Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
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A version of simple exponential smoothing can be
used to predict the outcome of sporting events. To illustrate,
consider pro football. We first assume that all games are
played on a neutral field. Before each day of play, we assume
that each team has a rating. For example, if the Bears’ rating
is 10 and the Bengals’ rating is 6, we would predict the
Bears to beat the Bengals by 10 6 4 points. Suppose
the Bears play the Bengals and win by 20 points. For this
observation, we “underpredicted” the Bears’ performance
by 20 4 16 points. The best a for pro football is 0.10.
After the game, we therefore increase the Bears’ rating by
16(0.1) 1.6 and decrease the Bengals’ rating by 1.6 points.
In a rematch, the Bears would be favored by (10 1.6)
(6 1.6) 7.2 points.
a How does this approach relate to the equation At
At1 a(et)?
b Suppose the home-field advantage in pro football is
3 points; that is, home teams tend to outscore visiting
teams by an average of 3 points a game. How could the
home-field advantage be incorporated into this system?
c How could we determine the best a for pro football?
d How might we determine ratings for each team at
the beginning of the season?
e Suppose we tried to apply the above method to pre-
dict pro football (16-game
(11-game schedule), college basketball (30-game sched-
ule), and pro basketball (82-game schedule). Which
sport would have the smallest optimal a? Which sport
would have the largest optimal a?
f Why would this approach probably yield poor fore-
casts for major league baseball?
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