THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE DEPARTMENT OF FINANCE THE ANATOMY OF A WIN: A STATISTICAL ANALYSIS OF SUCCESS IN THE NATIONAL BASKETBALL ASSOCIATION FRANK RAMON OLIU Spring 2020 A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Finance with honors in Finance Reviewed and approved* by the following: David Haushalter Associate Professor of Finance Thesis Supervisor Brian Davis Professor of Finance Honors Advisor *Electronic approvals are on file ii Abstract Anyone with an opinion on sports will have different thoughts on what they believe produces the best team. This thesis poses this debate in the realm of basketball, seeking to find the statistics that directly correlate to the most wins. Business-minded individuals can relate this to financial investing, specifically factor investing, by analyzing what elements are associated with a security’s returns and whether that change is positive or negative. In a day when sports analytics are sweeping through leagues (see the “Moneyball” effect in baseball), having such data that can be interpreted to find how a company can optimize its success, is key to executives. The observations have found that of the several data points tested, maximizing Effective Field Goal Percentage, as well as Defensive Rating, is correlated with more total wins. iii Table of Contents LIST OF FIGURES ............................................................................................................ iv LIST OF TABLES ............................................................................................................... v ACKNOWLEDGEMENTS ................................................................................................. vi Chapter 1: An Ever-changing Landscape ............................................................................ 1 Chapter 2: Statistical Management ...................................................................................... 2 Chapter 3: Not Just Offense and Defense ............................................................................. 4 Chapter 4: Data and Methodology ....................................................................................... 5 Chapter 5: Results .............................................................................................................. 10 Scatter Plots for Each Independent Variable ..................................................................... 13 Chapter 6: Prominent Team Analyses ............................................................................... 16 Chapter 7: Conclusions ...................................................................................................... 21 BIBLIOGRAPHY .............................................................................................................. 24 iv LIST OF FIGURES Panel A: Free Throw Percentage and Wins ........................................................................................ 13 Panel B: Assist-to-Turnover Ratio and Wins ...................................................................................... 14 Panel C: Pace and Wins ...................................................................................................................... 14 Panel D: Effective Field Goal Percentage and Wins ........................................................................... 15 Panel E: Defensive Rating and Wins ................................................................................................... 15 v LIST OF TABLES Regression Results .................................................................................................................. 10 Correlation Table .................................................................................................................... 11 Revised Regression Table ....................................................................................................... 12 Boston Celtics Regression....................................................................................................... 17 Chicago Bulls Regression ....................................................................................................... 18 Golden State Warriors Regression ........................................................................................ 20 vi ACKNOWLEDGEMENTS I would like to dedicate this section to those who have helped me through this whole process, including, but limited to: Professor Brian Davis, who consistently worked with me to overcome any obstacles and challenges I encountered. Professor David Haushalter, who I could always turn to for aid in understanding my data and constantly reminded me why I was performing this research. My family, for their overall support and encouragement through any rough times, but especially this semester. My friends, who were always there for a much-needed break. 1 Chapter 1: An Ever-changing Landscape Each year following the NBA Finals, a deliberation is held on what contributed to a specific team hoisting the Larry O’Brien trophy. This past season, commentators argued that the Toronto Raptors won because of the versatile abilities of Finals MVP Kawhi Leonard or the depth of Toronto’s roster, among other reasons. This argument always diverges into a comparison of what made every celebrated team so fortunate. At the root of this debate is what these teams did so much better than those that failed. Analysts will bring up certain statistics, players, events, or circumstances that they believe led to the win. However, there has to be a reason why these teams got that far. Every season, the National Basketball Association makes changes and alterations to the league to better the quality of the game, and ultimately increase viewership. These adjustments cause transformations in the way basketball is played and cause the surface-level comparisons of teams from different times to be more difficult. This includes, but is not limited to, the introduction of the three-point line, it being decreased, then lengthened again, and the removal of the “hand check” on defense (NBA). All others, including the former, were implemented to increase scoring, which could confuse those conflicts even further. As per research from The Washington Post, these rule changes have had direct consequences in the NBA. Judging off the 2018 season, scoring across the league was on an uptick, as it continued to build upon continuous seasons of improvement. Utilizing league-wide statistics from www.basketball-reference.com, it is evident that an average team’s points per game is the highest it has been since the 1970-71 season (Greenberg). Different analysts will credit a number of reasons for this, but some of the agreed logic contains a mixed belief of a lack of defense and advances in shot selection; which can be attributed to stemming from rule 2 changes. In the span of 4 years from the 2001 to 2005 season, the NBA decided to pursue an agenda of making the game more favorable for players on offense. No defensive players were allowed inside the painted area for more than three seconds, half-court advancement of the ball went down to 8 seconds, and checking of any kind was announced to be taken more seriously (Verrier). All of this, merging with a boost in the use of analytics and influx of talented players led the NBA to a positive correlation of rising efficient offense and lax defense. So, with a dynamic league rife with new regulations, playing styles, and trends, it is even more difficult to discover what aids a team, and what hurts it. The purpose of this study is to identify and examine what observable statistics contribute to success in the NBA. By then recognizing what variables correlate to a better team record, one can recommend what actions a front office could take. Chapter 2: Statistical Management Now, as basketball looks to accept an analytical view of the sport, Daryl Morey (General Manager of the Houston Rockets), is leading the way. His team has speared the “three-point revolution”. Most teams have shied away from taking long shots within the three-point arc and instead opt for a higher reward shot a few steps back. The Washington Post article backs this up by showing that just a year difference (2017 to 2018) saw a jump in three-point attempts from 33% to 35% of the total field goals attempted. This also lead to a decrease in midrange attempts, lowering to 16% from 19% (Greenberg). The year-to-year fluctuation can be seen in this graph created by the Washington Post: 3 Finally, the number of possessions a team has per a 48-minute game, or pace, has been noted as a key component in the NBA’s scoring revival. As was the case with points per game, the league has not seen such a quick and sufficient game in decades. Basketball-reference.com shows that last season, the average pace clocked in at 100.0, a high since 100.6 in the 1988-89 season. So, what does this all mean? With the various rule changes and alterations in the way the game is played, it makes it even more difficult to discern what makes a team more successful then another. It seems as though the most effective way to look at this argument is to primarily focus on statistics. By judging a team solely on the figures that they have produced, not their 4 players, events surrounding the season, or other components, one should be able to find what drives a successful basketball team. Chapter 3: Not Just Offense and Defense There is a plethora of different statistics through time