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Guest post by Jonathan Taplin, Professor at The University of Southern California and the Director of the school’s Annenberg Innovation Lab.

For baseball fans, October is the month they all look forward to as the playoffs commence and champions are crowned. A culmination of great individual and team play makes this time of year “must see television.” However, with teams from smaller markets heading to the World Series, you’d believe the national fan base and viewership would dwindle. That’s if you focused entirely on traditional TV rating measurements.

With social media now a staple of professional sports, fan sentiment on this new, growing medium should not go unnoticed. Today, fans have an opportunity to both share and learn from others instantly; and they’re providing researchers with an unfiltered voice that is ripe for analysis.

That’s why the University of Southern California (USC) Annenberg Innovation Lab (AIL) and IBM are embarking on a new social media analysis project using advanced analytics software to catch fan favorites during the World Series. The USC Annenberg Social Sentiment Index is being compiled by students and relies on IBM Social Analytics technology to analyze millions of tweets in order to assess public social media engagement and opinion from sports and film to retail and fashion.

USC students have done an initial analysis of the National League Championship Series (NLCS), and are now broadening the index to follow the games starting tonight. The goal is to uncover hidden insights from Twitter followers that could help better understand player and team sentiment; and illustrate how advanced analytics technologies can identify important trends.

So far the students have mined an initial test of more 1.5 million public baseball-related tweets during the National League Championship Series (NLCS), gauging positive and negative nuances and establishing overall sentiment rankings among a sampling of NLCS players. They determined that the Cardinals’ Chris Carpenter garnered the highest number of tweets indicating sentiment at 1,573 — 61.4 percent positive and 21.6 percent negative. While fellow Cardinal David Freese, a fan favorite and official NLCS MVP of the pennant race, garnered fewer tweets than Carpenter (768 tweets total), he had a more uniformly positive twitter following — a ‘T’ score of 89.3 percent positive tweets compared with only 15.4 percent negative. From the overall team view, the Texas Rangers are winning the social media buzz battle. They’ve earned a whopping 56,600 tweets or five times more than St. Louis –  but the Cardinals matched the Rangers’ level of enthusiasm in their tweets. St. Louis garnered 11,500 tweets, 80 percent of which were upbeat. We also found that the enthusiasm for the ALCS teams was reflected in the TV ratings.

All this data affirms what we’ve known for some time.  Statistics are an important part of baseball. The popularity of Moneyball and the Oakland A’s Billy Beane approach to roster building is another reminder that the sport, like many other areas of business and society, has been part of the analytics revolution. Today, insights into data is an even bigger part of understanding not only player performance but also the views of fans.

Our work with IBM is a great demonstration of how Watson-inspired technologies, like sophisticated semantic and linguistic analysis software, can crunch Big Data to quickly gain temperature checks on timely issues. Throughout the past year, our students have developed analyses across other industries including movies and fashion retail. By taking a deep dive into the Twitterverse — concentrating on the short 140 character tweets and polling large data sets over a number of days – students are able to determine positive or negative sentiments in a matter of minutes. The results are helping students gain highly sought skills in analytics that can set them apart in the business world.

I’ll be discussing the MLB index in more detail at next week’s IBM Information on Demand and Business Analytics Forum (IOD11) in Las Vegas and encourage you to check back on this blog for updates on fan sentiment results during the World Series. Enjoy the games!

• Read about analysis of Games 3 and 4.
• Read about analysis of Games 1 and 2.
• Learn about how IBM and USC AIL are conducting the social media analysis project.
• Check out images from the World Series Social Sentiment Index

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