The method for rating teams in Example 7.8 is based on actual and predicted point spreads. This…

The method for rating teams in Example 7.8 is based on
actual and predicted point spreads. This method can be biased if some teams
“run up the score” in a few games. An alternative possibility is to base the
ratings only on wins and losses. For each game, we observe whether the home
team wins. Then from the proposed ratings, we predict whether the home team
will win. (We predict the home team will win if the home team advantage plus
the home team’s rating is greater than the visitor team’s rating.) We want the
ratings such that the number of predictions that match the actual outcomes is
»

The method for rating teams in Example 7.8 is based on
actual and predicted point spreads. This method can be biased if some teams
“run up the score” in a few games. An alternative possibility is to base the
ratings only on wins and losses. For each game, we observe whether the home
team wins. Then from the proposed ratings, we predict whether the home team
will win. (We predict the home team will win if the home team advantage plus
the home team’s rating is greater than the visitor team’s rating.) We want the
ratings such that the number of predictions that match the actual outcomes is
maximized. Try modeling this. Do you run into difficulties? (Remember that
Solver doesn’t like IF functions.)

 

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