Randy Rodríguez

P San Francisco Giants

Age 27 RHP 6'0" / 195 lbs MLB Svc 2.0 · Ctrl thru 2030 peak 0.8 · 2028 ILInjured 60-Day (D60)

Overview

MLBRPIL-6040-MANArb-1FA 2031

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	23	MIX3 levels	45	2	70	4.37	3.84	26.4%	17.6%	—	K 22.0% · BB 16.9% · FIP 4.18
2024	24	MIX2 levels	45	1	63	3.86	2.97	24.0%	8.4%	1.2	K 24.0% · BB 8.1% · FIP 3.18
2025	25	MLBSF	50	0	51	1.78	2.13	33.8%	5.6%	1.5
2026	26	No MLB games played
2027PROJ	27	MLBSF	38	0	40	3.06	3.04	32.0%	5.5%	0.8
2028PROJ92% MLB	28	MLBSF	61	0	64	2.98	3.00	31.9%	5.6%	1.1
2029PROJ93% MLB	29	MLBSF	56	0	59	3.08	3.15	31.3%	5.9%	0.9

Last 3 seasons WAR 2.7 · Next 3 projected WAR 2.8Projected rows: MLB playing time only, park and league context applied.

ContractESTIMATED

Control thru2030Service2.860First FA2031Super-2

2027Arb-1$1M2028Arb-2$2M2029Arb-3$3M2030Arb-4$4M

Skills · percentileStatcast 2025

FB Velo90

K%90

xwOBA vs90

CompsJustin Slaten (age 26) · Nate Jones (age 27) · Luke Jackson (age 27)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8273vR 65

ERA3.06

FIP3.04

K%32.0%

BB%5.5%

K-BB%26.5%

Proj IP45

Value2027 · WAR

Runs saved vs league avg · 45 IP

━ projected WAR━ paid WAR ($/8M) surplus deficit+$17M ctrl surplus

Grades · Statcast · Contract

Scouting Grades

STUF6060

CTRL5555

CMD6565

K†7070

Statcast Percentiles

FB Velo

p9097.4

Whiff%

p8731.1%

p9033.8%

xwOBA vs

p900.251

Contract ESTIMATED · service-time + WAR-based estimator (needs verification)

Status Pre-Arb SUPER-2

MLB Debut 2024

Service 2.860 yrs.days

Team Control Through 2030

First FA Year 2031

Year	Status	Salary	Proj WAR	Value	Surplus
2027	Arb-1	$1M	+0.8	$3M	+$2M
2028	Arb-2	$2M	+1.1	$8M	+$6M
2029	Arb-3	$3M	+0.9	$3M	+$0.5M
2030	Arb-4	$4M	+0.7	$2M	+$0.0M (cut)
Totals		$7M		$17M	+$8M

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)
2018	18	ROK MLE	DSL Giants	10	2	27	116	34	9	1	20%	2.7	19.9%	9.7%	10.2%	1.2%	4.28	3.31	—
2018	18	ROK RAW	DSL Giants	10	2	27	116	34	9	1	20%	2.7	29.3%	7.8%	21.6%	0.9%	4.28	2.08	—
2019	19	ROK MLE	AZL Giants Black	16	0	25	116	29	16	1	0%	1.6	17.0%	17.2%	-0.2%	1.2%	5.40	4.51	—
2019	19	ROK RAW	AZL Giants Black	16	0	25	116	29	16	1	0%	1.6	25.0%	13.8%	11.2%	0.9%	5.40	3.22	—
2021	21	A MLE	San Jose Giants	32	0	62	258	101	23	0	0%	1.9	29.0%	10.5%	18.4%	0.0%	1.74	1.99	—
2021	21	A RAW	San Jose Giants	32	0	62	258	101	23	0	0%	1.9	39.1%	8.9%	30.2%	0.0%	1.74	0.95	—
2022	22	MIX MLE	3 levels	27	13	67	297	97	43	7	48%	2.5	25.3%	12.8%	12.5%	2.6%	4.46	4.00	—
2022	22	MIX RAW	3 levels	27	13	67	297	97	43	7	48%	2.5	32.7%	14.5%	18.2%	2.4%	4.46	3.49	—
2023	23	MIX MLE	3 levels	45	2	70	318	84	56	4	4%	1.6	22.0%	16.9%	5.0%	1.4%	4.37	4.18	—
2023	23	MIX RAW	3 levels	45	2	70	318	84	56	4	4%	1.6	26.4%	17.6%	8.8%	1.3%	4.37	3.84	—
2024	24	MIX MLE	2 levels	45	1	63	263	63	22	4	2%	1.4	24.0%	8.1%	15.8%	2.0%	3.86	3.18	+1.0
2024	24	MIX MLE	2 levels	45	1	63	263	63	22	4	2%	1.4	24.0%	8.1%	15.8%	2.0%	3.86	3.18	+1.0
2024	24	MIX RAW	2 levels	45	1	63	263	63	22	4	2%	1.4	24.0%	8.4%	15.6%	1.5%	3.86	2.97	+1.2
2025	25	MLB MLE	San Francisco Giants	50	0	51	198	67	11	4	0%	1.0	33.8%	5.6%	28.3%	2.2%	1.78	2.15	+1.5
2025	25	MLB MLE	San Francisco Giants	50	0	51	198	67	11	4	0%	1.0	33.8%	5.6%	28.3%	2.2%	1.78	2.15	+1.5
2025	25	MLB RAW	San Francisco Giants	50	0	51	198	67	11	4	0%	1.0	33.8%	5.6%	28.3%	2.0%	1.78	2.13	+1.5
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			85	1	103	419	120	29	8	1%	1.2	28.6%	6.9%	21.7%	1.9%	3.06	2.62	+2.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	27	MLB	SF	38	0	40	169	54	9	5	0%	1.1	32.0%	5.5%	26.5%	2.9%	3.06	3.04	+0.8
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	28	MLB MLE	SF	66	0	69	293	93	16	9	0%	1.1	31.9%	5.6%	26.2%	3.0%	3.10	3.12	+1.2
2029	29	MLB MLE	SF	61	0	64	270	85	16	8	0%	1.0	31.3%	5.9%	25.4%	3.1%	3.21	3.28	+1.0
2030	30	MLB MLE	SF	55	0	58	245	75	15	8	0%	1.1	30.6%	6.2%	24.4%	3.3%	3.38	3.49	+0.8
2028	28	MLB CTX	SF	61	0	64	270	86	15	7	0%	1.0	31.9%	5.6%	26.2%	2.7%	2.98	3.00	+1.1
2029	29	MLB CTX	SF	56	0	59	249	78	15	7	0%	1.1	31.3%	5.9%	25.4%	2.9%	3.08	3.15	+0.9
2030	30	MLB CTX	SF	51	0	54	228	70	14	7	0%	1.1	30.6%	6.2%	24.4%	3.1%	3.24	3.35	+0.7
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		254	1	280	1166	354	73	29	0%	1.1	30.3%	6.2%	24.1%	2.5%	3.08	2.72	+6.1
/ 200 IP	—	RAW		181	1	200	833	253	52	21	0%	1.1	30.3%	6.2%	24.1%	2.5%	3.08	2.72	+4.4
Career	—	MLE		225	18	365	1566	393	183	25	8%	1.6	25.1%	11.7%	13.4%	1.6%	3.55	3.32	+5.3
/ 200 IP	—	MLE		123	10	200	859	215	100	13	8%	1.6	25.1%	11.7%	13.4%	1.6%	3.55	3.32	+3.6

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 AAASacramento River Cats · 11 IPFIP 2.30 → 2.46 (+0.16)K% 23.8% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.81%	—	×0.860	20.48%
BB% (unintentional) /BF	9.52%	—	×0.900	8.57%
HR / BF /BF	0.00%	÷1.010	×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 102).

Result: FIP 2.30 → 2.46, 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 · 38 IPFIP 4.63 → 4.69 (+0.05)K% 22.9% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.91%	—	×0.860	19.70%
BB% (unintentional) /BF	20.11%	—	×0.900	18.10%
HR / BF /BF	1.68%	÷1.057	×1.100	1.74%

Result: FIP 4.63 → 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).

2023 AARichmond Flying Squirrels · 30 IPFIP 2.67 → 3.49 (+0.81)K% 31.0% → 25.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	31.01%	—	×0.810	25.12%
BB% (unintentional) /BF	13.95%	—	×1.100	15.35%
HR / BF /BF	0.78%	÷0.906	×1.200	1.03%

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 81).

Result: FIP 2.67 → 3.49, 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 · 51 IPFIP 3.00 → 4.00 (+1.00)K% 32.9% → 25.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.87%	—	×0.770	25.31%
BB% (unintentional) /BF	11.11%	—	×1.150	12.78%
HR / BF /BF	2.31%	÷1.120	×1.250	2.58%

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 3.00 → 4.00, 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 · 10 IPFIP 4.16 → 5.67 (+1.51)K% 39.6% → 32.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.58%	—	×0.810	32.06%
BB% (unintentional) /BF	16.67%	—	×1.100	18.33%
HR / BF /BF	4.17%	÷0.929	×1.200	5.38%

Result: FIP 4.16 → 5.67, 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 · 6 IPFIP 5.53 → 5.96 (+0.43)K% 21.2% → 18.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.21%	—	×0.880	18.67%
BB% (unintentional) /BF	33.33%	—	×1.050	35.00%
HR / BF /BF	0.00%	÷0.989	×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 98).

Result: FIP 5.53 → 5.96, 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).

2021 ASan Jose Giants · 62 IPFIP 0.92 → 1.99 (+1.06)K% 39.1% → 29.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	39.15%	—	×0.740	28.97%
BB% (unintentional) /BF	8.91%	—	×1.180	10.52%
HR / BF /BF	0.00%	÷1.008	×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 102).

Result: FIP 0.92 → 1.99, 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).

2019 ROKAZL Giants Black · 25 IPFIP 3.21 → 4.51 (+1.30)K% 25.0% → 17.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.680	17.00%
BB% (unintentional) /BF	13.79%	—	×1.250	17.24%
HR / BF /BF	0.86%	÷1.000	×1.400	1.21%

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 3.21 → 4.51, 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
Randy Rodríguez (OGILVIE)	—	—	3.04	32.0%	5.5%	2.9%	—	3.29	31.3%	25.4%	192	+0.25	—	—	—
Justin Slaten R 2024 · 2.43 FIP	26	SU	2.43	25.9%	4.0%	1.8%	.281	3.31	23.1%	17.3%	65	+0.88	2.43	305	71
Nate Jones R 2013 · 2.58 FIP	27	SU	2.58	28.3%	7.9%	1.6%	.327	3.03	31.0%	25.7%	90	+0.45	2.57	307	70
Luke Jackson R 2019 · 2.89 FIP	27	SU	2.89	33.7%	7.0%	3.2%	.373	3.55	23.4%	13.3%	90	+0.66	2.67	580	70
Pedro Báez R 2015 · 2.29 FIP	27	SU	2.29	28.8%	4.8%	1.8%	.321	3.57	25.8%	17.2%	194	+1.28	2.29	356	69
Liam Hendriks R 2016 · 2.74 FIP	27	SU	2.74	25.8%	4.0%	2.4%	.337	2.36	32.4%	25.9%	198	-0.38	0.63	684	69
Shawn Tolleson R 2015 · 3.05 FIP	27	SU	3.05	25.5%	4.0%	2.9%	.284	4.76	17.3%	12.5%	36	+1.71	3.05	218	69
Connor Brogdon R 2022 · 3.23 FIP	27	SU	3.23	26.6%	5.9%	3.0%	.314	4.84	23.5%	14.7%	139	+1.61	2.98	402	69
Cole Sands R 2024 · 2.28 FIP	27	SU	2.28	29.1%	4.1%	2.0%	.280	3.61	21.1%	15.0%	84	+1.33	2.28	474	69
Alex Colomé R 2016 · 2.73 FIP	27	SU	2.73	31.4%	6.2%	2.7%	.274	3.28	22.8%	15.9%	196	+0.55	2.72	569	68
Ken Giles R 2018 · 2.96 FIP	27	SU	2.96	25.0%	3.3%	2.8%	.329	2.02	39.9%	32.2%	53	-0.94	0.92	414	68
Cory Rasmus R 2015 · 2.34 FIP	27	SU	2.34	33.3%	7.6%	2.0%	.259	5.40	14.9%	1.7%	25	+3.06	2.34	138	68
Mychal Givens R 2017 · 3.28 FIP	27	SU	3.28	27.9%	7.6%	2.9%	.244	3.48	28.4%	19.3%	162	+0.20	1.77	526	68
Raisel Iglesias R 2017 · 2.47 FIP	27	SU	2.47	30.1%	8.5%	1.5%	.284	3.30	30.3%	23.6%	162	+0.83	1.34	825	67
Danny Farquhar R 2014 · 2.63 FIP	27	SU	2.63	27.9%	7.2%	1.8%	.290	3.84	22.1%	13.9%	224	+1.21	1.67	351	67
Jumbo Diaz R 2014 · 2.87 FIP	30	SU	2.87	26.1%	7.0%	2.0%	.283	3.64	23.0%	15.0%	186	+0.77	2.80	319	67
Ben Heller R 2017 · 2.86 FIP	25	SU	2.86	32.4%	9.4%	2.4%	.243	—	—	—	—	—	2.49	246	67
Héctor Neris R 2016 · 2.92 FIP	27	SU	2.92	31.1%	8.2%	2.6%	.263	3.16	32.0%	24.1%	209	+0.24	2.03	682	67
Silvino Bracho R 2018 · 2.55 FIP	26	SU	2.55	29.0%	6.7%	1.9%	.355	4.60	25.5%	18.0%	50	+2.05	2.49	395	67
Dellin Betances R 2015 · 2.15 FIP	27	SU	2.15	39.5%	11.4%	1.7%	.248	2.02	41.0%	29.6%	199	-0.13	1.21	398	67
David Hernandez R 2012 · 1.86 FIP	27	SU	1.86	35.3%	7.6%	1.4%	.284	4.15	24.3%	16.7%	96	+2.29	1.86	649	67

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	1.7	57	30.6%	6.1%	1.05	3.15
Sean Doolittle ('22)	2.3	56	29.0%	5.7%	0.96	3.20
Giovanny Gallegos ('24)	1.7	56	29.6%	6.6%	1.18	3.49
Chad Green ('25)	2.5	62	30.0%	6.4%	1.42	3.43
Mike Schooler ('88-'93, 5 WAR*)	1.5	49	26.9%*	8.0%*	0.96*	3.68*
Joe Sambito ('76-'87, 8 WAR*)	3.1	70	26.8%*	7.5%*	1.06*	3.65*
Tom Henke ('82-'95, 25 WAR*)	3.9	66	35.9%*	7.7%*	0.93*	2.72*