Ryan Borucki

P San Francisco Giants

Age 33 LHP 6'4" / 210 lbs MLB

Overview

MLBRP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MIX3 levels	29	0	26	5.96	4.15	32.5%	10.5%	0.1	K 25.5% · BB 7.8% · FIP 4.27
2025	31	MIX3 levels	47	1	44	3.89	3.74	23.2%	11.0%	0.4	K 22.1% · BB 10.3% · FIP 4.06
2026	32	MIX2 levels	34	0	35	3.60	3.73	16.7%	7.3%	0.3	K 14.4% · BB 7.7% · FIP 4.57
2027PROJ	33	MLBSF	167	0	175	4.65	4.66	18.9%	8.7%	-0.2
2028PROJ95% MLB	34	MLBSF	79	0	83	4.84	4.97	17.9%	9.2%	-0.4
2029PROJ91% MLB	35	MLBSF	38	0	40	5.12	5.30	17.0%	9.8%	-0.3

Last 3 seasons WAR 0.9 · Next 3 projected WAR -0.9Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs52

FB Velo36

K%10

CompsColt Hynes (age 32) · Alejandro Soto (age 33) · Brandon Mann (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 105114vR 122

ERA4.65

FIP4.66

K%18.9%

BB%8.7%

K-BB%10.1%

Proj IP175

Value2027 · WAR

Runs saved vs league avg · 175 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p3693.5

Whiff%

p6928.2%

p1014.4%

xwOBA vs

p520.310

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	21	ROK MLE	GCL Blue Jays	1	0	1	4	1	0	0	0%	1.0	25.0%	0.0%	25.0%	0.0%	0.00	0.99	—
2015	21	ROK RAW	GCL Blue Jays	1	0	1	4	1	0	0	0%	1.0	25.0%	0.0%	25.0%	0.0%	0.00	1.10	—
2016	22	MIX MLE	2 levels	26	26	136	570	117	38	11	100%	5.2	15.2%	7.8%	7.4%	2.6%	4.18	4.25	—
2016	22	MIX RAW	2 levels	26	26	136	570	117	38	11	100%	5.2	20.5%	6.7%	13.9%	1.9%	4.18	3.27	—
2017	23	MIX MLE	3 levels	27	26	150	613	157	35	7	96%	5.6	20.0%	6.6%	13.4%	1.2%	2.93	2.90	—
2017	23	MIX RAW	3 levels	27	26	150	613	157	35	7	96%	5.6	25.6%	5.7%	19.9%	1.1%	2.93	2.32	—
2018	24	MIX MLE	2 levels	30	30	175	731	125	58	13	100%	5.8	16.1%	8.1%	8.0%	1.8%	3.61	3.75	+3.5
2018	24	MIX RAW	2 levels	30	30	175	731	125	58	13	100%	5.8	17.1%	7.9%	9.2%	1.8%	3.61	3.63	+3.8
2019	25	MIX MLE	4 levels	6	6	25	107	25	9	6	100%	4.1	23.4%	8.4%	15.0%	5.6%	5.11	5.27	+0.0
2019	25	MIX RAW	4 levels	6	6	25	107	25	9	6	100%	4.1	23.4%	8.4%	15.0%	5.6%	5.11	5.33	+0.0
2020	26	MLB MLE	Toronto Blue Jays	21	0	17	73	21	11	1	0%	0.8	28.8%	15.1%	13.7%	1.4%	2.70	3.33	+0.2
2020	26	MLB RAW	Toronto Blue Jays	21	0	17	73	21	11	1	0%	0.8	28.8%	15.1%	13.7%	1.4%	2.70	3.34	+0.2
2021	27	MIX MLE	2 levels	33	0	33	137	33	19	5	0%	1.0	21.4%	11.2%	10.2%	5.1%	4.36	5.51	-0.4
2021	27	MIX RAW	2 levels	33	0	33	137	33	19	5	0%	1.0	24.1%	13.9%	10.2%	3.6%	4.36	4.80	-0.1
2022	28	MLB MLE	Seattle Mariners	32	0	25	111	21	11	6	0%	0.8	18.9%	9.9%	9.0%	5.5%	5.68	5.80	-0.4
2022	28	MLB RAW	Seattle Mariners	32	0	25	111	21	11	6	0%	0.8	18.9%	9.9%	9.0%	5.4%	5.68	5.82	-0.4
2023	29	MIX MLE	2 levels	54	2	58	237	48	16	5	4%	1.1	19.4%	6.2%	13.1%	2.2%	3.59	3.49	+0.7
2023	29	MIX RAW	2 levels	54	2	58	237	48	16	5	4%	1.1	20.3%	6.8%	13.5%	2.1%	3.59	3.39	+0.8
2024	30	MIX MLE	3 levels	29	0	26	114	37	12	5	0%	0.9	25.5%	7.8%	17.6%	4.2%	5.96	4.27	+0.1
2024	30	MIX MLE	3 levels	29	0	26	114	37	12	5	0%	0.9	25.5%	7.8%	17.6%	4.2%	5.96	4.27	+0.1
2024	30	MIX RAW	3 levels	29	0	26	114	37	12	5	0%	0.9	32.5%	10.5%	21.9%	4.4%	5.96	4.15	+0.1
2025	31	MIX MLE	3 levels	47	1	44	181	42	20	4	2%	0.9	22.1%	10.3%	11.7%	2.8%	3.89	4.06	+0.3
2025	31	MIX MLE	3 levels	47	1	44	181	42	20	4	2%	0.9	22.1%	10.3%	11.7%	2.8%	3.89	4.06	+0.3
2025	31	MIX RAW	3 levels	47	1	44	181	42	20	4	2%	0.9	23.2%	11.0%	12.2%	2.2%	3.89	3.74	+0.4
2026	32	MIX MLE	2 levels	34	0	35	150	25	11	3	0%	1.0	14.4%	7.7%	6.7%	3.1%	3.60	4.57	-0.0
2026	32	MIX MLE	2 levels	34	0	35	150	25	11	3	0%	1.0	14.4%	7.7%	6.7%	3.1%	3.60	4.57	-0.0
2026	32	MIX RAW	2 levels	34	0	35	150	25	11	3	0%	1.0	16.7%	7.3%	9.3%	2.0%	3.60	3.73	+0.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			208	21	280	1189	229	100	34	10%	1.3	19.3%	8.4%	10.8%	2.9%	4.34	4.11	+1.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	SF	167	0	175	739	139	65	22	0%	1.0	18.9%	8.7%	10.1%	3.0%	4.65	4.66	-0.2
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	SF	83	0	87	368	66	34	12	0%	1.1	17.9%	9.2%	8.7%	3.3%	4.84	4.97	-0.4
2029	35	MLB MLE	SF	42	0	44	187	32	18	7	0%	1.1	17.0%	9.8%	7.2%	3.7%	5.12	5.30	-0.4
2028	34	MLB CTX	SF	79	0	83	350	63	32	12	0%	1.1	17.9%	9.2%	8.7%	3.3%	4.84	4.97	-0.4
2029	35	MLB CTX	SF	38	0	40	169	29	17	6	0%	1.1	17.0%	9.8%	7.2%	3.7%	5.12	5.30	-0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		325	21	403	1709	321	149	52	6%	1.2	18.8%	8.7%	10.0%	3.0%	4.52	4.29	+1.3
/ 200 IP	—	RAW		161	10	200	848	159	74	26	6%	1.2	18.8%	8.7%	10.0%	3.0%	4.52	4.29	+0.6
Career	—	MLE		340	91	724	3028	561	245	75	27%	2.1	18.5%	8.1%	10.5%	2.5%	3.81	3.92	+0.6
/ 200 IP	—	MLE		94	25	200	837	155	68	21	27%	2.1	18.5%	8.1%	10.5%	2.5%	3.81	3.92	+0.3

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 AAAColumbus Clippers · 11 IPFIP 2.09 → 2.26 (+0.17)K% 21.7% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.860	18.70%
BB% (unintentional) /BF	6.52%	—	×0.900	5.87%
HR / BF /BF	0.00%	÷1.000	×1.100	0.00%

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 2.09 → 2.26, 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 AAABuffalo Bisons · 8 IPFIP 2.72 → 2.85 (+0.13)K% 27.3% → 23.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.27%	—	×0.860	23.45%
BB% (unintentional) /BF	15.15%	—	×0.900	13.64%
HR / BF /BF	0.00%	÷0.847	×1.100	0.00%

Result: FIP 2.72 → 2.85, 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 AAAIndianapolis Indians · 11 IPFIP 4.42 → 5.50 (+1.08)K% 43.8% → 37.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	43.75%	—	×0.860	37.62%
BB% (unintentional) /BF	12.50%	—	×0.900	11.25%
HR / BF /BF	6.25%	÷0.909	×1.100	7.57%

Result: FIP 4.42 → 5.50, 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 AAAIndianapolis Indians · 17 IPFIP 4.04 → 4.08 (+0.03)K% 17.6% → 15.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.65%	—	×0.860	15.18%
BB% (unintentional) /BF	14.12%	—	×0.900	12.71%
HR / BF /BF	1.18%	÷1.095	×1.100	1.18%

Result: FIP 4.04 → 4.08, 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 AAABuffalo Bisons · 9 IPFIP 3.10 → 3.54 (+0.44)K% 30.8% → 27.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	30.77%	—	×0.880	27.08%
BB% (unintentional) /BF	20.51%	—	×1.050	21.54%
HR / BF /BF	0.00%	÷1.081	×1.100	0.00%

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 116).

Result: FIP 3.10 → 3.54, 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 AAABuffalo Bisons · 11 IPFIP 7.13 → 7.87 (+0.73)K% 20.0% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.00%	—	×0.880	17.60%
BB% (unintentional) /BF	6.67%	—	×1.050	7.00%
HR / BF /BF	8.89%	÷1.000	×1.100	9.78%

Result: FIP 7.13 → 7.87, 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 AAABuffalo Bisons · 77 IPFIP 3.71 → 3.98 (+0.26)K% 18.4% → 16.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	18.35%	—	×0.880	16.15%
BB% (unintentional) /BF	8.86%	—	×1.050	9.30%
HR / BF /BF	1.90%	÷1.080	×1.100	1.93%

Result: FIP 3.71 → 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).

2017 A+Dunedin Blue Jays · 98 IPFIP 2.36 → 2.99 (+0.63)K% 26.5% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.52%	—	×0.770	20.42%
BB% (unintentional) /BF	6.57%	—	×1.150	7.55%
HR / BF /BF	1.22%	÷1.265	×1.250	1.20%

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 153).

Result: FIP 2.36 → 2.99, 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
Ryan Borucki (OGILVIE)	—	—	—	—	—	3.0%	—	5.08	17.6%	8.2%	132	+0.42	—	—	—
Colt Hynes L 2017 · 3.75 FIP	32	SW	3.75	14.1%	4.6%	2.3%	.306	—	—	—	—	—	3.42	169	72
Alejandro Soto L 2019 · 4.03 FIP	33	SW	4.03	17.1%	6.9%	2.7%	.376	—	—	—	—	—	3.96	280	71
Brandon Mann L 2017 · 4.24 FIP	33	SU	4.24	19.8%	11.3%	2.5%	.286	3.59	17.8%	6.4%	52	-0.65	3.59	190	71
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	70
Hideki Okajima L 2009 · 3.95 FIP	33	SU	3.95	20.5%	7.0%	3.1%	.268	4.27	15.5%	8.5%	46	+0.32	3.05	238	69
Zach Phillips L 2019 · 3.67 FIP	32	SU	3.67	19.1%	8.5%	2.0%	.355	—	—	—	—	—	2.33	202	68
Kelly Wunsch L 2005 · 3.82 FIP	33	SU	3.82	21.0%	11.4%	1.9%	.261	—	—	—	—	—	3.36	175	67
Doug Creek L 2002 · 4.99 FIP	33	SU	4.99	21.4%	12.6%	3.8%	.288	5.79	17.1%	7.9%	36	+0.80	4.48	277	67
Clay Rapada L 2015 · 3.84 FIP	34	SU	3.84	14.3%	8.0%	1.7%	.299	—	—	—	—	—	3.00	120	67
Tommy Phelps L 2005 · 3.98 FIP	31	SU	3.98	13.2%	7.5%	1.9%	.280	—	—	—	—	—	3.39	120	66
Brian Moran L 2022 · 4.20 FIP	33	SU	4.20	21.7%	7.0%	3.7%	.304	3.72	21.5%	14.5%	152	-0.48	1.80	421	66
Juan Pablo Perez L 2013 · 3.68 FIP	34	SU	3.68	25.2%	11.5%	2.3%	.250	3.68	26.5%	11.2%	51	-0.00	1.75	104	66
Brian Shouse L 2002 · 5.54 FIP	33	SU	5.54	15.5%	11.3%	4.2%	.245	3.31	16.3%	10.7%	159	-2.23	2.32	339	66
Marlon Arias L 2017 · 3.73 FIP	32	SU	3.73	17.2%	7.7%	2.0%	.443	4.83	19.8%	8.6%	74	+1.10	3.73	154	65
Scott Sauerbeck L 2005 · 3.75 FIP	33	SU	3.75	22.3%	8.9%	2.5%	.298	5.05	14.0%	0.0%	25	+1.30	2.79	386	64
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	64
Francisley Bueno L 2014 · 3.61 FIP	33	SU	3.61	14.1%	3.5%	2.3%	.289	3.94	13.6%	6.9%	138	+0.33	2.43	188	63
Edward Paredes L 2019 · 5.10 FIP	32	SU	5.10	21.5%	7.3%	5.3%	.364	—	—	—	—	—	2.36	130	63
Micah Bowie L 2007 · 4.60 FIP	32	SW	4.60	16.9%	10.9%	2.8%	.279	—	—	—	—	—	3.78	140	62
Joey Eischen L 2003 · 4.00 FIP	33	SU	4.00	18.1%	5.4%	3.2%	.309	3.44	19.6%	10.3%	69	-0.56	2.13	294	62

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	2.9	65	18.8%	9.0%	1.18	4.57
Dustin McGowan ('17)	3.3	65	19.2%	9.9%	1.08	4.50
Paul Gibson ('88-'96, 2 WAR*)	2.8	80	19.0%*	9.5%*	1.18*	4.37*
Adrian Devine ('73-'80, 2 WAR*)	1.9	65	16.7%*	7.8%*	1.12*	4.45*
Jerry Reed ('81-'90, 3 WAR*)	2.4	60	16.6%*	8.2%*	1.08*	3.97*
Donne Wall ('02)	2.4	59	16.1%	7.7%	1.18	4.20
Robert Gsellman ('22)	1.8	73	18.0%	7.8%	1.03	4.60