Jason Foley

P San Francisco Giants

Age 31 RHP 6'4" / 215 lbs MLB

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	28	MLBDET	69	0	60	3.15	3.55	18.4%	7.2%	0.7
2025	29	AAADET	5	0	7	0.00	0.40	40.9%	0.0%	—	K 40.9% · BB 0.0% · FIP -0.35
2026	30	MIX3 levels	43	1	44	5.56	3.79	15.2%	8.6%	0.4	K 14.1% · BB 8.0% · FIP 3.85
2027PROJ	31	MLBSF	59	0	62	4.37	4.50	17.0%	8.4%	0.0
2028PROJ95% MLB	32	MLBSF	40	0	42	4.60	4.75	16.2%	8.8%	-0.1
2029PROJ95% MLB	33	MLBSF	31	0	33	4.86	5.03	15.3%	9.2%	-0.2

Last 3 seasons WAR 1.1 · Next 3 projected WAR -0.2Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo72

xwOBA vs53

K%10

Form4.26 ERA (proj 4.37) · K 8.3% (proj 17.0%) · BB 10.0% (proj 8.4%) ▼▼ last 60d · 13 IP through Oct 27

CompsBo Schultz (age 30) · Jose Manuel Lopez (age 31) · Juan Sandoval (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 119111vR 102

ERA4.37

FIP4.50

K%17.0%

BB%8.4%

K-BB%8.6%

Proj IP62

Value2027 · WAR

Runs saved vs league avg · 62 IP

Grades · Statcast · Contract

Scouting Grades

STUF4545

CTRL5050

CMD4040

K†4040

Statcast Percentiles

FB Velo

p7296.1

Whiff%

p1015.6%

p1012.8%

xwOBA vs

p530.309

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	20	ROK MLE	GCL Tigers West	1	0	1	4	1	0	0	0%	1.0	25.0%	0.0%	25.0%	0.0%	0.00	0.99	—
2016	20	ROK RAW	GCL Tigers West	1	0	1	4	1	0	0	0%	1.0	25.0%	0.0%	25.0%	0.0%	0.00	1.10	—
2017	21	MIX MLE	2 levels	24	0	36	147	41	5	1	0%	1.5	23.2%	4.1%	19.1%	0.0%	2.48	1.66	—
2017	21	MIX RAW	2 levels	24	0	36	147	41	5	1	0%	1.5	27.9%	3.4%	24.5%	0.7%	2.48	1.61	—
2019	23	A+ MLE	Lakeland Flying Tigers	36	0	44	201	43	16	3	0%	1.2	16.5%	9.2%	7.3%	1.9%	3.89	3.93	—
2019	23	A+ RAW	Lakeland Flying Tigers	36	0	44	201	43	16	3	0%	1.2	21.4%	8.0%	13.4%	1.5%	3.89	3.12	—
2021	25	MIX MLE	2 levels	43	0	45	198	42	23	6	0%	1.0	20.7%	12.4%	8.4%	3.4%	4.00	4.79	-0.1
2021	25	MIX RAW	2 levels	43	0	45	198	42	23	6	0%	1.0	21.2%	11.6%	9.6%	3.0%	4.00	4.50	+0.0
2022	26	MIX MLE	2 levels	64	0	65	277	49	12	3	0%	1.0	16.8%	4.3%	12.5%	0.8%	3.86	2.67	+1.5
2022	26	MIX RAW	2 levels	64	0	65	277	49	12	3	0%	1.0	17.7%	4.3%	13.4%	1.1%	3.86	2.75	+1.4
2023	27	MLB MLE	Detroit Tigers	70	0	69	276	55	13	2	0%	1.0	19.9%	4.7%	15.2%	0.8%	2.61	2.43	+1.8
2023	27	MLB RAW	Detroit Tigers	70	0	69	276	55	13	2	0%	1.0	19.9%	4.7%	15.2%	0.7%	2.61	2.45	+1.7
2024	28	MLB MLE	Detroit Tigers	69	0	60	250	46	18	5	0%	0.9	18.4%	7.2%	11.2%	2.1%	3.15	3.60	+0.7
2024	28	MLB MLE	Detroit Tigers	69	0	60	250	46	18	5	0%	0.9	18.4%	7.2%	11.2%	2.1%	3.15	3.60	+0.7
2024	28	MLB RAW	Detroit Tigers	69	0	60	250	46	18	5	0%	0.9	18.4%	7.2%	11.2%	2.0%	3.15	3.55	+0.7
2025	29	AAA MLE	Toledo Mud Hens	5	0	7	22	9	0	0	0%	1.3	40.9%	0.0%	40.9%	0.0%	0.00	-0.35	—
2025	29	AAA MLE	Toledo Mud Hens	5	0	7	22	9	0	0	0%	1.3	40.9%	0.0%	40.9%	0.0%	0.00	-0.35	—
2025	29	AAA RAW	Toledo Mud Hens	5	0	7	22	9	0	0	0%	1.3	40.9%	0.0%	40.9%	0.0%	0.00	0.40	—
2026	30	MIX MLE	3 levels	43	1	44	198	30	17	3	2%	1.0	14.1%	8.0%	6.1%	1.7%	5.56	3.85	+0.4
2026	30	MIX MLE	3 levels	43	1	44	198	30	17	3	2%	1.0	14.1%	8.0%	6.1%	1.7%	5.56	3.85	+0.4
2026	30	MIX RAW	3 levels	43	1	44	198	30	17	3	2%	1.0	15.2%	8.6%	6.6%	1.5%	5.56	3.79	+0.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			234	0	226	944	165	56	12	0%	1.0	17.5%	5.9%	11.5%	1.3%	3.27	3.07	+4.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	SF	59	0	62	262	44	22	7	0%	1.1	17.0%	8.4%	8.6%	2.5%	4.37	4.50	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	SF	42	0	44	187	30	16	5	0%	1.1	16.2%	8.8%	7.4%	2.8%	4.60	4.75	-0.1
2029	33	MLB MLE	SF	33	0	34	145	22	13	4	0%	1.0	15.3%	9.2%	6.1%	3.0%	4.86	5.03	-0.2
2028	32	MLB CTX	SF	40	0	42	177	29	16	5	0%	1.1	16.2%	8.8%	7.4%	2.8%	4.60	4.75	-0.1
2029	33	MLB CTX	SF	31	0	33	139	21	13	4	0%	1.1	15.3%	9.2%	6.1%	3.0%	4.86	5.03	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		305	0	300	1259	215	84	21	0%	1.0	17.1%	6.7%	10.4%	1.7%	3.63	3.43	+4.0
/ 200 IP	—	RAW		203	0	200	839	143	56	14	0%	1.0	17.1%	6.7%	10.4%	1.7%	3.63	3.43	+2.7
Career	—	MLE		355	1	371	1573	294	108	24	0%	1.0	18.7%	6.8%	11.8%	1.5%	3.52	3.21	+3.7
/ 200 IP	—	MLE		191	1	200	848	158	58	13	0%	1.0	18.7%	6.8%	11.8%	1.5%	3.52	3.21	+2.4

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.

2026 AAASacramento River Cats · 17 IPFIP 3.44 → 3.61 (+0.18)K% 18.7% → 16.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.67%	—	×0.860	16.05%
BB% (unintentional) /BF	9.33%	—	×0.900	8.40%
HR / BF /BF	1.33%	÷1.000	×1.100	1.47%

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 100).

Result: FIP 3.44 → 3.61, 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).

2025 AAAToledo Mud Hens · 7 IPFIP -0.35 → 0.13 (+0.48)K% 40.9% → 35.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	40.91%	—	×0.860	35.18%
BB% (unintentional) /BF	0.00%	—	×0.900	0.00%
HR / BF /BF	0.00%	÷0.872	×1.100	0.00%

Result: FIP -0.35 → 0.13, 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).

2022 AAAToledo Mud Hens · 5 IPFIP 3.90 → 4.41 (+0.51)K% 28.6% → 25.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.880	25.14%
BB% (unintentional) /BF	4.76%	—	×1.050	5.00%
HR / BF /BF	4.76%	÷1.026	×1.100	5.11%

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 105).

Result: FIP 3.90 → 4.41, 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 AAAToledo Mud Hens · 35 IPFIP 4.40 → 4.79 (+0.40)K% 23.5% → 20.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.53%	—	×0.880	20.71%
BB% (unintentional) /BF	11.76%	—	×1.050	12.35%
HR / BF /BF	3.27%	÷1.052	×1.100	3.42%

Result: FIP 4.40 → 4.79, 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 A+Lakeland Flying Tigers · 44 IPFIP 3.12 → 3.93 (+0.81)K% 21.4% → 16.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.39%	—	×0.770	16.47%
BB% (unintentional) /BF	7.96%	—	×1.150	9.15%
HR / BF /BF	1.49%	÷0.963	×1.250	1.94%

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 93).

Result: FIP 3.12 → 3.93, 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 AWest Michigan Whitecaps · 29 IPFIP 0.90 → 1.66 (+0.77)K% 31.3% → 23.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.30%	—	×0.740	23.17%
BB% (unintentional) /BF	3.48%	—	×1.180	4.10%
HR / BF /BF	0.00%	÷0.849	×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 70).

Result: FIP 0.90 → 1.66, 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 A+Lakeland Flying Tigers · 7 IPFIP 3.89 → 3.77 (−0.13)K% 15.6% → 12.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.62%	—	×0.770	12.03%
BB% (unintentional) /BF	3.12%	—	×1.150	3.59%
HR / BF /BF	3.12%	÷1.747	×1.250	2.24%

Result: FIP 3.89 → 3.77, 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).

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
Jason Foley (OGILVIE)	—	—	4.50	17.0%	8.4%	2.5%	—	4.87	15.8%	6.8%	79	+0.37	—	—	—
Bo Schultz R 2016 · 3.90 FIP	30	SU	3.90	14.4%	5.2%	2.5%	.276	2.73	16.4%	8.4%	27	-1.17	2.73	141	79
Jose Manuel Lopez R 2016 · 3.50 FIP	31	SU	3.50	16.4%	8.4%	1.3%	.308	4.77	19.3%	7.3%	90	+1.27	3.50	170	78
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	78
Mike Buddie R 2002 · 4.26 FIP	31	SU	4.26	15.1%	7.6%	2.7%	.297	—	—	—	—	—	3.76	123	77
Luis Angel Daniel R 2016 · 4.56 FIP	31	SU	4.56	13.4%	8.9%	2.7%	.286	4.16	9.8%	0.3%	53	-0.40	4.16	156	77
Angel Castro R 2015 · 4.50 FIP	32	SU	4.50	15.9%	8.0%	3.1%	.268	4.28	13.6%	5.5%	231	-0.22	3.51	292	77
Miguel Mejia R 2019 · 4.41 FIP	31	SU	4.41	15.7%	10.7%	2.3%	.341	—	—	—	—	—	3.37	108	77
Jeff Fulchino R 2011 · 4.44 FIP	31	SU	4.44	20.4%	11.1%	3.0%	.302	—	—	—	—	—	3.36	178	76
Erik Davis R 2018 · 3.52 FIP	31	SU	3.52	17.4%	8.9%	1.4%	.317	—	—	—	—	—	3.05	227	75
Neil Wagner R 2016 · 4.05 FIP	32	SU	4.05	19.8%	12.3%	1.9%	.360	3.11	20.7%	12.1%	54	-0.94	3.11	135	75
Ryan Kelly R 2019 · 4.33 FIP	31	SU	4.33	14.7%	7.2%	2.8%	.307	—	—	—	—	—	2.49	131	75
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	75
Mike Broadway R 2018 · 4.65 FIP	31	SU	4.65	23.0%	10.6%	3.9%	.282	—	—	—	—	—	1.74	195	74
Ivan Zavala R 2017 · 5.02 FIP	30	SU	5.02	12.8%	8.0%	3.6%	.286	5.15	12.7%	5.3%	61	+0.13	4.27	209	74
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	74
Fernando Villalobos R 2016 · 4.00 FIP	31	SU	4.00	12.3%	8.6%	1.6%	.305	4.70	10.6%	2.6%	55	+0.70	4.00	152	74
Jason Urquidez R 2015 · 3.59 FIP	32	SU	3.59	15.6%	9.3%	1.1%	.386	1.81	25.5%	20.7%	90	-1.78	1.25	123	74
Esteban Haro R 2017 · 3.91 FIP	31	SU	3.91	13.9%	5.1%	2.4%	.386	3.76	9.5%	3.9%	39	-0.15	3.76	164	73
Rodney Myers R 2001 · 4.70 FIP	32	SU	4.70	13.7%	9.5%	2.8%	.301	3.98	10.9%	1.0%	21	-0.72	3.98	218	73
Dustin Molleken R 2016 · 4.04 FIP	31	SU	4.04	19.6%	12.1%	1.9%	.276	3.38	24.3%	11.9%	16	-0.66	3.38	129	73

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	1.8	52	17.0%	7.4%	0.71	3.77
Adam Cimber ('24)	1.6	54	18.0%	6.5%	0.75	3.75
Alex Wilson ('19)	1.7	55	16.5%	6.6%	0.84	3.44
Eric O'Flaherty ('17)	2.1	52	17.7%	8.5%	0.61	3.70
Mike Perez ('90-'97, 3 WAR*)	1.7	58	18.5%*	8.2%*	0.88*	3.86*
Jeff Innis ('87-'93, 4 WAR*)	1.8	60	16.2%*	8.1%*	0.82*	3.56*
Fred Gladding ('61-'73, 6 WAR*)	3.0	67	19.6%*	8.6%*	0.76*	3.76*