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Cost Efficiency in Major League Soccer: Using the Audi Player Index to Optimize a Lineup

This paper examines cost efficiency in the 2016 Major League Soccer (MLS) season through utilizing data from the Audi Player Index (API) and the MLS Players’ Union. The data was collected from MLS’s website and then player scores were calculated in terms of their average API score per game. The cost efficiency was measured in Dollar per API, or the average cost of a player to score 1 API point in a game. By using binary integer programming, a cost efficient lineup was constructed that maximized the optimal team’s API score under the constraint of $1,279,300 which is the median MLS salary multiplied by 11. Ultimately, this binary integer programming iterates and compares different combinations of players to form an optimal lineup where the teams’ API production is maximized while having a salary less than the constraint.

Co-author: Dr. Noni Zaharia

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