Big data and basketball: A ‘magic’ combination

Orlando Magic CEO on the benefits of big data

These days, decisions have to be made in the board room almost as fast as they’re made on the basketball court. And that’s a challenge since customers and businesses are churning out data faster than ever. Blink, and you'll miss an opportunity.

SAS Senior VP and Chief Marketing Officer Jim Davis sat down with Orlando Magic CEO Alex Martins to discuss those challenges and analytics in sports. Whether using analytics to improve the game or to improve customer relationships, Martins understands the pressures and opportunities of doing it right.

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Alex Martins is the CEO of the Orlando Magic.

Jim Davis: Are analytics changing the way you do business?
Alex Martins: We adopted an analytics approach years ago, and we're seeing it transform our entire organization. Analytics helps us understand customers better, helps in business planning (ticket pricing, etc.), and provides game-to-game and year-to-year data on demand by game and even by seat.

For example, analytics helps us forecast ticket sales. It has helped transform the game itself. Moneyball is truly at play in professional sports now. GMs and analytics teams look at every aspect of the game, including movements of players on the court, to transform data to predict defense against certain teams. We can now ask ourselves, "What are the most efficient lineups in a game? Which team can produce more points vs. another lineup? Which team is better defensively than another?"

We used to produce a series of reports manually, but now we can do it with five clicks of a mouse (instead of five hours overnight in anticipation of tomorrow's game). We can have dozens of reports available to staff in minutes. Analytics has made us smarter.

Davis: What is big data to you?
Martins: Big data helps us understand customers better. It helps us gather personal preferences to customize the experience they have with our organization. Big data helps us forecast our business – on a year-to-year, game-to-game, hour-to-hour basis – to determine demand for a specific seat. Before, we priced seats on lower vs. upper bowl. Now, based on our big data, we can measure demands for specific seats, not whole sections. Big data helps us figure out what to charge for tickets by opponent and by game.

Big data helps us predict our business moving forward. It has helped by giving us a predictive renewal model – the core of our business is season ticket holders. SAS helped us create a model based on use of tickets, merchandise purchases, season ticket holder events and more to help us predict who we need to reach out to for renewal.

Davis: What are the critical decisions you rely on?
Martins: The core of our business is our season ticket holder base. We work hard to get 10,000 season ticket holders every season. We have to determine what will create greatest opportunity to sell out every night.

In the past, we had a touch point program with sales reps who interacted periodically throughout the year, without knowing a lot about the customers. Now the analytics allows us to model out the perfect circumstance by which a season holder will renew. We now know who their favorite players are, and can reach out to them with things that make a difference, and make it personal. Through our scoring model, we can reach out to them with resell and merchandising options that appeal to them personally. Predictive modeling is where we succeed.

Davis: You can buy the best data repository, best analytics or fastest platform, but you won't succeed unless you deal with cultural issues within your organization. What are the issues that organizations face in terms of adoption and how to get over them?
Martins: You have to have belief and buy-in at the top of the organization for analytics teams to have support to grow your approach. Other CEOs in the NBA have been slow to adapt. A lot of our execs and coaches are used to how it's always been done. Saying "I know in my gut that guy can play" without data to support it makes it hard to adapt. Our teams have developed models and forecasts that hit on the money. It only takes a couple of proof instances to help transform folks into believers of analytics. Our analytics team reports to the CFO. We meet biweekly to see how it's working, what we want to do in the future. For us, you have to have buy-in from the top down to transform it across all the silos.

Davis: You've heard us talk a lot about high-performance in-memory analytics – loading billions of rows in memory to turn processing time from hours to minutes. Have you thought about what that means to your organization?
Martins: Getting real-time data is the next step for us in our analytical growth process. On a game day, getting real-time data to track what tickets are available and how to maximize yield of those tickets is critical. Additionally, you're going to see major technological changes and acceptance of the technology on the bench to see how the games are played moving forward. Maybe as soon as next season you'll see our assistant coaches with iPad® tablets getting real-time data, learning what the opponent is doing and what plays are working. It'll be necessary in the future.

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