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How Analytics is Changing the Game for Sports – and Academia

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, the country’s first-ever sports analytics major. Students use big data to answer pressing business questions about sports teams: who are their audiences, how do they engage them, and how can they turn that engagement into real revenue opportunities?

This new field of study is remarkable in two ways. First, it is a brand new field of study, 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.

How does the Center help college and professional sports organizations? Students and faculty have two primary use cases. The first is on the team side. Students leverage a host of data sets to glean insights that teams can use to inform recruitment strategy, as well as make in-game decisions, such as formation, plays, and when to sub one player for another. 

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

A university asked the Center to help it drive attendance at its 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. They then looked at another group of alumni who had attended one or more games in order to identify behavior predictors that indicate a propensity to attend a game. Once those predictors were identified, the students applied it to the 200,000 non-game attending alumni, and identified 7,000 people who, if engaged correctly, could be prompted to buy tickets to a game. The results were spectacular. The university saw a healthy percentage of that 7,000 attend a game.

Curious readers may wonder what I mean by, “if engaged correctly.” How is that even knowable? Over the past decade there have been a number of Big Data analytics tools to help users parse Big Data, and to home in on meaningful trends. The students at Samford use Affinio, which is 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 such factors as: inclusion of the team name in their social media bios, or if they follow the Twitter account of, say, the head coach. But that’s just the start. Students can identify the news sites the fans read, celebrities they follow, 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, such as Mercedes-Benz, spend tens of millions of dollars sponsoring sports arenas. Traditionally, sponsors measure effectiveness of a relationship based on calculated impressions, i.e. the number of fans that 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 the 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. In fact, sponsorship analysis is one of the key use cases students and the Center for Sports Analytics use most frequently.

The Power of Social Data

The power of social data has been one of the most surprising – and, frankly, awe inspiring – data sets available. I’ll give you another example. Going into the 2018 football season, all traditional marketing research predicted that the NFL would have another bad year in terms of TV ratings. If you remember, 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, the Center was convinced that the NFL would have a very good year in terms of TV rankings, and publicly stated as much. By November, the predictions were validated

About the Author

Darin W. White, Ph.D. is Executive Director, Center for Sports Analytics, Samford University and Chair of the Entrepreneurship, Management & Marketing Department. Dr. White also serves as the Founding Director of the Sports Marketing program in the Brock School of Business. He has crafted business relationships with some of the most prestigious sports organizations & corporate sponsors, including NFL, MLB, MLS, NBA, Dallas Cowboys, Bayern Munich, Miami Dolphins, Atlanta Falcons, Atlanta United, Adidas, SEC, ACC, Nike, NASCAR, Atlanta Braves, Harlequins Rugby, Indy Car, Peach Bowl, PGA, Tampa Bay Lightning, MiLB, IFL, Coca Cola, Honda, BCBS, & Manchester United’s sponsor AON.

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Comments

  1. It is of great help. Especially for marketers like me, who want to explore the field of data science. Thanks a lot

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