The information provided by correlations allows for predicting any outcome, especially sports. With multiple regression techniques and a little software, you can guess the winner before the game is played. The trick is picking the right predictors.
The conventional use of correlations [Hack #11] is to find out how much two variables share in common—or, more technically, how much variance is shared between the two variables.
Shared variance is a mathematical term to describe the amount of redundant information reflected in two variables. When lots of variance is shared, prediction is easy and accurate because knowledge of one variable leads to knowledge about a second. Shared variance is estimated by squaring the correlation.
But our everyday world consists of way more than only one variable predicting another. In fact, in most cases there are several or multiple variables that predict a particular outcome. Here we are not dealing with the prediction of just one variable from another, but the prediction of one variable from several. This tool is called multiple regression (because there is more than one predictor variable).
Serious sports gamblers, bookies, and casino operators are familiar with multiple regression, or at least they should be. So much information is available about sports teams that there are almost certainly all sorts of variables that, in the right combinations, can fairly accurately predict which team will win.
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