By: Edward Egros

Analytics in Sports Marketing

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While much of my work and this website is devoted to sports analytics from performance, the marketing industry also uses similar tools to promote their teams, leagues and venues. By finding trends within data, marketers are in a better position to maximize profit through creating the best product fans are likeliest to spend their money on.

I emceed a sports marketing conference on behalf of Hispanic Communicators of DFW. The panelists are: Javier Villalobos with Sports Marketing Monterrey, Jason Hines with Red Bull, Gregg Elkin with Texas Motor Speedway, Chris Yates with Huddle Productions, Erin Finegold with the Dallas Mavericks and Carmen Branch with the March of Dimes. This group discussed how they use analytics in their work, as well as other pressing issues within their industry.

To watch a recording of this conference,
click here.

(Please note, the first three videos are of the conference)

Analyzing and Aiding Sports Analytics in Television

Sports analytics serve a variety of purposes, from quantitative measurements for how successful an athlete or team is, to predicting the outcome of a sporting event. Much of the development of analytics comes from athletes and teams implementing these tools for a competitive advantage.

Unfortunately for the media, there are several outside forces preventing sports analytics from being used more frequently on television. Still, some sports journalists have found ways to use and enhance these tools. In this paper, three nationally televised sports shows are analyzed for how they discuss analytics for their respective sport.

Using text mining techniques, it is possible to see the frequency of sports analytics and the complexity of the tools used in broadcasting, as well as—ultimately its primary purpose of the paper—how sports analytics are used to supplement a preexisting argument. This paper concludes by looking at other ways sports analytics can be used to enhance journalism.


To read this paper, click here.

Forecasting MLB World Champions Using Data Mining

Major League Baseball features an abundance of statistics and metrics used to measure how well players and teams perform during a season. Not only do those in the sport keep finding additional analytics means to determine success, but also more recently, data scientists have used this evolving information to try and forecast certain things within the sport.

It might be possible to find the right combination of measurements that will forecast which team is the likeliest to win the World Series, an event often tougher to predict than the larger regular season.


To read this paper, click here.