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Measuring Production and Predicting Outcomes in the National Basketball Association

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Measuring Production and Predicting Outcomes in the National Basketball Association Measuring Production and Predicting Outcomes in the National Basketball Association Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Michael Steven Milano, M.S. Graduate Program in Education The Ohio State University 2011 Dissertation Committee: Packianathan Chelladurai, Advisor Brian Turner Sarah Fields Stephen Cosslett Copyright by Michael Steven Milano 2011 Abstract Building on the research of Loeffelholz, Bednar and Bauer (2009), the current study analyzed the relationship between previously compiled team performance measures and the outcome of an “un-played” game. While past studies have relied solely on statistics traditionally found in a box score, this study included scheduling fatigue and team depth. Multiple models were constructed in which the performance statistics of the competing teams were operationalized in different ways. Absolute models consisted of performance measures as unmodified traditional box score statistics. Relative models defined performance measures as a series of ratios, which compared a team‟s statistics to its opponents‟ statistics. Possession models included possessions as an indicator of pace, and offensive rating and defensive rating as composite measures of efficiency. Play models were composed of offensive plays and defensive plays as measures of pace, and offensive points-per-play and defensive points-per-play as indicators of efficiency. Under each of the above general models, additional models were created to include streak variables, which averaged performance measures only over the previous five games, as well as logarithmic variables. Game outcomes were operationalized and analyzed in two distinct manners - score differential and game winner. Multiple regression analysis was used to explain the relationships between predictors and the “un-played” game‟s score differential, and logistic regression analysis was used when the game winner was the ii dependent variable. The process of entering each model‟s respective variables into the regression equations was accomplished through simultaneous entry, stepwise entry, and hierarchal entry. Statistical analyses were conducted on both the 2007-2008 and 2008- 2009 National Basketball Association seasons, which served as two populations. Taking into account goodness-of-fit measures and parsimony, superior models were identified. In regards to explained variance in score differential, the possession model with stepwise entry emerged as the best model for the 2007-2008 and 2008-2009 seasons. For predicting game winner the best models for the 2007-2008 and 2008-2009 seasons were the play stepwise entry model and the possession stepwise entry model respectively. As a whole, non-streak models were substantially more successful at explaining game outcomes than streak models. The increase in explained variance due to the entry of scheduling fatigue variables and team depth contribution factors in the second stage of the hierarchal multiple regression analysis varied among the models as well as the variables that reached statistical significance. Overall, the findings of the present study indicate little generalizability between the two NBA seasons selected for the study. In general, the variables selected for inclusion into the regressions equations, as well as their relative importance differed from one season to the next. However, the possession models were found to be the best models in terms of predictive capabilities and parsimony, and they were the most stable over the two populations. These findings serve to support the use of composite measures of pace and efficiency in future basketball research as well as decisions made by management and members of the media. iii Acknowledgements While my journey through the dissertation process has been both long and arduous, it would have been nearly impossible to complete without the support of a host of other individuals. For all the people who provided assistance along the way, I would like to express my appreciation. First, I would like to gratefully and sincerely thank Dr. Packianathan Chelladurai, Dr. Brian Turner, Dr. Sarah Fields, and Dr. Stephen Cosslett, for serving as my dissertation committee members. The feedback and suggestions you all provided at various stages in the process was invaluable, and for that I am thankful. Specifically, I would like to express my gratitude to my advisor, Dr. Chelladurai, for his guidance, patience, and support throughout my graduate studies at The Ohio State University. You are truly a wealth of knowledge, and without your assistance I would have undoubtedly struggled to have success in the program. I would also like to thank all my good friends, who over the years provided an immeasurable amount of support and encouragement. The underlying inspirations for the current research can be traced back to our countless in-depth conversations on predicting outcomes within the domain of sport. During the dissertation process, the much welcomed distractions you all provided helped me maintain my sanity. In particular, I would like to thank Oleg Mishchenko, for without your assistance in the creation of iv the two statistic conversion programs, this research endeavor would not have been possible. Finally, and most importantly, I would like to thank my parents, Robert and Priscilla, and my brother, Bryan. Words cannot express how important you all were throughout this entire process. Thank you so much for supporting and believing in me. v Vita July 4, 1982…………………………………………………..Born – Brooklyn, New York 2004………………………………………………………….B.S. Computer Engineering, Lehigh University 2005………………………………………………………….M.S. Sport Administration, Florida State University 2007 – present…………………………….…………………The Ohio State University Publications Milano, M., & Chelladurai, P. (2011). Gross domestic sports product: The size of the sport industry in the United States. Journal of Sport Management, 25(1), 24-35. Fields of Study Major Field: Education Focus: Sport Management Minor: Research Methods in Human Resources Development vi Table of Contents Abstract…………………………………………………………………….……………...ii Acknowledgements…………………………………………………………………….…iv Vita………………………………………………………………………………………..vi List of Tables……………………………………………………………………………xiii List of Figures………………………………………………………………….........…...xx Chapter 1: Introduction……………………………………………………………………1 Problem Statement………………………………………………………………...1 Theoretical Foundation……………………………………………………………9 Linear Production Function……………………………………………...13 Cobb-Douglas Production Function……………………………………..16 Constant Elasticity of Substitution Production Function (CES)…………21 Transcendental Logarithmic Production Function…………………….....21 Production Function Applications in Sport………………………………………23 Selection and Justification of Production Function Formulations………….……24 Research Questions………………………………………………………………27 Models of Performance…………………………………………………………..27 Absolute Models…………………………...………………………….…28 vii Relative Models………………………………………………….………31 Possession Models……………..…………………………….…………..34 Play Models…………………………………………………………...…37 Definition of Terms………………………………………………………………38 Assist……………………………………………………………………..40 Blocked Shot……………………………...……..……………………….40 Defensive Rating………………………………………………...……….41 Defensive Rebound………………………………………………..……..41 Depth Contribution Factor……………………...……………….….……41 Free Throw………………………………………………….……………42 Game Location…………………………………………...………………42 Offensive Rating…………………………………………………………43 Offensive Rebound………………………………………………………43 Personal Foul………………………………………….…………………44 “Played” Games………………………………………………………….45 Scheduling Fatigue……………………………………………………….45 Steal……………………………………………………………………....47 Streak………………………………………………………………...…..49 Three Point Field Goal………………………………………...…………49 Turnover………………………………………………….………………50 Two Point Field Goal…………………………………….………………50 “Un-played” Games…………………………………………………..….50 viii Significance of the Problem…………………………………………….………..50 Chapter 2: Review of Literature…………………………………………..……………..53 Individual Performance Measures……………………………………………….53 Team Based Production Research……………………………………………….59 Chapter 3: Methodology…………………………………………………………………75 Research Design…………………………………………………………….……75 Conversion of Statistics……………………………………………….…79 Subject Selection……………………………………………………………..…..81 Outcome Measures……………………………………………………………….83 Data Analysis…………………………………………………………………….85 Chapter 4: Results……………………………………………………………….……….95 Multiple Linear Regression ………………………………………….…………..95 Absolute model 2007-2008………………………….…………...……....95 Absolute model 2008-2009………………………….………………….101 Absolute model (streak variables) 2007-2008……………………...…..106 Absolute model (streak variables) 2008-2009…………………...…..…110 Absolute model (logarithmic variables) 2007-2008……………………114 Absolute model (logarithmic variables) 2008-2009………………....…119 Absolute model (streak logarithmic variables) 2007-2008……………..123 Absolute model (streak logarithmic variables) 2008-2009…………..…127 Relative model 2007-2008………………………………………..….…131 Relative model 2008-2009………………………………………..….…137 ix Relative model (streak variables) 2007-2008…………………………..141 Relative model (streak variables) 2008-2009………………………..…144 Relative model (logarithmic variables) 2007-2008………………….....148 Relative model (logarithmic variables) 2008-2009………………….....152 Relative model (streak logarithmic variables) 2007-2008…………...…155 Relative model (streak logarithmic variables) 2008-2009…………...…159 Possession model 2007-2008…………………………………..……….163
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