DataBaseball
2026

Head to head · Career pitching

Cliff Lee vs Zack Wheeler

In 2014 Zack Wheeler has been worth 0.7 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 LeeZack Wheeler
G284311
GS280311
IP1,915.01,890.1
W126127
L7980
SV00
SO1,6132,013
BB355537
ERA3.353.26
WHIP1.171.13
K/97.69.6
BB/91.72.6
HR/90.90.9
WAR41.741.3

2014 season

2014 pitching. The stronger figure in each row is highlighted — for ERA, FIP, WHIP and walks that is the lower one.
StatisticCliff LeeZack Wheeler
G1332
GS1332
IP81.1185.1
W411
SO72187
ERA3.653.54
FIP2.963.55
WHIP1.381.33
K/98.09.1
BB/91.33.8
FIP−7995
WAR1.72.4

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 LeeZack Wheeler
G1113
GS1111
IP82.074.1
W74
SV00
SO8980
BB1017
ERA2.522.30
WHIP0.930.77
K/99.89.7
BB/91.12.1

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.