DataBaseball
2026

Head to head · Career pitching

Kevin Gausman vs Cliff Lee

In 2014 the two have been worth the same 1.7 wins. Both lines are computed from play-by-play on the same constants, so they are measured the same way rather than quoted from two sources.

Career

Totalled over the seasons published here, which is not the same as a whole career: a player who debuted before 2005 begins at 2005, not at his real first game.

Career pitching totals. ERA, WHIP and the per-nine rates are recomputed from the components rather than averaged; FIP needs each season’s own constant and is left to the season table.
StatisticKevin GausmanCliff Lee
G393284
GS354280
IP2,088.21,915.0
W121126
L12579
SV00
SO2,1381,613
BB582355
ERA3.873.35
WHIP1.241.17
K/99.27.6
BB/92.51.7
HR/91.10.9
WAR37.941.7

2014 season

2014 pitching. The stronger figure in each row is highlighted — for ERA, FIP, WHIP and walks that is the lower one.
StatisticKevin GausmanCliff Lee
G2013
GS2013
IP113.181.1
W74
SO8872
ERA3.573.65
FIP3.412.96
WHIP1.311.38
K/97.08.0
BB/93.01.3
FIP−9179
WAR1.71.7

Postseason

Box-score figures only: October is kept out of the season constants, so there is no wOBA, FIP or dbWAR for it.

Every published postseason, pitching, summed with the rates recomputed from the counts.
StatisticKevin GausmanCliff Lee
G1511
GS911
IP59.282.0
W27
SV00
SO5789
BB2210
ERA4.072.52
WHIP1.040.93
K/98.69.8
BB/93.31.1

More head to heads

The same seasons, measured against somebody else.

How these are computed

FIP uses the constant solved from this season’s own runs, and FIP− is park-adjusted against the same league. dbWAR is our own open implementation — not fWAR or bWAR — and the full derivation, including its simplifications, is on the methodology page. To put these two beside a third, the comparison page takes up to four.