Statistical analysis in sports has been around for a long time, but the topic of sports and analytics has attracted more attention in the last decade. The release in 2011 of the movie Moneyball (based on the book Moneyball: The Art of Winning an Unfair Game, by Michael Lewis, published in 2003) made the use of analytics a popular subject for public consumption.
In the movie, the general manager of the Oakland Athletics baseball team, Billy Beane (played by Brad Pitt), struggles to put together a competitive team as the franchise faces financial challenges. Beane employs an approach called sabermetrics, which relies on evidence-based analysis in areas such as evaluating players, measuring game activity, and scouting. Thanks in part to this methodology, the team wins 20 consecutive games — a record in the American League.
The advancement of technology, especially in the areas of big data, in-memory computing, the cloud, and mobile, further put the spotlight on analytics in the sports and entertainment industries. Today, these technology solutions make it possible for sports organizations to effectively leverage their rich data assets and take their analytical capabilities to the next level beyond the rudimentary game, player, or scouting stats.
When we think about sports organizations in this context, we may realize that they are not much different than traditional businesses. Sports organizations face some of the same challenges as their counterparts in more conventional industries such as retail or consumer products. They operate in similar terrains, where they need to effectively manage their scarce resources and deliver value to their customers and other stakeholders.
One of the main reasons why sports and analytics is a perfect combination is the data sources that include data points from topics as diverse as game scores and attendance and revenue figures. These considerable data repositories are conducive for cross-fertilization and until now have been mostly untapped. Moreover, just as, for example, a consumer-products company may be interested in understanding all aspects of their customers (products, stores, or service offerings), sports organizations seek greater insight in understanding all aspects of their fans, whether they study fan engagement, team performance, or venue management.
An integrated business intelligence (BI) approach that brings these unique and diverse data points together gives sports organizations the ability to see the big picture and execute growth faster with better-informed decisions. It enables them to address new business models and complex challenges, which increasingly require greater access to data coming from sources both structured (for example, revenue and attendance data) and unstructured (such as data collected on fan experience from social media that cannot be easily queried with traditional tools and technologies).
Innovative technologies and strategic BI solutions, which go beyond just game-day analytics to manage big data on mobile platforms and which can be delivered on the premises or in the cloud, can not only transform their operations but also continue to deliver value-added products and services, improving the overall fan experience.
About the author:
Kaan Turnali is a Global Senior Director, Business Intelligence (BI), for SAP’s Global Customer Operations (GCO) Reporting & Analytics Platform, Kaan is responsible for the development, oversight, and execution of strategy for the BI platform across GCO’s worldwide user base. In addition, he manages special mobile BI projects for the Office of co-CEO Bill McDermott and the GCO senior management team. His background and experience in the integration of business and technology span over two decades. He is also an adjunct professor, teaching BI in the doctor of business administration program at Wilmington University. Read more at http://www.turnali.com/ or Follow @KaanTurnali on Twitter or on LinkedIn
Originally posted on Forbes