Keaton Winn

P San Francisco Giants

Age 29 RHP 6'4" / 230 lbs MLB ILInjured 60-Day (D60)

Overview

MLBRPIL-6040-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	26	MIX2 levels	13	12	59	6.86	4.59	21.3%	8.7%	0.6	K 20.2% · BB 8.4% · FIP 4.71
2025	27	MIX4 levels	25	4	37	6.99	3.85	20.3%	6.4%	0.3	K 16.7% · BB 6.4% · FIP 4.77
2026	28	MIX4 levels	47	2	46	3.13	3.56	19.3%	9.6%	0.5	K 18.8% · BB 9.1% · FIP 3.68
2027PROJ	29	MLBSF	22	0	23	4.65	4.47	20.1%	8.9%	0.0
2028PROJ95% MLB	30	MLBSF	27	0	28	4.67	4.68	19.4%	9.2%	-0.0
2029PROJ95% MLB	31	MLBSF	18	0	19	4.81	4.91	18.7%	9.6%	-0.1

Last 3 seasons WAR 1.4 · Next 3 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo72

xwOBA vs54

K%27

CompsRamon Delgado (age 29) · Yoanner Negrin (age 31) · Ryan Jensen (age 29)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 113105vR 96

ERA4.65

FIP4.47

K%20.1%

BB%8.9%

K-BB%11.2%

Proj IP23

Value2027 · WAR

Runs saved vs league avg · 23 IP

Grades · Statcast · Contract

Scouting Grades

STUF5050

CTRL5050

CMD4545

K†5050

Statcast Percentiles

FB Velo

p7296.1

Whiff%

p3423.0%

p2718.8%

xwOBA vs

p540.308

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)
2019	21	A MLE	Augusta GreenJackets	26	20	127	526	99	26	9	77%	4.9	13.9%	5.8%	8.1%	2.1%	3.32	3.79	—
2019	21	A RAW	Augusta GreenJackets	26	20	127	526	99	26	9	77%	4.9	18.8%	4.9%	13.9%	1.7%	3.32	3.08	—
2022	24	MIX MLE	3 levels	27	25	108	461	125	32	9	93%	4.0	20.7%	8.0%	12.7%	2.4%	4.08	3.68	—
2022	24	MIX RAW	3 levels	27	25	108	461	125	32	9	93%	4.0	27.1%	6.9%	20.2%	2.0%	4.08	2.76	—
2023	25	MIX MLE	2 levels	26	19	100	435	101	34	13	73%	3.9	21.1%	7.2%	13.9%	3.2%	4.75	3.97	+1.7
2023	25	MIX RAW	2 levels	26	19	100	435	101	34	13	73%	3.9	23.2%	7.8%	15.4%	3.0%	4.75	3.79	+1.9
2024	26	MIX MLE	2 levels	13	12	59	253	54	22	10	92%	4.5	20.2%	8.4%	11.8%	4.1%	6.86	4.71	+0.5
2024	26	MIX MLE	2 levels	13	12	59	253	54	22	10	92%	4.5	20.2%	8.4%	11.8%	4.1%	6.86	4.71	+0.5
2024	26	MIX RAW	2 levels	13	12	59	253	54	22	10	92%	4.5	21.3%	8.7%	12.6%	4.0%	6.86	4.59	+0.6
2025	27	MIX MLE	4 levels	25	4	37	172	35	11	5	16%	1.5	16.7%	6.4%	10.4%	4.2%	6.99	4.77	-0.1
2025	27	MIX MLE	4 levels	25	4	37	172	35	11	5	16%	1.5	16.7%	6.4%	10.4%	4.2%	6.99	4.77	-0.1
2025	27	MIX RAW	4 levels	25	4	37	172	35	11	5	16%	1.5	20.3%	6.4%	14.0%	2.9%	6.99	3.85	+0.3
2026	28	MIX MLE	4 levels	47	2	46	187	36	18	3	4%	1.0	18.8%	9.1%	9.7%	1.8%	3.13	3.68	+0.5
2026	28	MIX MLE	4 levels	47	2	46	187	36	18	3	4%	1.0	18.8%	9.1%	9.7%	1.8%	3.13	3.68	+0.5
2026	28	MIX RAW	4 levels	47	2	46	187	36	18	3	4%	1.0	19.3%	9.6%	9.6%	1.6%	3.13	3.56	+0.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			72	17	151	628	125	46	19	24%	2.1	19.9%	7.3%	12.6%	3.0%	5.20	4.00	+1.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	SF	22	0	23	97	20	9	3	0%	1.0	20.1%	8.9%	11.2%	2.8%	4.65	4.47	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	SF	28	0	29	122	24	11	4	0%	1.0	19.4%	9.2%	10.2%	3.0%	4.67	4.68	-0.0
2029	31	MLB MLE	SF	19	0	20	86	16	8	3	0%	1.1	18.7%	9.6%	9.1%	3.3%	4.81	4.91	-0.1
2028	30	MLB CTX	SF	27	0	28	118	23	11	4	0%	1.0	19.4%	9.2%	10.2%	3.0%	4.67	4.68	-0.0
2029	31	MLB CTX	SF	18	0	19	80	15	8	3	0%	1.1	18.7%	9.6%	9.1%	3.3%	4.81	4.91	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		117	17	198	826	163	65	25	15%	1.7	19.7%	7.8%	11.9%	3.1%	5.09	4.10	+1.1
/ 200 IP	—	RAW		118	17	200	836	165	66	25	15%	1.7	19.7%	7.8%	11.9%	3.1%	5.09	4.10	+1.1
Career	—	MLE		164	82	478	2034	375	148	57	50%	2.9	18.5%	7.3%	11.2%	2.8%	4.50	3.99	+2.9
/ 200 IP	—	MLE		69	34	200	851	157	62	24	50%	2.9	18.5%	7.3%	11.2%	2.8%	4.50	3.99	+2.9

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 AAASacramento River Cats · 23 IPFIP 4.30 → 4.77 (+0.48)K% 19.5% → 16.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.47%	—	×0.860	16.74%
BB% (unintentional) /BF	7.08%	—	×0.900	6.37%
HR / BF /BF	3.54%	÷0.938	×1.100	4.15%

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 88).

Result: FIP 4.30 → 4.77, 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).

2023 AAASacramento River Cats · 58 IPFIP 3.69 → 3.93 (+0.23)K% 25.1% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.10%	—	×0.860	21.58%
BB% (unintentional) /BF	9.89%	—	×0.900	8.90%
HR / BF /BF	2.66%	÷1.057	×1.100	2.77%

Result: FIP 3.69 → 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).

2022 ASan Jose Giants · 41 IPFIP 2.54 → 3.66 (+1.11)K% 31.6% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.61%	—	×0.740	23.39%
BB% (unintentional) /BF	9.20%	—	×1.180	10.85%
HR / BF /BF	1.72%	÷1.063	×1.300	2.11%

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 2.54 → 3.66, 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).

2022 A+Eugene Emeralds · 37 IPFIP 2.14 → 2.91 (+0.76)K% 28.9% → 22.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.93%	—	×0.770	22.28%
BB% (unintentional) /BF	6.29%	—	×1.150	7.23%
HR / BF /BF	1.26%	÷1.120	×1.250	1.40%

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 124).

Result: FIP 2.14 → 2.91, 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 AARichmond Flying Squirrels · 30 IPFIP 3.83 → 4.69 (+0.86)K% 18.8% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.75%	—	×0.810	15.19%
BB% (unintentional) /BF	4.69%	—	×1.100	5.16%
HR / BF /BF	3.12%	÷0.929	×1.200	4.04%

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 86).

Result: FIP 3.83 → 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).

2019 AAugusta GreenJackets · 127 IPFIP 3.08 → 3.79 (+0.71)K% 18.8% → 13.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.82%	—	×0.740	13.93%
BB% (unintentional) /BF	4.94%	—	×1.180	5.83%
HR / BF /BF	1.71%	÷1.082	×1.300	2.06%

Result: FIP 3.08 → 3.79, 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).

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
Keaton Winn (OGILVIE)	—	—	4.47	20.1%	8.9%	2.8%	—	4.77	19.1%	9.7%	49	+0.30	—	—	—
Ramon Delgado R 2016 · 4.60 FIP	29	SW	4.60	13.8%	5.9%	3.5%	.332	4.31	11.2%	7.2%	74	-0.29	4.31	135	69
Yoanner Negrin R 2015 · 3.79 FIP	31	SW	3.79	15.1%	7.5%	1.9%	.256	3.29	15.0%	8.5%	270	-0.50	3.15	485	69
Ryan Jensen R 2005 · 4.35 FIP	29	SW	4.35	15.7%	5.2%	3.5%	.310	—	—	—	—	—	4.35	253	66
Rodolfo Aguirre R 2015 · 4.29 FIP	30	SW	4.29	12.4%	6.9%	2.5%	.288	4.81	11.4%	1.3%	125	+0.52	3.39	212	65
Carlos Frías R 2019 · 4.42 FIP	29	SP-	4.42	16.9%	11.1%	2.4%	.369	—	—	—	—	—	3.40	236	65
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	64
Jose Pina R 2015 · 4.90 FIP	29	SW	4.90	16.9%	9.2%	3.8%	.231	3.66	13.6%	6.9%	246	-1.24	3.63	424	64
Fredy Quintero R 2017 · 4.45 FIP	29	SW	4.45	14.0%	8.7%	2.6%	.335	4.62	17.2%	3.5%	43	+0.17	4.29	241	64
Edgar Alberto Garcia R 2017 · 4.01 FIP	29	SP-	4.01	17.3%	10.9%	1.8%	.305	—	—	—	—	—	4.01	272	63
Mike Wright Jr. R 2019 · 3.76 FIP	29	SW	3.76	19.9%	7.2%	2.6%	.347	4.19	17.6%	7.3%	169	+0.43	3.76	725	63
Aldo Montes R 2019 · 4.69 FIP	29	SW	4.69	18.2%	10.6%	3.3%	.336	—	—	—	—	—	4.69	210	63
Felipe Arredondo R 2016 · 3.92 FIP	29	SW	3.92	17.2%	6.6%	2.6%	.281	4.71	15.4%	8.2%	139	+0.79	3.92	337	62
Murphy Smith R 2017 · 4.32 FIP	29	SW	4.32	13.6%	4.9%	3.2%	.267	4.57	13.6%	4.3%	63	+0.25	3.16	300	62
Alfonso Sanchez R 2017 · 4.51 FIP	29	SW	4.51	12.5%	9.8%	2.2%	.346	7.58	8.6%	1.4%	39	+3.07	4.51	224	61
Zach Stewart R 2015 · 4.43 FIP	28	SP-	4.43	14.4%	8.3%	2.7%	.322	5.17	11.7%	2.1%	51	+0.74	4.27	194	61
Michael Mariot R 2018 · 4.62 FIP	29	SU	4.62	21.8%	7.5%	4.4%	.328	4.65	17.7%	9.5%	136	+0.03	2.40	701	60
Esmil Rogers R 2015 · 3.51 FIP	29	SW	3.51	18.5%	8.9%	1.6%	.348	2.86	21.7%	17.9%	40	-0.65	2.86	616	60
Chris Jakubauskas R 2009 · 4.97 FIP	30	SW	4.97	12.1%	6.2%	3.9%	.250	4.42	15.7%	7.8%	72	-0.55	4.42	165	60
Willie Eyre R 2007 · 4.53 FIP	29	SU	4.53	13.7%	9.1%	2.6%	.306	2.87	11.1%	5.5%	18	-1.66	2.87	164	60
Dinelson Lamet R 2021 · 3.67 FIP	29	SW	3.67	27.3%	10.5%	2.8%	.339	4.61	20.7%	8.6%	176	+0.94	2.11	778	60

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	1.1	38	19.8%	8.1%	1.17	5.03
Tom Dukes ('67-'72, -2 WAR*)	1.1	43	22.7%*	8.7%*	1.16*	5.37*
Steve Kealey ('68-'73, -1 WAR*)	1.1	54	18.5%*	7.2%*	1.32*	5.26*
Drew VerHagen ('23)	1.4	47	19.1%	9.4%	1.37	4.98
Randy St. Claire ('84-'94, -0 WAR*)	1.3	50	19.0%*	8.4%*	1.39*	4.61*
Sam Long ('25)	1.1	42	19.5%	9.7%	1.28	4.65
Josh Lindblom ('21)	1.0	52	22.1%	9.6%	1.25	4.78