Dylan Cease

P Toronto Blue Jays

Age 31 RHP 6'2" / 200 lbs MLB Svc 7.0 · Ctrl thru 2026 peak 4.2 · 2027

Overview

MLBSP40-MANFA 2027

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	28	MLBSD	33	33	189	3.47	3.00	29.4%	8.5%	5.7
2025	29	MLBSD	32	32	168	4.55	3.40	29.8%	9.6%	4.1
2026	30	MIX2 levels	30	30	173	2.60	2.61	34.8%	11.2%	6.2	K 34.9% · BB 11.4% · FIP 2.48
2027PROJ	31	MLBTOR	24	24	146	3.31	3.22	33.4%	10.5%	3.9
2028PROJ	32	MLBTOR	22	22	129	3.41	3.47	32.6%	10.9%	3.0
2029PROJ	33	MLBTOR	19	19	114	3.62	3.75	31.8%	11.3%	2.3

Last 3 seasons WAR 16.0 · Next 3 projected WAR 9.2Projected rows: MLB playing time only, park and league context applied.

ContractCONFIRMED

Control thru2026Service7.517First FA2027

Skills · percentileStatcast 2026

K%90

xwOBA vs90

FB Velo82

Form2.89 ERA (proj 3.31) · K 26.2% (proj 33.4%) · BB 15.5% (proj 10.5%) ▼▼ last 60d · 19 IP through Oct 27

CompsJacob deGrom (age 31) · Kenta Maeda (age 31) · Chase Anderson (age 31)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8779vR 71

ERA3.31

FIP3.22

K%33.4%

BB%10.5%

K-BB%23.0%

Proj IP146

Value2027 · WAR

Runs saved vs league avg · 146 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p8296.8

Whiff%

p9033.3%

p9034.9%

xwOBA vs

p900.265

Contract CONFIRMED · hand-curated extension

Status FA

MLB Debut 2019

Service 7.517 yrs.days

Team Control Through 2026

First FA Year 2027

Year	Status	Salary	Proj WAR	Value	Surplus
Totals		$0.0M		$0.0M	+$0.0M

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	19	ROK MLE	AZL Cubs	11	8	24	101	25	16	0	73%	2.2	16.8%	19.8%	-3.0%	0.0%	2.62	4.19	—
2015	19	ROK RAW	AZL Cubs	11	8	24	101	25	16	0	73%	2.2	24.8%	15.8%	8.9%	0.0%	2.62	3.02	—
2017	21	A MLE	Kannapolis Intimidators	22	22	93	388	126	44	3	100%	4.2	24.0%	13.4%	10.6%	1.2%	3.28	3.44	—
2017	21	A RAW	Kannapolis Intimidators	22	22	93	388	126	44	3	100%	4.2	32.5%	11.3%	21.1%	0.8%	3.28	2.23	—
2018	22	MIX MLE	2 levels	23	23	124	492	160	50	8	100%	5.4	25.7%	11.5%	14.2%	2.0%	2.40	3.49	—
2018	22	MIX RAW	2 levels	23	23	124	492	160	50	8	100%	5.4	32.5%	10.2%	22.4%	1.6%	2.40	2.57	—
2019	23	MIX MLE	2 levels	29	29	141	630	154	66	19	100%	4.9	23.1%	10.7%	12.3%	2.9%	5.16	4.11	+2.2
2019	23	MIX RAW	2 levels	29	29	141	630	154	66	19	100%	4.9	24.4%	10.5%	14.0%	3.0%	5.16	4.07	+2.2
2020	24	MLB MLE	Chicago White Sox	12	12	58	255	44	33	12	100%	4.9	17.3%	12.9%	4.3%	4.6%	4.01	5.82	-0.3
2020	24	MLB RAW	Chicago White Sox	12	12	58	255	44	33	12	100%	4.9	17.3%	12.9%	4.3%	4.7%	4.01	5.96	-0.4
2021	25	MLB MLE	Chicago White Sox	32	32	166	708	226	68	20	100%	5.2	31.9%	9.6%	22.3%	2.8%	3.91	3.14	+4.6
2021	25	MLB RAW	Chicago White Sox	32	32	166	708	226	68	20	100%	5.2	31.9%	9.6%	22.3%	2.8%	3.91	3.17	+4.6
2022	26	MLB MLE	Chicago White Sox	32	32	184	747	227	76	16	100%	5.8	30.4%	10.2%	20.2%	2.1%	2.20	2.98	+5.6
2022	26	MLB RAW	Chicago White Sox	32	32	184	747	227	76	16	100%	5.8	30.4%	10.2%	20.2%	2.1%	2.20	3.00	+5.5
2023	27	MLB MLE	Chicago White Sox	33	33	177	784	214	76	19	100%	5.4	27.3%	9.7%	17.6%	2.4%	4.58	3.33	+4.5
2023	27	MLB RAW	Chicago White Sox	33	33	177	784	214	76	19	100%	5.4	27.3%	9.7%	17.6%	2.4%	4.58	3.37	+4.4
2024	28	MLB MLE	San Diego Padres	33	33	189	762	224	65	18	100%	5.7	29.4%	8.5%	20.9%	2.3%	3.47	2.96	+5.8
2024	28	MLB MLE	San Diego Padres	33	33	189	762	224	65	18	100%	5.7	29.4%	8.5%	20.9%	2.3%	3.47	2.96	+5.8
2024	28	MLB RAW	San Diego Padres	33	33	189	762	224	65	18	100%	5.7	29.4%	8.5%	20.9%	2.4%	3.47	3.00	+5.7
2025	29	MLB MLE	San Diego Padres	32	32	168	722	215	69	21	100%	5.2	29.8%	9.6%	20.2%	2.8%	4.55	3.35	+4.2
2025	29	MLB MLE	San Diego Padres	32	32	168	722	215	69	21	100%	5.2	29.8%	9.6%	20.2%	2.8%	4.55	3.35	+4.2
2025	29	MLB RAW	San Diego Padres	32	32	168	722	215	69	21	100%	5.2	29.8%	9.6%	20.2%	2.9%	4.55	3.40	+4.1
2026	30	MIX MLE	2 levels	30	30	173	704	245	79	13	100%	5.8	34.9%	11.4%	23.5%	1.6%	2.60	2.48	+6.5
2026	30	MIX MLE	2 levels	30	30	173	704	245	79	13	100%	5.8	34.9%	11.4%	23.5%	1.6%	2.60	2.48	+6.5
2026	30	MIX RAW	2 levels	30	30	173	704	245	79	13	100%	5.8	34.8%	11.2%	23.6%	1.8%	2.60	2.61	+6.2
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			217	217	1184	4989	1470	499	132	100%	5.5	29.5%	10.0%	19.5%	2.6%	3.67	3.33	+30.2
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	31	MLB	TOR	24	24	146	616	206	65	14	100%	6.1	33.4%	10.5%	23.0%	2.2%	3.31	3.22	+3.9
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	32	MLB MLE	TOR	22	22	129	545	178	59	14	100%	5.9	32.6%	10.9%	21.8%	2.5%	3.41	3.47	+3.0
2029	33	MLB MLE	TOR	19	19	114	481	153	55	13	100%	6.0	31.8%	11.3%	20.5%	2.8%	3.62	3.75	+2.3
2030	34	MLB MLE	TOR	16	16	98	414	128	49	13	100%	6.1	30.9%	11.8%	19.1%	3.1%	3.88	4.06	+1.6
2031	35	MLB MLE	TOR	13	13	78	331	99	41	11	100%	6.0	29.9%	12.4%	17.6%	3.4%	4.19	4.39	+0.9
2032	36	MLB MLE	TOR	9	9	56	236	68	31	9	100%	6.2	28.9%	13.0%	15.9%	3.8%	4.53	4.76	+0.4
2028	32	MLB CTX	TOR	22	22	129	545	178	59	14	100%	5.9	32.6%	10.9%	21.8%	2.5%	3.41	3.47	+3.0
2029	33	MLB CTX	TOR	19	19	114	481	153	55	13	100%	6.0	31.8%	11.3%	20.5%	2.8%	3.62	3.75	+2.3
2030	34	MLB CTX	TOR	16	16	98	414	128	49	13	100%	6.1	30.9%	11.8%	19.1%	3.1%	3.88	4.06	+1.6
2031	35	MLB CTX	TOR	13	13	78	329	99	41	11	100%	6.0	29.9%	12.4%	17.6%	3.4%	4.19	4.39	+0.9
2032	36	MLB CTX	TOR	9	9	56	236	68	31	9	100%	6.2	28.9%	13.0%	15.9%	3.8%	4.53	4.76	+0.4
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		296	296	1660	6997	2096	734	192	100%	5.6	30.0%	10.5%	19.5%	2.7%	3.71	3.40	+40.7
/ 200 IP	—	RAW		36	36	200	843	253	88	23	100%	5.6	30.0%	10.5%	19.5%	2.7%	3.71	3.40	+4.9
Career	—	MLE		289	286	1498	6293	1777	663	148	99%	5.2	28.2%	10.5%	17.7%	2.4%	3.58	3.34	+38.2
/ 200 IP	—	MLE		39	38	200	840	237	89	20	99%	5.2	28.2%	10.5%	17.7%	2.4%	3.58	3.34	+4.6

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 AAACharlotte Knights · 68 IPFIP 3.13 → 3.37 (+0.24)K% 24.0% → 21.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.01%	—	×0.880	21.13%
BB% (unintentional) /BF	10.53%	—	×1.050	11.05%
HR / BF /BF	1.32%	÷1.212	×1.100	1.19%

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 142).

Result: FIP 3.13 → 3.37, 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 A+Winston-Salem Dash · 72 IPFIP 2.88 → 3.83 (+0.95)K% 28.3% → 21.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.28%	—	×0.770	21.77%
BB% (unintentional) /BF	9.66%	—	×1.150	11.10%
HR / BF /BF	1.72%	÷1.014	×1.250	2.12%

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 103).

Result: FIP 2.88 → 3.83, 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 AABirmingham Barons · 52 IPFIP 2.03 → 3.01 (+0.97)K% 38.6% → 31.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	38.61%	—	×0.810	31.28%
BB% (unintentional) /BF	10.89%	—	×1.100	11.98%
HR / BF /BF	1.49%	÷0.949	×1.200	1.88%

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 90).

Result: FIP 2.03 → 3.01, 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 AKannapolis Intimidators · 93 IPFIP 2.22 → 3.44 (+1.22)K% 32.5% → 24.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.47%	—	×0.740	24.03%
BB% (unintentional) /BF	11.34%	—	×1.180	13.38%
HR / BF /BF	0.77%	÷0.824	×1.300	1.22%

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 65).

Result: FIP 2.22 → 3.44, 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 ROKAZL Cubs · 24 IPFIP 3.02 → 4.19 (+1.17)K% 24.8% → 16.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.75%	—	×0.680	16.83%
BB% (unintentional) /BF	15.84%	—	×1.250	19.80%
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 3.02 → 4.19, 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
Dylan Cease (OGILVIE)	—	—	—	—	—	2.2%	—	3.73	31.9%	20.5%	341	+0.51	—	—	—
Jacob deGrom R 2019 · 2.40 FIP	31	SP	2.40	31.7%	5.3%	2.4%	.277	1.51	42.5%	38.1%	224	-0.89	0.74	1,692	60
Kenta Maeda R 2019 · 3.68 FIP	31	SP-	3.68	27.1%	8.0%	3.4%	.240	3.44	27.8%	22.0%	173	-0.24	2.87	1,092	49
Chase Anderson R 2019 · 4.39 FIP	31	SP	4.39	20.9%	8.1%	3.7%	.259	5.39	19.2%	10.0%	218	+1.00	3.30	1,260	47
Corey Kluber R 2017 · 2.26 FIP	31	SP	2.26	34.1%	4.4%	2.7%	.263	3.11	25.9%	21.2%	251	+0.85	2.16	1,636	44
Tanner Roark R 2018 · 3.99 FIP	31	SP	3.99	19.2%	6.2%	3.2%	.289	4.57	21.1%	13.1%	259	+0.58	2.40	1,194	41
Tim Adleman R 2019 · 4.30 FIP	31	SP	4.30	22.8%	7.8%	3.9%	.266	4.71	18.5%	11.1%	90	+0.41	3.63	605	39
Mike Fiers R 2016 · 4.30 FIP	31	SP	4.30	18.5%	5.8%	3.7%	.308	4.96	19.2%	12.2%	510	+0.66	2.83	1,140	34
Brad Peacock R 2019 · 4.22 FIP	31	SP-	4.22	25.1%	8.1%	4.0%	.261	4.15	22.8%	12.5%	96	-0.07	2.97	748	32
Josh Lowey R 2016 · 2.22 FIP	31	SP	2.22	29.1%	6.1%	1.5%	.278	2.68	24.0%	17.2%	180	+0.46	2.22	426	30
Matt Shoemaker R 2018 · 3.17 FIP	31	SP	3.17	25.4%	7.7%	2.3%	.310	4.87	19.3%	11.5%	188	+1.70	3.12	728	29
Miles Mikolas R 2019 · 4.11 FIP	30	SP	4.11	18.8%	4.1%	3.8%	.295	4.02	18.4%	13.4%	266	-0.09	3.13	1,386	28
Cory Lidle R 2003 · 4.40 FIP	31	SP	4.40	13.3%	6.8%	2.9%	.297	4.23	15.4%	9.5%	567	-0.17	3.48	1,318	25
Jeremy Guthrie R 2010 · 4.08 FIP	31	SP	4.08	13.6%	5.6%	2.6%	.247	4.68	13.2%	6.8%	601	+0.60	4.08	1,834	25
Hideki Irabu R 2000 · 4.38 FIP	31	SP	4.38	17.0%	5.7%	3.6%	.374	5.19	17.2%	11.0%	64	+0.81	3.78	514	25
Chuck Smith R 2001 · 3.99 FIP	31	SP	3.99	18.4%	8.1%	2.6%	.289	—	—	—	—	—	3.08	211	25
Miguel Batista R 2002 · 3.81 FIP	31	SP-	3.81	14.2%	8.5%	1.5%	.267	4.13	14.9%	6.3%	467	+0.32	3.39	1,943	24
Jarrett Grube R 2015 · 3.75 FIP	33	SP	3.75	18.7%	4.2%	3.1%	.285	4.57	17.3%	9.6%	253	+0.82	3.75	393	23
Jose Samayoa R 2019 · 4.05 FIP	29	SP	4.05	19.4%	7.0%	3.1%	.314	—	—	—	—	—	3.44	262	23
Jose Oyervides R 2015 · 3.01 FIP	33	SP	3.01	19.1%	6.2%	1.4%	.333	4.09	21.9%	14.5%	184	+1.08	3.01	407	23
Wandy Rodriguez L 2010 · 3.37 FIP	31	SP	3.37	21.7%	7.9%	2.0%	.297	4.06	18.1%	11.6%	459	+0.69	3.37	1,572	21

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	9.0	129	30.3%	10.6%	1.05	3.68
Jose DeLeon ('83-'95, 22 WAR*)	9.5	146	25.8%*	10.4%*	1.01*	4.17*
Francisco Liriano ('19)	9.1	130	23.3%	10.5%	0.91	4.15
Jose Rijo ('84-'02, 33 WAR*)	9.4	145	25.9%*	8.4%*	0.93*	3.56*
Bob Veale ('62-'74, 32 WAR*)	9.6	175	26.6%*	11.3%*	0.62*	3.77*
Rich Harden ('11)	4.6	103	24.2%	10.7%	1.02	3.76
Danny Salazar ('19)	3.0	118	27.7%	8.6%	1.14	3.82