Landen Roupp — OGILVIE

Landen Roupp P San Francisco Giants

Age 27 RHP 6'2" / 220 lbs MLB Svc 1.0 · Ctrl thru 2030 peak 1.5 · 2026

2026 ROS PROJECTION151 act + 14 ip · rates below = ROS only

showingROS

wRC+ AGAINSTvL 10496vR 87

ERA3.75

FIP3.93

K%21.8%

BB%10.9%

K-BB%10.9%

ROS IP14

Value2026 · WAR

Runs saved vs league avg · 164 IP

━ projected WAR━ paid WAR ($/8M) surplus deficit━ post-control+$6M ctrl surplus

Grades · Statcast · Arsenal · Contract

Scouting Grades

STUF5050

CTRL4040

CMD4545

K†5050

Statcast Percentiles

FB Velo

p2792.9

Whiff%

p4624.7%

p4721.8%

xwOBA vs

p640.298

FB+3.6 (40%)

CH+3.0 (21%)

SI (34%)

CU (26%)

CT-2.4 (14%)

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

Status Pre-Arb

MLB Debut 2024

Service 1.494 yrs.days

Team Control Through 2030

First FA Year 2031

Year	Status	Salary	Proj WAR	Value	Surplus
2026ROS	Ext	$0.1M	+0.2	$0.7M	+$0.7M
2027	Pre-Arb	$0.8M	+0.8	$3M	+$2M
2028	Arb-1	$2M	+0.6	$2M	+$0.0M
2029	Arb-2	$3M	+0.3	$1.0M	+$0.0M (cut)
2030	Arb-3	$3M	+0.1	$0.3M	+$0.0M (cut)
Totals		$3M		$7M	+$3M

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 industry-standard fWAR.

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 + 2026 so far)
2021	22	MIX MLE	2 levels	5	0	8	31	14	1	0	0%	1.6	45.2%	3.2%	41.9%	0.0%	2.25	-0.31	—
2021	22	MIX RAW	2 levels	5	0	8	31	14	1	0	0%	1.6	45.2%	3.2%	41.9%	0.0%	2.25	-0.02	—
2022	23	MIX MLE	3 levels	26	14	107	432	152	37	6	54%	4.1	26.9%	9.8%	17.1%	1.7%	2.60	3.02	—
2022	23	MIX RAW	3 levels	26	14	107	432	152	37	6	54%	4.1	35.2%	8.6%	26.6%	1.4%	2.60	2.03	—
2023	24	AA MLE	Richmond Flying Squirrels	10	10	31	120	42	9	1	100%	3.1	28.3%	8.2%	20.1%	1.1%	1.74	2.36	—
2023	24	AA MLE	Richmond Flying Squirrels	10	10	31	120	42	9	1	100%	3.1	28.3%	8.2%	20.1%	1.1%	1.74	2.36	—
2023	24	AA RAW	Richmond Flying Squirrels	10	10	31	120	42	9	1	100%	3.1	35.0%	7.5%	27.5%	0.8%	1.74	1.68	—
2024	25	MIX MLE	3 levels	34	12	77	324	89	40	3	35%	2.3	23.4%	12.6%	10.8%	1.1%	3.40	3.30	+1.2
2024	25	MIX MLE	3 levels	34	12	77	324	89	40	3	35%	2.3	23.4%	12.6%	10.8%	1.1%	3.40	3.30	+1.2
2024	25	MIX RAW	3 levels	34	12	77	324	89	40	3	35%	2.3	27.5%	12.3%	15.1%	0.9%	3.40	2.85	+1.6
2025	26	MIX MLE	2 levels	23	23	110	488	106	44	11	100%	4.8	21.4%	9.2%	12.2%	2.5%	3.86	3.83	+1.4
2025	26	MIX MLE	2 levels	23	23	110	488	106	44	11	100%	4.8	21.4%	9.2%	12.2%	2.5%	3.86	3.83	+1.4
2025	26	MIX RAW	2 levels	23	23	110	488	106	44	11	100%	4.8	21.7%	9.0%	12.7%	2.3%	3.86	3.67	+1.6
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			73	54	308	1330	288	142	24	74%	4.2	21.7%	10.7%	11.0%	1.8%	3.86	3.63	+4.7
▸ 2026 season
YTD	27	MLB MLE	San Francisco Giants	28	28	151	637	139	72	11	100%	5.4	21.8%	11.3%	10.5%	1.9%	4.00	3.71	+2.2
YTD	27	MLB MLE	San Francisco Giants	28	28	151	637	139	72	11	100%	5.4	21.8%	11.3%	10.5%	1.9%	4.00	3.71	+2.2
▸ 2026 season — so far · projected rest · full
2026	27	MLB RAW	SF	28	28	151	637	139	72	11	100%	5.4	21.8%	11.3%	10.5%	1.7%	4.00	3.64	+2.3
SO FAR	27	MLB RAW	SF	28	28	151	637	139	72	11	100%	5.4	21.8%	11.3%	10.5%	1.7%	4.00	3.64	+2.3
ROS	27	MLB MLE	SF	2	2	14	58	13	6	1	100%	6.9	21.8%	10.9%	10.9%	2.3%	3.75	3.93	+0.2
ROS	27	MLB CTX	SF	2	2	14	59	13	6	1	100%	7.0	21.8%	10.9%	10.9%	2.2%	3.65	3.83	+0.2
2026 TOT	27	MLB MLE	SF	—	—	164	695	152	78	12	—	—	21.8%	11.3%	10.5%	1.8%	3.98	3.73	+2.3
FULL	27	MLB A+C	SF	—	—	164	694	152	78	12	—	—	21.8%	11.3%	10.6%	1.8%	3.97	3.65	+2.3
▸ Career projection (2027+) — MLB context, MLB playing time (IP × MLB%)
2027	28	MLB MLE	SF	18	18	107	451	97	50	10	100%	5.9	21.6%	11.0%	10.6%	2.2%	4.13	4.24	+0.8
2028	29	MLB MLE	SF	17	17	102	430	90	48	10	100%	6.0	21.0%	11.3%	9.8%	2.3%	4.29	4.40	+0.6
2029	30	MLB MLE	SF	16	16	96	404	82	47	10	100%	6.0	20.3%	11.6%	8.8%	2.5%	4.48	4.61	+0.3
2030	31	MLB MLE	SF	15	15	89	377	74	45	10	100%	6.0	19.6%	11.9%	7.7%	2.7%	4.70	4.84	+0.1
2031	32	MLB MLE	SF	14	14	83	349	65	43	10	100%	5.9	18.8%	12.3%	6.5%	3.0%	4.94	5.09	-0.2
2027	28	MLB CTX	SF	17	17	101	426	92	47	9	100%	5.9	21.6%	11.0%	10.6%	2.0%	4.04	4.15	+0.8
2028	29	MLB CTX	SF	16	16	97	410	86	46	9	100%	6.1	21.0%	11.3%	9.8%	2.1%	4.19	4.30	+0.6
2029	30	MLB CTX	SF	15	15	91	384	78	44	9	100%	6.1	20.3%	11.6%	8.8%	2.3%	4.38	4.51	+0.3
2030	31	MLB CTX	SF	14	14	85	359	70	43	9	100%	6.1	19.6%	11.9%	7.7%	2.5%	4.59	4.73	+0.1
2031	32	MLB CTX	SF	13	13	78	329	62	41	9	100%	6.0	18.8%	12.3%	6.5%	2.8%	4.82	4.97	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		151	132	774	3299	690	370	70	87%	5.1	20.9%	11.2%	9.7%	2.1%	4.16	3.91	+9.1
/ 200 IP	—	RAW		39	34	200	852	178	96	18	87%	5.1	20.9%	11.2%	9.7%	2.1%	4.16	3.91	+2.4
Career	—	MLE		126	87	483	2032	484	211	36	69%	3.8	23.8%	10.4%	13.4%	1.8%	3.39	3.39	+6.4
/ 200 IP	—	MLE		52	36	200	841	200	87	15	69%	3.8	23.8%	10.4%	13.4%	1.8%	3.39	3.39	+1.6

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
Landen Roupp (OGILVIE)	—	—	4.31	21.6%	10.1%	2.5%	—	4.41	21.0%	9.7%	304	+0.10	—	—	—
Taylor Jungmann R 2017 · 3.88 FIP	27	SP-	3.88	18.8%	11.1%	1.8%	.279	—	—	—	—	—	3.59	435	83
Tyler Chatwood R 2017 · 4.54 FIP	27	SP-	4.54	19.0%	11.9%	2.8%	.279	4.53	20.6%	5.2%	199	-0.01	3.30	537	77
Daniel Ponce de Leon R 2019 · 4.09 FIP	27	SP	4.09	22.8%	12.0%	2.5%	.246	5.09	22.7%	10.1%	182	+1.00	3.35	768	75
Hector Noesí R 2015 · 4.37 FIP	28	SP-	4.37	17.6%	8.1%	3.2%	.261	—	—	—	—	—	4.37	250	75
Patrick Johnson R 2016 · 3.82 FIP	27	SP-	3.82	17.3%	6.9%	2.4%	.286	4.14	15.7%	6.4%	120	+0.32	3.82	248	73
Michael Darrell-Hicks R 2023 · 3.71 FIP	25	SP-	3.71	20.3%	8.7%	2.2%	.309	3.50	21.1%	13.0%	142	-0.21	1.87	251	73
Mitch Atkins R 2016 · 3.65 FIP	30	SP-	3.65	21.4%	8.2%	2.4%	.341	4.77	14.3%	6.2%	78	+1.12	3.65	205	73
Jared Mortensen R 2015 · 4.42 FIP	27	SP-	4.42	16.9%	7.8%	3.2%	.287	4.99	20.9%	2.1%	72	+0.57	4.42	200	73
Cameron Foster R 2023 · 4.31 FIP	24	SP	4.31	18.9%	10.3%	2.7%	.329	4.05	20.9%	10.9%	132	-0.26	2.54	213	73
Alejandro Martinez R 2015 · 4.46 FIP	26	SP-	4.46	17.5%	10.9%	2.7%	.393	4.24	9.2%	3.9%	53	-0.22	4.24	166	72
Mat Latos R 2015 · 3.60 FIP	27	SP	3.60	20.2%	6.3%	2.6%	.306	5.16	14.5%	4.4%	128	+1.56	3.60	244	72
David Phelps R 2015 · 3.94 FIP	28	SP	3.94	16.0%	6.8%	2.4%	.299	2.82	29.9%	20.6%	142	-1.12	2.50	373	72
David Hale R 2015 · 4.15 FIP	27	SP-	4.15	16.2%	6.8%	2.8%	.343	4.11	13.8%	8.1%	251	-0.04	3.30	493	71
Brad Peacock R 2016 · 3.97 FIP	28	SP-	3.97	20.9%	8.7%	2.8%	.302	3.45	29.4%	20.4%	289	-0.52	2.97	533	71
Daniel Corcino R 2018 · 3.84 FIP	27	SP-	3.84	20.8%	10.0%	2.2%	.265	5.83	17.5%	5.3%	119	+1.99	3.84	299	70
Jacob Turner R 2018 · 4.48 FIP	27	SP-	4.48	14.9%	8.8%	2.8%	.297	—	—	—	—	—	4.29	340	70
Cooper Chandler R 2023 · 4.33 FIP	24	SP-	4.33	17.8%	9.3%	2.9%	.300	3.78	23.5%	17.1%	18	-0.55	3.78	107	70
Burch Smith R 2017 · 3.93 FIP	27	SP	3.93	19.5%	12.6%	1.6%	.234	4.43	22.2%	10.5%	192	+0.50	3.50	382	69
Bradin Hagens R 2015 · 3.79 FIP	26	SP-	3.79	14.6%	9.8%	1.2%	.277	4.92	14.9%	5.0%	55	+1.13	3.79	254	69
Paxton Schultz R 2023 · 4.90 FIP	25	SP-	4.90	19.1%	11.9%	3.5%	.319	4.05	22.7%	14.3%	222	-0.85	3.90	538	69

Career Comps

Retired MLB careers whose totals most resemble what we're projecting for this player. Pool: 133 completed pitcher careers (2015-2024).

Player	162s	IP/yr	K%	BB%	HR/9	ERA
OGILVIE CAREER PROJECTION	2.9	97	20.6%	11.3%	0.97	4.42
Aaron Sanchez ('22)	3.3	93	18.1%	10.0%	0.98	4.32
Ian Anderson ('22)	1.4	91	22.5%	10.4%	0.96	3.97
Dakota Hudson ('24)	2.8	92	15.0%	11.1%	0.91	4.25
James Kaprielian ('23)	1.6	105	20.5%	9.6%	1.26	4.58
Spencer Turnbull ('24)	1.7	68	22.0%	9.2%	0.77	4.18
Tyler Skaggs ('19)	1.7	85	22.9%	8.2%	1.09	4.24