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Evaluating College Prospects and Their Potential to Succeed in the National Basketball Association: Identifiying Significant Quantifiable Measures

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Evaluating College Prospects and Their Potential to Succeed in the National Basketball Association: Identifiying Significant Quantifiable Measures EVALUATING COLLEGE PROSPECTS AND THEIR POTENTIAL TO SUCCEED IN THE NATIONAL BASKETBALL ASSOCIATION: IDENTIFIYING SIGNIFICANT QUANTIFIABLE MEASURES A THESIS Presented to The Faculty of the Department of Economics and Business Colorado College In Partial Fulfillment of the Requirements for the Degree Bachelor of Arts By Samuel L. Markin May 2018 EVALUATING COLLEGE PROSPECTS AND THEIR POTENTIAL TO SUCCEED IN THE NATIONAL BASKETBALL ASSOCIATION: IDENTIFIYING SIGNIFICANT QUANTIFIABLE MEASURES Samuel L. Markin May 2018 Economics Abstract As more money is committed to players, it is more pivotal than ever for NBA teams to find ways to accurately and comprehensively find young, cheaper talent in the draft better than their competitors. In this study we use all publicly available information about players that is available. The focus of this paper is to examine factors that contribute to early career success among professional players in the National Basketball Association (NBA) and to better understand these quantifiable measures available on draft day that can aide in predicting players' future performance. In this study we run six separate OLS regressions with different sections of the data. One regression with all the player data, then we separate the others by one and two year players, three and four year players, guards, forwards, and big men. The independent variables used in this study are player position (point guard, shooting guard, small forward, power forward, center), the player's college win shares per year, win shares in their first year, average college win shares per year, quality of conference, NBA combine agility, combine no-step vertical leap, and dummy variables for when they came out of college. The dependent variable in all of the regressions is the average wins per year through five years of each players NBA career. Key Words: National Basketball Association, Player Development, Sports Economics JEL Codes: (L83, Z2, Z20) ii ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS Signature iii TABLE OF CONTENTS ABSTRACT…………………………………………………………………….….. ii. 1. INTRODUCTION………………………………………………….……… 1. 2. Previous Literature and Theoretical Considerations………………….……. 3. 2.1 Win Shares………………………………………………………….. 7. 2.2 Formula for Crediting Win Shares……………………………...…... 9. 3. DATA & METHODOLOGY………………………………………..…....... 10. 3.1 Regression Equations…………………………………………...…… 14. 4. RESULTS & ANALASYS………………………………………………..… 16. 4.1 General Findings……………………………………………….…… 18. 4.2 All Player Data…………………………………………………….... 18. 4.3 One & Two Year Players…………………………………………..... 20. 4.4 Three & Four Year Players………………………………………….. 20. 4.5 Guards Wings and Big Men…………………………………………. 21. 5. CONCLUSION……………………………………………………………… 22. 6. REFERENCES………………………………………………………………. 24. iv Introduction In an age where football seems to be on the decline as America’s number one sport per viewership and revenue, engagement in basketball, specifically the National Basketball Association has only increased. The NBA has become the sport people look to for superstar personalities. Players like Lebron James and Stephen Curry are continually in the news, either because of their incredible play, or because they are speaking on important social issues. Total league revenue has jumped from 2.66 billion dollars in 2001-02 season to 5.87 billion dollars in 2015-16, and finally up to 7.37 billion just a year later from last season alone (Statista). As revenues for the league have increased, player salaries have increased as well with the average salary around six million for the 2017-2018 season (basketball-reference). We are now seeing average players like Tim Hardaway make (72 million dollars) on a 4-year contract. Teams seem to be reaching for free agent talent, and handing out large sums of money in the hopes that an unproven player pans out. While the large market teams might not need to be good to generate the most revenue in the league, other smaller market teams do not have the same luxury. Small market teams do not usually attract top free agents, so the only way to acquire a superstar talent without breaking the bank for overrated players is through the draft. This brings us to the discussion about what quantifiable measures we should be using as we look to evaluate players entering the NBA draft. Many of these smaller market teams rely on their performance to drive ticket sales and revenue as well, despite the revenue sharing statues currently in place via the latest 1 collective bargaining agreement (CBA). In addition to this, tanking, or purposely losing in order to get a better spot in the draft, continues to be put under the microscope. It is discouraged to lose on purpose in order to benefit your future and so the league has considered implementing a new rule to make it so this does not take place. One potential solution that has been mentioned is that the bottom 4-8 teams will have an equal opportunity to get the top pick in the lottery. As the stakes become higher, and more money is committed to players, it is more pivotal than ever for NBA teams to find ways to accurately and comprehensively find young, cheaper talent in the draft better than their competitors. In the latest version of the CBA, rookies have a scaled contract for the first three years based on their draft position and there is a team option on the fourth year. Expiring rookie contracts also qualify for the Larry Bird exception that allows teams to exceed the cap to resign their own free agents. Teams are also able to offer their own free agents more than the max if they meet certain criteria. Every year there are tens of billions of dollars in exchanged in the NBA between teams, players, and other corporate entities with a stake in the league’s success, involved through sponsorships and advertisements. The collective bargaining agreement has been put into place to help the smaller market teams stay competitive. If a franchise can consistently find hidden talent in the draft, these rules make it so that they have a chance to keep their talent and grow organically with a salary cap friendly roster. 2 Previous Literature and Theoretical Considerations Although we have seen an increase in technology and data collection in the present day, it seems as though it has not been utilized to its full potential. Many analysts and scouts may still argue that statistics can only tell you so much, and I do agree to an extent. However, there is a reason players perceived as the best tend to have the best statistics as well. We have seen statistical analysis put to use effectively, and it has gained traction since the release of Michael Lewis’ highly publicized book, Moneyball (2003), a true story where Lewis uses statistical analysis to put together a winning team on a budget in the MLB. While I am not here to say that statistical analysis and statistical analysis alone should be used to evaluate players and decide on personnel, I do believe that it does have a place in the world of player evaluation and analysis. As we proceed we will look at previous studies that identify how players are likely to reach the NBA level, as well as studies aimed at predicting draft orders, and studies that use college statistics to predict success and evolution at the NBA level. A previous study by Musch and Grondin (2001) suggests that factors such as being born on the right side of the age cutoff as a youth may contribute to an athlete being identified as better as a result of being more physically and cognitively mature at an early age, and thus are given more opportunities and coaching to improve to reach their potential, the professional level. This has come to be known as the “season-of-birth effect” or “relative age effect”. The effect has been identified in hockey, soccer, baseball, gymnastics, and other sports. This study only shows who may be more likely to make it to the professional level, but tells us little about how the athlete will preform once they arrive. 3 There have been studies like the one of Groothius, Hill, and Perri (2007) that compares measures of NBA efficiency to draft spot of which they were chosen. They find in this study that draft position is negatively related to NBA efficiency in a given year. This shows us that scouts and franchises do get player evaluation right for the most part, however it does not explain why certain players taken earlier in the draft tend to be better, and why there are some hidden gems taken later in the draft that excel at the NBA level. A study by Berri, Brook, and Fenn (2011) showed that the most important performance variable for predicting draft order was scoring. Also in this study, many other variables were used to predict NBA performance, which includes rebounds, steals, shooting efficiency, and team success. Additionally, they find that college performance measures and age predict both draft status and performance, but differ in that other variables such as height and team success predict draft status but not NBA performance. The youth advantage, which suggests that age predicts relative success, has also been confirmed by separate analyses (Rodenberg & Kim, 2011). This leads to the explanation that franchise scouts and evaluators believe that certain measures, like scoring, are better indicators than others to use in order to make rational decisions come draft time. Many different papers have discussed the effectiveness of teams in professional sports leagues to pick valuable prospects in their respective amateur drafts. Coates and Oguntimein (2008), Camerer and Weber (1999), and Groothuis, Hill, and Perri (2007a, 2007b), all study this effectiveness as it relates to the NBA. Coates and Oguntimein (2008) were the first to use college performance to predict draft performance and NBA success.
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