How analytics is changing the game for sports—and academia

Students are putting big data to use to boost engagement, revenue
By: | December 11, 2019
(gettyimages.com: Getty Images)
Darin W. White is the executive director of the Center for Sports Analytics at Samford University in Homewood, Alabama, and chair of the Entrepreneurship, Management & Marketing Department. He is also the founding director of the Sports Marketing program in the Brock School of Business.

Darin W. White is the executive director of the Center for Sports Analytics at Samford University in Homewood, Alabama, and chair of the Entrepreneurship, Management & Marketing Department.

Big data has given rise to new opportunities, both in academia and business. Take the Center for Sports Analytics at Samford University in Homewood, Alabama, which offers a new sports analytics major. Students use big data to answer pressing business questions about sports teams: Who are their audiences? How do they engage them? How can they turn that engagement into real revenue opportunities?  

This new field of study is remarkable in two ways. First, it was developed specifically to help sports organizations drive team efficiencies and increase revenue. Second, the students are using big data and a suite of tools to deliver actionable insights to real sports organizations. It’s not academic to them; it’s real.

Driving engagement 

At the center, students and faculty have two primary use cases. Students leverage a host of datasets to glean insights that teams can use to inform recruitment strategy and to make in-game decisions, such as formation, plays, and when to substitute one player for another. 


Read: How higher ed is shaping the business of esports


The other use case is advising sports organizations on how they can increase their revenue,  primarily through improved fan engagement. Sports organizations have four sources of revenue: selling media rights, sponsorships, licensed merchandising and ticket sales. All are dependent on fan enthusiasm, and that’s precisely where Samford students come in. Following is an example.

Leaders at one university asked the center to help it drive attendance at football games. The students began by compiling a list of more than 200,000 alumni who had donated to the university, but had never been to a football game. Then, they looked at another group of alumni who had attended one or more games so they could identify behavior predictors that indicated a propensity to attend a game. Once those predictors were identified, the students applied them to the 200,000 nongame attending alumni, and identified 7,000 people who, if engaged correctly, could be prompted to buy tickets to a game. The result: The university saw a healthy percentage of that 7,000 attend a game.

Students use big data to answer pressing business questions about sports teams: Who are their audiences? How do they engage them? How can they turn that engagement into real revenue opportunities?

Homing in on meaningful trends

How can we engage people correctly? Over the past decade, there have been a number of analytics tools to help users parse big data and home in on meaningful trends. The students at Samford use an augmented analytics tool that groups people by interests. For instance, students can examine all of the users who follow a specific sports team and identify which are the hardcore fans by factors such as the inclusion of the team name in their social media bios, or if they follow the Twitter account of the head coach, for example. But that’s just the start. Students can identify the news sites the fans read, the celebrities they follow, the brands they favor, and so on. This data becomes a virtual road map for the sports organization’s marketing team to engage fans.

Sponsorship is another key area of focus. Brands spend tens of millions of dollars sponsoring sports arenas. Traditionally, sponsors measure effectiveness of a relationship based on calculated impressions—the number of fans who attend a game, along with estimated TV viewership. But these metrics aren’t particularly correlated with the marketing objectives of the brand, which is to sell products. They’re good secondary indicators of the potential value, but cannot help the marketer calculate the sales lift that results from a sponsorship. Students have found that there is a higher correlation between people who talk about brands in social media, and then go on to actually make a purchase. 

Harnessing the power of social data

The power of social data has been one of the most surprising datasets available. Going into the 2018 football season, all traditional marketing research predicted that the NFL would have another bad year in terms of TV ratings. The 2017 NFL season was racked by controversy, and the pundits agreed that 2018 wouldn’t be any better. Such predictions have devastating consequences to the cost of in-game advertising and sponsorships. The stakes were high.

But our students disagreed with the seasoned pundits. Why? Rather than calling people and asking what they thought of the NFL, they tracked what people were saying about teams on social media. Beginning in February, they saw an increase in relevance. By May, they were convinced the NFL would have a very good year in terms of TV rankings, and publicly stated as much. By November, the predictions were validated.


Darin W. White is the executive director of the Center for Sports Analytics at Samford University in Homewood, Alabama, and chair of the Entrepreneurship, Management & Marketing Department. He is also the founding director of the Sports Marketing program in the Brock School of Business.

 


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