Share with your friends










Submit

Analytics Magazine

Beyond Moneyball: The future of sports analytics

Headlines

By Benjamin Alamar and Vijay Mehrotra

beyond moneyballIt has been an eventful off-season for many Major League Baseball (MLB) teams as they frantically examine opportunities to improve their rosters, including many that require significant financial investments. In particular, the Los Angeles Angels of Anaheim lured free agent Albert Pujols away from the St. Louis Cardinals with a 10-year deal worth over $240 million, while the Detroit Tigers won the bidding for Milwaukee’s Prince Fielder with a nine-year contract valued at $214 million.

Less than an hour away from the Tigers’ home ballpark, Jason Winfree is almost surely shaking his head. Winfree, an associate professor of Sports Management at the University of Michigan, recently published a paper with colleague Chris Annala in which the authors argue empirically that an MLB team’s winning percentage is negatively correlated to the level of payroll inequity across its players. Fielder’s massive contract, however, represents 18 percent of the Tigers’ payroll – and locks in nearly 60 percent of its payroll to its four highest paid players.

While Winfree and Annala’s paper may not be the final word on this very complicated question, their results and analysis certainly provide an additional piece to the decision-making process that is often overlooked. Indeed, when juxtaposed against Winfree and Annala’s research findings [1], the massive contracts offered to Pujols and Fielder provide an almost perfect illustration of the state of sports analytics in the year 2012.

Over the past several years, the sports analytics field has been growing rapidly and has attracted a great deal of interest. Many of the unconventional strategies presented in the book “Moneyball” have become part of the new conventional wisdom, while the film based on the book has grossed over $75 million and been nominated for several Academy Awards. Despite the increase in data and analysis that can potentially inform more management decisions than ever before, too many teams do not engage with the tools and results of sports analytics, even when they are lurking in their own backyard.

Why?

Barriers to Adoption of Analytics in Professional Sports

From our perspective, a variety of cultural and structural barriers are preventing a true “revolution” in the way analytics are utilized in professional sports. While some are common across all industries, several others are unique to the world of sports analytics.

Individuals and organizations both in and out of sports are naturally inclined to resist change. A shift toward basing major decisions based on data- and model-driven analysis represents a significant paradigm change, and as such it has for a variety of reasons encountered resistance. Many team executives have little or no formal experience with statistical models, and as such have a natural discomfort with and distrust for what these tools may have to offer. In addition, new ways of making decisions threaten not only individuals but also tightknit and longstanding communities. Without careful planning, the integration of new types of information and processes within the decision making process, adding analytics can lead to the culture clashes that were caricatured in the “Moneyball” film [2].

There is also a deeper, ancient cultural conflict at play in the world of sports. Traditionally, most executives on the sports side (rather than the business side) have grown up in the game, often spending years playing, coaching and/or scouting. As such, many have strongly held beliefs about the sport and its workings that are informed by a great deal of personal experience. By contrast, proponents of increased use of data and models are often not considered credible by these traditionalists because of their lack of first-hand experience in the sport.

This conflict, which occurs in front offices across the world of professional sports, is perhaps most vividly embodied by Paraag Marathe of the San Francisco 49ers. Marathe, who has been with the team for 11 years and presently serves as the 49ers’ chief operating officer, is a Stanford MBA and former Bain consultant with a passion for both football and numbers. Yet his involvement in football decisions has often been criticized by traditionalists both inside and outside of the team because he is not a “football guy” [3].

This type of distrust is often exacerbated by the general communication barrier between analysts and professionals in sports. The language of analytics is often foreign to sports professionals, just as the language of sports professionals may seem foreign to most quantitative analysts. Building an effective communication bridge between these two groups is vital to the success of the sports analytics program.

On one NBA team, an analyst quickly gained the reputation as being the smartest guy in the room but had virtually no impact on the decision-making process. His reputation was built on his delivery of analysis in highly technical terms. So while the work he was producing was innovative, it was wasted because he did not have the ability to communicate it in a manner that was understandable to the decision-makers.

Just as these cultural issues impede the progress of sports analytics, so does the current mindset about how analytics should be utilized. Today, many sports organizations view data summaries and analytic results as another type of information to be “thrown into the hopper.” This thinking has actually proven to be a significant impediment to real adoption of analytics.

In particular, this mindset positions analytics as just one more thing that too-busy decision-makers have to somehow find time for – and also something that may cause conflict within the organization. One MLB team recently found that the conflict around personnel decisions was already very intense, and that including analytic results in the process was creating a toxic atmosphere within the organization. Instead of abandoning the use of analytics however, the team brought in experts in information theory to redefine their process of combining the analytics with the scouting information, so that analysts and scouts were no longer working at cross purposes, but rather jointly toward the goal of improving the team. This allowed the scouts to see the data, models and information systems as a set of tools that could help them work more efficiently.

We believe that the organizations that are able to get past these obstacles will have the best chance to gain significant competitive advantage through the use of analytics. In particular, like the MLB team described above, the franchises who are successful in truly leveraging analytics will be those that come to see data and model results as the mechanism through which information (unstructured text as well structured data) is transformed to deliver insight to decision makers in a well-contextualized format.

So how do we get there from here?

An Emerging Industry Structure

As data and computing power continue to grow, the possibilities for the use of analytics are also rapidly growing and changing. As such it is not possible for managers and executives, even those with an interest in and a basic understanding of statistics and models, to be fully aware of the many different ways in which analytics might help their team gain a competitive advantage.

Meanwhile, there are all sorts of people – including software vendors, consulting firms, professors, passionate hobbyists and energetic fans – making all sorts of claims to all sorts of people within professional sports organizations about how their data and models can help the team win. For most decision-makers, trying to determine which proposals might actually be able to create real value for the organization – that is, separating the truly credible from the merely fanciful – is a daunting task for which they are simply ill-equipped, having neither the time to focus on the many possibilities nor the proper training to accurately assess them. As such, some organizations choose to start small and think very incrementally about what they might do in the future, with many teams doing just enough so that they cannot be accused of ignoring analytics altogether. Still other teams basically freeze in place, having either no clear idea of how to get started or any real framework to evaluate and develop an analytics program if they have somehow managed to get started.

However, the structures of the field are slowly starting to emerge. Conferences and courses that we have mentioned in previous parts of this series are the beginning. The conferences provide venues for executives to discuss what is happening in the field as well as what might be on their “wish lists” and for practitioners and researchers in the field to establish their credibility and demonstrate their skills. In turn, the existence and improvement of university courses signal that sports analytics is a serious discipline in which students can develop a course of study. These are important first steps.

The next key steps will include the development of research and training resources that are institutionalized rather than being reliant on a small number of dedicated individuals for their continued existence. We foresee the establishment of research centers at major academic institutions, possibly with partial funding from professional sports teams or leagues (in the model of the MIT Media Lab). Such research centers will allow researchers (ranging from undergraduate students to senior faculty) to interact, discuss, investigate and ultimately create innovative and relevant applications to sports; to understand existing decision-making processes; and to improve communication skills around sports and analysis in order to help revise and improve them. Like the emergent graduate training programs in analytics and data science [4], these sports analytics research centers will likely draw on faculty from disciplines such as computer science, economics, statistics and business analytics to push the boundaries of the field. Ideally, such centers will also examine the implementation of sports analytics programs and in doing so help to identify best practices (see below for more on this).

In terms of training tomorrow’s sports analytics professionals, we envision the development of rigorous programs that might be housed in business schools or in departments such as sports management, statistics or computer science. The emergence of several different types of programs would allow aspiring students to develop their skills in the area in which they are most interested, while gaining exposure to the core areas of sports analytics.

Perhaps just as important as developing the practitioners of sports analytics is developing the skills needed to become intelligent consumers. Just as many MBA programs prepare their graduates to understand possible applications of quantitative tools in a specific context (e.g., marketing, finance, operations), so too would managerially oriented courses in sports analytics enable future professionals to better understand the range of analytic applications for professional sports. Students would ideally emerge from such courses with a sense of how to establish and evaluate an analytic program within their organizations and an understanding of the nature and magnitude of competitive advantages that could be gained from them.

Professional sports organizations will see two significant benefits once these institutions are in place. First of all, they will have a larger, more credible and more broadly skilled pool of talent to choose from. Right now organizations are essentially reliant on individuals who may have some of the skills needed and are passionate about sports. As the institutions are developed, organizations will be able to more efficiently look for and identify individuals with the right mix of capabilities and training, and will also have a wider range of people to draw on.

The second major benefit is the emergence of best practices for beginning, developing and maintaining a sports analytics program that is well-grounded in research and experience. Many sports organizations may hesitate to expand their investment in their sports analytics program without an understanding of a clear, proven way forward and sense of the potential value. Well-developed road maps and ongoing research into these areas will significantly expand teams’ understanding of the possibilities of sports analytics.

Conclusion

Sports analytics will continue to evolve as a field. The pace of this evolution and adoption, however, will depend largely on how quickly leaders in sports become convinced that significant investments into analytics (data, models, information systems, and skilled personnel) will deliver a true competitive advantage. For the benefits of the explosion in data described in Part I of this series or advances in technology and analysis described in Part II to be captured by a team, the decision-makers will have to believe there is value in investing in these technologies and processes. This will not happen until analysts (and analytics champions, either inside or outside the organization) can effectively communicate that potential value to those executives.

Today, while some teams are wading into the field, many still do not utilize sports analytics in any significant way, in part because they do not know where to start or how to integrate analytics into their organization. This is where the institutional structures described here in Part III can have a significant impact on the field. These training programs and research centers can provide well trained and experienced analysts, a trusted place for teams to find these analysts, and a source of advice on how to build an analytics program.

The end result, we believe, will be a better set of data-driven tools and processes for more efficiently managing and making decisions in the world of professional sports, multibillion dollar industries that feature multimillion dollar payrolls, a variety of highly visible and complex decisions, and a growing set of opportunities for analytics.

Benjamin Alamar (quantsports@gmail.com) is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He is co-author of the annual “Football Outsiders Almanac” and a regular contributor to the Wall Street Journal.

Vijay Mehrotra (vmehrotra@usfca.edu) is an associate professor, Department of Finance and Quantitative Analytics, School of Business and Professional Studies, University of San Francisco. He is also an experienced analytics consultant and entrepreneur, an angel investor in several successful analytics companies and a San Francisco Giants season-ticket holder.

References & Further Reading

1. Annala, C. and Winfree, J., 2011, “Salary distribution and team performance in Major League Baseball,” Sport Management Review, Vol. 14, pp. 167-175.

2. See, for example, Rob Neyer’s detailed “live-blogging” of the film at http://mlb.sbnation.com/2012/1/18/2697216/live-blogging-moneyball.

3. For more details, see http://www.sfweekly.com/2005-12-28/news/offensive-line/.

4. For more on emerging graduate programs in analytics, see http://www.analytics-magazine.org/january-february-2011/85-profit-center-an-an and http://www.networkworld.com/community/blog/new-analytics-education-programs .

Note: In the first two parts of this series (accessible online: Part I and Part II), the authors discussed the evolution of the use of data and analytic models in professional sports.

Related Articles

Headlines

ITIF: Expand computer science education to keep up with demand

Computer skills are in high demand among employers in a wide range of industries, not just tech-related fields, yet despite growing interest in the subject, a new report from the Information Technology and Innovation Foundation (ITIF) finds that too few U.S. students are taking quality computer science classes at the high school and university levels. ITIF makes the case for public action to support and maintain the groundswell of interest in computer science and capture the economic and social benefits that will come from fostering a more highly skilled workforce. Read more →

Report outlines benefits of data-driven customer experiences

According to a new report by Forbes Insights and SAS, “Data Elevates the Customer Experience: New Ways of Discovering and Applying Customer Insights,” the benefits of brands evolving to data-driven customer experiences (data-driven CX) are wide-ranging. The study notes data-driven methods enhance revenue generation and enable cost reduction, as well as accelerate process efficiencies and quality improvements. Read more →

Debunking myths about harm from artificial intelligence

Artificial intelligence (AI) holds great promise for economic growth and social progress, but pervasive, inaccurate myths about hypothetical harms could encourage policymakers to retard further innovation in the technology, according to a new report from the Information Technology and Innovation Foundation (ITIF). ITIF, a leading tech policy think tank, makes the case that policymakers should actively support further development and use of artificial intelligence if we want society to reap the myriad benefits it has to offer. Read more →

Report: Technology rarely used to detect fraud

Technology is an important tool to help companies fight fraud, but many are not succeeding in using data analytics as a primary tool for fraud detection. Meanwhile, fraudsters are leveraging technology to perpetrate fraud, according to a new report by KPMG International, “Global Profiles of the Fraudster.” Read more →

CAP® EXAM SCHEDULE

CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:


 
For more information, go to 
https://www.certifiedanalytics.org.