Analytics

Cracking the Code: Analyzing the Audi Player Index by Kevin Shank

In my Sports Analytics class at Saint Joseph's University, my professor would always stress the importance of having a valid data source; “Put garbage in, get garbage out,” he would tell the class. If the data has a bias, isn’t random, or is miscalculated, then any resulting conclusion is not credible. In order to have a sound analytic method, it is imperative that the data source is not “garbage.” For the course’s final project, I chose to analyze players’ cost efficiency and also use binary integer programming to build an optimal lineup. Ironically enough, I decided to have my data source be none other than the Audi Player Index.

More after the jump.

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