Tyler Rogers

P Toronto Blue Jays

Age 36 RHP 6'3" / 180 lbs MLB peak 0.2 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	IP	ERA	FIP	K%	BB%	WAR
2024	33	MLBSF	77	70	2.82	3.11	17.6%	1.4%	1.2
2025	34	MLBNYM	81	77	1.98	2.80	16.1%	2.3%	1.6
2026	35	MLBTOR	74	71	1.77	3.06	13.6%	5.8%	1.3
2027PROJ	36	MLBTOR	46	48	3.36	4.04	11.3%	4.6%	0.3
2028PROJ	37	MLBTOR	17	18	4.01	4.44	10.2%	5.2%	0.0
2029PROJ	38	MLBTOR	7	7	4.48	4.79	10.0%	5.9%	-0.0

Last 3 seasons WAR 4.1 · Next 3 projected WAR 0.3Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs90

FB Velo10

K%10

Form1.96 ERA (proj 3.36) · K 13.4% (proj 11.3%) · BB 8.2% (proj 4.6%) · last 90d · 23 IP through Oct 27

CompsPeter Moylan (age 36) · Erubiel Gonzalez (age 36) · Juan Sandoval (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 9789vR 81

ERA3.36

FIP4.04

K%11.3%

BB%4.6%

K-BB%6.7%

Proj IP48

Value2027 · WAR

Runs saved vs league avg · 48 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1083.2

Whiff%

p1014.5%

p1013.6%

xwOBA vs

p900.263

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	24	MIX MLE	2 levels	52	0	90	355	101	25	5	0%	1.7	21.4%	7.4%	14.0%	1.6%	2.00	3.09	—
2015	24	MIX RAW	2 levels	52	0	90	355	101	25	5	0%	1.7	28.5%	7.0%	21.4%	1.4%	2.00	2.41	—
2016	25	MIX MLE	2 levels	59	1	66	285	46	22	1	2%	1.1	13.6%	8.2%	5.4%	0.4%	3.27	3.19	—
2016	25	MIX RAW	2 levels	59	1	66	285	46	22	1	2%	1.1	16.1%	7.7%	8.4%	0.4%	3.27	2.90	—
2017	26	AAA MLE	Sacramento River Cats	55	0	76	314	43	25	2	0%	1.4	12.1%	8.4%	3.7%	0.8%	2.37	3.57	—
2017	26	AAA RAW	Sacramento River Cats	55	0	76	314	43	25	2	0%	1.4	13.7%	8.0%	5.7%	0.6%	2.37	3.30	—
2018	27	AAA MLE	Sacramento River Cats	51	0	68	273	60	21	4	0%	1.3	19.3%	8.1%	11.3%	1.6%	2.13	3.38	—
2018	27	AAA RAW	Sacramento River Cats	51	0	68	273	60	21	4	0%	1.3	22.0%	7.7%	14.3%	1.5%	2.13	3.03	—
2019	28	MIX MLE	2 levels	66	1	80	340	71	29	6	2%	1.2	18.9%	8.9%	10.0%	1.9%	3.50	3.67	+0.8
2019	28	MIX RAW	2 levels	66	1	80	340	71	29	6	2%	1.2	20.9%	8.5%	12.4%	1.8%	3.50	3.39	+1.1
2020	29	MLB MLE	San Francisco Giants	29	0	28	123	27	6	2	0%	1.0	22.0%	4.9%	17.1%	1.8%	4.50	2.83	+0.6
2020	29	MLB RAW	San Francisco Giants	29	0	28	123	27	6	2	0%	1.0	22.0%	4.9%	17.1%	1.6%	4.50	2.74	+0.6
2021	30	MLB MLE	San Francisco Giants	80	0	81	326	55	13	5	0%	1.0	16.9%	4.0%	12.9%	1.7%	2.22	3.09	+1.4
2021	30	MLB RAW	San Francisco Giants	80	0	81	326	55	13	5	0%	1.0	16.9%	4.0%	12.9%	1.5%	2.22	3.03	+1.5
2022	31	MLB MLE	San Francisco Giants	68	0	76	319	49	23	3	0%	1.1	15.4%	7.2%	8.2%	1.0%	3.57	3.27	+1.2
2022	31	MLB RAW	San Francisco Giants	68	0	76	319	49	23	3	0%	1.1	15.4%	7.2%	8.2%	0.9%	3.57	3.23	+1.2
2023	32	MLB MLE	San Francisco Giants	68	0	74	309	60	17	7	0%	1.1	19.4%	5.5%	13.9%	2.4%	3.04	3.50	+0.9
2023	32	MLB RAW	San Francisco Giants	68	0	74	309	60	17	7	0%	1.1	19.4%	5.5%	13.9%	2.3%	3.04	3.40	+1.0
2024	33	MLB MLE	San Francisco Giants	77	0	70	290	51	4	7	0%	0.9	17.6%	1.4%	16.2%	2.6%	2.82	3.22	+1.1
2024	33	MLB MLE	San Francisco Giants	77	0	70	290	51	4	7	0%	0.9	17.6%	1.4%	16.2%	2.6%	2.82	3.22	+1.1
2024	33	MLB RAW	San Francisco Giants	77	0	70	290	51	4	7	0%	0.9	17.6%	1.4%	16.2%	2.4%	2.82	3.11	+1.2
2025	34	MLB MLE	New York Mets	81	0	77	299	48	7	4	0%	1.0	16.1%	2.3%	13.7%	1.3%	1.98	2.78	+1.6
2025	34	MLB MLE	New York Mets	81	0	77	299	48	7	4	0%	1.0	16.1%	2.3%	13.7%	1.3%	1.98	2.78	+1.6
2025	34	MLB RAW	New York Mets	81	0	77	299	48	7	4	0%	1.0	16.1%	2.3%	13.7%	1.3%	1.98	2.80	+1.6
2026	35	MLB MLE	Toronto Blue Jays	74	0	71	294	40	17	2	0%	1.0	13.6%	5.8%	7.8%	0.7%	1.77	3.06	+1.3
2026	35	MLB MLE	Toronto Blue Jays	74	0	71	294	40	17	2	0%	1.0	13.6%	5.8%	7.8%	0.7%	1.77	3.06	+1.3
2026	35	MLB RAW	Toronto Blue Jays	74	0	71	294	40	17	2	0%	1.0	13.6%	5.8%	7.8%	0.7%	1.77	3.06	+1.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			494	0	495	2030	346	90	30	0%	1.0	17.0%	4.4%	12.6%	1.5%	2.62	3.04	+8.9
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	TOR	46	0	48	203	23	9	3	0%	1.0	11.3%	4.6%	6.7%	1.7%	3.36	4.04	+0.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	TOR	18	0	18	78	8	4	2	0%	1.0	10.2%	5.2%	5.0%	2.1%	4.01	4.44	+0.0
2029	38	MLB MLE	TOR	7	0	7	31	3	2	1	0%	1.1	10.0%	5.9%	4.1%	2.6%	4.48	4.79	-0.0
2030	39	MLB MLE	TOR	3	0	3	14	1	1	0	0%	1.1	10.0%	6.6%	3.4%	3.0%	4.87	5.13	-0.0
2028	37	MLB CTX	TOR	17	0	18	76	8	4	2	0%	1.1	10.2%	5.2%	5.0%	2.1%	4.01	4.44	+0.0
2029	38	MLB CTX	TOR	7	0	7	30	3	2	1	0%	1.0	10.0%	5.9%	4.1%	2.6%	4.48	4.79	-0.0
2030	39	MLB CTX	TOR	3	0	3	13	1	1	0	0%	1.0	10.0%	6.6%	3.4%	3.0%	4.87	5.13	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		522	0	524	2153	358	97	33	0%	1.0	16.6%	4.5%	12.2%	1.5%	2.71	3.10	+9.1
/ 200 IP	—	RAW		199	0	200	822	137	37	13	0%	1.0	16.6%	4.5%	12.2%	1.5%	2.71	3.10	+3.5
Career	—	MLE		760	2	857	3527	600	215	52	0%	1.1	17.0%	6.1%	10.9%	1.5%	2.66	3.24	+8.9
/ 200 IP	—	MLE		177	0	200	823	140	50	12	0%	1.1	17.0%	6.1%	10.9%	1.5%	2.66	3.24	+3.4

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.

2019 AAASacramento River Cats · 62 IPFIP 3.82 → 4.17 (+0.35)K% 20.4% → 17.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.37%	—	×0.880	17.93%
BB% (unintentional) /BF	9.63%	—	×1.050	10.11%
HR / BF /BF	2.22%	÷1.027	×1.100	2.38%

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 105).

Result: FIP 3.82 → 4.17, 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).

2018 AAASacramento River Cats · 68 IPFIP 3.02 → 3.38 (+0.35)K% 22.0% → 19.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.98%	—	×0.880	19.34%
BB% (unintentional) /BF	7.69%	—	×1.050	8.08%
HR / BF /BF	1.47%	÷0.998	×1.100	1.62%

Result: FIP 3.02 → 3.38, 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).

2017 AAASacramento River Cats · 76 IPFIP 3.30 → 3.57 (+0.27)K% 13.7% → 12.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	13.69%	—	×0.880	12.05%
BB% (unintentional) /BF	7.96%	—	×1.050	8.36%
HR / BF /BF	0.64%	÷0.889	×1.100	0.79%

Result: FIP 3.30 → 3.57, 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).

2016 AAASacramento River Cats · 31 IPFIP 3.50 → 3.72 (+0.22)K% 15.0% → 13.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	14.97%	—	×0.880	13.17%
BB% (unintentional) /BF	10.20%	—	×1.050	10.71%
HR / BF /BF	0.68%	÷1.082	×1.100	0.69%

Result: FIP 3.50 → 3.72, 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).

2016 AARichmond Flying Squirrels · 35 IPFIP 2.27 → 2.62 (+0.34)K% 17.4% → 14.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.39%	—	×0.810	14.09%
BB% (unintentional) /BF	5.07%	—	×1.100	5.58%
HR / BF /BF	0.00%	÷0.978	×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 96).

Result: FIP 2.27 → 2.62, 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).

2015 A+San Jose Giants · 79 IPFIP 2.28 → 3.09 (+0.81)K% 27.8% → 21.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	27.83%	—	×0.770	21.43%
BB% (unintentional) /BF	6.47%	—	×1.150	7.44%
HR / BF /BF	1.29%	÷1.031	×1.250	1.57%

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 106).

Result: FIP 2.28 → 3.09, 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 AARichmond Flying Squirrels · 11 IPFIP 2.92 → 4.05 (+1.13)K% 32.6% → 26.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.61%	—	×0.810	26.41%
BB% (unintentional) /BF	10.87%	—	×1.100	11.96%
HR / BF /BF	2.17%	÷0.860	×1.200	3.03%

Result: FIP 2.92 → 4.05, 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
Tyler Rogers (OGILVIE)	—	—	—	—	—	1.7%	—	4.44	10.2%	5.0%	18	+0.40	—	—	—
Peter Moylan R 2015 · 3.11 FIP	36	SU	3.11	18.4%	8.2%	0.9%	.259	3.84	18.2%	9.1%	145	+0.73	2.54	431	54
Erubiel Gonzalez R 2015 · 3.43 FIP	36	SU	3.43	12.4%	6.0%	1.1%	.328	3.26	14.8%	10.4%	69	-0.17	2.99	119	52
Juan Sandoval R 2017 · 3.42 FIP	36	SU	3.42	16.7%	7.2%	1.5%	.298	5.41	15.9%	8.7%	57	+1.99	3.24	225	50
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	48
Brian Sweeney R 2010 · 4.63 FIP	36	SU	4.63	9.5%	3.4%	3.5%	.226	—	—	—	—	—	3.03	108	47
Kazuhiro Sasaki R 2003 · 3.30 FIP	35	SU	3.30	19.3%	8.7%	1.3%	.274	—	—	—	—	—	2.76	223	46
Tony Pena Jr. R 2017 · 3.43 FIP	36	SU	3.43	10.9%	4.3%	1.3%	.312	—	—	—	—	—	2.80	128	46
Yoshihisa Hirano R 2020 · 5.02 FIP	36	SU	5.02	17.5%	12.7%	3.3%	.381	—	—	—	—	—	3.45	132	45
Pete Walker R 2005 · 4.79 FIP	36	SU	4.79	12.0%	9.2%	2.8%	.261	4.45	19.6%	11.6%	30	-0.34	4.45	327	45
Jesus Adrian Castillo R 2019 · 5.13 FIP	35	SU	5.13	13.1%	7.8%	3.9%	.319	—	—	—	—	—	2.23	173	44
Rudy Gonzalez R 2017 · 4.72 FIP	36	SU	4.72	15.8%	7.1%	3.8%	.354	—	—	—	—	—	3.31	119	44
Roy Corcoran R 2015 · 2.87 FIP	35	SU	2.87	17.7%	8.2%	0.4%	.359	—	—	—	—	—	2.87	129	44
Leo Rosales R 2017 · 4.24 FIP	36	SU	4.24	7.8%	8.2%	1.4%	.203	—	—	—	—	—	3.85	130	44
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	42
Chris Nichting R 2002 · 4.67 FIP	36	SU	4.67	16.6%	3.3%	4.6%	.289	—	—	—	—	—	4.11	103	42
Rafael Martín R 2019 · 4.71 FIP	35	SU	4.71	22.5%	6.0%	5.0%	.333	—	—	—	—	—	2.94	227	42
Salvador Valdez R 2019 · 4.30 FIP	36	SW	4.30	17.1%	8.2%	2.9%	.405	—	—	—	—	—	3.60	326	40
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	39
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	39
Matt Miller R 2006 · 4.98 FIP	34	SU	4.98	18.5%	13.8%	3.1%	.214	—	—	—	—	—	2.13	101	38

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	2.9	52	16.2%	4.9%	0.60	2.76
Chad Bradford ('09)	2.6	57	14.4%	6.3%	0.49	3.26
Richard Bleier ('23)	1.7	47	13.6%	3.9%	0.65	3.27
Mark Melancon ('22)	3.6	56	21.6%	6.4%	0.54	2.94
Ramon Hernandez ('67-'77, 6 WAR*)	2.2	61	19.7%*	7.3%*	0.77*	3.65*
Doug Jones ('82-'00, 23 WAR*)	5.6	81	24.1%*	5.0%*	0.81*	3.31*
Dan Quisenberry ('79-'90, 26 WAR*)	5.2	95	13.1%*	3.8%*	0.67*	2.91*