Cal Riehl

P Lake Elsinore Storm

Age 24 LHP A

Overview

APROSPECT

ETA2028MLB%0%

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2026	23	MIX2 levels	21	27	4.73	4.90	18.1%	11.0%	—	K 13.6% · BB 12.4% · FIP 6.03
2027PROJ	24	MLBSD	13	14	5.05	5.05	18.7%	11.5%	-0.1
2028PROJ43% MLB	25	MLBSD	10	10	4.89	4.83	19.9%	10.6%	-0.0

Next 2 projected WAR -0.1Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Career WAR0 WAR median (P10 0 / P90 0, comp-based) · star 0% · didn't stick 100%

CompsGlen Perkins (age 24) · Jason Kershner (age 25) · Gabriel Sequeira (age 24)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 113121vR 129

ERA5.05

FIP5.05

K%18.7%

BB%11.5%

K-BB%7.3%

Proj IP14

Value2027 · WAR

Runs saved vs league avg · 14 IP

Grades · Contract

Scouting Grades

K†4545

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 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 + 2027 so far)
2026	23	MIX MLE	2 levels	21	0	27	127	23	14	4	0%	1.3	13.6%	12.4%	1.2%	4.6%	4.73	6.03	—
2026	23	MIX MLE	2 levels	21	0	27	127	23	14	4	0%	1.3	13.6%	12.4%	1.2%	4.6%	4.73	6.03	—
2026	23	MIX RAW	2 levels	21	0	27	127	23	14	4	0%	1.3	18.1%	11.0%	7.1%	3.1%	4.73	4.90	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	24	MLB	SD	13	0	14	59	11	7	2	0%	1.1	18.7%	11.5%	7.3%	3.1%	5.05	5.05	-0.1
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	25	MLB MLE	SD	23	0	24	103	20	11	3	0%	1.1	19.9%	10.6%	9.3%	3.1%	4.89	4.83	-0.1
2028	25	MLB CTX	SD	10	0	10	42	8	4	1	0%	1.0	19.9%	10.6%	9.3%	3.1%	4.89	4.83	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		10	0	10	44	9	5	1	0%	1.0	19.9%	10.6%	9.3%	3.1%	4.89	4.45	+0.0
/ 200 IP	—	RAW		190	0	200	844	173	96	19	0%	1.1	19.9%	10.6%	9.3%	3.1%	4.89	4.45	+0.2
Career	—	MLE		21	0	27	127	17	16	6	0%	1.3	13.6%	12.4%	1.2%	4.6%	4.73	6.03	-0.1
/ 200 IP	—	MLE		158	0	200	953	130	118	43	0%	1.3	13.6%	12.4%	1.2%	4.6%	4.73	6.03	-0.7

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
Cal Riehl (OGILVIE)	—	—	5.05	18.7%	11.5%	3.1%	—	4.83	19.9%	9.3%	24	-0.22	—	—	—
Glen Perkins L 2007 · 3.91 FIP	24	SU	3.91	17.4%	10.4%	1.7%	.259	4.76	11.2%	5.7%	269	+0.85	2.16	611	87
Jason Kershner L 2002 · 4.76 FIP	25	SU	4.76	16.8%	12.1%	2.8%	.233	4.04	14.5%	8.1%	76	-0.72	3.87	100	87
Gabriel Sequeira L 2022 · 4.60 FIP	24	SU	4.60	19.3%	13.1%	2.7%	.294	3.57	21.4%	10.0%	63	-1.03	2.62	160	86
Hector Mercado L 2000 · 4.79 FIP	26	SU	4.79	21.7%	13.3%	3.3%	.270	4.22	22.9%	10.0%	111	-0.57	3.47	125	86
Mitchell Tyranski L 2023 · 5.14 FIP	25	SU	5.14	20.4%	13.6%	3.7%	.269	4.10	20.7%	6.1%	103	-1.04	3.46	145	86
Will Ohman L 2001 · 4.66 FIP	23	SU	4.66	22.2%	11.1%	3.7%	.353	—	—	—	—	—	2.88	350	85
Mike Zagurski L 2007 · 4.10 FIP	24	SU	4.10	20.8%	8.9%	3.0%	.324	—	—	—	—	—	2.94	120	85
Nick Hagadone L 2012 · 4.74 FIP	26	SU	4.74	22.4%	12.9%	3.5%	.310	3.88	24.6%	14.7%	82	-0.86	2.97	141	84
Houston Harding L 2022 · 5.30 FIP	24	SU	5.30	20.2%	10.5%	4.7%	.307	4.87	17.3%	4.9%	158	-0.43	3.41	239	84
Michael Roth L 2014 · 5.10 FIP	24	SU	5.10	15.0%	11.7%	3.3%	.333	4.48	13.4%	6.1%	381	-0.62	2.34	515	84
Bryan Harper L 2015 · 4.90 FIP	25	SU	4.90	15.2%	9.4%	3.5%	.236	4.38	17.1%	5.3%	92	-0.52	4.13	133	83
Keylan Killgore L 2019 · 4.23 FIP	22	SU	4.23	20.7%	12.7%	2.3%	.307	—	—	—	—	—	3.73	161	83
Matt Minnick L 2019 · 5.59 FIP	23	SU	5.59	17.2%	14.1%	3.9%	.302	4.67	23.5%	12.1%	105	-0.92	3.81	147	83
Brian Moran L 2015 · 4.50 FIP	26	SU	4.50	17.7%	14.1%	2.0%	.318	2.74	25.0%	17.4%	80	-1.76	1.80	421	82
José Manuel Fernández L 2017 · 4.18 FIP	24	SU	4.18	18.1%	12.3%	1.9%	.345	4.91	17.5%	6.6%	121	+0.73	3.35	263	82
Ryan Sherriff L 2015 · 4.27 FIP	25	SU	4.27	17.1%	8.2%	2.9%	.317	3.28	18.8%	11.9%	135	-0.99	2.96	245	82
Kelly Secrest L 2016 · 4.42 FIP	24	SU	4.42	18.6%	13.7%	2.1%	.267	3.47	20.8%	10.4%	38	-0.95	3.11	151	82
Daniel Gibson L 2016 · 4.30 FIP	24	SU	4.30	14.7%	10.3%	2.1%	.309	4.12	15.6%	4.3%	63	-0.18	2.95	159	81
Andre Wheeler L 2016 · 5.25 FIP	24	SU	5.25	17.5%	12.1%	3.8%	.347	5.01	14.4%	2.4%	29	-0.24	3.82	150	81
Michael Boyle L 2018 · 4.93 FIP	24	SU	4.93	18.3%	11.4%	3.5%	.237	5.45	19.6%	7.4%	54	+0.52	2.97	406	81