Francisco Liriano

P Pittsburgh Pirates

Age 43 LHP 6'3" / 220 lbs MLB

Overview

MLBRPLAST SEASON 2019

Year	Age	Lvl / Team	G	GS	IP	ERA	FIP	K%	BB%	WAR	MLE rates
2017	33	MIX2 levels	39	19	101	5.60	4.26	20.1%	11.6%	0.3	K 19.4% · BB 11.6% · FIP 4.35
2018	34	MIX2 levels	28	27	138	4.97	4.97	19.2%	12.0%	0.7	K 18.8% · BB 12.5% · FIP 4.95
2019	35	MLBPIT	69	0	70	3.47	4.24	20.9%	11.3%	0.3
2020	36	No MLB games played
2021	37	No MLB games played
2022	38	No MLB games played
2023	39	No MLB games played
2024	40	No MLB games played
2025	41	No MLB games played
2026	42	No MLB games played

Last 3 seasons WAR 1.3

Contract

No contract data on file.

Skills · percentileStatcast 2019

xwOBA vs57

K%41

FB Velo26

ERA—

FIP—

K%—

BB%—

K-BB%—

Proj IP—

Statcast · Contract

Statcast Percentiles

FB Velo

p2692.8

Whiff%

p8330.4%

p4120.9%

xwOBA vs

p570.305

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)
2005	21	MLB MLE	Minnesota Twins	6	4	24	93	33	7	4	67%	3.9	35.5%	7.5%	28.0%	4.3%	5.70	3.42	+0.6
2005	21	MLB RAW	Minnesota Twins	6	4	24	93	33	7	4	67%	3.9	35.5%	7.5%	28.0%	4.3%	5.70	3.40	+0.6
2006	22	MLB MLE	Minnesota Twins	28	16	121	473	144	32	9	57%	4.3	30.4%	6.8%	23.7%	1.9%	2.16	2.43	+4.5
2006	22	MLB RAW	Minnesota Twins	28	16	121	473	144	32	9	57%	4.3	30.4%	6.8%	23.7%	1.9%	2.16	2.48	+4.5
2008	24	MLB MLE	Minnesota Twins	14	14	76	329	67	31	7	100%	5.4	20.4%	9.4%	10.9%	2.1%	3.91	3.74	+1.5
2008	24	MLB RAW	Minnesota Twins	14	14	76	329	67	31	7	100%	5.4	20.4%	9.4%	10.9%	2.1%	3.91	3.76	+1.5
2009	25	MLB MLE	Minnesota Twins	29	24	137	609	122	65	21	83%	4.7	20.0%	10.7%	9.4%	3.4%	5.80	4.65	+1.2
2009	25	MLB RAW	Minnesota Twins	29	24	137	609	122	65	21	83%	4.7	20.0%	10.7%	9.4%	3.4%	5.80	4.74	+1.0
2010	26	MLB MLE	Minnesota Twins	31	31	192	806	201	58	9	100%	6.2	24.9%	7.2%	17.7%	1.1%	3.62	2.52	+7.2
2010	26	MLB RAW	Minnesota Twins	31	31	192	806	201	58	9	100%	6.2	24.9%	7.2%	17.7%	1.1%	3.62	2.52	+7.2
2011	27	MLB MLE	Minnesota Twins	26	24	134	591	112	74	14	92%	5.2	19.0%	12.5%	6.4%	2.4%	5.09	4.38	+1.6
2011	27	MLB RAW	Minnesota Twins	26	24	134	591	112	74	14	92%	5.2	19.0%	12.5%	6.4%	2.4%	5.09	4.44	+1.5
2012	28	MLB MLE	Chicago White Sox	34	28	157	693	167	82	19	82%	4.6	24.1%	11.8%	12.3%	2.7%	5.34	4.04	+2.5
2012	28	MLB RAW	Chicago White Sox	34	28	157	693	167	82	19	82%	4.6	24.1%	11.8%	12.3%	2.7%	5.34	4.11	+2.4
2013	29	MLB MLE	Pittsburgh Pirates	26	26	161	666	163	63	9	100%	6.2	24.5%	9.5%	15.0%	1.5%	3.02	3.03	+4.8
2013	29	MLB RAW	Pittsburgh Pirates	26	26	161	666	163	63	9	100%	6.2	24.5%	9.5%	15.0%	1.4%	3.02	2.98	+4.9
2014	30	MLB MLE	Pittsburgh Pirates	29	29	162	691	175	78	13	100%	5.6	25.3%	11.3%	14.0%	2.0%	3.38	3.51	+3.8
2014	30	MLB RAW	Pittsburgh Pirates	29	29	162	691	175	78	13	100%	5.6	25.3%	11.3%	14.0%	1.9%	3.38	3.43	+3.9
2015	31	MLB MLE	Pittsburgh Pirates	31	31	187	773	205	69	15	100%	6.0	26.5%	8.9%	17.6%	2.1%	3.38	3.14	+5.3
2015	31	MLB RAW	Pittsburgh Pirates	31	31	187	773	205	69	15	100%	6.0	26.5%	8.9%	17.6%	1.9%	3.38	3.06	+5.5
2016	32	MLB MLE	Toronto Blue Jays	31	29	163	731	168	84	26	94%	5.3	23.0%	11.5%	11.5%	3.6%	4.69	4.57	+1.6
2016	32	MLB RAW	Toronto Blue Jays	31	29	163	731	168	84	26	94%	5.3	23.0%	11.5%	11.5%	3.6%	4.69	4.66	+1.4
2017	33	MIX MLE	2 levels	39	19	101	458	92	53	11	49%	2.6	19.4%	11.6%	7.7%	2.6%	5.60	4.35	+0.2
2017	33	MIX RAW	2 levels	39	19	101	458	92	53	11	49%	2.6	20.1%	11.6%	8.5%	2.4%	5.60	4.26	+0.3
2018	34	MIX MLE	2 levels	28	27	138	610	117	73	21	96%	4.9	18.8%	12.5%	6.3%	3.4%	4.97	4.95	+0.7
2018	34	MIX RAW	2 levels	28	27	138	610	117	73	21	96%	4.9	19.2%	12.0%	7.2%	3.4%	4.97	4.97	+0.7
2019	35	MLB MLE	Pittsburgh Pirates	69	0	70	301	63	34	8	0%	1.0	20.9%	11.3%	9.6%	2.9%	3.47	4.34	+0.2
2019	35	MLB RAW	Pittsburgh Pirates	69	0	70	301	63	34	8	0%	1.0	20.9%	11.3%	9.6%	2.7%	3.47	4.24	+0.3
▸ Career totals (raw MLB only — as recorded)
MLB Career	—			419	300	1814	7781	1815	801	184	72%	4.3	23.3%	10.3%	13.0%	2.4%	4.15	3.74	+36.1
▸ Career totals — MLB only (actual + projected)
Career	—	RAW		419	300	1814	7781	1815	801	184	72%	4.3	23.3%	10.3%	13.0%	2.4%	4.15	3.73	+36.3
/ 200 IP	—	RAW		46	33	200	858	200	88	20	72%	4.3	23.3%	10.3%	13.0%	2.4%	4.15	3.73	+4.0
Career	—	MLE		421	302	1822	7824	1823	806	190	72%	4.3	23.3%	10.3%	13.0%	2.4%	4.18	3.77	+36.1
/ 200 IP	—	MLE		46	33	200	859	200	88	21	72%	4.3	23.3%	10.3%	13.0%	2.4%	4.18	3.77	+4.0

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.

2018 AAAToledo Mud Hens · 4 IPFIP 5.21 → 6.56 (+1.35)K% 29.2% → 25.7%

Component	Raw	Park ÷	Level factor ×	= MLE
K% /BF	29.17%	—	×0.880	25.67%
BB% (unintentional) /BF	0.00%	—	×1.050	0.00%
HR / BF /BF	8.33%	÷0.893	×1.100	10.26%

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 79).

Result: FIP 5.21 → 6.56, 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).