Data and analytics have the potential to provide sports organizations with competitive advantages both on and off the field. But how are organizations incorporating analytics into their decision-making process? What challenges do they encounter in the use of analytics, and what effect do changes in technology have on these organizations? In the second edition of Sports Analytics, Benjamin C. Alamar shares insights from the Sports Analytics Use Survey 2.0. In this Q&A, Alamar discusses some of the findings in this survey, changes in field since 2013, and how businesses can improve their use of analytics. 

Q: As part of the foundation of the second edition, you surveyed executives in sports about analytics in their organizations. What were some of the most surprising findings?

Benjamin C. Alamar: I conducted the  Sports Analytics Use Survey 2.0, which aimed to provide the sports industry with a baseline understanding of how analytics are used. In total, 163 individuals who work for 119 different organizations from 15 different sports answered the survey. The respondents have over 1,000 years of collective experience. From this group, 53% of respondents reported that statistical analysis is used regularly in decision making; 58% report data in their organization is mostly or fully centralized; but only 32% of respondents report data are regularly presented clearly and consistently. What this suggests is that while sports organizations have grown significantly around the availability of data and the use of analysis in decision making, there is still a major opportunity to improve how data and analysis are communicated. Organizations can clearly improve the value that they get from their investment in data and analytics by refining how  data are shared and discussed. When data and analysis are presented consistently, then decision makers will move through the steepest parts of the learning curve and become more comfortable with the information and where it fits into their decision-making process—which leads to more effective and strategic uses of the data.

Additionally, 83% of respondents said that their analytics group would grow over the next five years. This demonstrates how sports organizations continue to get value from data and, as data and technology evolve, are looking to continue to increase their use of data.

Q: What have been the most significant changes in the field of sports analytics since the first edition was published in 2013?

Alamar: Technology around data collection and analysis continues to grow at incredible rates. In 2013, NBA teams were just starting to get access to player tracking data that gave the location of every player twenty-five times a second. This type of data has fueled the expansion of analytics teams in every league where it has been introduced, which includes the NFL, NBA, MLB, NHL, Premier League, and more. The datasets are much larger—millions of rows of data per game—than the play-by-play data used previously and are far more complex. Player tracking data also level up the type of questions that data can help answer. In the NFL, for example, play-by-play data allowed analysts to answer broad strategic questions like whether a team should pass more often or go for it more often on fourth down; player tracking data now allow for automated metrics that measure the skill of offensive and defensive linemen as well as how effective a wide receiver is at getting open. These data are now evolving to where, instead of just a whole-body location, twenty or more points on a player’s body are tracked at the same time, allowing for example for deep analysis. for example, of a basketball player’s shooting mechanics. At the same time as the data have improved, tools to analyze those data, such as machine learning and artificial intelligence, have advanced and become widespread so that the depth and speed with which data can be analyzed have grown. The types of questions that decision makers can ask as well as the speed with which those questions can be answered have expanded exponentially since 2013.

Q: The book addresses executives and decision makers in sports. Why is it important for them to understand the concepts discussed in the book?

Alamar: Organizations are investing millions of dollars in people and technology to handle the massive growth in data in order to gain a competitive advantage on both the sport side and the business side of the organization. Understanding how to integrate this investment effectively into how the organization operates is key to maximizing the return on that investment. From the structure of the data group to effectively understanding how to use different types of data in decision making, the topics in the book are key to truly capturing a competitive advantage. For example, organizations are often faced with whether to build their own systems or hire consultants to build it for them. Understanding the different dimensions of these choices and how they can affect the organization for years to come is vital to reaching the right decision.

Q: What are the areas with the most room for improvement for organizations when it comes to their use of analytics?

Alamar: The two areas with the greatest potential for improvement for teams and organizations generally are at the opposite ends of the process. The first area is around data management. As the number of data sources continues to expand, organizations increasingly struggle with integrating new data sources into their existing systems so that they can merge the data with existing analysis. This can be caused by so-called tech debt, in which an organization’s systems are relatively old and not built to effectively handle an ever-expanding data landscape. Or it could be because each data provider has its own technological challenges and structures data in unique ways that can make it a challenge to fully integrate with even the most sophisticated data systems. As organizations improve in this area, their data will become more accurate and more complete, which will, in turn, make the information being delivered to decision makers more effective.

At the other end of the data-analytics process is how the data are actually used in decision making. Decision-making processes need to be engineered to effectively make use of the kind of data that is available. For example, the process for using the data on the effectiveness of a marketing campaign that leverages millions of visits to an organization’s website is different from the process for using data from the thirty games of an eighteen-year-old’s college career. Data analytics is more prominent in most organizations’ decision making than it was a decade ago, but there is still significant room to grow around the sophistication with which data are used.