An Investigation of Three-point Shooting through an Analysis of NBA Player Tracking Data By Bradley A. Sliz Thesis Project Submitted in partial fulfillment of the Requirements for the degree of MASTER OF SCIENCE IN PREDICTIVE ANALYTICS December, 2016 Dr. Alianna JeanAnn Maren, First Reader Thomas Robinson, Second Reader 2 Abstract In my thesis, I address the difficult challenge of measuring the relative influence of competing basketball game strategies, and I apply my analysis to plays resulting in three-point shots. I use a glut of SportVU player tracking data from over 600 NBA games to derive custom position-based features that capture tangible game strategies from game-play data, such as teamwork, player matchups, and on-ball defender distances. Then, I demonstrate statistical methods for measuring the relative importance of any given basketball strategy. In doing so, I highlight the high importance of teamwork based strategies in affecting three-point shot success. By coupling SportVU data with an advanced variable importance algorithm I am able to extract meaningful results that would have been impossible to achieve even 3 years ago. Further, I demonstrate how player-tracking based features can be used to measure the three- point shooting propensity of players, and I show how this measurement can identify effective shooters that are either highly-utilized or under-utilized. Altogether, my findings provide a substantial body of work for influencing basketball strategy, and for measuring the effectiveness of basketball players. 3 Acknowledgements Firstly, I would like to express my sincere appreciation to my thesis committee, Dr. Alianna Maren and Thomas Robinson. Their patience and support was a beacon that helped guide me to the end. Thank you! Lastly I would like to express my deepest gratitude to Dr. Rajiv Shah. His advice, collaboration, and excitement provided me the energy needed to finish my thesis amid the stresses of family life and full time employment. Without his friendship and cooperation, my work would have been only a shell of what it is. Thank you! 4 Table of Contents Abstract ................................................................................................................................. 2 Acknowledgements ................................................................................................................ 3 Introduction ........................................................................................................................... 5 Background ............................................................................................................................ 7 Review of the Literature ......................................................................................................... 9 SportVU ........................................................................................................................................... 9 Variable Importance ....................................................................................................................... 13 Methods ................................................................................................................................ 16 Make-Miss Model .......................................................................................................................... 16 Player Model .................................................................................................................................. 17 Results .................................................................................................................................. 23 Make-Miss Model .......................................................................................................................... 23 Player Model .................................................................................................................................. 25 Conclusions ........................................................................................................................... 32 References ............................................................................................................................ 35 5 Introduction Basketball is a game of athleticism, skill, positioning, and teamwork. Teams that optimize each of these facets of their game can generally expect to be successful. However, it is difficult to measure the degree to which a given strategy can influence basketball success, because there are many competing influencers (i.e. did a player make a shot because they were open, or because they are a good shooter?), and because there is so much noise mixed in with the signal (i.e. even great three-point shooters only make 40% of their shots). With the advent of player tracking data, it has become possible to explore game strategies in a new light. Player tracking data enables measurements that were not before measureable beyond subjective suppositions and terse remarks. In fact, across sports, player tracking is revolutionizing the sports-analytics movement with copious collections of fine-grained game observations, enabling an assortment of (literally) game-changing analyses. In basketball research, much work has been done to leverage player tracking data, but little work has used it to analyze three-point shooting. In my thesis: • I analyze player tracking data from over 600 games from the first half of the 2015-2016 NBA season, to find plays resulting in three-point shots. • I derive custom position-based features that capture tangible game strategies from game- play data. 6 • I propose statistical methods for measuring the relative importance of any given basketball strategy. • I demonstrate how these position-based features can be used to measure the three-point shooting propensity of players. • Finally, I show how this propensity metric can identify effective shooters that are either highly-utilized or under-utilized. 7 Background Between 2010 and 2013, the NBA equipped all of its arenas with motion capture cameras. Throughout the subsequent basketball seasons, positional data were collected in every regular season and post season game. During each game, the positions of the ball and each player on the court were recorded at a rate of 25 observations per second. This rich dataset has enabled researchers, analysts, and basketball aficionados alike to explore the game of basketball in ways that were never before possible. YonggangNiu [2014] offers an excellent description on the background of the technology that enables the collection of this data in their paper Application of the SportVU Motion Capture System in the Technical Statistics and Analysis in Basketball Games. The following paraphrases the discussion in that paper on the SportVU technology: The SportVU system (Multi-lens Tracing System) was invented in 2005, by Israeli scientist Mickey Tamir, and was originally intended for missile tracking in a military setting. The technology was also shown to have functional applications in sports. In 2008, the sports analytics firm STATS acquired the SportVU technology and focused it on the analysis of basketball games. Today, this system has been installed in every NBA teams’ home court and has captured motion data for over 1000 professional basketball games. To date, this NBA SportVU data has already occupied an important position in the academic world. The annual Sloan Sports Analytics Conference at the Massachusetts 8 Institute of Technology is the top technology event in the sports world. Among the papers submitted to Sloan about basketball last year, half were based on the data captured for the NBA by the SportVU system. The SportVU system is run by STATS Data Corporation Limited. The ceiling of every basketball gymnasium in the NBA is equipped with 6 cameras and every half-court has 3 cameras, all synchronized to each other. Collectively, these cameras capture player and ball movements, and extract XYZ locations relative to the court at a rate of 25 frames per second. Furthermore, these positional data are collected with a foreign key that can be used to join onto each game’s Play-by-Play records. This decision by the NBA to equip all of its arenas with STATS SportVU systems was pivotal in ushering in a new age of data driven strategy to the game of professional basketball. 9 Review of the Literature SportVU In his paper CourtVision: New Visual and Spatial Analytics for the NBA, Goldsberry [2012] proposed the use of spatial analytical techniques to assess NBA player’s shooting abilities. His work was one of a number of efforts beginning to challenge box-score analytics as the status quo for basketball performance assessment. He suggested that spatial analysis was vital to the study of NBA basketball, and this suggestion has only become more true in the past five years. Indeed, his work helped pave the way for the NBA to buy in to collecting player tracking data with STATS SportVU, which spawned a flurry of in-depth NBA spatial analyses that continue to contribute substantially to the domain of basketball analytics. With the advent of STATS SportVU tracking data in the NBA, basketball researchers have been able to explore in-game interactions, strategies, and player performance in innovative ways that have not before been possible. Specifically, the granularity at which the SportVU data are collected enable a precision of measurement that before was not possible in analyzing the game of basketball. Indeed, in the four years since goldsberry's seminal