Matthew Boyd

P Chicago Cubs

Age 36 LHP 6'3" / 223 lbs MLB peak 1.3 · 2027

Overview

MLBSP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	33	MIX4 levels	13	13	61	2.05	2.30	30.0%	6.2%	2.4	K 27.7% · BB 7.8% · FIP 3.08
2025	34	MLBCHC	31	31	180	3.21	3.46	21.4%	5.8%	4.3
2026	35	MIX3 levels	26	26	143	4.08	3.99	19.7%	7.1%	2.4	K 18.6% · BB 7.3% · FIP 4.25
2027PROJ	36	MLBCHC	17	17	103	4.07	4.34	19.7%	6.8%	1.3
2028PROJ	37	MLBCHC	11	11	65	4.47	4.74	18.6%	7.5%	0.5
2029PROJ	38	MLBCHC	6	6	35	4.89	5.17	17.4%	8.2%	0.1

Last 3 seasons WAR 9.1 · Next 3 projected WAR 1.9Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs34

FB Velo26

K%25

Form3.64 ERA (proj 4.07) · K 16.8% (proj 19.7%) · BB 7.6% (proj 6.8%) ▼ last 60d · 30 IP through Oct 27

CompsTsuyoshi Wada (age 34) · Mauricio Lara (age 36) · Arturo Lopez (age 36)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 100108vR 116

ERA4.07

FIP4.34

K%19.7%

BB%6.8%

K-BB%12.8%

Proj IP103

Value2027 · WAR

Runs saved vs league avg · 103 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p2692.8

Whiff%

p2922.3%

p2518.5%

xwOBA vs

p340.330

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	3 levels	32	31	172	687	151	47	25	97%	5.4	19.4%	7.2%	12.2%	3.9%	3.61	4.53	+1.7
2015	24	MIX RAW	3 levels	32	31	172	687	151	47	25	97%	5.4	22.0%	6.8%	15.1%	3.6%	3.61	4.05	+2.8
2016	25	MIX MLE	2 levels	31	29	161	671	139	46	22	94%	5.2	19.7%	7.0%	12.7%	3.6%	3.63	4.27	+2.1
2016	25	MIX RAW	2 levels	31	29	161	671	139	46	22	94%	5.2	20.7%	6.9%	13.9%	3.3%	3.63	4.00	+2.7
2017	26	MIX MLE	2 levels	34	33	186	804	163	63	25	97%	5.5	19.5%	7.9%	11.6%	3.3%	4.60	4.24	+2.5
2017	26	MIX RAW	2 levels	34	33	186	804	163	63	25	97%	5.5	20.3%	7.8%	12.4%	3.1%	4.60	4.11	+2.8
2018	27	MLB MLE	Detroit Tigers	31	31	170	709	159	51	27	100%	5.5	22.4%	7.2%	15.2%	4.0%	4.39	4.30	+2.2
2018	27	MLB RAW	Detroit Tigers	31	31	170	709	159	51	27	100%	5.5	22.4%	7.2%	15.2%	3.8%	4.39	4.19	+2.4
2019	28	MLB MLE	Detroit Tigers	32	32	185	788	238	49	39	100%	5.8	30.2%	6.2%	24.0%	5.2%	4.56	4.17	+2.7
2019	28	MLB RAW	Detroit Tigers	32	32	185	788	238	49	39	100%	5.8	30.2%	6.2%	24.0%	4.9%	4.56	4.06	+2.9
2020	29	MLB MLE	Detroit Tigers	12	12	60	271	60	22	15	100%	5.0	22.1%	8.1%	14.0%	5.8%	6.71	5.43	-0.0
2020	29	MLB RAW	Detroit Tigers	12	12	60	271	60	22	15	100%	5.0	22.1%	8.1%	14.0%	5.5%	6.71	5.44	-0.0
2021	30	MIX MLE	2 levels	18	18	88	368	78	23	9	100%	4.9	19.9%	6.8%	13.1%	2.8%	3.48	3.82	+1.7
2021	30	MIX RAW	2 levels	18	18	88	368	78	23	9	100%	4.9	21.2%	6.2%	14.9%	2.4%	3.48	3.44	+2.1
2022	31	MIX MLE	2 levels	16	0	21	82	27	5	1	0%	1.3	24.5%	9.4%	15.1%	0.0%	1.69	2.22	+0.6
2022	31	MIX RAW	2 levels	16	0	21	82	27	5	1	0%	1.3	32.9%	6.1%	26.8%	1.2%	1.69	1.88	+0.7
2023	32	MLB MLE	Detroit Tigers	15	15	71	303	73	25	11	100%	4.7	24.1%	8.3%	15.8%	3.8%	5.45	4.19	+1.0
2023	32	MLB RAW	Detroit Tigers	15	15	71	303	73	25	11	100%	4.7	24.1%	8.3%	15.8%	3.6%	5.45	4.11	+1.1
2024	33	MIX MLE	4 levels	13	13	61	243	73	15	4	100%	4.7	27.7%	7.8%	19.9%	2.4%	2.05	3.08	+1.8
2024	33	MIX MLE	4 levels	13	13	61	243	73	15	4	100%	4.7	27.7%	7.8%	19.9%	2.4%	2.05	3.08	+1.8
2024	33	MIX RAW	4 levels	13	13	61	243	73	15	4	100%	4.7	30.0%	6.2%	23.9%	1.6%	2.05	2.30	+2.4
2025	34	MLB MLE	Chicago Cubs	31	31	180	718	154	42	19	100%	5.8	21.4%	5.8%	15.6%	2.7%	3.21	3.54	+4.1
2025	34	MLB MLE	Chicago Cubs	31	31	180	718	154	42	19	100%	5.8	21.4%	5.8%	15.6%	2.7%	3.21	3.54	+4.1
2025	34	MLB RAW	Chicago Cubs	31	31	180	718	154	42	19	100%	5.8	21.4%	5.8%	15.6%	2.6%	3.21	3.46	+4.3
2026	35	MIX MLE	3 levels	26	26	143	589	116	42	18	100%	5.5	18.6%	7.3%	11.4%	3.3%	4.08	4.25	+1.9
2026	35	MIX MLE	3 levels	26	26	143	589	116	42	18	100%	5.5	18.6%	7.3%	11.4%	3.3%	4.08	4.25	+1.9
2026	35	MIX RAW	3 levels	26	26	143	589	116	42	18	100%	5.5	19.7%	7.1%	12.6%	3.1%	4.08	3.99	+2.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			235	221	1215	5134	1141	368	191	94%	5.2	22.2%	7.2%	15.1%	3.7%	4.50	4.17	+17.6
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	36	MLB	CHC	17	17	103	435	86	30	13	100%	6.1	19.7%	6.8%	12.8%	3.0%	4.07	4.34	+1.3
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	37	MLB MLE	CHC	11	11	65	275	51	21	9	100%	5.9	18.6%	7.5%	11.1%	3.4%	4.47	4.74	+0.5
2029	38	MLB MLE	CHC	6	6	35	149	26	12	6	100%	5.9	17.4%	8.2%	9.3%	3.9%	4.89	5.17	+0.1
2028	37	MLB CTX	CHC	11	11	65	274	51	21	9	100%	5.9	18.6%	7.5%	11.1%	3.4%	4.47	4.74	+0.5
2029	38	MLB CTX	CHC	6	6	35	148	26	12	6	100%	5.8	17.4%	8.2%	9.3%	3.9%	4.89	5.17	+0.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		252	238	1315	5559	1218	401	206	94%	5.2	21.9%	7.2%	14.7%	3.7%	4.51	4.20	+18.6
/ 200 IP	—	RAW		38	36	200	845	185	61	31	94%	5.2	21.9%	7.2%	14.7%	3.7%	4.51	4.20	+2.8
Career	—	MLE		291	271	1500	6233	1376	443	228	93%	5.2	22.1%	7.1%	15.0%	3.7%	4.05	4.15	+18.1
/ 200 IP	—	MLE		39	36	200	831	184	59	30	93%	5.2	22.1%	7.1%	15.0%	3.7%	4.05	4.15	+2.8

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.

2026 AAAIowa Cubs · 13 IPFIP 4.79 → 5.30 (+0.51)K% 23.6% → 20.3%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	23.64%	—	×0.860	20.33%
BB% (unintentional) /BF	5.45%	—	×0.900	4.91%
HR / BF /BF	5.45%	÷1.000	×1.100	6.00%

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 100).

Result: FIP 4.79 → 5.30, 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 AAAColumbus Clippers · 10 IPFIP -0.07 → 0.38 (+0.44)K% 37.5% → 32.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	37.50%	—	×0.860	32.25%
BB% (unintentional) /BF	0.00%	—	×0.900	0.00%
HR / BF /BF	0.00%	÷1.060	×1.100	0.00%

Result: FIP -0.07 → 0.38, 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).

2024 AAAkron RubberDucks · 9 IPFIP 1.38 → 1.83 (+0.44)K% 25.0% → 20.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.00%	—	×0.810	20.25%
BB% (unintentional) /BF	3.12%	—	×1.100	3.44%
HR / BF /BF	0.00%	÷0.966	×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 93).

Result: FIP 1.38 → 1.83, 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).

2022 AAATacoma Rainiers · 8 IPFIP 0.92 → 1.33 (+0.42)K% 48.3% → 42.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	48.28%	—	×0.880	42.48%
BB% (unintentional) /BF	0.00%	—	×1.050	0.00%
HR / BF /BF	3.45%	÷1.144	×1.100	3.32%

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 129).

Result: FIP 0.92 → 1.33, 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).

2021 AAAToledo Mud Hens · 9 IPFIP 0.10 → 0.46 (+0.36)K% 35.5% → 31.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	35.48%	—	×0.880	31.23%
BB% (unintentional) /BF	0.00%	—	×1.050	0.00%
HR / BF /BF	0.00%	÷1.052	×1.100	0.00%

Result: FIP 0.10 → 0.46, 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 AAAToledo Mud Hens · 51 IPFIP 3.61 → 4.01 (+0.41)K% 26.6% → 23.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	26.63%	—	×0.880	23.44%
BB% (unintentional) /BF	6.53%	—	×1.050	6.86%
HR / BF /BF	3.52%	÷1.049	×1.100	3.69%

Result: FIP 3.61 → 4.01, 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).

2016 AAAToledo Mud Hens · 64 IPFIP 3.13 → 3.63 (+0.50)K% 22.0% → 19.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	22.01%	—	×0.880	19.37%
BB% (unintentional) /BF	6.56%	—	×1.050	6.89%
HR / BF /BF	1.93%	÷0.899	×1.100	2.36%

Result: FIP 3.13 → 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).

2015 AANew Hampshire Fisher Cats · 74 IPFIP 2.37 → 2.91 (+0.53)K% 25.6% → 20.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	25.64%	—	×0.810	20.77%
BB% (unintentional) /BF	6.59%	—	×1.100	7.25%
HR / BF /BF	1.10%	÷1.130	×1.200	1.17%

Result: FIP 2.37 → 2.91, 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
Matthew Boyd (OGILVIE)	—	—	—	—	—	3.0%	—	4.89	18.2%	10.5%	101	+0.55	—	—	—
Tsuyoshi Wada L 2015 · 3.66 FIP	34	SP	3.66	16.8%	7.1%	2.0%	.307	—	—	—	—	—	3.60	188	58
Mauricio Lara L 2015 · 3.62 FIP	36	SP	3.62	13.9%	6.9%	1.5%	.310	3.69	12.1%	5.1%	220	+0.07	3.62	444	57
Arturo Lopez L 2019 · 5.46 FIP	36	SP	5.46	12.2%	6.8%	4.6%	.320	—	—	—	—	—	4.34	445	54
Juan Antonio Pena L 2015 · 4.20 FIP	37	SP-	4.20	13.6%	9.9%	1.8%	.287	—	—	—	—	—	4.20	149	50
Marlon Arias L 2019 · 4.83 FIP	34	SP	4.83	19.8%	11.2%	3.6%	.273	—	—	—	—	—	3.73	154	50
Francisley Bueno L 2017 · 4.44 FIP	36	SP	4.44	11.7%	8.4%	2.3%	.351	—	—	—	—	—	2.43	188	50
Jorge Castillo L 2017 · 4.97 FIP	35	SP	4.97	11.2%	6.7%	3.6%	.337	4.91	19.6%	11.5%	119	-0.06	4.50	408	44
Kenshin Kawakami R 2010 · 4.06 FIP	35	SP	4.06	15.1%	5.6%	2.8%	.293	—	—	—	—	—	3.97	244	33
Hiroki Kuroda R 2011 · 3.64 FIP	36	SP	3.64	19.2%	5.1%	2.8%	.282	3.50	18.2%	13.3%	620	-0.14	2.96	1,319	33
Jarrett Grube R 2017 · 4.41 FIP	35	SP	4.41	15.8%	8.1%	3.0%	.296	—	—	—	—	—	3.75	393	30
Yasutomo Kubo R 2019 · 3.98 FIP	38	SP	3.98	20.4%	4.7%	3.6%	.327	—	—	—	—	—	3.98	152	29
Jose Contreras R 2008 · 4.08 FIP	36	SP	4.08	13.4%	6.7%	2.3%	.291	3.52	20.1%	12.1%	202	-0.56	2.43	1,279	29
Yoanner Negrin R 2019 · 3.87 FIP	35	SP	3.87	17.5%	8.1%	2.2%	.280	—	—	—	—	—	3.15	485	28
Jorge Martinez R 2019 · 3.71 FIP	34	SP	3.71	16.3%	5.7%	2.3%	.348	—	—	—	—	—	3.53	214	28
Walter Silva R 2015 · 4.15 FIP	38	SP	4.15	14.8%	7.4%	2.5%	.311	3.91	10.4%	3.7%	209	-0.24	3.67	453	27
Austin Bibens-Dirkx R 2021 · 4.20 FIP	36	SP-	4.20	15.8%	5.6%	3.1%	.333	—	—	—	—	—	3.97	567	26
Frankie De La Cruz R 2019 · 4.38 FIP	35	SP-	4.38	16.2%	8.0%	3.0%	.322	—	—	—	—	—	2.87	262	26
Jose Oyervides R 2017 · 5.38 FIP	35	SP	5.38	18.0%	6.3%	5.5%	.254	5.66	16.3%	5.1%	115	+0.28	3.01	407	26
Jesse Estrada R 2019 · 4.34 FIP	35	SP-	4.34	14.4%	6.6%	2.9%	.312	—	—	—	—	—	3.80	257	24
Pablo Ortega R 2015 · 4.52 FIP	38	SP	4.52	8.6%	7.3%	2.2%	.291	4.24	11.5%	6.0%	236	-0.28	4.01	375	24

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	7.1	101	21.7%	7.3%	1.42	4.48
Derek Holland ('21)	7.3	113	19.7%	8.4%	1.35	4.62
Chase Anderson ('24)	5.5	101	19.7%	8.1%	1.48	4.41
Mark Leiter ('90-'01, 5 WAR*)	5.9	118	20.7%*	7.8%*	1.36*	4.61*
Marco Estrada ('19)	6.3	139	21.0%	7.6%	1.43	4.29
Larry McWilliams ('78-'90, 10 WAR*)	7.8	130	19.0%*	8.1%*	1.24*	4.56*
Don Robinson ('78-'92, 16 WAR*)	9.8	131	20.5%*	7.8%*	1.26*	4.41*