Inside sports analytics: 10 lessons for business leaders

By Thomas H. Davenport

Not many people were aware of sports analytics before Moneyball, Michael Lewis’ 2003 book about the Oakland Athletics baseball team and its general manager Billy Beane (played by Brad Pitt in the 2011 movie). In the book, Lewis chronicles Beane’s analytical, sabermetric approach to assembling a competitive baseball team on a tight budget.

It was the first popular look at how analytics can transform a team and a sport, and since Moneyball the use of analytics by sports teams has grown tremendously. Almost every major league baseball team now employs at least one quantitative analyst, and so do many professional football and basketball teams. Analytical professionals are also employed by college, and even high school, teams.

Even though sports teams are relatively new to the analytics game, there are several important lessons that businesses can learn from them. The following 10 lessons come from research I conducted on more than 25 professional teams that resulted in a report on sports analytics.

1. The importance of aligned leadership. In sports, key decisions have to be made and overseen at multiple management levels. Alignment about the methods used in making such decisions is critical for a sustained, consistent approach. The Dallas Mavericks, a National Basketball Association (NBA) team, are a prime example of alignment around analytics. They’ve hired well-known analyst Roland Beech, who sits on the bench during games. After the Mavericks won the 2011 NBA championship, team owner Mark Cuban told ESPN, “Roland was a key part to all this. I give a lot of credit to Coach Carlisle for putting Roland on the bench and interfacing with him, and making sure we understood exactly what was going on. Knowing what lineups work, what the issues were in terms of play calls and training.”
2. Aggressive use of external data, often from video and locational devices. Sports teams are increasingly using video and locational data to improve performance and decision-making. In the NBA there are six cameras in the ceiling of each arena that capture all movements of the players and ball. In Major League Soccer, each player wears a GPS-based locational device that captures all movements around the field. All Major League Baseball ballparks have cameras that track every pitch, and many teams also track every hit and fielding play with video cameras. Aside from aggressive adoption of new technology for data capture, the key lesson for businesses is the need to develop capabilities to analyze the data, the amount of which can quickly become overwhelming.
3. It’s all about the people who play the game. Sports analytics have primarily been focused on players – which ones to seek in the draft, which ones to put in the game, which ones might be overpaid. Most statistics measure individual player performance, and everyone on and off the team knows how a player is doing. Businesses, on the other hand, have been relatively slow to address HR analytics. Poorly performing businesspeople can typically keep their jobs far longer than poor athletes. Human performance analytics should be much more of a focus in the future of business.
4. Ultimately, it’s how the team performs, not the individuals. The reason professional baseball was an early adopter of analytics is that it is relatively easy to measure the performance of individual players. But baseball, like most other professional sports and all businesses, is a team sport. In the NBA, teams focus on how the team performs when a player, or group of players, is in the game versus out of it. Even if a player has mediocre point and rebound totals, the team may perform better when that player is on the court. Variations on this type of “plus/minus” analysis could be applied to businesses as well. How does a group or unit perform when a particular manager is overseeing it, and how does performance change when that manager leaves?
5. Focus on analytical amateurs. In some sports – especially baseball – individual players have begun to analyze their own performance and create improvement programs based on the results. Pitcher Brian Bannister is perhaps the best example. Bannister analyzed relatively obscure metrics on his own performance, including batting average for balls in play, and expected fielding independent pitching. While Bannister was somewhat successful in improving his own performance – he inspired Kansas City Royals teammate Zack Greinke to adopt similar analyses of his own performance. In 2009, Greinke won the American League Cy Young award for best pitcher for the season.
6. Get help from the ecosystem. Pro sports teams are essentially small businesses. They can’t afford to build up big IT or analytical staffs, to gather all their own data, or write their own software. Smart teams like the Orlando Magic and San Francisco Giants, for example, have created strategic partnerships with vendors of data, analytical software and hardware, and communications. The same is true, of course, for small-to-medium businesses – they need just as much help from their ecosystem. Even large organizations can often benefit from strategic partnerships with external suppliers of analytical resources.
7. Take an enterprise approach to organizing analytics. Most teams don’t nurture much contact between analysts working on team and player performance and analysts working on business analytics, such as ticket pricing and targeted marketing. But given their small size, teams would be better served by combining these groups of analysts and prioritizing the problems they work on. Teams like the Orlando Magic and the Phoenix Suns have done this. Businesses are more likely than pro teams to have taken an enterprise approach, but there are still many companies with analytical silos that don’t collaborate.
8. Communication is critical. I have often heard coaches and general managers say that analytics are useless in sports unless the “quants” are good at explaining their analyses and results in clear, sports-related terms. It’s often argued that if analysts don’t know baseball (or basketball or football or soccer), they won’t be able to effectively relate their analytics to those who make decisions. The same is true in business. I hear requests for people who can “tell a clear story with data” almost daily.
9. Work closely with your technologists. The technology for analyzing all these new data sources is critical – and changing fast. Analysts likely won’t get much done without a close partnership with the people who manage the video server or those who can bring season ticket-holder information into the team’s data warehouse. Similarly in business analytics, marketers, HR professionals, and product developers need to establish close relationships with IT. In sports and business, analytics is a team sport.
10. Enlist the external public to help with analysis. Amateur fans have long been some of the most talented analyzers of sports data. Some very capable amateurs, such as Bill James in baseball and Roland Beech in basketball, have been hired by teams (the Boston Red Sox and Dallas Mavericks, respectively). Some teams, such as Manchester City in the English Premier League in soccer, have released their own player and performance data to any fan who wants to analyze it. So far more than 5,000 have registered and downloaded the data. Businesses should think about open data initiatives and enlisting their customers in solving key problems.