Ken Giles

P Gwinnett Stripers

Age 36 RHP 6'3" / 197 lbs AAA peak 0.2 · 2027

Overview

AAARP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	31	MIX4 levels	23	3	20	7.08	6.35	24.8%	16.8%	-0.4	K 14.1% · BB 18.9% · FIP 9.19
2023	32	MIX2 levels	21	2	20	8.55	4.55	30.8%	17.3%	—	K 24.6% · BB 16.5% · FIP 4.96
2024	33	AAAATL	42	0	39	6.28	4.50	33.9%	12.9%	—	K 29.2% · BB 11.6% · FIP 5.02
2025	34	No MLB games played
2026	35	No MLB games played
2027PROJ	36	MLBATL	5	0	5	4.15	4.14	26.2%	9.2%	0.0
2028PROJ	37	MLBATL	36	0	38	4.38	4.54	25.2%	9.9%	0.0

Last 3 seasons WAR -0.4 · Next 2 projected WAR 0.0Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2019

K%90

xwOBA vs90

FB Velo84

CompsHirokazu Sawamura (age 34) · Keiichi Yabu (age 36) · Matt Bush (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9486vR 77

ERA4.15

FIP4.14

K%26.2%

BB%9.2%

K-BB%17.0%

Proj IP51

Value2027 · WAR

Runs saved vs league avg · 51 IP

Grades · Statcast · Contract

Scouting Grades

STUF7070

CTRL5555

CMD7575

K†6060

Statcast Percentiles

FB Velo

p8496.9

Whiff%

p9040.0%

p9039.9%

xwOBA vs

p900.245

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)
2014	23	MLB MLE	Philadelphia Phillies	44	0	46	166	64	10	1	0%	1.0	38.6%	6.0%	32.5%	0.6%	1.18	0.92	+2.0
2014	23	MLB RAW	Philadelphia Phillies	44	0	46	166	64	10	1	0%	1.0	38.6%	6.0%	32.5%	0.6%	1.18	1.24	+1.8
2015	24	MLB MLE	Philadelphia Phillies	69	0	70	298	87	23	2	0%	1.0	29.2%	7.7%	21.5%	0.6%	1.80	1.96	+2.2
2015	24	MLB RAW	Philadelphia Phillies	69	0	70	298	87	23	2	0%	1.0	29.2%	7.7%	21.5%	0.7%	1.80	1.97	+2.2
2016	25	MLB MLE	Houston Astros	69	0	66	286	102	24	8	0%	1.0	35.7%	8.4%	27.3%	2.9%	4.11	2.73	+1.4
2016	25	MLB RAW	Houston Astros	69	0	66	286	102	24	8	0%	1.0	35.7%	8.4%	27.3%	2.8%	4.11	2.67	+1.5
2017	26	MLB MLE	Houston Astros	63	0	63	247	83	21	4	0%	1.0	33.6%	8.5%	25.1%	1.7%	2.30	2.25	+1.7
2017	26	MLB RAW	Houston Astros	63	0	63	247	83	21	4	0%	1.0	33.6%	8.5%	25.1%	1.6%	2.30	2.29	+1.7
2018	27	MIX MLE	2 levels	61	0	56	239	61	9	6	0%	0.9	25.0%	3.3%	21.7%	2.8%	5.01	2.96	+1.1
2018	27	MIX RAW	2 levels	61	0	56	239	61	9	6	0%	0.9	25.5%	3.8%	21.8%	2.5%	5.01	2.79	+1.2
2019	28	MLB MLE	Toronto Blue Jays	53	0	53	208	83	16	5	0%	1.0	39.9%	7.7%	32.2%	2.4%	1.87	2.02	+1.6
2019	28	MLB RAW	Toronto Blue Jays	53	0	53	208	83	16	5	0%	1.0	39.9%	7.7%	32.2%	2.4%	1.87	2.10	+1.6
2020	29	MLB MLE	Toronto Blue Jays	4	0	4	19	6	4	2	0%	0.9	31.6%	21.1%	10.5%	10.5%	9.82	8.88	-0.2
2020	29	MLB RAW	Toronto Blue Jays	4	0	4	19	6	4	2	0%	0.9	31.6%	21.1%	10.5%	10.5%	9.82	10.19	-0.2
2022	31	MIX MLE	4 levels	23	3	20	101	25	17	5	13%	0.9	14.1%	18.9%	-4.8%	8.9%	7.08	9.19	-1.1
2022	31	MIX RAW	4 levels	23	3	20	101	25	17	5	13%	0.9	24.8%	16.8%	7.9%	5.0%	7.08	6.35	-0.4
2023	32	MIX MLE	2 levels	21	2	20	104	32	18	3	10%	1.0	24.6%	16.5%	8.0%	3.3%	8.55	4.96	—
2023	32	MIX RAW	2 levels	21	2	20	104	32	18	3	10%	1.0	30.8%	17.3%	13.5%	2.9%	8.55	4.55	—
2024	33	AAA MLE	Gwinnett Stripers	42	0	39	171	58	22	8	0%	0.9	29.2%	11.6%	17.6%	5.3%	6.28	5.02	—
2024	33	AAA MLE	Gwinnett Stripers	42	0	39	171	58	22	8	0%	0.9	29.2%	11.6%	17.6%	5.3%	6.28	5.02	—
2024	33	AAA RAW	Gwinnett Stripers	42	0	39	171	58	22	8	0%	0.9	33.9%	12.9%	21.1%	4.7%	6.28	4.50	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			362	0	355	1454	484	109	28	0%	1.0	33.3%	7.5%	25.8%	1.9%	2.71	2.32	+9.5
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	ATL	5	0	5	21	6	2	1	0%	1.0	26.2%	9.2%	17.0%	3.1%	4.15	4.14	+0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	ATL	36	0	38	158	40	16	6	0%	1.0	25.2%	9.9%	15.3%	3.5%	4.38	4.54	+0.0
2028	37	MLB CTX	ATL	36	0	38	160	40	16	6	0%	1.1	25.2%	9.9%	15.3%	3.5%	4.38	4.54	+0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		398	0	393	1612	524	125	34	0%	1.0	32.5%	7.7%	24.8%	2.1%	2.87	2.48	+9.8
/ 200 IP	—	RAW		202	0	200	821	267	64	17	0%	1.0	32.5%	7.7%	24.8%	2.1%	2.87	2.48	+5.0
Career	—	MLE		449	5	435	1839	574	162	50	1%	1.0	31.2%	8.8%	22.4%	2.7%	3.60	3.08	+9.5
/ 200 IP	—	MLE		206	2	200	845	264	74	23	1%	1.0	31.2%	8.8%	22.4%	2.7%	3.60	3.08	+4.8

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 AAAGwinnett Stripers · 39 IPFIP 4.43 → 5.02 (+0.59)K% 33.9% → 29.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.92%	—	×0.860	29.17%
BB% (unintentional) /BF	12.87%	—	×0.900	11.58%
HR / BF /BF	4.68%	÷0.968	×1.100	5.32%

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 94).

Result: FIP 4.43 → 5.02, 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 AAAOklahoma City Dodgers · 18 IPFIP 4.69 → 4.96 (+0.26)K% 28.6% → 24.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.57%	—	×0.860	24.57%
BB% (unintentional) /BF	18.37%	—	×0.900	16.53%
HR / BF /BF	3.06%	÷1.006	×1.100	3.35%

Result: FIP 4.69 → 4.96, 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 AAASacramento River Cats · 10 IPFIP 8.42 → 9.19 (+0.77)K% 16.0% → 14.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.00%	—	×0.880	14.08%
BB% (unintentional) /BF	18.00%	—	×1.050	18.90%
HR / BF /BF	8.00%	÷0.989	×1.100	8.90%

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 98).

Result: FIP 8.42 → 9.19, 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+Everett AquaSox · 5 IPFIP 3.73 → 4.61 (+0.88)K% 33.3% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	33.33%	—	×0.770	25.67%
BB% (unintentional) /BF	11.11%	—	×1.150	12.78%
HR / BF /BF	3.70%	÷1.237	×1.250	3.74%

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 147).

Result: FIP 3.73 → 4.61, 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).

2018 AAAFresno Grizzlies · 5 IPFIP 1.54 → 1.88 (+0.35)K% 29.6% → 26.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.63%	—	×0.880	26.07%
BB% (unintentional) /BF	7.41%	—	×1.050	7.78%
HR / BF /BF	0.00%	÷0.951	×1.100	0.00%

Result: FIP 1.54 → 1.88, 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).

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
Ken Giles (OGILVIE)	—	—	4.14	26.2%	9.2%	3.1%	—	4.54	25.2%	15.3%	38	+0.40	—	—	—
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	67
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	65
Matt Bush R 2022 · 3.73 FIP	36	SU	3.73	30.3%	6.6%	4.3%	.224	3.79	26.2%	17.5%	36	+0.06	2.87	249	65
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	65
Daniel Bard R 2021 · 3.62 FIP	36	SU	3.62	26.3%	11.5%	2.3%	.337	3.64	24.6%	10.3%	110	+0.02	2.56	456	61
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	60
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	59
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	59
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	58
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	58
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	58
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	56
Jay Jackson R 2024 · 5.31 FIP	36	SU	5.31	25.4%	7.9%	6.1%	.304	—	—	—	—	—	2.36	261	56
Peter Moylan R 2015 · 3.11 FIP	36	SU	3.11	18.4%	8.2%	0.9%	.259	3.84	18.2%	9.1%	145	+0.73	2.54	431	54
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	52
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	51
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	51
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	43
Tony Pena Jr. R 2017 · 3.43 FIP	36	SU	3.43	10.9%	4.3%	1.3%	.312	—	—	—	—	—	2.80	128	41
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	41

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	2.2	55	31.7%	8.2%	0.85	3.02
Felipe Vázquez ('19)	1.7	66	29.8%	7.5%	0.60	2.61
Will Harris ('21)	2.1	60	26.5%	6.7%	0.86	2.94
J.J. Putz ('14)	2.8	57	25.7%	7.9%	0.79	3.08
Bryan Harvey ('87-'94, 12 WAR*)	1.9	64	37.8%*	8.9%*	0.94*	2.59*
Tom Henke ('82-'95, 25 WAR*)	3.9	66	35.9%*	7.7%*	0.93*	2.72*
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*