Michael Lorenzen

P Toronto Blue Jays

Age 35 RHP 6'1" / 210 lbs MLB

Overview

MLBSP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	32	MIX3 levels	31	29	147	3.68	4.68	18.8%	11.7%	1.2	K 18.4% · BB 11.6% · FIP 4.85
2025	33	MIX2 levels	29	28	150	4.79	4.52	21.0%	7.0%	1.5	K 21.0% · BB 6.4% · FIP 4.59
2026	34	MLBTOR	32	25	127	7.21	5.31	14.5%	8.7%	0.1
2027PROJ	35	MLBTOR	84	0	88	5.11	5.16	16.9%	8.4%	-0.6
2028PROJ	36	MLBTOR	38	0	40	5.36	5.53	15.9%	9.0%	-0.4
2029PROJ	37	MLBTOR	16	0	17	5.70	5.93	14.8%	9.7%	-0.2

Last 3 seasons WAR 2.9 · Next 3 projected WAR -1.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

FB Velo43

K%10

xwOBA vs10

Form10.32 ERA (proj 5.11) · K 8.5% (proj 16.9%) · BB 12.0% (proj 8.4%) ▼▼ last 90d · 23 IP through Oct 27

CompsJean Machi (age 35) · Pete Walker (age 34) · Brendan Donnelly (age 35)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 136128vR 120

ERA5.11

FIP5.16

K%16.9%

BB%8.4%

K-BB%8.5%

Proj IP88

Value2027 · WAR

Runs saved vs league avg · 88 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p4394.0

Whiff%

p1018.0%

p1014.5%

xwOBA vs

p100.372

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)
2015	23	MIX MLE	2 levels	33	27	156	680	102	59	21	82%	4.7	14.7%	8.7%	5.9%	2.9%	4.43	4.58	+1.5
2015	23	MIX RAW	2 levels	33	27	156	680	102	59	21	82%	4.7	15.0%	8.7%	6.3%	3.1%	4.43	4.67	+1.3
2016	24	MIX MLE	2 levels	39	0	54	215	55	13	5	0%	1.4	23.8%	6.4%	17.3%	2.3%	2.67	3.17	+0.9
2016	24	MIX RAW	2 levels	39	0	54	215	55	13	5	0%	1.4	25.6%	6.0%	19.5%	2.3%	2.67	2.99	+1.0
2017	25	MLB MLE	Cincinnati Reds	70	0	83	361	80	29	9	0%	1.2	22.2%	8.0%	14.1%	2.3%	4.45	3.51	+1.0
2017	25	MLB RAW	Cincinnati Reds	70	0	83	361	80	29	9	0%	1.2	22.2%	8.0%	14.1%	2.5%	4.45	3.63	+0.9
2018	26	MIX MLE	2 levels	48	3	85	358	57	33	6	6%	1.8	15.7%	9.3%	6.4%	1.6%	2.96	3.84	+0.7
2018	26	MIX RAW	2 levels	48	3	85	358	57	33	6	6%	1.8	15.9%	9.2%	6.7%	1.7%	2.96	3.84	+0.7
2019	27	MLB MLE	Cincinnati Reds	73	0	83	343	85	27	9	0%	1.1	24.8%	7.9%	16.9%	2.4%	2.92	3.34	+1.2
2019	27	MLB RAW	Cincinnati Reds	73	0	83	343	85	27	9	0%	1.1	24.8%	7.9%	16.9%	2.6%	2.92	3.44	+1.1
2020	28	MLB MLE	Cincinnati Reds	18	2	34	147	35	16	3	11%	1.9	23.8%	10.9%	12.9%	1.9%	4.28	3.51	+0.4
2020	28	MLB RAW	Cincinnati Reds	18	2	34	147	35	16	3	11%	1.9	23.8%	10.9%	12.9%	2.0%	4.28	3.60	+0.4
2021	29	MIX MLE	3 levels	32	1	34	144	29	15	3	3%	1.1	16.8%	11.2%	5.6%	1.5%	4.98	3.91	+0.3
2021	29	MIX RAW	3 levels	32	1	34	144	29	15	3	3%	1.1	20.1%	10.4%	9.7%	2.1%	4.98	3.86	+0.3
2022	30	MIX MLE	3 levels	21	21	110	466	98	48	14	100%	5.3	20.7%	10.7%	10.0%	2.6%	4.57	4.15	+1.6
2022	30	MIX RAW	3 levels	21	21	110	466	98	48	14	100%	5.3	21.0%	10.3%	10.7%	3.0%	4.57	4.28	+1.5
2023	31	MIX MLE	2 levels	30	26	155	636	113	50	20	87%	5.2	17.8%	7.5%	10.3%	3.0%	4.35	4.19	+2.2
2023	31	MIX RAW	2 levels	30	26	155	636	113	50	20	87%	5.2	17.8%	7.9%	9.9%	3.1%	4.35	4.28	+2.0
2024	32	MIX MLE	3 levels	31	29	147	616	116	72	19	94%	4.7	18.4%	11.6%	6.9%	3.4%	3.68	4.85	+0.9
2024	32	MIX MLE	3 levels	31	29	147	616	116	72	19	94%	4.7	18.4%	11.6%	6.9%	3.4%	3.68	4.85	+0.9
2024	32	MIX RAW	3 levels	31	29	147	616	116	72	19	94%	4.7	18.8%	11.7%	7.1%	3.1%	3.68	4.68	+1.2
2025	33	MIX MLE	2 levels	29	28	150	647	136	45	27	97%	5.2	21.0%	6.4%	14.5%	4.5%	4.79	4.59	+1.4
2025	33	MIX MLE	2 levels	29	28	150	647	136	45	27	97%	5.2	21.0%	6.4%	14.5%	4.5%	4.79	4.59	+1.4
2025	33	MIX RAW	2 levels	29	28	150	647	136	45	27	97%	5.2	21.0%	7.0%	14.1%	4.2%	4.79	4.52	+1.5
2026	34	MLB MLE	Toronto Blue Jays	32	25	127	619	90	54	23	78%	4.0	14.5%	8.7%	5.8%	3.7%	7.21	5.02	+0.6
2026	34	MLB MLE	Toronto Blue Jays	32	25	127	619	90	54	23	78%	4.0	14.5%	8.7%	5.8%	3.7%	7.21	5.02	+0.6
2026	34	MLB RAW	Toronto Blue Jays	32	25	127	619	90	54	23	78%	4.0	14.5%	8.7%	5.8%	3.7%	7.21	5.31	+0.1
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			427	144	1123	4833	916	426	148	34%	2.6	19.0%	8.8%	10.1%	3.1%	4.44	4.32	+3.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	TOR	84	0	88	372	63	31	14	0%	1.0	16.9%	8.4%	8.5%	3.7%	5.11	5.16	-0.6
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	TOR	38	0	40	168	27	15	7	0%	1.0	15.9%	9.0%	6.9%	4.1%	5.36	5.53	-0.4
2029	37	MLB MLE	TOR	16	0	17	72	11	7	3	0%	1.1	14.8%	9.7%	5.2%	4.5%	5.70	5.93	-0.3
2028	36	MLB CTX	TOR	38	0	40	169	27	15	7	0%	1.1	15.9%	9.0%	6.9%	4.1%	5.36	5.53	-0.4
2029	37	MLB CTX	TOR	16	0	17	72	11	7	3	0%	1.1	14.8%	9.7%	5.2%	4.5%	5.70	5.93	-0.2
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		481	144	1180	5073	953	448	158	30%	2.5	18.8%	8.8%	10.0%	3.1%	4.49	4.34	+2.9
/ 200 IP	—	RAW		82	24	200	860	161	76	27	30%	2.5	18.8%	8.8%	10.0%	3.1%	4.49	4.34	+0.5
Career	—	MLE		456	162	1220	5232	980	459	156	36%	2.7	18.7%	8.8%	10.0%	3.0%	4.41	4.27	+2.3
/ 200 IP	—	MLE		75	27	200	858	161	75	26	36%	2.7	18.7%	8.8%	10.0%	3.0%	4.41	4.27	+0.4

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 AANorthwest Arkansas Naturals · 9 IPFIP 5.78 → 6.62 (+0.84)K% 22.0% → 17.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.95%	—	×0.810	17.78%
BB% (unintentional) /BF	14.63%	—	×1.100	16.10%
HR / BF /BF	4.88%	÷1.077	×1.200	5.44%

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 115).

Result: FIP 5.78 → 6.62, 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 AAAOmaha Storm Chasers · 15 IPFIP 4.63 → 4.70 (+0.07)K% 24.6% → 21.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.64%	—	×0.860	21.19%
BB% (unintentional) /BF	15.94%	—	×0.900	14.35%
HR / BF /BF	2.90%	÷1.111	×1.100	2.87%

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 122).

Result: FIP 4.63 → 4.70, 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 AAASalt Lake Bees · 7 IPFIP 5.21 → 5.93 (+0.72)K% 25.0% → 22.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.880	22.00%
BB% (unintentional) /BF	6.25%	—	×1.050	6.56%
HR / BF /BF	6.25%	÷0.978	×1.100	7.03%

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 96).

Result: FIP 5.21 → 5.93, 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 AInland Empire 66ers · 6 IPFIP 4.75 → 6.53 (+1.78)K% 21.7% → 16.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.74%	—	×0.740	16.09%
BB% (unintentional) /BF	8.70%	—	×1.180	10.26%
HR / BF /BF	4.35%	÷0.889	×1.300	6.36%

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 78).

Result: FIP 4.75 → 6.53, 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).

2015 AAALouisville Bats · 43 IPFIP 3.74 → 3.98 (+0.24)K% 11.5% → 10.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	11.52%	—	×0.880	10.13%
BB% (unintentional) /BF	4.85%	—	×1.050	5.09%
HR / BF /BF	1.82%	÷1.009	×1.100	1.98%

Result: FIP 3.74 → 3.98, 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
Michael Lorenzen (OGILVIE)	—	—	—	—	—	3.7%	—	5.65	15.6%	6.4%	57	+0.49	—	—	—
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	55
Pete Walker R 2003 · 5.73 FIP	34	SW	5.73	12.0%	9.1%	4.5%	.267	4.70	14.0%	5.1%	114	-1.03	4.45	327	51
Brendan Donnelly R 2006 · 4.21 FIP	35	SU	4.21	19.1%	9.0%	2.9%	.260	3.19	18.6%	10.1%	60	-1.02	2.23	385	50
Ivan Maldonado R 2015 · 4.60 FIP	35	SW	4.60	12.8%	8.7%	2.7%	.269	5.41	6.4%	-3.3%	104	+0.81	4.60	172	48
Jesse Estrada R 2019 · 4.34 FIP	35	SP-	4.34	14.4%	6.6%	2.9%	.312	—	—	—	—	—	3.80	257	46
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	42
Keiichi Yabu R 2005 · 4.05 FIP	36	SU	4.05	16.8%	8.8%	2.3%	.307	3.48	15.9%	6.6%	68	-0.57	3.48	126	41
Brian Schlitter R 2021 · 4.70 FIP	35	SU	4.70	14.9%	9.1%	3.1%	.318	—	—	—	—	—	3.27	300	40
Yohan Pino R 2017 · 5.33 FIP	33	SP-	5.33	15.2%	3.6%	5.6%	.296	—	—	—	—	—	3.84	193	38
Joe Nelson R 2009 · 5.35 FIP	34	SU	5.35	19.8%	14.3%	3.9%	.221	—	—	—	—	—	3.08	139	36
Spencer Patton R 2023 · 4.33 FIP	35	SU	4.33	18.9%	9.7%	2.9%	.295	—	—	—	—	—	2.79	239	36
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	36
Francisco Del Rosario R 2017 · 4.15 FIP	36	SU	4.15	16.4%	10.0%	2.1%	.267	6.35	11.9%	-0.6%	77	+2.20	3.93	194	36
Giovanni Carrara R 2003 · 6.00 FIP	35	SU	6.00	9.5%	10.2%	4.4%	.327	3.53	19.3%	10.8%	157	-2.47	2.50	462	35
Fernando Rodriguez Jr. R 2019 · 5.11 FIP	35	SW	5.11	25.0%	11.2%	4.9%	.312	—	—	—	—	—	3.34	335	35
Kenshin Kawakami R 2010 · 4.06 FIP	35	SP	4.06	15.1%	5.6%	2.8%	.293	—	—	—	—	—	3.97	244	35
Hector Navarro R 2015 · 4.35 FIP	35	SU	4.35	15.0%	5.4%	3.3%	.281	3.00	15.2%	9.0%	81	-1.35	2.30	126	35
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	34
Todd Williams R 2006 · 4.79 FIP	35	SU	4.79	9.2%	6.2%	3.1%	.321	4.42	14.1%	7.9%	14	-0.37	3.51	223	34
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	33

Career Comps

Completed MLB careers (no pro action in 2+ yrs) whose totals — actual + projected — most resemble this player's. Pool: 2,161 pitcher careers (1,251 from 1990-2025 + 910 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	6.3	85	18.7%	9.1%	1.22	4.53
Dan Schatzeder ('77-'91, 8 WAR*)	6.6	94	18.8%*	8.5%*	1.28*	4.25*
Gene Nelson ('81-'93, 6 WAR*)	5.4	83	19.6%*	8.8%*	1.17*	4.31*
Mike Bielecki ('84-'97, 9 WAR*)	6.2	95	18.4%*	9.1%*	1.15*	4.47*
Carlos Villanueva ('16)	5.0	91	20.4%	7.6%	1.25	4.31
Tom Gorzelanny ('16)	4.4	88	18.4%	10.0%	1.00	4.40
Jorge De La Rosa ('18)	7.6	109	19.1%	10.3%	1.05	4.58