Luis Cessa

P Indianapolis Indians

Age 35 RHP 6'0" / 222 lbs AAA

Overview

AAASP

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2022	30	MLBCIN	46	10	81	4.57	4.93	17.6%	8.3%	-0.4
2023	31	MIX2 levels	29	13	68	8.74	5.20	16.0%	9.3%	-0.5	K 14.2% · BB 8.8% · FIP 5.02
2024	32	AAAPIT	26	23	103	4.73	4.76	17.3%	9.6%	—	K 14.8% · BB 8.7% · FIP 5.18
2025	33	No MLB games played
2026	34	No MLB games played
2027PROJ	35	MLBPIT	6	0	6	4.61	4.60	20.8%	9.1%	-0.0
2028PROJ	36	MLBPIT	41	0	43	4.83	4.97	19.8%	9.7%	-0.2
2029PROJ	37	MLBPIT	30	0	31	5.16	5.37	18.7%	10.3%	-0.3

Last 3 seasons WAR -0.9 · Next 3 projected WAR -0.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2023

FB Velo14

K%10

xwOBA vs10

CompsIvan Maldonado (age 35) · Jean Machi (age 35) · Tiago Da Silva (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 121112vR 104

ERA4.61

FIP4.60

K%20.8%

BB%9.1%

K-BB%11.8%

Proj IP62

Value2027 · WAR

Runs saved vs league avg · 62 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1491.9

Whiff%

p1014.6%

p108.3%

xwOBA vs

p100.433

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	25	25	139	607	119	35	7	100%	5.6	16.5%	6.2%	10.4%	1.4%	4.52	3.23	—
2015	23	MIX RAW	2 levels	25	25	139	607	119	35	7	100%	5.6	19.6%	5.8%	13.8%	1.2%	4.52	2.80	—
2016	24	MIX MLE	2 levels	32	23	148	594	115	37	24	72%	4.6	18.0%	6.4%	11.5%	4.0%	3.66	4.60	+1.4
2016	24	MIX RAW	2 levels	32	23	148	594	115	37	24	72%	4.6	19.4%	6.2%	13.1%	4.0%	3.66	4.41	+1.7
2017	25	MIX MLE	2 levels	24	18	114	486	97	43	14	75%	4.8	18.3%	9.1%	9.2%	2.6%	3.86	4.15	+1.7
2017	25	MIX RAW	2 levels	24	18	114	486	97	43	14	75%	4.8	20.0%	8.8%	11.1%	2.9%	3.86	4.12	+1.7
2018	26	MIX MLE	4 levels	25	13	83	346	77	22	6	52%	3.3	20.5%	5.8%	14.7%	1.9%	4.25	3.16	+2.3
2018	26	MIX RAW	4 levels	25	13	83	346	77	22	6	52%	3.3	22.3%	6.4%	15.9%	1.7%	4.25	2.98	+2.4
2019	27	MLB MLE	New York Yankees	43	0	81	343	75	30	14	0%	1.9	21.9%	8.7%	13.1%	3.8%	4.11	4.46	+0.1
2019	27	MLB RAW	New York Yankees	43	0	81	343	75	30	14	0%	1.9	21.9%	8.7%	13.1%	4.1%	4.11	4.61	-0.1
2020	28	MLB MLE	New York Yankees	16	0	22	93	17	7	2	0%	1.4	18.3%	7.5%	10.8%	2.0%	3.32	3.62	+0.2
2020	28	MLB RAW	New York Yankees	16	0	22	93	17	7	2	0%	1.4	18.3%	7.5%	10.8%	2.2%	3.32	3.70	+0.2
2021	29	MLB MLE	Cincinnati Reds	53	0	65	261	54	18	5	0%	1.2	20.7%	6.9%	13.8%	1.8%	2.51	3.20	+1.0
2021	29	MLB RAW	Cincinnati Reds	53	0	65	261	54	18	5	0%	1.2	20.7%	6.9%	13.8%	1.9%	2.51	3.27	+1.0
2022	30	MLB MLE	Cincinnati Reds	46	10	81	336	59	28	14	22%	1.8	17.6%	8.3%	9.2%	3.9%	4.57	4.79	-0.2
2022	30	MLB RAW	Cincinnati Reds	46	10	81	336	59	28	14	22%	1.8	17.6%	8.3%	9.2%	4.2%	4.57	4.93	-0.4
2023	31	MIX MLE	2 levels	29	13	68	332	53	31	12	45%	2.3	14.2%	8.8%	5.4%	3.7%	8.74	5.02	-0.4
2023	31	MIX RAW	2 levels	29	13	68	332	53	31	12	45%	2.3	16.0%	9.3%	6.6%	3.6%	8.74	5.20	-0.5
2024	32	AAA MLE	Indianapolis Indians	26	23	103	446	77	43	15	88%	3.9	14.8%	8.7%	6.2%	4.1%	4.73	5.18	—
2024	32	AAA MLE	Indianapolis Indians	26	23	103	446	77	43	15	88%	3.9	14.8%	8.7%	6.2%	4.1%	4.73	5.18	—
2024	32	AAA RAW	Indianapolis Indians	26	23	103	446	77	43	15	88%	3.9	17.3%	9.6%	7.6%	3.4%	4.73	4.76	—
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			208	35	425	1805	331	139	66	17%	2.0	18.3%	7.7%	10.6%	3.7%	4.43	4.54	+0.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	PIT	6	0	6	25	5	2	1	0%	1.0	20.8%	9.1%	11.8%	3.2%	4.61	4.60	-0.0
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	PIT	41	0	43	180	36	17	6	0%	1.0	19.8%	9.7%	10.1%	3.5%	4.83	4.97	-0.2
2029	37	MLB MLE	PIT	29	0	31	130	24	13	5	0%	1.1	18.7%	10.3%	8.4%	3.9%	5.16	5.37	-0.3
2030	38	MLB MLE	PIT	22	0	24	100	17	11	4	0%	1.1	17.5%	11.0%	6.5%	4.4%	5.55	5.81	-0.3
2028	36	MLB CTX	PIT	41	0	43	182	36	18	6	0%	1.0	19.8%	9.7%	10.1%	3.5%	4.83	4.97	-0.2
2029	37	MLB CTX	PIT	30	0	31	131	24	13	5	0%	1.0	18.7%	10.3%	8.4%	3.9%	5.16	5.37	-0.3
2030	38	MLB CTX	PIT	23	0	24	101	18	11	4	0%	1.0	17.5%	11.0%	6.5%	4.4%	5.55	5.81	-0.3
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		301	35	522	2215	409	181	82	12%	1.7	18.4%	8.2%	10.3%	3.7%	4.55	4.61	-0.3
/ 200 IP	—	RAW		115	13	200	848	157	69	31	12%	1.7	18.4%	8.2%	10.3%	3.7%	4.55	4.61	-0.1
Career	—	MLE		319	125	903	3844	686	291	114	39%	2.8	17.8%	7.6%	10.3%	3.0%	4.41	4.19	-0.8
/ 200 IP	—	MLE		71	28	200	852	152	64	25	39%	2.8	17.8%	7.6%	10.3%	3.0%	4.41	4.19	-0.3

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.

2024 AAAIndianapolis Indians · 103 IPFIP 4.71 → 5.18 (+0.47)K% 17.3% → 14.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.26%	—	×0.860	14.85%
BB% (unintentional) /BF	9.64%	—	×0.900	8.68%
HR / BF /BF	3.36%	÷0.909	×1.100	4.07%

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 82).

Result: FIP 4.71 → 5.18, 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 AAARochester Red Wings · 42 IPFIP 5.00 → 5.23 (+0.23)K% 21.0% → 18.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.00%	—	×0.860	18.06%
BB% (unintentional) /BF	9.50%	—	×0.900	8.55%
HR / BF /BF	4.50%	÷1.058	×1.100	4.68%

Result: FIP 5.00 → 5.23, 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 AAAScranton/Wilkes-Barre RailRiders · 26 IPFIP 2.07 → 2.37 (+0.30)K% 24.5% → 21.6%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.51%	—	×0.880	21.57%
BB% (unintentional) /BF	3.92%	—	×1.050	4.12%
HR / BF /BF	0.98%	÷1.046	×1.100	1.03%

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 109).

Result: FIP 2.07 → 2.37, 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 AATrenton Thunder · 10 IPFIP 0.70 → 1.26 (+0.55)K% 32.4% → 26.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.43%	—	×0.810	26.27%
BB% (unintentional) /BF	2.70%	—	×1.100	2.97%
HR / BF /BF	0.00%	÷1.034	×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 107).

Result: FIP 0.70 → 1.26, 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 AAAScranton/Wilkes-Barre RailRiders · 78 IPFIP 3.55 → 3.67 (+0.12)K% 20.6% → 18.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.55%	—	×0.880	18.09%
BB% (unintentional) /BF	7.98%	—	×1.050	8.37%
HR / BF /BF	2.15%	÷1.247	×1.100	1.89%

Result: FIP 3.55 → 3.67, 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 AAAScranton/Wilkes-Barre RailRiders · 77 IPFIP 3.58 → 4.00 (+0.42)K% 22.3% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.33%	—	×0.880	19.65%
BB% (unintentional) /BF	7.44%	—	×1.050	7.82%
HR / BF /BF	2.59%	÷0.996	×1.100	2.86%

Result: FIP 3.58 → 4.00, 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 AABinghamton Mets · 77 IPFIP 2.46 → 2.87 (+0.41)K% 19.2% → 15.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.18%	—	×0.810	15.54%
BB% (unintentional) /BF	5.03%	—	×1.100	5.53%
HR / BF /BF	0.63%	÷1.088	×1.200	0.69%

Result: FIP 2.46 → 2.87, 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 AAAToledo Mud Hens · 62 IPFIP 3.19 → 3.63 (+0.44)K% 20.1% → 17.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.07%	—	×0.880	17.66%
BB% (unintentional) /BF	6.57%	—	×1.050	6.90%
HR / BF /BF	1.73%	÷0.911	×1.100	2.09%

Result: FIP 3.19 → 3.63, 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
Luis Cessa (OGILVIE)	—	—	—	—	—	3.2%	—	5.30	18.9%	8.7%	97	+0.70	—	—	—
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	68
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	68
Tiago Da Silva R 2019 · 3.95 FIP	34	SW	3.95	15.0%	7.2%	2.2%	.365	—	—	—	—	—	2.21	287	67
Jesse Estrada R 2019 · 4.34 FIP	35	SP-	4.34	14.4%	6.6%	2.9%	.312	—	—	—	—	—	3.80	257	66
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	62
Brian Schlitter R 2021 · 4.70 FIP	35	SU	4.70	14.9%	9.1%	3.1%	.318	—	—	—	—	—	3.27	300	62
Daniel Guerrero R 2019 · 5.59 FIP	34	SW	5.59	14.7%	11.1%	4.2%	.353	—	—	—	—	—	4.61	269	61
Fernando Rodriguez Jr. R 2019 · 5.11 FIP	35	SW	5.11	25.0%	11.2%	4.9%	.312	—	—	—	—	—	3.34	335	60
Mark Serrano R 2019 · 5.25 FIP	33	SU	5.25	20.2%	7.9%	5.2%	.289	—	—	—	—	—	3.16	120	56
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	56
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	55
Yohan Pino R 2017 · 5.33 FIP	33	SP-	5.33	15.2%	3.6%	5.6%	.296	—	—	—	—	—	3.84	193	55
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	54
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	53
Toby Borland R 2004 · 4.28 FIP	35	SU	4.28	20.9%	8.1%	3.5%	.259	—	—	—	—	—	2.92	248	53
Spencer Patton R 2023 · 4.33 FIP	35	SU	4.33	18.9%	9.7%	2.9%	.295	—	—	—	—	—	2.79	239	52
Hirokazu Sawamura R 2022 · 4.02 FIP	34	SU	4.02	18.1%	11.3%	1.9%	.270	—	—	—	—	—	4.02	104	52
Zack Segovia R 2019 · 4.55 FIP	36	SU	4.55	17.6%	13.6%	2.2%	.353	—	—	—	—	—	3.33	144	52
Jim Miller R 2016 · 5.43 FIP	34	SU	5.43	20.4%	14.1%	4.1%	.347	—	—	—	—	—	3.95	140	52
Leo Rosales R 2015 · 3.99 FIP	34	SU	3.99	18.1%	10.8%	1.9%	.261	4.24	7.8%	-0.4%	22	+0.25	3.85	130	51

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	49	18.7%	8.3%	1.41	4.56
Lance Painter ('03)	2.2	56	16.7%	9.0%	1.32	5.24
Randy St. Claire ('84-'94, -0 WAR*)	1.3	50	19.0%*	8.4%*	1.39*	4.61*
Matt Wisler ('22)	2.5	63	20.1%	7.6%	1.40	4.59
Russ Springer ('10)	4.3	66	20.9%	9.4%	1.27	4.52
Dennis Cook ('88-'02, 7 WAR*)	5.1	72	21.4%*	8.6%*	1.39*	4.02*
Bob Patterson ('85-'98, 4 WAR*)	3.1	51	23.0%*	6.7%*	1.32*	4.33*