Sports Performance

Sports Analytics: Decoding Data to Gain Competitive Edge

Using data analytics to make better decisions for teams and their players, increase revenues, reduce costs, and create a more engaging fan experience. Statistical data has always been central to sports. Box scores are published in newspapers, baseball cards show players’ career stats, and radio announcers provide commentary on statistics like points scored or yards thrown. But the growing prominence of sports analytics is taking this information to a whole new level. Sports analytics are a collection of relevant, historical, statistics that can provide a competitive advantage to a team or individual. These numbers are often used to inform coaching and management decisions both during and before sporting events. They can also help scouts evaluate players and determine whether they are worth drafting, signing as free agents or acquiring in trades.

With the proliferation of big data and advanced analysis, all major sports teams have developed an analytics department or at least a team statistician on staff. The analytics evolution is not only changing the way teams play, but is impacting all aspects of a professional sports organization including ticket sales, marketing and fan engagement. Traditionally, coaches and general managers would make player evaluations based on a combination of subjective gut feeling, player performance during previous seasons and recent games, and basic statistical metrics like points per game, batting average, or touchdown passes thrown. However, the increasing popularity of sports analytics has created a new way for players to get noticed and for scouts to find the next big star.

While traditional methods for assessing athlete talent will still be important, teams are now using analytics to optimize their team and gain a competitive edge. Analytics is being used in many different ways, such as evaluating player injuries, identifying the most valuable fanbases for sponsorship, and predicting future trends. Analytics can even help identify what type of fan experiences may be most effective in converting single-game fans to season ticket holders. This course provides an overview of the latest trends and developments in sports analytics, with a particular focus on data science techniques. Students will be taught how to apply these techniques to real-world sports datasets and develop their own results.

Students will also learn how to work with various cloud-based data processing tools such as Amazon Web Services, which will enable them to become independent producers of sports analytics instead of simply consumers. Albert Cohen is a professor of mathematics at MSU and the Director of the new Spartan Analytics Certificate program. He is a lifelong hockey, soccer and football fan and has been combining his passion for sport with his expertise in applied mathematics for over a decade. Previously, he was a research mathematician in the field of computational finance at Carnegie Mellon University.

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