2016	23	AA RAW	Biloxi Shuckers	13	13	70	304	56	22	5	100%	5.4	18.4%	7.2%	11.2%	1.6%	5.25	3.37	—
2017	24	MIX MLE	2 levels	9	8	18	67	27	4	1	89%	2.0	40.3%	6.0%	34.3%	1.5%	1.02	1.27	—
2017	24	MIX RAW	2 levels	9	8	18	67	27	4	1	89%	2.0	40.3%	6.0%	34.3%	1.5%	1.02	1.46	—
2018	25	MIX MLE	3 levels	28	21	92	406	75	32	9	75%	3.3	16.0%	8.3%	7.7%	2.3%	4.78	4.04	+1.5
2018	25	MIX RAW	3 levels	28	21	92	406	75	32	9	75%	3.3	18.5%	7.9%	10.6%	2.2%	4.78	3.78	+1.8
2019	26	MIX MLE	2 levels	39	22	133	546	140	39	16	56%	3.4	25.1%	7.2%	18.0%	2.9%	3.32	3.45	+3.1
2019	26	MIX RAW	2 levels	39	22	133	546	140	39	16	56%	3.4	25.6%	7.1%	18.5%	2.9%	3.32	3.44	+3.1
2020	27	MLB MLE	Milwaukee Brewers	12	11	56	246	44	21	8	92%	4.7	17.9%	8.5%	9.3%	3.1%	5.30	4.37	+0.7
2020	27	MLB RAW	Milwaukee Brewers	12	11	56	246	44	21	8	92%	4.7	17.9%	8.5%	9.3%	3.3%	5.30	4.51	+0.6
2021	28	MLB MLE	Milwaukee Brewers	28	26	142	599	105	64	12	93%	5.1	17.5%	10.7%	6.8%	1.9%	3.22	4.02	+2.3
2021	28	MLB RAW	Milwaukee Brewers	28	26	142	599	105	64	12	93%	5.1	17.5%	10.7%	6.8%	2.0%	3.22	4.07	+2.2
2022	29	MIX MLE	2 levels	25	24	111	492	79	53	9	96%	4.4	15.2%	10.1%	5.1%	1.7%	4.62	4.02	+1.8
2022	29	MIX RAW	2 levels	25	24	111	492	79	53	9	96%	4.4	16.1%	10.8%	5.3%	1.8%	4.62	4.16	+1.6
2023	30	MIX MLE	2 levels	27	25	126	539	108	39	15	93%	4.7	19.7%	7.1%	12.6%	2.7%	4.00	3.85	+2.3
2023	30	MIX RAW	2 levels	27	25	126	539	108	39	15	93%	4.7	20.0%	7.2%	12.8%	2.8%	4.00	3.86	+2.3
2024	31	MIX MLE	2 levels	30	14	105	464	70	44	11	47%	3.5	14.3%	9.2%	5.1%	2.4%	6.02	4.40	+0.2
2024	31	MIX MLE	2 levels	30	14	105	464	70	44	11	47%	3.5	14.3%	9.2%	5.1%	2.4%	6.02	4.40	+0.2
2024	31	MIX RAW	2 levels	30	14	105	464	70	44	11	47%	3.5	15.1%	9.5%	5.6%	2.4%	6.02	4.39	+0.2
2025	32	MIX MLE	2 levels	30	29	164	680	129	53	14	97%	5.5	18.2%	7.6%	10.6%	2.1%	3.72	3.69	+3.4
2025	32	MIX MLE	2 levels	30	29	164	680	129	53	14	97%	5.5	18.2%	7.6%	10.6%	2.1%	3.72	3.69	+3.4
2025	32	MIX RAW	2 levels	30	29	164	680	129	53	14	97%	5.5	19.0%	7.8%	11.2%	2.1%	3.72	3.61	+3.6
2026	33	MLB MLE	San Francisco Giants	26	16	110	470	75	34	15	62%	4.2	16.0%	7.2%	8.7%	3.5%	4.02	4.56	+1.1
2026	33	MLB MLE	San Francisco Giants	26	16	110	470	75	34	15	62%	4.2	16.0%	7.2%	8.7%	3.5%	4.02	4.56	+1.1
2026	33	MLB RAW	San Francisco Giants	26	16	110	470	75	34	15	62%	4.2	16.0%	7.2%	8.7%	3.2%	4.02	4.44	+1.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			199	141	843	3607	651	313	88	71%	4.2	18.0%	8.7%	9.4%	2.4%	4.06	4.03	+13.8
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	34	MLB	SF	7	7	42	177	30	14	5	100%	6.0	17.1%	7.7%	9.3%	2.9%	4.50	4.61	+0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	35	MLB MLE	SF	8	8	46	195	31	16	6	100%	5.8	16.1%	8.3%	7.8%	3.2%	4.77	4.95	+0.2
2029	36	MLB MLE	SF	6	6	37	157	27	13	6	100%	6.2	17.1%	8.1%	9.0%	3.6%	4.94	5.05	+0.1
2028	35	MLB CTX	SF	8	8	46	194	31	16	6	100%	5.8	16.1%	8.3%	7.8%	3.0%	4.64	4.82	+0.2
2029	36	MLB CTX	SF	6	6	37	156	27	13	5	100%	6.2	17.1%	8.1%	9.0%	3.3%	4.79	4.90	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		213	155	927	3958	709	342	99	73%	4.3	17.9%	8.6%	9.3%	2.5%	4.11	4.05	+14.8
/ 200 IP	—	RAW		46	33	200	854	153	74	21	73%	4.4	17.9%	8.6%	9.3%	2.5%	4.11	4.05	+3.2
Career	—	MLE		295	229	1249	5344	958	449	130	78%	4.2	17.9%	8.4%	9.5%	2.4%	4.21	3.99	+14.1
/ 200 IP	—	MLE		47	37	200	856	153	72	21	78%	4.2	17.9%	8.4%	9.5%	2.4%	4.21	3.99	+3.0

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 AAARound Rock Express · 39 IPFIP 3.70 → 4.13 (+0.43)K% 22.8% → 19.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.84%	—	×0.860	19.64%
BB% (unintentional) /BF	9.26%	—	×0.900	8.33%
HR / BF /BF	2.47%	÷0.913	×1.100	2.97%

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 83).

Result: FIP 3.70 → 4.13, 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 AAANorfolk Tides · 35 IPFIP 3.78 → 3.98 (+0.20)K% 16.1% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.13%	—	×0.860	13.87%
BB% (unintentional) /BF	7.74%	—	×0.900	6.97%
HR / BF /BF	1.94%	÷0.996	×1.100	2.14%

Result: FIP 3.78 → 3.98, 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).

2023 AAANashville Sounds · 15 IPFIP 4.34 → 4.88 (+0.54)K% 20.7% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.69%	—	×0.860	17.79%
BB% (unintentional) /BF	8.62%	—	×0.900	7.76%
HR / BF /BF	3.45%	÷0.905	×1.100	4.19%

Result: FIP 4.34 → 4.88, 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).

2022 AAANashville Sounds · 8 IPFIP 4.70 → 5.33 (+0.63)K% 27.0% → 23.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.03%	—	×0.880	23.78%
BB% (unintentional) /BF	18.92%	—	×1.050	19.86%
HR / BF /BF	2.70%	÷0.946	×1.100	3.14%

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 89).

Result: FIP 4.70 → 5.33, 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).

2019 AAASan Antonio Missions · 21 IPFIP 2.70 → 3.17 (+0.47)K% 27.4% → 24.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.38%	—	×0.880	24.10%
BB% (unintentional) /BF	4.76%	—	×1.050	5.00%
HR / BF /BF	2.38%	÷0.977	×1.100	2.68%

Result: FIP 2.70 → 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 AAAColorado Springs Sky Sox · 52 IPFIP 4.16 → 4.34 (+0.18)K% 15.9% → 14.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.95%	—	×0.880	14.03%
BB% (unintentional) /BF	7.76%	—	×1.050	8.15%
HR / BF /BF	2.59%	÷1.124	×1.100	2.53%

Result: FIP 4.16 → 4.34, 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 AABiloxi Shuckers · 27 IPFIP 3.10 → 3.76 (+0.66)K% 26.1% → 21.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.09%	—	×0.810	21.13%
BB% (unintentional) /BF	6.09%	—	×1.100	6.70%
HR / BF /BF	2.61%	÷1.078	×1.200	2.90%

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 116).

Result: FIP 3.10 → 3.76, 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 ROKAZL Brewers · 9 IPFIP 2.26 → 4.48 (+2.22)K% 45.7% → 31.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	45.71%	—	×0.680	31.09%
BB% (unintentional) /BF	11.43%	—	×1.250	14.29%
HR / BF /BF	2.86%	÷1.000	×1.400	4.00%

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 100).

Result: FIP 2.26 → 4.48, 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
Adrian Houser (OGILVIE)	—	—	—	—	—	2.9%	—	4.99	16.5%	8.4%	83	+0.38	—	—	—
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	62
Jarrett Grube R 2016 · 4.74 FIP	34	SP-	4.74	18.9%	7.2%	4.2%	.280	4.41	15.8%	7.7%	134	-0.33	3.75	393	62
Hiroki Kuroda R 2009 · 3.54 FIP	34	SP	3.54	17.9%	4.7%	2.5%	.270	3.43	19.1%	14.2%	618	-0.11	2.96	1,319	62
Masato Yoshii R 2000 · 5.32 FIP	35	SP	5.32	12.1%	6.5%	4.4%	.302	4.38	13.1%	8.0%	244	-0.94	4.15	757	60
Jonathan Castellanos R 2016 · 3.40 FIP	34	SP	3.40	15.2%	7.4%	1.2%	.314	5.04	9.4%	0.3%	174	+1.64	3.40	394	59
Andres Ivan Meza R 2019 · 4.33 FIP	32	SP	4.33	11.7%	7.7%	2.3%	.349	—	—	—	—	—	4.14	275	59
Orlando Hernandez R 2004 · 3.77 FIP	34	SP	3.77	23.4%	10.0%	2.5%	.278	4.34	20.5%	11.7%	438	+0.57	3.35	1,315	58
Leo Medrano R 2016 · 4.34 FIP	33	SP	4.34	11.0%	7.3%	2.3%	.312	—	—	—	—	—	3.76	222	58
Manuel Flores R 2019 · 4.32 FIP	33	SP	4.32	17.1%	7.4%	3.1%	.291	—	—	—	—	—	3.46	515	58
Guillermo Moscoso R 2018 · 5.07 FIP	34	SP-	5.07	18.2%	8.4%	4.5%	.318	4.35	12.6%	5.9%	104	-0.72	3.56	486	56
Jorge Martinez R 2019 · 3.71 FIP	34	SP	3.71	16.3%	5.7%	2.3%	.348	—	—	—	—	—	3.53	214	55
Sergio Valenzuela R 2017 · 5.66 FIP	32	SP	5.66	10.6%	6.5%	4.8%	.355	—	—	—	—	—	3.66	244	55
Matt Buschmann R 2016 · 4.84 FIP	32	SP	4.84	12.8%	8.4%	3.2%	.325	—	—	—	—	—	4.03	277	54
Albert Suárez R 2024 · 4.00 FIP	34	SP-	4.00	19.5%	6.8%	3.1%	.293	4.14	17.8%	10.6%	80	+0.14	3.58	616	51
Francisco Rodriguez R 2017 · 4.43 FIP	34	SP	4.43	11.4%	8.2%	2.3%	.305	5.18	10.7%	1.4%	83	+0.75	3.16	334	51
Salvador Valdez R 2017 · 3.87 FIP	34	SP	3.87	9.4%	6.1%	1.4%	.328	4.30	17.1%	8.9%	38	+0.43	3.60	326	51
Michael Nix R 2016 · 4.60 FIP	33	SP	4.60	11.9%	11.6%	1.9%	.322	—	—	—	—	—	3.82	234	50
Juan Delgadillo R 2017 · 4.47 FIP	34	SP	4.47	10.9%	6.2%	2.7%	.311	—	—	—	—	—	4.47	143	50
Jesse Estrada R 2017 · 5.39 FIP	33	SP	5.39	8.3%	5.6%	4.2%	.285	4.34	14.4%	7.8%	85	-1.05	3.80	257	49
David Buchanan R 2024 · 4.26 FIP	35	SP-	4.26	14.7%	6.5%	2.9%	.337	5.56	12.2%	4.1%	29	+1.30	3.88	560	49

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	4.9	88	17.9%	8.7%	0.99	4.14
Jim Acker ('83-'92, 5 WAR*)	4.5	90	16.7%*	8.3%*	1.06*	4.12*
Tom Gorzelanny ('16)	4.4	88	18.4%	10.0%	1.00	4.40
Don Hood ('73-'83, 4 WAR*)	4.2	85	16.0%*	9.9%*	0.85*	4.22*
Ron Robinson ('84-'92, 8 WAR*)	4.0	100	18.0%*	7.3%*	0.97*	3.99*
Tim Stauffer ('15)	3.0	85	17.8%	8.0%	0.97	3.97
Kendall Graveman ('25)	3.3	96	17.6%	8.1%	1.06	4.03