Decision-Making in the National Basketball Association
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DECISION-MAKING IN THE NATIONAL BASKETBALL ASSOCIATION: THE INTERACTION OF ADVANCED ANALYTICS AND TRADITIONAL EVALUATION METHODS by JONATHAN MILLS A THESIS Presented to the Lundquist College of Business and the Robert D. Clark Honors College in partial fulfillment of the requirements for the degree of Bachelor of Science June 2015 An Abstract of the Thesis of Jonathan Mills for the degree of Bachelor of Science in the Lundquist College of Business to be taken June 2015 Title: Decision-Making in the NBA: The Interaction of Advanced Analytics and Traditional Evaluation Methods Approved: _______________________________________ Joshua Gordon, Primary Thesis Advisor Decision-making in the National Basketball Association (NBA) occurs in the three general contexts of player evaluation, team evaluation, and roster construction. This thesis will explore the interaction of two primary methods of evaluating basketball—“advanced analytics” and “traditional evaluation methods”—that must be integrated together to reduce risk in decision-making and achieve competitive advantage. “Advanced analytics” does not have a precise definition, but can broadly be defined in this thesis as “insights gained from quantitative data analysis about basketball.” “Traditional evaluation methods” include any form of observation, such as on-site scouting, live coaching, and game film analysis. Through an interview and survey protocol, this thesis explores methods for quantifying human intangibles, the role of “gut instinct,” NBA organizational structures, as well as the specific strategies and tools in place for key decision-makers to balance all available information. The findings of this thesis are that NBA organizations should formalize their decision- making processes with repeatable strategies and specific tools that align with the strategic plan and vision of the organization, in order to maximize team performance and pursue NBA championships. ii Acknowledgements I want to acknowledge everyone who provided help and support for the completion of this thesis. Thank you to Professor Joshua Gordon for serving as my primary thesis advisor and helping me to fully examine the various perspectives and contexts related to this subject matter. I would also like to thank Professor Whitney Wagoner for helping remove barriers, and for always being there to talk. I would like to thank Dean Terry Hunt for assisting with my project throughout the thesis prospectus course. To the rest of the Warsaw Sports Marketing Center and Clark Honors College communities, I am incredibly lucky to have had such excellent people and resources to help guide me through this strenuous and rewarding process. Thank you to the many NBA basketball operations personnel that responded to my persistent emails, met with me at the 2015 Sloan Sports Analytics Conference, and participated in my interview and survey protocol. Your insights are sincerely appreciated, and this project would absolutely not be possible without you. Thank you to my friends and roommates for being understanding when I disappeared to write for long periods of time throughout our senior year. You know who you are—you have helped me grow tremendously, and I will never forget our times together at the University of Oregon. Finally, thank you to my parents, Geoff and Donna Mills, for their never-ending love and support throughout this entire process. My dad has acknowledged me in his educational research textbooks for years—now it’s my turn to return the favor: Dad, your research expertise was incredibly helpful, and I never could have done this without your reassurance and encouragement. Thank you! iii Table of Contents Chapter 1: Introduction 1 Background 1 Defining Analytics 4 Defining Traditional Evaluation Methods 6 Player Evaluation, Team Evaluation, and Roster Construction 7 Research Question 8 Contribution to the Field 9 Outline 10 Chapter 2: Literature Review 11 History of Sports Analytics- MONEYBALL 11 The NBA Adopts Analytics- BASKETBALL ON PAPER 12 Daryl Morey and the MIT Sloan Sports Analytics Conference 14 Recent Developments and Current Landscape of NBA Analytics 17 Important Analytics Trends 17 Plus-Minus and Player Efficiency Rating 20 SportVU Data 21 Synergy 22 Implementing Analytics 23 Organizational Structure 26 NBA Analytics Case Studies 28 Houston Rockets 28 Dallas Mavericks 31 San Antonio Spurs 32 Philadelphia 76ers 33 Toronto Raptors 34 Nonbelievers and Holdouts 36 Flaws in Current Processes 39 Limitations of Analytics 39 Limitations of Traditional Methods 40 Human Intangibles 42 Milwaukee Bucks Facial Coding 43 CogSports’ “ATHLETT” 47 iv Importance of Leadership and a Strategic Plan 50 Chapter 3: Research Design 52 Participants 52 Instrument 53 Procedure 55 Limitations 57 Chapter 4: Results 59 Defining Advanced Analytics 59 Importance of Analytics 61 Advantages of Analytics 61 Limitations of Analytics 62 Importance of Traditional Evaluation Methods 64 Advantages of Traditional Evaluation Methods 67 Limitations of Traditional Evaluation Methods 69 The Interaction Between Advanced Analytics and Traditional Evaluation 70 Importance of Human Intangibles 72 Scientific or Psychological Methods to Evaluate Human Intangibles 74 “Gut Instinct” 75 Tension Between Analytics Personnel and Traditional Evaluators 76 Importance of Basketball Experience/Understanding for Analytics Personnel 77 Organizational Structure 79 Specific Strategy to Balance all Available Information? 81 Tools for Assigning Values to Different Information 82 Decision-Making Processes to Balance All Available Information 84 Chapter 5: Discussion 87 Chapter 6: Prescriptive Next Steps 100 Mixed Methods Research Design for Decision-Making in the NBA 100 Balanced Scorecards 102 Quantifying Human Intangibles 105 Biometrics and Sports Science 105 Salary Cap Considerations 106 Chapter 7: Conclusion 108 Bibliography 119 v List of Accompanying Materials 1. Interview/Survey Protocol 113 2. Email Cover Letter 117 3. Follow-up Email After Three Days 118 4. Last Email After Three Days 118 vi List of Figures Figure 1: Sports Analytics Framework 5 Figure 2: Comparing Mid Range to 3-Point Jump Shot Frequencies of 30 Teams 18 Figure 3: Data Integration 24 Figure 4: Player-Related Information 26 Figure 5: Houston Rockets Midrange and 3-Point Jump Shot Frequencies 30 Figure 6: Toronto Raptors “Ghost System” 36 Figure 7: Typical Emotions in a Successful Player and a Problem Player 45 Figure 8: What Expressions Can Say About a Player 46 Figure 9: Specific Strategy in Place for Balancing All Available Information? 94 Figure 10: NBA Organizations With Tools in Place to Balance All Available Information 96 Figure 11: Dashboard Design for Player Evaluation Decision 98 Figure 12: Convergent Mixed Methods Research Design for Decision-Making in the NBA 101 Figure 13: Visual Representation of a Generic Business Balanced Scorecard 103 Figure 14: Framework for Basketball Operations Decision-Making in the NBA 110 vii List of Tables Table 1: “Analytics are important for evaluation and decision-making in your organization” 61 Table 2: “The following ‘traditional evaluation methods’ are important for evaluation and decision-making in your organization” 65 Table 3: “Human intangibles are important for player evaluation in your organization” 73 Table 4: “Your organization uses scientific or psychological methods to evaluate human intangibles” 74 Table 5: “There is an element of ‘gut instinct’ required for decision-making” 76 Table 6: “There is tension between analytics personnel and ‘traditional’ evaluators in organizations around the league” 77 Table 7: “It is important for analytics personnel to have basketball experience and/or an understanding of the game” 78 Table 8: “Which option below best describes the organizational structure for analytics and ‘traditional evaluation’ in your organization’s basketball operations?” 80 Table 9: “Your organization has a specific strategy in place for how to balance all information gathered from analytics and traditional evaluation methods.” 81 viii Chapter 1: Introduction “With a philosophical battle brewing between the old-school Eye Test guys and the new-wave numbers guys, it’s been funny (and a little ridiculous) to watch this turn into an either/or thing. Ideally, you should blend both worlds into one larger vision.”– Bill Simmons1 Background Decision-making is critical in every walk of life. Some decisions have enormous consequences, while others are relatively meaningless. No matter the context, it is imperative to rigorously consider all available information from every source in order to make the best possible decision. As data sources continue to become more sophisticated around the world, it will be increasingly important to effectively balance all available information about a decision. “Because of big data,” according to the Harvard Business Review, “managers can measure, and hence know, radically more about their businesses, and directly translate that knowledge into improved decision-making and performance.” 2 Restaurants, retailers, and insurance companies, for example, are all using massive amounts of data to make informed, intelligent decisions, and gain competitive advantages in their industries.3 Likewise, the global sports industry has seen an enormous increase in the use of “advanced analytics,” or statistical and numerical data analysis in order to drive strategic decisions. This evidence-based approach was first widely used for player evaluation in Major League Baseball (MLB), but has since expanded to every other