Tyler Alexander

P Texas Rangers

Age 33 LHP 6'1" / 214 lbs MLB peak 0.4 · 2027

Overview

MLBRP40-MAN

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2024	30	MIX2 levels	30	14	143	4.96	4.53	20.3%	4.8%	0.0	K 19.5% · BB 4.7% · FIP 4.66
2025	31	MLBCWS	52	5	98	4.98	3.39	19.4%	5.9%	1.4
2026	32	MLBTEX	75	5	73	3.70	2.89	19.2%	7.1%	1.4
2027PROJ	33	MLBTEX	58	0	61	4.13	4.00	18.2%	6.4%	0.4
2028PROJ	34	MLBTEX	34	0	36	4.23	4.30	17.3%	6.9%	0.1
2029PROJ	35	MLBTEX	19	0	20	4.47	4.64	16.3%	7.5%	-0.0

Last 3 seasons WAR 2.8 · Next 3 projected WAR 0.5Projected rows: MLB playing time only, park and league context applied.

Contract

No contract data on file.

Skills · percentileStatcast 2026

xwOBA vs80

K%30

FB Velo14

Form4.60 ERA (proj 4.13) · K 13.8% (proj 18.2%) · BB 9.2% (proj 6.4%) ▼▼ last 60d · 16 IP through Oct 27

CompsColt Hynes (age 32) · Hisanori Takahashi (age 35) · Mark Hendrickson (age 33)

2027 FULL-SEASON PROJECTIONrates below = full-season projection

showingPROJ

wRC+ AGAINSTvL 8593vR 101

ERA4.13

FIP4.00

K%18.2%

BB%6.4%

K-BB%11.7%

Proj IP61

Value2027 · WAR

Runs saved vs league avg · 61 IP

Statcast · Contract

Statcast Percentiles

FB Velo

p1491.9

Whiff%

p1920.9%

p3019.2%

xwOBA vs

p800.283

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)
2016	22	MIX MLE	2 levels	25	24	136	545	105	20	11	96%	5.5	15.0%	4.2%	10.8%	2.2%	2.44	3.56	—
2016	22	MIX RAW	2 levels	25	24	136	545	105	20	11	96%	5.5	19.3%	3.7%	15.6%	2.0%	2.44	3.05	—
2017	23	AA MLE	Erie SeaWolves	27	26	138	614	120	22	20	96%	5.1	15.8%	3.9%	11.9%	3.1%	5.07	3.97	—
2017	23	AA RAW	Erie SeaWolves	27	26	138	614	120	22	20	96%	5.1	19.5%	3.6%	16.0%	3.3%	5.07	3.72	—
2018	24	MIX MLE	2 levels	26	24	140	611	95	22	16	92%	5.4	13.3%	3.9%	9.4%	3.0%	4.44	4.14	—
2018	24	MIX RAW	2 levels	26	24	140	611	95	22	16	92%	5.4	15.5%	3.6%	11.9%	2.6%	4.44	3.70	—
2019	25	MIX MLE	2 levels	33	24	152	668	155	30	27	73%	4.6	21.3%	4.7%	16.6%	4.7%	5.03	4.50	+1.6
2019	25	MIX RAW	2 levels	33	24	152	668	155	30	27	73%	4.6	23.2%	4.5%	18.7%	4.0%	5.03	3.96	+2.6
2020	26	MLB MLE	Detroit Tigers	14	2	36	152	34	9	8	14%	2.6	22.4%	5.9%	16.4%	5.5%	3.96	4.98	-0.2
2020	26	MLB RAW	Detroit Tigers	14	2	36	152	34	9	8	14%	2.6	22.4%	5.9%	16.4%	5.3%	3.96	4.83	-0.1
2021	27	MLB MLE	Detroit Tigers	41	15	106	451	87	28	16	37%	2.6	19.3%	6.2%	13.1%	3.7%	3.81	4.29	+0.3
2021	27	MLB RAW	Detroit Tigers	41	15	106	451	87	28	16	37%	2.6	19.3%	6.2%	13.1%	3.5%	3.81	4.21	+0.4
2022	28	MIX MLE	2 levels	32	18	110	468	69	25	19	56%	3.4	14.3%	5.6%	8.7%	4.4%	5.07	5.02	+0.5
2022	28	MIX RAW	2 levels	32	18	110	468	69	25	19	56%	3.4	14.7%	5.3%	9.4%	4.1%	5.07	4.77	+0.8
2023	29	MLB MLE	Detroit Tigers	25	1	44	181	44	5	8	4%	1.8	24.3%	2.8%	21.5%	4.6%	4.50	3.93	+0.3
2023	29	MLB RAW	Detroit Tigers	25	1	44	181	44	5	8	4%	1.8	24.3%	2.8%	21.5%	4.4%	4.50	3.80	+0.4
2024	30	MIX MLE	2 levels	30	14	143	606	123	29	28	47%	4.8	19.5%	4.7%	14.8%	4.8%	4.96	4.66	-0.2
2024	30	MIX MLE	2 levels	30	14	143	606	123	29	28	47%	4.8	19.5%	4.7%	14.8%	4.8%	4.96	4.66	-0.2
2024	30	MIX RAW	2 levels	30	14	143	606	123	29	28	47%	4.8	20.3%	4.8%	15.5%	4.6%	4.96	4.53	+0.0
2025	31	MLB MLE	Chicago White Sox	52	5	98	423	82	25	9	10%	1.9	19.4%	5.9%	13.5%	2.1%	4.98	3.36	+1.4
2025	31	MLB MLE	Chicago White Sox	52	5	98	423	82	25	9	10%	1.9	19.4%	5.9%	13.5%	2.1%	4.98	3.36	+1.4
2025	31	MLB RAW	Chicago White Sox	52	5	98	423	82	25	9	10%	1.9	19.4%	5.9%	13.5%	2.1%	4.98	3.39	+1.4
2026	32	MLB MLE	Texas Rangers	75	5	73	312	60	22	3	7%	1.0	19.2%	7.1%	12.2%	0.9%	3.70	2.86	+1.5
2026	32	MLB MLE	Texas Rangers	75	5	73	312	60	22	3	7%	1.0	19.2%	7.1%	12.2%	0.9%	3.70	2.86	+1.5
2026	32	MLB RAW	Texas Rangers	75	5	73	312	60	22	3	7%	1.0	19.2%	7.1%	12.2%	1.0%	3.70	2.89	+1.4
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			270	62	620	2636	505	143	94	23%	2.3	19.2%	5.4%	13.7%	3.6%	4.52	4.13	+3.0
▸ 2027 season — projected remainder (no 2027 stats recorded)
2027	33	MLB	TEX	58	0	61	258	47	17	6	0%	1.1	18.2%	6.4%	11.7%	2.3%	4.13	4.00	+0.4
▸ Career projection (2028+) — MLB context, MLB playing time (IP × MLB%)
2028	34	MLB MLE	TEX	35	0	36	153	26	11	4	0%	1.0	17.3%	6.9%	10.3%	2.6%	4.23	4.30	+0.1
2029	35	MLB MLE	TEX	19	0	20	86	14	6	2	0%	1.1	16.3%	7.5%	8.8%	2.9%	4.47	4.64	-0.0
2028	34	MLB CTX	TEX	34	0	36	152	26	11	4	0%	1.1	17.3%	6.9%	10.3%	2.6%	4.23	4.30	+0.1
2029	35	MLB CTX	TEX	19	0	20	84	14	6	2	0%	1.1	16.3%	7.5%	8.8%	2.9%	4.47	4.64	-0.0
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		324	62	676	2875	545	160	100	19%	2.1	19.0%	5.6%	13.4%	3.5%	4.50	4.12	+3.4
/ 200 IP	—	RAW		96	18	200	850	161	47	30	19%	2.1	19.0%	5.6%	13.4%	3.5%	4.50	4.12	+1.0
Career	—	MLE		380	158	1177	5031	894	245	176	42%	3.1	17.8%	4.9%	12.9%	3.5%	4.41	4.13	+3.1
/ 200 IP	—	MLE		65	27	200	855	152	42	30	42%	3.1	17.8%	4.9%	12.9%	3.5%	4.41	4.13	+0.9

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 AAADurham Bulls · 36 IPFIP 3.58 → 3.85 (+0.27)K% 21.9% → 18.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	21.85%	—	×0.860	18.79%
BB% (unintentional) /BF	3.97%	—	×0.900	3.58%
HR / BF /BF	3.31%	÷1.061	×1.100	3.43%

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 112).

Result: FIP 3.58 → 3.85, 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 AAAToledo Mud Hens · 9 IPFIP 3.10 → 3.41 (+0.31)K% 19.5% → 17.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.51%	—	×0.880	17.17%
BB% (unintentional) /BF	2.44%	—	×1.050	2.56%
HR / BF /BF	2.44%	÷1.026	×1.100	2.62%

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 3.10 → 3.41, 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).

2019 AAAToledo Mud Hens · 98 IPFIP 3.95 → 4.78 (+0.83)K% 24.9% → 21.9%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	24.94%	—	×0.880	21.95%
BB% (unintentional) /BF	5.31%	—	×1.050	5.58%
HR / BF /BF	4.16%	÷0.888	×1.100	5.15%

Result: FIP 3.95 → 4.78, 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).

2018 AAAToledo Mud Hens · 92 IPFIP 3.48 → 3.94 (+0.46)K% 15.0% → 13.2%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	15.04%	—	×0.880	13.23%
BB% (unintentional) /BF	3.26%	—	×1.050	3.42%
HR / BF /BF	2.26%	÷0.893	×1.100	2.78%

Result: FIP 3.48 → 3.94, 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 AAErie SeaWolves · 48 IPFIP 4.06 → 4.52 (+0.46)K% 16.5% → 13.4%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.51%	—	×0.810	13.37%
BB% (unintentional) /BF	4.25%	—	×1.100	4.67%
HR / BF /BF	3.30%	÷1.112	×1.200	3.56%

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 122).

Result: FIP 4.06 → 4.52, 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 AAErie SeaWolves · 138 IPFIP 3.69 → 3.97 (+0.27)K% 19.5% → 15.8%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	19.54%	—	×0.810	15.83%
BB% (unintentional) /BF	3.58%	—	×1.100	3.94%
HR / BF /BF	3.26%	÷1.259	×1.200	3.10%

Result: FIP 3.69 → 3.97, 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+Lakeland Flying Tigers · 102 IPFIP 2.84 → 3.38 (+0.54)K% 20.1% → 15.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	20.15%	—	×0.770	15.51%
BB% (unintentional) /BF	3.93%	—	×1.150	4.52%
HR / BF /BF	1.72%	÷1.161	×1.250	1.85%

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 132).

Result: FIP 2.84 → 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).

2016 AAErie SeaWolves · 34 IPFIP 3.65 → 4.11 (+0.46)K% 16.7% → 13.5%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	16.67%	—	×0.810	13.50%
BB% (unintentional) /BF	2.90%	—	×1.100	3.19%
HR / BF /BF	2.90%	÷1.095	×1.200	3.18%

Result: FIP 3.65 → 4.11, 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
Tyler Alexander (OGILVIE)	—	—	—	—	—	2.3%	—	4.42	16.9%	9.8%	57	+0.42	—	—	—
Colt Hynes L 2017 · 3.75 FIP	32	SW	3.75	14.1%	4.6%	2.3%	.306	—	—	—	—	—	3.42	169	61
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	60
Mark Hendrickson L 2007 · 3.78 FIP	33	SW	3.78	17.3%	4.7%	2.8%	.318	4.32	14.2%	8.0%	314	+0.54	3.04	1,169	57
Alejandro Soto L 2019 · 4.03 FIP	33	SW	4.03	17.1%	6.9%	2.7%	.376	—	—	—	—	—	3.96	280	55
Hideki Okajima L 2009 · 3.95 FIP	33	SU	3.95	20.5%	7.0%	3.1%	.268	4.27	15.5%	8.5%	46	+0.32	3.05	238	53
Edgar Osuna L 2019 · 5.09 FIP	31	SW	5.09	10.0%	7.2%	3.5%	.373	—	—	—	—	—	5.05	175	53
Brandon Mann L 2017 · 4.24 FIP	33	SU	4.24	19.8%	11.3%	2.5%	.286	3.59	17.8%	6.4%	52	-0.65	3.59	190	51
Chris Narveson L 2015 · 4.79 FIP	33	SW	4.79	23.6%	8.0%	4.9%	.333	4.59	14.1%	4.5%	193	-0.20	4.00	625	51
Luke Westphal L 2022 · 4.35 FIP	33	SW	4.35	24.2%	13.4%	2.9%	.302	5.19	15.7%	-0.7%	26	+0.84	2.97	294	51
Chad Zerbe L 2003 · 3.89 FIP	31	SU	3.89	7.8%	5.5%	1.4%	.306	—	—	—	—	—	3.75	145	50
Kwang Hyun Kim L 2021 · 4.21 FIP	33	SP-	4.21	17.7%	8.2%	2.9%	.266	—	—	—	—	—	3.76	146	50
Nick Ramirez L 2023 · 2.91 FIP	33	SU	2.91	17.9%	5.3%	1.2%	.283	3.55	14.2%	8.5%	50	+0.64	2.91	478	49
Micah Bowie L 2007 · 4.60 FIP	32	SW	4.60	16.9%	10.9%	2.8%	.279	—	—	—	—	—	3.78	140	48
Everett Teaford L 2015 · 4.42 FIP	31	SP-	4.42	13.9%	7.6%	2.8%	.324	—	—	—	—	—	4.42	208	48
Adam Liberatore L 2018 · 4.28 FIP	31	SU	4.28	18.6%	9.1%	2.9%	.354	—	—	—	—	—	2.38	124	48
Doug Creek L 2002 · 4.99 FIP	33	SU	4.99	21.4%	12.6%	3.8%	.288	5.79	17.1%	7.9%	36	+0.80	4.48	277	47
Ryan O'Rourke L 2019 · 4.08 FIP	31	SU	4.08	22.1%	14.3%	1.9%	.290	—	—	—	—	—	3.05	145	47
Clay Rapada L 2015 · 3.84 FIP	34	SU	3.84	14.3%	8.0%	1.7%	.299	—	—	—	—	—	3.00	120	46
Joey Eischen L 2003 · 4.00 FIP	33	SU	4.00	18.1%	5.4%	3.2%	.309	3.44	19.6%	10.3%	69	-0.56	2.13	294	46
Tommy Phelps L 2005 · 3.98 FIP	31	SU	3.98	13.2%	7.5%	1.9%	.280	—	—	—	—	—	3.39	120	46

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	3.7	67	18.9%	5.9%	1.31	4.47
Yusmeiro Petit ('21)	4.5	70	20.7%	5.5%	1.36	3.93
Ron Taylor ('62-'72, 3 WAR*)	4.0	80	17.8%*	6.8%*	1.26*	4.74*
Anthony Swarzak ('21)	3.3	73	17.8%	7.3%	1.20	4.42
Tommy Hunter ('23)	4.6	71	16.7%	5.4%	1.27	4.07
Jim Willoughby ('71-'78, 4 WAR*)	2.8	79	16.0%*	6.2%*	1.42*	4.13*
Tom House ('71-'78, 3 WAR*)	2.7	77	17.3%*	7.8%*	1.25*	4.14*