Anyone who’s seen the 2011 movie Moneyball—the inspiring story of the 2002 Oakland Athletics baseball team and its general manager Billy Beane’s new way of analyzing player performance that literally changed the game—knows the impact data analytics can have on outcomes in sports. Fast forward to today and the sports industry continues to take data-driven performance to new levels, offering lessons for both business leaders and their teams.

In a recent MIT Sloan Management Review podcast, titled Why Sports Still Leads the Analytics Revolution, MIT senior lecturer Ben Shields provides an up-to-date look at the role of data science in sports. According to Shields, “With its keen focus on using data to gain direct competitive advantage, the sports industry sets the pace for how analytics can drive business success.” The podcast discusses the industry’s success not only in collecting data, but also in applying it across the organization to win. According to Shields, “This data is being communicated from the front office to the coaching staff and then, ultimately, to the players, who apply it to win games.”

Below are some of the lessons sports analytics can provide businesses trying to do the same today:

Start with Data Strategy

Sports teams and organizations tend to have very specific goals—like winning or beating the competition—with very clear views on the problems they need to solve to get there. Improve the offense. Replace an injured player. Experiment with a new defense. Depending on the sport, the list can go on and on. As a result, when turning to data analytics, sports organizations have a natural tendency to start with clearly defining the problem, and then putting a data strategy in place to solve it. This could involve factoring in or even creating more data sources to deliver greater intelligence and more finite strategies for success.

Invest in Data Capture

Indeed, the sports industry has taken data collection to a whole new level, including the introduction of player tracking across all major sports to capture better information on each game. The National Basketball Association (NBA) in the late 2000s introduced sport-view cameras that provide insights into the “most efficient” basketball plays—the plays that helped teams score the most points or the spots from the court where shots were most likely to go in. “If you don’t have the information you need, you need to find new ways to create and capture it,” said Shields.

Data-Driven Decision Making is Science and Art

Data needs to get into the hands of the athletes in order to improve results, and this takes a mix of both good communication and building a culture that values data. According to Shields, “One of the unique aspects of sports is that the analytics insights are making their way from the analytics team to the coach and then ultimately to the player, who is the end user.” One of the main reasons that data analytics is successful in sports is that each individual athlete—or end user—usually has a clear understanding of the value of the data and how it can help them improve. As a result, sports organizations tend to be leaders in building data-driven organizations that value analytics best practices from the front office to the playing field. 

If you’d like to learn more, you can listen to the complete MIT SMR podcast.