By: Edward Egros

Advanced Modeling Techniques for Forecasting College Football Games

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This July, Charles South, an instructor at UT Southwestern, and I, gave a presentation at the R Users Group at the University of Dallas. In this talk, Charles and I discuss how to predict college football games. Using data from Clemson University and 247Sports, we used advanced modeling techniques to see what best predicts an out-of-sample set of games.

To see the powerpoint of our talk,
click here.

Analytics in Television Journalism

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I recently was in Stevens Point, Wisconsin to deliver a talk about sports analytics in television journalism. This discussion is essentially how I do my "day job" as the weekend sports anchor at Fox 4 in Dallas. I began with an observation: while other media have found ways to incorporate analytics into its sports coverage, television is noticeably lagging. This medium poses its own challenges highlighting quantitative analysis, but instead of ignoring it, there are ways to use the tools. At the Great Lakes Analytics in Sports Conference, I discussed three of these challenges and how to address them during reports.

To listen to this podcast,
click here.

To learn more about the conference and how you can get involved next year,
click here.

Finally, to get the slides of this talk including videos showcasing examples of concepts,
click here.

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.