Ballpark
Effects
Ballpark effects matter 
they greatly affect a player's statistics. Ballpark adjustments attempt
to normalize a player's statistics in order to remove the effect of a
player's ballpark. For instance, since 1982, players at Fenway Park have
batted about 20 points higher than when they bat at other ballparks;
because Red Sox players play roughly half there games at Fenway, one
might imagine that Fenway Park boosts the batting average of the typical
Red Sox player by 10 points. One measure is called the Park Index, which is an estimate of the
amount by which a given statistic is inflated by the home ballpark. For
example, take a stat like runs  the Park Run Index is simply
the ratio of runs at home to runs on the road. Importantly,
the Park Index looks at runs scored by both the home and visiting teams,
since home teams usually hit better at home than they do on the road.
Example: Coors Field in 1996 registered the highest run index of
all time. The Rockies scored 658 runs at home, while opponents scored
559 runs at Coors. On the road, the Rockies scored 303 runs and gave up
405. So the Run Index is: Coors
RI = (658+559)/(303+405) = 1.72 The ratio is usually stated as a percentage rather than as a decimal 
thus, we say that Coors Field had a run index of 172.
The same process can be used to develop an index for runs scored, hits,
extrabase hits, home runs, or any other stat.
The next step is
to convert this index to a Park Factor. Say a ballpark boosts run
production by 72% (i.e. has a Park Index of 172)  intuitively, one
might think that a team's run production has been exaggerated by 36%
throughout the year  thus, a Park Factor of 136 can be used to adjust a
team's statistics, meaning that we should divide Colorado's runs by 1.36
to get a true measure of their ability to score runs. But two adjustments have to be made. First of all, teams may play more
innings on the road than at home  remember, the home team doesn't
always have to hit in the bottom of the ninth. If the number of innings
played at home and on the road differ, than the Park Factor shouldn't be
the simple average of the Park Index and 100, but a weighted
average. Second,
simply creating a Park Factor of 136 for all the players on the Colorado
Rockies of 1996 will actually overcorrect the problem. That's
because all the other players in the National League got to play some
portion of their road games in Coors; the Rockies players did not get
this advantage, so their road statistics will be depressed relative to
what other teams produced. Pretend that Colorado had played some portion  1 in 15, to be precise 
of their road games at Coors. Then their road statistics would be
a little higher, boosted by the same element that helped all other NL
teams. Ideally, to remove the park effects from all stats, we should
have each of the 16 teams in the NL play 1/16th of their games in each
park. Statistically,
we can get there by making the Park Factor a weighted average of
the Park Index (adjusted for the innings differential) and 100 (the
league average).
Example: The Park Factor for Coors Field in 1996 is calculated in
the following manner. There are 16 teams in the NL, so each team plays 1
in 15 road games at Coors.
So if PI is the Park Index: PF =
((15 x PI) + (16  PI))/ (15 x 2) = 1.336
Again, the Park Factor is
usually stated as a percentage rather than as a decimal  thus, we say
that Coors Field had a Park Factor of 133.6
