DataBaseball
2026

Head to head · Career pitching

Cliff Lee vs Chris Sale

In 2014 Chris Sale has been worth 2.9 more 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.
StatisticCliff LeeChris Sale
G284420
GS280339
IP1,915.02,246.2
W126159
L7997
SV012
SO1,6132,779
BB355518
ERA3.352.95
WHIP1.171.04
K/97.611.1
BB/91.72.1
HR/90.90.9
WAR41.760.0

2014 season

2014 pitching. The stronger figure in each row is highlighted — for ERA, FIP, WHIP and walks that is the lower one.
StatisticCliff LeeChris Sale
G1326
GS1326
IP81.1174.0
W412
SO72208
ERA3.652.17
FIP2.962.57
WHIP1.380.97
K/98.010.8
BB/91.32.0
FIP−7969
WAR1.74.6

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.
StatisticCliff LeeChris Sale
G1113
GS119
IP82.046.2
W71
SV00
SO8966
BB1014
ERA2.525.79
WHIP0.931.33
K/99.812.7
BB/91.12.7

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.