My recent report, “The Data-Driven Business”, presents examples of how data is changing so many things, from marketing Ice Cube to reducing the incidence of sepsis (infection) in surgery. But data is also changing the business of sports.

You may have read (or seen) Moneyball, the 2003 book by Michael Lewis and 2011 film starring Brad Pitt, but there are many more ways to use data beyond Sabermetrics, which originated long before big data was a glint in anyone’s eye.

I attended a session at Structure Data in San Francisco on March 9 entitled “How Orlando Magic Uses Data to Improve Their Game”, which featured Alex Martins, CEO of the Orlando Magic, John Paul, CEO and Founder of VenueNext, and which was moderated by Tom Krazit, Executive Editor, Structure Events.

Martins began by explaining that data isn’t just changing the way sports are played; it’s also changing the way teams make money inside the arena. A decade ago, the Orlando Magic began its fledgling analytics efforts with one person looking at how to optimize yield management and ticket pricing. Back then, he says, there was really no data that went into the ticketing process.

Today, however, The Magic is able to do dynamic ticket pricing based on variables such as demand, day of week (Mondays are bad), the team’s position in the league that season, the opponent they’ll be playing, and others. All those variables go into the ticket pricing system. Then they use analytics to determine the likelihood of season ticket renewals and use a predictive model to identify where sales reps should be spending the most time.

“Our team has been in a rebuilding mode in the past couple of years,” says Martins. “In the past we would set a price and it would stay pretty flat. Now we can drive volume on lower price.”

In addition to dynamic pricing, the Magic offers an app, built by VenueNext, which enables fans to engage even further within the venue. Fans can use the app to enter the building, order tickets, order food from their seats, or order via the app and go through a “fast pass” line on the concession stand, dramatically reducing the amount of time they spend waiting in lines. As a result, the franchise has dramatically increased the amount of data it’s able to collect and is able to deliver a personalized experience based on purchasing habits.

Part of the fan experience includes “Magic Money,” a monetary system where fans can accumulate value that they can use to buy tickets, food or merchandise, or save for future use, such as for tickets for friends or even for valet parking. “Now we are transforming what used to be a season ticket purchase into a membership,” Martins says.

The Magic is also reimagining couponing and sponsor promotions for the digital age. For example, he says, they have a promotion that, when player hits five 3-point shots in a game, all fans get a coupon for a spicy Chick-fil-A sandwich the next day. They’re also working on a system where, rather than having to use a physical coupon, fans will be able to redeem their promotion from the app, providing data on the number of redemptions for sandwiches. And, while Martins didn’t say so, this data would also be valuable for sponsoring companies so they can better understand the ROI of their investment, such as whether promotions lead to additional incremental revenue or new customers in the door.

In addition to its marketing uses, Martins also uses data to build better teams. Three of his eight-person staff focus on trying to estimate the potential of a draft pick, or identify the exact spot on the floor where a specific player should stand in a specific situation to optimize the team’s score. Today, he added, the Magic uses wearables in practice, but they are not able to use them in a game. But if that becomes possible it would be yet another data source and potentially another way to improve performance.

Because basketball (and soccer, actually) is so much more fluid and context-dependent than, say, baseball, where only one person can pitch or hit at a particular time, it’s a good analog for many aspects of business that depend on an understanding of multiple, shifting and often subtle cues. I’m curious to see where this will go, and what it will teach us about how to tell a real opportunity from something that just looks like one. Or, if you prefer, what’s a shot, and what’s a fake.