Fall is a great time to be a sports fan. It's the start of football season around the world, both for American football and what the rest of the world calls football. It's also the beginning of hockey season worldwide (except in North America, where there is a lockout), and the end of Australia rules football season (its Grand Final was on September 28th) and the start of Major League Baseball’s playoffs leading up to the World Series.
So in this season of all things sport, we are going to look at the role analytics play in sports management and marketing. Analytics are increasingly used to drive decisions in nearly every aspect of sports management, from labor agreements and player contracts, to ticket and merchandise sales, scheduling and TV and digital media deals.
First, and probably most famously, is talent acquisition ala the Moneyball era of the Oakland A’s baseball team. To field a competitive roster on a limited budget, the team employed analytics to identify and obtain relatively undervalued players. A bestselling book by Michael Lewis led to a movie starring Brad Pitt and heavy media coverage. What was at the time a game-changing use of analytics has become such a common practice that it is no longer considered a differentiator, but rather table stakes in professional sports. Although the A’s just clinched the Americal League Western Division Championship, so the team is still doing something right.
The second example is also from baseball: dynamic ticket pricing, made famous by the San Francisco Giants and being quickly adopted everywhere. Professional sports teams are increasingly in competition with third parties like Stub Hub that can price tickets based on demand. Leveraging analytics, sports marketers can determine which games will be most popular among their fans and price tickets accordingly. This way, teams can keep attendance up for less-popular games, and not leave money on the table for the most in-demand games. It seems to be a winning strategy since the Giants have sold out every home game since October 1, 2010.
Our third example involves game scheduling — a complex business involving home games, road games, fairness, league considerations, TV schedules and the fan quotient. In fact, FICO’s own optimization software is used to find the best schedule for the NFL’s 256 games, which involves trillions of scheduling permutations, 20,000 variables and 50,000 constraints. All for 16 games and 32 teams.
As Big Data matures, the analytics of sports will continue to evolve, making for better fan experiences, more exciting competitions and more profitable games. Talk about a win-win!