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Forecasting Most Valuable Players of the National Basketball Association

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Forecasting Most Valuable Players of the National Basketball Association FORECASTING MOST VALUABLE PLAYERS OF THE NATIONAL BASKETBALL ASSOCIATION by Jordan Malik McCorey A thesis submitted to the faculty of The University of North Carolina at Charlotte in partial fulfillment of the requirements for the degree of Master of Science in Engineering Management Charlotte 2021 Approved by: _______________________________ Dr. Tao Hong _______________________________ Dr. Linquan Bai _______________________________ Dr. Pu Wang ii ©2021 Jordan Malik McCorey ALL RIGHTS RESERVED iii ABSTRACT JORDAN MALIK MCCOREY. Forecasting Most Valuable Players of the National Basketball Association. (Under the direction of DR. TAO HONG) This thesis aims at developing models that would accurately forecast the Most Valuable Player (MVP) of the National Basketball Association (NBA). R programming language was used in this study to implement different techniques, such as Artificial Neural Networks (ANN), K- Nearest Neighbors (KNN), and Linear Regression Models (LRM). NBA statistics were extracted from all of the past MVP recipients and the top five runner-up MVP candidates from the last ten seasons (2009-2019). The objective is to forecast the Point Total Ratio (PTR) for MVP during the regular season. Seven different underlying models were created and applied to the three techniques in order to produce potential outputs for the 2018-19 season. The best models were then selected and optimized to form the MVP forecasting algorithm, which was validated by predicting the MVP of the 2019-20 season. Ultimately, two underlying models were most robust under the LRM framework, which is considered the champion approach. As a result, two combination models were constructed based on the champion approach and proved to be most efficient. The two finalized combination models then served as the forecasting algorithm used to predict players PTR. Using this algorithm, one of the top players in PTR will win the MVP award for the regular season of the NBA. Hence, this proposed algorithm can be used post All- Star selections to determine the Most Valuable Player. iv DEDICATION To my Father and Grandfather v ACKNOWLEDGEMENTS I would like to start by thanking God for putting me in this position in pursuing my graduate degree at the University of North Carolina at Charlotte. This experience has provided me a wealth of knowledge in the field of Engineering Management. Thank you to North Carolina A&T State University for providing me the foundation and basic skills of an engineer, as well as providing me essential experiences in my development. Thank you UNCC for prepping me throughout my graduate studies and providing me the opportunity to present this thesis capstone project. I would also like to thank my professor/thesis advisor, Dr. Tao Hong. When I initially joined UNC Charlotte’s Mechanical Engineering Management program, learning about forecasting was not in the plans. When seeking for a capstone project, I wanted to do the thesis option to give myself enough time to provide a captivating and prominent piece of work. I came across the opportunity to base my project on sports forecasting from Dr. Hong. Certainly, I already had a passion for sports, especially basketball. I then connected with Dr. Hong, who advised me to take his forecasting class so I could learn about programming and different forecasting techniques. The class went extremely well, and I grasped on quickly, thus allowing me to apply that knowledge to my project. Ironically, Dr. Hong was also a basketball fan, and has been a strong pillar of support. Thank you to Dr. Linquan Bai and Dr. Pu Wang who served on my thesis committee. They also played a pivotal part in formulating my thesis, providing me with beneficial feedback and concepts to help improve my work. In summary, with the support from advisor and committee, I selected the thesis topic of forecasting the Most Valuable Player vi of the regular season of the National Basketball Association and I can conclude that this was an exceptional project where I was able to apply my knowledge to my passion. Lastly, I would like to thank my family, girlfriend, and numerous friends who have endured this long process with me while always offering support and love. vii TABLE OF CONTENTS LIST OF TABLES ......................................................................................................................... ix LIST OF FIGURES ........................................................................................................................ x LIST OF EQUATIONS ................................................................................................................. xi LIST OF ABBREVIATIONS ...................................................................................................... xiii INTRODUCTION .......................................................................................................................... 1 1.1 The Game of Basketball.................................................................................................. 1 1.2 Sports Analytics .............................................................................................................. 4 1.3 Most Valuable Player ...................................................................................................... 7 1.4 Predicting Most Valuable Player .................................................................................... 9 LITERATURE REVIEW ............................................................................................................. 12 2.1 Sports Analytics ............................................................................................................ 12 2.2 Basketball Analytics ..................................................................................................... 17 2.3 Forecasting the MVP of the NBA ................................................................................. 19 THEORETICAL BACKGROUND .............................................................................................. 23 3.1 Linear Regression Model (LRM).................................................................................. 23 3.2 K-Nearest Neighbor (KNN) .......................................................................................... 25 3.3 Artificial Neural Networks (ANN) ............................................................................... 27 METHODOLOGY ....................................................................................................................... 29 4.1 Unanimous Votes .......................................................................................................... 29 4.2 Exploratory Analysis .................................................................................................... 33 4.3 Algorithm ...................................................................................................................... 51 4.4 Forecast Techniques...................................................................................................... 61 RESULTS ..................................................................................................................................... 66 5.1 LRM .................................................................................................................................... 66 5.2 KNN .................................................................................................................................... 69 5.3 ANN .................................................................................................................................... 73 5.4 MVP Algorithm .................................................................................................................. 77 5.5 Discussion of Results .......................................................................................................... 80 CONCLUSIONS........................................................................................................................... 83 viii REFERENCES ............................................................................................................................. 85 APPENDIX ................................................................................................................................... 88 APPENDIX A: Datasets ........................................................................................................... 88 APPENDIX B: R Program Models .......................................................................................... 88 ix LIST OF TABLES TABLE 4. 1 Shaquille O'Neal 1999-00 Statistical Highlights ..................................................... 29 TABLE 4. 2 Shaquille O'Neal 1999-00 Statistical Milestones ..................................................... 29 TABLE 4. 3 LeBron James 2012-13 Statistical Highlights .......................................................... 30 TABLE 4. 4 LeBron James 2012-13 Statistical Milestones ......................................................... 30 TABLE 4. 5 Stephen Curry 2015-16 Statistical Highlights ......................................................... 31 TABLE 4. 6 Stephen Curry 2015-16 Statistical Milestones ........................................................ 31 TABLE 4. 7 Reproduced Model Forecast Results........................................................................ 55 TABLE 5. 1 LRM Model Rankings ............................................................................................. 66 TABLE
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