Riley Thompson

P Iowa Cubs

Age 31 RHP 6'4" / 210 lbs AAA peak 0.6 · 2027

Overview

AAAPROSPECT

ETA2029MLB%10%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	27	AAACHC	25	19	81	5.64	5.50	21.5%	14.3%	—	K 18.5% · BB 12.9% · FIP 5.76
2024	28	AAACHC	34	15	107	5.95	4.41	20.5%	9.9%	—	K 17.6% · BB 8.9% · FIP 4.48
2027PROJ	31	MLBCHC	2	2	13	4.70	4.70	18.8%	9.0%	0.1
2028PROJ29% MLB	32	MLBCHC	5	5	29	4.79	4.88	18.7%	9.3%	0.2
2029PROJ44% MLB	33	MLBCHC	6	6	36	4.94	5.08	18.4%	9.6%	0.1

Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

CompsElian Leyva (age 30) · Kenshin Kawakami (age 34) · Scott McGregor (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 122114vR 106

ERA4.70

FIP4.70

K%18.8%

BB%9.0%

K-BB%9.8%

Proj IP127

Value2027 · WAR

Runs saved vs league avg · 127 IP

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	23	A MLE	South Bend Cubs	21	21	94	391	87	31	9	100%	4.5	16.5%	9.4%	7.1%	3.3%	3.06	4.69	—
2019	23	A RAW	South Bend Cubs	21	21	94	391	87	31	9	100%	4.5	22.3%	7.9%	14.3%	2.3%	3.06	3.48	—
2022	26	AA MLE	Tennessee Smokies	19	19	57	239	64	32	8	100%	3.0	21.7%	14.7%	7.0%	4.0%	4.42	5.32	—
2022	26	AA RAW	Tennessee Smokies	19	19	57	239	64	32	8	100%	3.0	26.8%	13.4%	13.4%	3.3%	4.42	4.36	—
2023	27	AAA MLE	Iowa Cubs	25	19	81	363	78	52	15	76%	3.3	18.5%	12.9%	5.6%	4.7%	5.64	5.76	—
2023	27	AAA RAW	Iowa Cubs	25	19	81	363	78	52	15	76%	3.3	21.5%	14.3%	7.2%	4.1%	5.64	5.50	—
2024	28	AAA MLE	Iowa Cubs	34	15	107	483	99	48	15	44%	3.2	17.6%	8.9%	8.7%	3.2%	5.95	4.48	—
2024	28	AAA MLE	Iowa Cubs	34	15	107	483	99	48	15	44%	3.2	17.6%	8.9%	8.7%	3.2%	5.95	4.48	—
2024	28	AAA RAW	Iowa Cubs	34	15	107	483	99	48	15	44%	3.2	20.5%	9.9%	10.6%	3.1%	5.95	4.41	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	CHC	2	2	13	55	10	5	2	100%	6.5	18.8%	9.0%	9.8%	3.0%	4.70	4.70	+0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	CHC	17	17	99	420	78	39	14	100%	5.8	18.7%	9.3%	9.4%	3.3%	4.79	4.88	+0.6
2029	33	MLB MLE	CHC	14	14	82	345	64	33	12	100%	5.8	18.4%	9.6%	8.8%	3.5%	4.94	5.08	+0.3
2028	32	MLB CTX	CHC	5	5	29	122	23	11	4	100%	5.8	18.7%	9.3%	9.4%	3.3%	4.79	4.88	+0.2
2029	33	MLB CTX	CHC	6	6	36	152	28	15	5	100%	6.0	18.4%	9.6%	8.8%	3.5%	4.94	5.08	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		11	11	65	273	51	26	9	100%	5.9	18.5%	9.5%	9.0%	3.4%	4.87	4.61	+0.6
/ 200 IP	—	RAW		33	33	200	844	158	80	28	100%	6.0	18.5%	9.5%	9.0%	3.4%	4.87	4.61	+1.8
Career	—	MLE		99	74	340	1476	268	162	55	75%	3.4	18.2%	11.0%	7.2%	3.7%	4.82	4.99	+0.9
/ 200 IP	—	MLE		58	44	200	869	158	95	32	75%	3.4	18.2%	11.0%	7.2%	3.7%	4.82	4.99	+1.0

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 AAAIowa Cubs · 107 IPFIP 4.33 → 4.48 (+0.15)K% 20.5% → 17.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.50%	—	×0.860	17.63%
BB% (unintentional) /BF	9.94%	—	×0.900	8.94%
HR / BF /BF	3.11%	÷1.079	×1.100	3.17%

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 116).

Result: FIP 4.33 → 4.48, 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 AAAIowa Cubs · 81 IPFIP 5.37 → 5.76 (+0.39)K% 21.5% → 18.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.49%	—	×0.860	18.48%
BB% (unintentional) /BF	14.33%	—	×0.900	12.89%
HR / BF /BF	4.13%	÷0.965	×1.100	4.71%

Result: FIP 5.37 → 5.76, 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 AATennessee Smokies · 57 IPFIP 4.37 → 5.32 (+0.94)K% 26.8% → 21.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.78%	—	×0.810	21.69%
BB% (unintentional) /BF	13.39%	—	×1.100	14.73%
HR / BF /BF	3.35%	÷1.010	×1.200	3.98%

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 102).

Result: FIP 4.37 → 5.32, 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 ASouth Bend Cubs · 94 IPFIP 3.49 → 4.69 (+1.20)K% 22.3% → 16.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.25%	—	×0.740	16.47%
BB% (unintentional) /BF	7.93%	—	×1.180	9.36%
HR / BF /BF	2.30%	÷0.914	×1.300	3.27%

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 83).

Result: FIP 3.49 → 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).

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
Riley Thompson (OGILVIE)	—	—	—	—	—	3.0%	—	4.97	18.6%	9.1%	181	+0.27	—	—	—
Elian Leyva R 2019 · 4.30 FIP	30	SP-	4.30	17.0%	8.5%	2.9%	.323	—	—	—	—	—	3.10	158	75
Kenshin Kawakami R 2009 · 3.97 FIP	34	SP-	3.97	15.7%	7.6%	2.2%	.277	4.06	15.1%	9.5%	87	+0.09	3.97	244	73
Scott McGregor R 2015 · 4.37 FIP	28	SP-	4.37	11.8%	7.6%	2.4%	.293	—	—	—	—	—	4.37	107	72
Hisashi Iwakuma R 2012 · 4.28 FIP	31	SP-	4.28	19.5%	7.7%	3.4%	.277	3.45	21.5%	17.8%	528	-0.83	3.19	884	71
Scott Copeland R 2019 · 5.62 FIP	31	SP-	5.62	17.1%	12.4%	4.4%	.311	—	—	—	—	—	3.55	569	71
Jorge Campillo R 2008 · 3.93 FIP	29	SP-	3.93	16.3%	5.5%	2.8%	.283	—	—	—	—	—	3.93	172	69
Kohei Arihara R 2022 · 4.58 FIP	29	SP-	4.58	16.4%	7.2%	3.5%	.333	—	—	—	—	—	4.58	135	69
Luke Hudson R 2006 · 3.81 FIP	29	SP-	3.81	14.5%	8.4%	1.6%	.307	—	—	—	—	—	3.81	235	68
Zack Segovia R 2015 · 4.78 FIP	32	SP	4.78	15.0%	8.3%	3.4%	.314	3.33	20.2%	14.3%	39	-1.45	3.33	144	65
Hector Galvan R 2019 · 4.84 FIP	31	SP-	4.84	14.3%	12.2%	2.6%	.357	—	—	—	—	—	3.59	406	65
Pete Walker R 2002 · 4.59 FIP	33	SP-	4.59	13.4%	7.7%	3.0%	.279	5.16	12.0%	2.8%	139	+0.57	4.45	327	65
Yohan Pino R 2015 · 4.13 FIP	31	SP-	4.13	17.8%	5.8%	3.3%	.312	5.33	15.2%	11.6%	35	+1.20	3.84	193	64
Daniel McCutchen R 2015 · 4.30 FIP	32	SP-	4.30	13.9%	4.4%	3.3%	.268	4.74	10.8%	4.9%	155	+0.44	4.13	476	64
Mike Wright Jr. R 2021 · 3.89 FIP	31	SP-	3.89	19.1%	8.8%	2.4%	.237	4.79	14.4%	1.1%	56	+0.90	3.76	725	64
Randy Wynne R 2024 · 5.75 FIP	31	SP-	5.75	13.6%	6.0%	5.5%	.283	6.55	10.7%	7.2%	60	+0.80	4.35	461	64
Juan Delgadillo R 2015 · 4.54 FIP	32	SP	4.54	15.5%	7.2%	3.3%	.316	4.47	10.9%	4.7%	39	-0.07	4.47	143	64
Danny Gutierrez R 2017 · 4.11 FIP	30	SP	4.11	15.1%	6.1%	2.8%	.362	—	—	—	—	—	3.34	239	64
Daniel Guerrero R 2016 · 4.81 FIP	31	SP	4.81	12.4%	11.6%	2.3%	.328	4.93	11.7%	2.0%	175	+0.12	4.61	269	63
Patrick Johnson R 2019 · 4.14 FIP	30	SP	4.14	15.7%	9.3%	2.2%	.344	—	—	—	—	—	3.82	248	63
Nate Long R 2015 · 4.67 FIP	29	SP	4.67	14.8%	10.6%	2.7%	.285	—	—	—	—	—	4.67	151	63

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 927 starter careers (497 from 1990-2025 + 430 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	1.5	103	18.7%	9.3%	1.25	4.79
Brandon Claussen ('06)	1.6	105	16.0%	8.5%	1.37	5.04
James Kaprielian ('23)	1.6	106	20.5%	9.7%	1.30	4.61
John Maine ('13)	3.0	99	19.5%	10.5%	1.23	4.45
Dave Stenhouse ('62-'64, 2 WAR*)	1.9	124	17.9%*	10.5%*	1.33*	4.51*
Tommy Boggs ('76-'85, 3 WAR*)	2.9	97	16.4%*	8.1%*	1.24*	4.77*
Greg Mathews ('86-'92, 4 WAR*)	2.6	103	14.7%*	9.2%*	1.09*	4.52*