Mitchell Tyranski

P Knoxville Smokies

Age 29 LHP 6'2" / 215 lbs AA

Overview

AAPROSPECT

ETA2029MLB%2%

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2023	25	A+LAD	36	1	42	3.83	3.90	26.6%	11.9%	—	K 20.4% · BB 13.6% · FIP 5.14
2024	26	MIX3 levels	34	0	53	3.25	3.40	27.7%	10.8%	—	K 21.0% · BB 12.3% · FIP 4.90
2025	27	AACHC	47	0	57	2.37	2.87	25.3%	15.0%	—	K 20.5% · BB 16.5% · FIP 3.46
2026	28	No MLB games played
2027PROJ	29	MLBCHC	1	0	1	5.20	5.20	21.0%	16.2%	-0.0
2028PROJ31% MLB	30	MLBCHC	14	0	15	5.22	5.29	20.9%	15.8%	-0.1
2029PROJ51% MLB	31	MLBCHC	19	0	20	5.31	5.42	20.7%	15.7%	-0.2

Next 3 projected WAR -0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Scouting grades · 20-80

Stuff50Control30Command40

CompsStephen Randolph (age 29) · Keith Hessler (age 29) · Braulio Lara (age 28)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 116124vR 132

ERA5.20

FIP5.20

K%21.0%

BB%16.2%

K-BB%4.8%

Proj IP69

Value2027 · WAR

Runs saved vs league avg · 69 IP

Grades · Contract

Scouting Grades

STUF5050

CTRL3030

CMD4040

K†5050

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	21	ROK MLE	AZL Dodgers Mota	6	0	8	29	13	1	0	0%	1.3	44.8%	3.4%	41.4%	0.0%	1.12	-0.25	—
2019	21	ROK RAW	AZL Dodgers Mota	6	0	8	29	13	1	0	0%	1.3	44.8%	3.4%	41.4%	0.0%	1.12	0.23	—
2023	25	A+ MLE	Great Lakes Loons	36	1	42	177	47	21	5	3%	1.2	20.4%	13.6%	6.8%	3.7%	3.83	5.14	—
2023	25	A+ RAW	Great Lakes Loons	36	1	42	177	47	21	5	3%	1.2	26.6%	11.9%	14.7%	2.8%	3.83	3.90	—
2024	26	MIX MLE	3 levels	34	0	53	213	59	23	5	0%	1.5	21.0%	12.3%	8.7%	3.7%	3.25	4.90	—
2024	26	MIX MLE	3 levels	34	0	53	213	59	23	5	0%	1.5	21.0%	12.3%	8.7%	3.7%	3.25	4.90	—
2024	26	MIX RAW	3 levels	34	0	53	213	59	23	5	0%	1.5	27.7%	10.8%	16.9%	2.3%	3.25	3.40	—
2025	27	AA MLE	Knoxville Smokies	47	0	57	233	59	35	0	0%	1.2	20.5%	16.5%	4.0%	0.0%	2.37	3.46	—
2025	27	AA MLE	Knoxville Smokies	47	0	57	233	59	35	0	0%	1.2	20.5%	16.5%	4.0%	0.0%	2.37	3.46	—
2025	27	AA RAW	Knoxville Smokies	47	0	57	233	59	35	0	0%	1.2	25.3%	15.0%	10.3%	0.0%	2.37	2.87	—
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	29	MLB	CHC	1	0	1	4	1	1	0	0%	1.0	21.0%	16.2%	4.8%	2.6%	5.20	5.20	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	30	MLB MLE	CHC	46	0	48	204	43	32	6	0%	1.1	20.9%	15.8%	5.1%	2.9%	5.22	5.29	-0.4
2029	31	MLB MLE	CHC	37	0	39	165	34	26	5	0%	1.1	20.7%	15.7%	5.0%	3.1%	5.31	5.42	-0.4
2028	30	MLB CTX	CHC	14	0	15	63	13	10	2	0%	1.1	20.9%	15.8%	5.1%	2.9%	5.22	5.29	-0.1
2029	31	MLB CTX	CHC	19	0	20	84	17	13	3	0%	1.1	20.7%	15.7%	5.0%	3.1%	5.31	5.42	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		34	0	35	149	31	23	4	0%	1.0	20.8%	15.7%	5.1%	3.0%	5.27	4.99	-0.2
/ 200 IP	—	RAW		190	0	200	844	176	131	23	0%	1.1	20.8%	15.7%	5.1%	3.0%	5.27	4.99	-1.0
Career	—	MLE		123	1	160	652	142	90	14	1%	1.3	21.7%	13.8%	8.0%	2.2%	2.98	4.22	-0.8
/ 200 IP	—	MLE		154	1	200	815	177	112	18	1%	1.3	21.7%	13.8%	8.0%	2.2%	2.98	4.22	-1.9

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.

2025 AAKnoxville Smokies · 57 IPFIP 2.86 → 3.46 (+0.60)K% 25.3% → 20.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.32%	—	×0.810	20.51%
BB% (unintentional) /BF	15.02%	—	×1.100	16.52%
HR / BF /BF	0.00%	÷0.911	×1.200	0.00%

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 82).

Result: FIP 2.86 → 3.46, 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).

2024 A+South Bend Cubs · 46 IPFIP 3.62 → 4.90 (+1.28)K% 27.3% → 21.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.27%	—	×0.770	21.00%
BB% (unintentional) /BF	10.70%	—	×1.150	12.30%
HR / BF /BF	2.67%	÷0.909	×1.250	3.68%

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 82).

Result: FIP 3.62 → 4.90, 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 A+Great Lakes Loons · 42 IPFIP 3.91 → 5.14 (+1.23)K% 26.6% → 20.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.55%	—	×0.770	20.45%
BB% (unintentional) /BF	11.86%	—	×1.150	13.64%
HR / BF /BF	2.82%	÷0.951	×1.250	3.71%

Result: FIP 3.91 → 5.14, 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 Dodgers Mota · 8 IPFIP -0.25 → 1.07 (+1.32)K% 44.8% → 30.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	44.83%	—	×0.680	30.48%
BB% (unintentional) /BF	3.45%	—	×1.250	4.31%
HR / BF /BF	0.00%	÷1.000	×1.400	0.00%

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 -0.25 → 1.07, 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
Mitchell Tyranski (OGILVIE)	—	—	5.20	21.0%	16.2%	2.6%	—	5.35	20.8%	5.1%	87	+0.15	—	—	—
Stephen Randolph L 2003 · 4.83 FIP	29	SU	4.83	18.5%	14.8%	2.6%	.247	5.69	15.8%	-3.0%	82	+0.86	4.83	155	89
Keith Hessler L 2018 · 4.96 FIP	29	SU	4.96	19.8%	13.3%	3.4%	.348	—	—	—	—	—	3.31	228	79
Braulio Lara L 2017 · 5.80 FIP	28	SU	5.80	19.8%	17.8%	3.9%	.361	—	—	—	—	—	3.96	118	77
Aaron Brown L 2021 · 4.13 FIP	29	SU	4.13	17.4%	12.8%	1.6%	.275	5.38	21.3%	8.8%	66	+1.25	4.04	269	77
Juan Pablo Perez L 2007 · 5.32 FIP	28	SU	5.32	17.5%	14.0%	3.5%	.324	—	—	—	—	—	1.75	104	76
Raul Valdes L 2010 · 4.02 FIP	32	SU	4.02	21.4%	9.9%	2.7%	.301	3.14	27.4%	22.5%	78	-0.88	2.29	160	75
Bryan Harper L 2018 · 4.63 FIP	28	SU	4.63	14.9%	13.9%	1.9%	.288	—	—	—	—	—	4.13	133	74
Jack Leathersich L 2018 · 4.55 FIP	28	SU	4.55	21.7%	19.8%	1.4%	.224	—	—	—	—	—	3.16	101	71
Andrew Barbosa L 2017 · 4.67 FIP	29	SU	4.67	20.2%	9.6%	3.8%	.301	—	—	—	—	—	2.70	175	71
Jose Meraz L 2017 · 4.34 FIP	29	SU	4.34	16.4%	10.3%	2.4%	.282	3.80	10.5%	-2.0%	30	-0.54	3.78	110	71
Luke Westphal L 2016 · 4.69 FIP	27	SU	4.69	21.2%	19.7%	1.6%	.353	—	—	—	—	—	2.97	294	70
Ashur Tolliver L 2017 · 5.03 FIP	29	SU	5.03	13.8%	18.9%	1.3%	.319	—	—	—	—	—	3.49	134	70
Robby Scott L 2019 · 5.59 FIP	29	SU	5.59	23.9%	15.9%	4.5%	.267	—	—	—	—	—	2.39	285	69
Jordan Norberto L 2015 · 4.30 FIP	28	SU	4.30	16.9%	14.1%	1.5%	.307	—	—	—	—	—	3.89	117	69
Matt Purke L 2018 · 4.29 FIP	28	SU	4.29	16.1%	16.0%	0.9%	.344	—	—	—	—	—	3.01	226	69
Mike Zagurski L 2012 · 4.48 FIP	29	SU	4.48	20.9%	11.7%	3.0%	.305	—	—	—	—	—	2.94	120	69
Travis Bergen L 2022 · 4.93 FIP	28	SU	4.93	19.3%	20.5%	1.6%	.314	—	—	—	—	—	2.48	143	69
David Purcey L 2011 · 4.66 FIP	29	SU	4.66	14.0%	15.9%	1.3%	.287	3.79	20.4%	8.9%	25	-0.87	3.60	206	68
Alex Torres L 2016 · 4.20 FIP	28	SU	4.20	18.5%	18.6%	0.6%	.300	—	—	—	—	—	2.10	184	68
Patrick McCoy L 2016 · 4.28 FIP	27	SU	4.28	15.4%	13.8%	1.3%	.350	—	—	—	—	—	3.56	127	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	0.8	52	20.9%	16.0%	1.09	5.23
Brian Bruney ('12)	1.3	43	21.2%	15.8%	0.94	4.68
Dwayne Henry ('84-'95, -1 WAR*)	1.7	67	23.8%*	14.2%*	0.90*	5.02*
Doug Creek ('05)	1.4	58	22.2%	15.1%	1.52	5.32
Matt Anderson ('05)	1.3	51	19.3%	13.5%	1.05	5.19
Ed Glynn ('75-'85, -0 WAR*)	1.3	53	23.5%*	12.9%*	1.22*	4.69*
Gene Harris ('89-'95, -1 WAR*)	1.2	40	20.0%*	12.5%*	0.98*	4.96*