Tim Mayza

P Philadelphia Phillies

Age 35 LHP 6'3" / 215 lbs MLB peak 0.3 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	32	MIX2 levels	59	0	51	5.65	3.90	15.7%	7.0%	0.4	K 14.4% · BB 6.7% · FIP 4.05
2025	33	MIX3 levels	19	1	21	3.37	3.05	22.6%	5.4%	0.4	K 20.3% · BB 6.8% · FIP 3.63
2026	34	MLBPHI	60	6	67	3.48	3.26	23.6%	5.9%	1.0
2027PROJ	35	MLBPHI	51	0	54	4.07	3.97	21.5%	6.4%	0.4
2028PROJ	36	MLBPHI	31	0	33	4.23	4.34	20.5%	7.0%	0.1
2029PROJ	37	MLBPHI	17	0	18	4.54	4.74	19.4%	7.7%	-0.0

Last 3 seasons WAR 1.8 · Next 3 projected WAR 0.4Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs75

K%58

FB Velo48

Form2.37 ERA (proj 4.07) · K 21.1% (proj 21.5%) · BB 9.2% (proj 6.4%) ▼ last 90d · 19 IP through Oct 27

CompsHeriberto Ruelas (age 35) · Brian Shouse (age 35) · Hideki Okajima (age 34)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8694vR 102

ERA4.07

FIP3.97

K%21.5%

BB%6.4%

K-BB%15.1%

Proj IP54

Value2027 · WAR

Runs saved vs league avg · 54 IP

Grades · Statcast · Contract

Scouting Grades

STUF4040

CTRL5555

CMD3535

K†5050

Statcast Percentiles

FB Velo

p4894.4

Whiff%

p2822.2%

p5823.6%

xwOBA vs

p750.288

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	A MLE	Lansing Lugnuts	26	1	56	236	62	26	0	4%	2.1	19.4%	13.0%	6.4%	0.0%	3.07	3.11	—
2015	23	A RAW	Lansing Lugnuts	26	1	56	236	62	26	0	4%	2.1	26.3%	11.0%	15.3%	0.0%	3.07	2.27	—
2016	24	MIX MLE	2 levels	42	0	64	274	65	30	1	0%	1.5	18.5%	12.3%	6.1%	0.5%	2.25	3.38	—
2016	24	MIX RAW	2 levels	42	0	64	274	65	30	1	0%	1.5	23.7%	10.9%	12.8%	0.4%	2.25	2.68	—
2017	25	MIX MLE	3 levels	59	0	70	306	85	24	8	0%	1.2	24.5%	8.4%	16.2%	2.7%	4.13	3.55	+0.8
2017	25	MIX RAW	3 levels	59	0	70	306	85	24	8	0%	1.2	27.8%	7.8%	19.9%	2.6%	4.13	3.19	+1.1
2018	26	MIX MLE	2 levels	57	0	61	261	76	20	5	0%	1.1	27.5%	7.9%	19.6%	1.9%	3.82	2.84	+1.3
2018	26	MIX RAW	2 levels	57	0	61	261	76	20	5	0%	1.1	29.1%	7.7%	21.5%	1.9%	3.82	2.66	+1.4
2019	27	MLB MLE	Toronto Blue Jays	68	0	51	227	55	25	8	0%	0.8	24.2%	11.0%	13.2%	3.5%	4.91	4.38	+0.1
2019	27	MLB RAW	Toronto Blue Jays	68	0	51	227	55	25	8	0%	0.8	24.2%	11.0%	13.2%	3.5%	4.91	4.44	+0.1
2021	29	MIX MLE	2 levels	62	0	54	214	57	12	6	0%	0.9	27.1%	5.7%	21.4%	2.4%	3.50	2.84	+1.1
2021	29	MIX RAW	2 levels	62	0	54	214	57	12	6	0%	0.9	26.6%	5.6%	21.0%	2.8%	3.50	3.10	+0.9
2022	30	MIX MLE	2 levels	65	1	50	199	45	11	7	2%	0.8	22.8%	5.2%	17.6%	3.6%	3.42	3.82	+0.4
2022	30	MIX RAW	2 levels	65	1	50	199	45	11	7	2%	0.8	22.6%	5.5%	17.1%	3.5%	3.42	3.78	+0.5
2023	31	MLB MLE	Toronto Blue Jays	69	0	53	215	53	12	2	0%	0.8	24.7%	5.6%	19.1%	0.9%	1.52	2.24	+1.5
2023	31	MLB RAW	Toronto Blue Jays	69	0	53	215	53	12	2	0%	0.8	24.7%	5.6%	19.1%	0.9%	1.52	2.28	+1.5
2024	32	MIX MLE	2 levels	59	0	51	230	36	16	5	0%	0.9	14.4%	6.7%	7.7%	2.4%	5.65	4.05	+0.3
2024	32	MIX MLE	2 levels	59	0	51	230	36	16	5	0%	0.9	14.4%	6.7%	7.7%	2.4%	5.65	4.05	+0.3
2024	32	MIX RAW	2 levels	59	0	51	230	36	16	5	0%	0.9	15.7%	7.0%	8.7%	2.2%	5.65	3.90	+0.4
2025	33	MIX MLE	3 levels	19	1	21	93	21	5	2	5%	1.1	20.3%	6.8%	13.5%	2.5%	3.37	3.63	+0.2
2025	33	MIX MLE	3 levels	19	1	21	93	21	5	2	5%	1.1	20.3%	6.8%	13.5%	2.5%	3.37	3.63	+0.2
2025	33	MIX RAW	3 levels	19	1	21	93	21	5	2	5%	1.1	22.6%	5.4%	17.2%	2.2%	3.37	3.05	+0.4
2026	34	MLB MLE	Philadelphia Phillies	60	6	67	271	64	16	7	10%	1.1	23.6%	5.9%	17.7%	2.4%	3.48	3.18	+1.1
2026	34	MLB MLE	Philadelphia Phillies	60	6	67	271	64	16	7	10%	1.1	23.6%	5.9%	17.7%	2.4%	3.48	3.18	+1.1
2026	34	MLB RAW	Philadelphia Phillies	60	6	67	271	64	16	7	10%	1.1	23.6%	5.9%	17.7%	2.6%	3.48	3.26	+1.0
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			442	6	386	1615	383	107	42	1%	0.9	23.7%	6.6%	17.1%	2.6%	3.80	3.36	+5.4
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	35	MLB	PHI	51	0	54	228	49	15	6	0%	1.1	21.5%	6.4%	15.1%	2.7%	4.07	3.97	+0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	36	MLB MLE	PHI	31	0	33	138	28	10	4	0%	1.1	20.5%	7.0%	13.5%	3.1%	4.23	4.34	+0.1
2029	37	MLB MLE	PHI	17	0	18	74	14	6	3	0%	1.0	19.4%	7.7%	11.7%	3.5%	4.54	4.74	-0.0
2030	38	MLB MLE	PHI	8	0	8	35	6	3	1	0%	1.0	18.3%	8.4%	9.9%	4.0%	4.92	5.18	-0.1
2028	36	MLB CTX	PHI	31	0	33	139	29	10	4	0%	1.1	20.5%	7.0%	13.5%	3.1%	4.23	4.34	+0.1
2029	37	MLB CTX	PHI	17	0	18	76	15	6	3	0%	1.1	19.4%	7.7%	11.7%	3.5%	4.54	4.74	-0.0
2030	38	MLB CTX	PHI	8	0	8	34	6	3	1	0%	1.0	18.3%	8.4%	9.9%	4.0%	4.92	5.18	-0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		498	6	444	1863	432	125	50	1%	0.9	23.2%	6.7%	16.5%	2.7%	3.89	3.48	+5.7
/ 200 IP	—	RAW		224	3	200	838	194	56	23	1%	0.9	23.2%	6.7%	16.5%	2.7%	3.89	3.48	+2.6
Career	—	MLE		586	9	599	2526	571	208	51	2%	1.0	22.6%	8.2%	14.4%	2.0%	3.55	3.35	+5.4
/ 200 IP	—	MLE		196	3	200	843	191	69	17	2%	1.0	22.6%	8.2%	14.4%	2.0%	3.55	3.35	+2.4

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.

2024 AAAScranton/Wilkes-Barre RailRiders · 8 IPFIP 2.26 → 2.42 (+0.16)K% 22.9% → 19.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.86%	—	×0.860	19.66%
BB% (unintentional) /BF	8.57%	—	×0.900	7.71%
HR / BF /BF	0.00%	÷1.045	×1.100	0.00%

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 109).

Result: FIP 2.26 → 2.42, 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 AAABuffalo Bisons · 26 IPFIP 2.49 → 2.89 (+0.41)K% 32.7% → 28.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	32.73%	—	×0.880	28.80%
BB% (unintentional) /BF	9.09%	—	×1.050	9.55%
HR / BF /BF	1.82%	÷1.080	×1.100	1.85%

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 116).

Result: FIP 2.49 → 2.89, 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 AANew Hampshire Fisher Cats · 33 IPFIP 3.68 → 4.31 (+0.63)K% 28.8% → 23.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	28.77%	—	×0.810	23.30%
BB% (unintentional) /BF	8.90%	—	×1.100	9.79%
HR / BF /BF	3.42%	÷1.165	×1.200	3.53%

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 133).

Result: FIP 3.68 → 4.31, 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 AAABuffalo Bisons · 19 IPFIP 2.53 → 2.78 (+0.25)K% 19.8% → 17.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.75%	—	×0.880	17.38%
BB% (unintentional) /BF	8.64%	—	×1.050	9.07%
HR / BF /BF	0.00%	÷0.903	×1.100	0.00%

Result: FIP 2.53 → 2.78, 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 A+Dunedin Blue Jays · 49 IPFIP 2.12 → 2.88 (+0.75)K% 26.1% → 20.1%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.13%	—	×0.770	20.12%
BB% (unintentional) /BF	7.54%	—	×1.150	8.67%
HR / BF /BF	0.50%	÷0.912	×1.250	0.69%

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 2.12 → 2.88, 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 AANew Hampshire Fisher Cats · 15 IPFIP 4.17 → 4.70 (+0.53)K% 17.3% → 14.0%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	17.33%	—	×0.810	14.04%
BB% (unintentional) /BF	20.00%	—	×1.100	22.00%
HR / BF /BF	0.00%	÷1.292	×1.200	0.00%

Result: FIP 4.17 → 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).

2015 ALansing Lugnuts · 56 IPFIP 2.28 → 3.11 (+0.83)K% 26.3% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.27%	—	×0.740	19.44%
BB% (unintentional) /BF	11.02%	—	×1.180	13.00%
HR / BF /BF	0.00%	÷1.007	×1.300	0.00%

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 101).

Result: FIP 2.28 → 3.11, 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
Tim Mayza (OGILVIE)	—	—	3.97	21.5%	6.4%	2.7%	—	4.48	20.1%	12.9%	50	+0.51	—	—	—
Heriberto Ruelas L 2017 · 3.42 FIP	35	SU	3.42	17.2%	6.7%	1.7%	.294	—	—	—	—	—	2.95	117	74
Brian Shouse L 2004 · 3.47 FIP	35	SU	3.47	18.5%	8.2%	1.6%	.250	3.57	14.8%	8.9%	139	+0.10	2.32	339	73
Hideki Okajima L 2010 · 4.27 FIP	34	SU	4.27	15.5%	7.0%	2.9%	.333	—	—	—	—	—	3.05	238	71
Raul Valdes L 2013 · 4.07 FIP	35	SU	4.07	24.3%	5.3%	4.3%	.350	—	—	—	—	—	2.29	160	70
Clay Rapada L 2015 · 3.84 FIP	34	SU	3.84	14.3%	8.0%	1.7%	.299	—	—	—	—	—	3.00	120	65
Hisanori Takahashi L 2010 · 3.50 FIP	35	SW	3.50	22.1%	7.0%	2.5%	.292	3.81	21.1%	15.0%	118	+0.31	3.50	240	65
Juan Pablo Perez L 2013 · 3.68 FIP	34	SU	3.68	25.2%	11.5%	2.3%	.250	3.68	26.5%	11.2%	51	-0.00	1.75	104	64
John Bale L 2009 · 4.31 FIP	35	SU	4.31	17.6%	11.8%	2.2%	.333	—	—	—	—	—	2.49	168	63
Brandon Mann L 2018 · 3.59 FIP	34	SU	3.59	17.8%	11.4%	1.0%	.232	—	—	—	—	—	3.59	190	60
Tim Byrdak L 2009 · 5.07 FIP	35	SU	5.07	22.2%	13.8%	3.8%	.185	3.48	23.8%	13.5%	107	-1.59	2.85	320	59
Brooks Raley L 2023 · 3.03 FIP	35	SU	3.03	25.8%	9.3%	1.7%	.268	3.00	23.6%	15.9%	78	-0.03	1.74	294	58
Mike Zagurski L 2018 · 3.12 FIP	35	SU	3.12	30.4%	9.3%	2.6%	.330	—	—	—	—	—	2.94	120	53
Scott Rice L 2015 · 3.67 FIP	33	SU	3.67	22.9%	17.2%	0.6%	.266	—	—	—	—	—	3.10	104	49
Jean Machi R 2017 · 4.12 FIP	35	SU	4.12	18.6%	6.6%	3.2%	.285	—	—	—	—	—	2.16	306	40
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	39
Yoshihisa Hirano R 2019 · 3.72 FIP	35	SU	3.72	26.2%	8.6%	3.2%	.303	5.02	17.5%	4.8%	12	+1.30	3.45	132	39
Tony Pena Jr. R 2016 · 2.87 FIP	35	SU	2.87	21.5%	4.3%	1.9%	.249	3.43	10.9%	6.6%	22	+0.56	2.80	128	37
Neil Wagner R 2017 · 3.11 FIP	33	SU	3.11	20.7%	8.6%	1.2%	.257	—	—	—	—	—	3.11	135	37
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	37
Jumbo Diaz R 2019 · 2.98 FIP	35	SU	2.98	25.0%	7.7%	1.8%	.290	—	—	—	—	—	2.80	319	37

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	2.5	45	23.0%	7.4%	1.05	3.91
Fernando Salas ('19)	2.4	54	23.1%	7.3%	1.05	3.91
Héctor Rondón ('20)	2.2	54	24.2%	7.4%	1.03	3.49
Jason Motte ('17)	2.0	50	23.0%	7.7%	1.11	3.30
Joe Hoerner ('63-'77, 8 WAR*)	2.8	51	23.2%*	7.9%*	1.21*	3.52*
Tony Fossas ('88-'99, 2 WAR*)	2.1	42	21.9%*	9.3%*	0.99*	3.88*
Tony Castillo ('88-'98, 4 WAR*)	2.6	59	18.5%*	7.3%*	0.98*	3.76*