THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE

DEPARTMENT OF FINANCE

THE ANATOMY OF A WIN: A STATISTICAL ANALYSIS OF SUCCESS IN THE NATIONAL 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

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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 , is correlated with more total wins.

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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

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LIST OF FIGURES Panel A: Percentage and Wins ...... 13 Panel B: -to- Ratio and Wins ...... 14 Panel C: Pace and Wins ...... 14 Panel D: Effective Percentage and Wins ...... 15 Panel E: Defensive Rating and Wins ...... 15

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LIST OF TABLES Regression Results ...... 10 Correlation Table ...... 11 Revised Regression Table ...... 12 Regression...... 17 Bulls Regression ...... 18 Regression ...... 20

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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.

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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 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- 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 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 is the highest it has been since the 1970-71 season (Greenberg). Different analysts will credit a 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 ), 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:

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Finally, the number of possessions a team has per a 48- 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 that one could use to compare teams, but some hold more significance than others. The first and most obvious is PPG or Points per Game. It is simply an average of total number of points scored per game and is the most compelling offensive statistic. After that, staying on the attacking side of the ball, there is shooting percentages. There are several to use, but all employ an equation of successful attempts divided by total attempts. Three-point percentage and then total can be indicators of how the offense is performing. Both of these data points can be used to create an effective field goal percentage, which helps to take into account the increased value of taking, and making, a three-point shot, rather than a two.

(Greenberg 2017)

Rounding out the offense, is Offensive Rebounds per Game (ORG) and Offensive Rating

(ORtg). ORG is merely the average number of rebounds recovered by the shooting team, while

ORtg is the number of points scored by a team per 100 possessions.

On the other side of the ball is Defensive Rebounds per Game and Defensive Rating.

These are the opposite of their counterparts on offense, explained above. Some miscellaneous statistics include the Assist/Turnover Ratio, which measures basically how the team is helping 5 versus hampering itself, by either aiding other players to score or turning the ball over to the other team.

Finally, as mentioned previously, pace can help to showcase offense and is found by the equation: ((Team Possession + Opponent Possession) / (2 * (Team Minutes Played/ 5)))

(“Glossary.”). Now, by applying these main statistics and a few others, how does one find what data points correlate to success?

Boiling this down to its most straightforward level, searching for the vital component in a prosperous basketball team is equal to a financial strategy called factor investing. As per an

MSCI Inc. research paper, a factor is, “any characteristic relating a group of securities that is important in explaining their return and risk” (Bender). After all, factor investing is at the basis of every financial decision, in trying to determine what will affect a security and how.

Such factors are typically split into two broad categories, either “common” or “individual firm characteristics”. As the name, would suggest, common, refers to expansive aspects found across an asset class. On the other hand, a characteristic factor has more to do with components inside one specific class (Harvey). MSCI lists six equity risk premia factors in their paper; being Value,

Low Size, Low Volatility, High Yield, Quality and Momentum (Bender). Each of these elements explains how a security return can move. For example, dealing with Momentum, explains how a security with positive recent history can earn high returns.

Chapter 4: Data and Methodology

The different facets through distinct eras of basketball make pinpointing a discernible aspect that is the catalyst of success almost impossible. The three-point revolution currently occurring, the dominance of huge centers in the 1980’s, or the tenacious defense of the 1990’s 6 are all examples. So, how does a General Manager, in charge of presenting a team consisting of differently skilled players, with the ultimate goal of maximizing the number of wins, make his decisions? Most would probably base it off players’ past performances, budget constraints and a dose of analytics. Yet some of these GM’s consistently fail in their quest, so what is it that they are doing wrong? Is there some sort of key to understanding what eventually leads to a successful NBA team?

In terms of basketball, it would be most effective to look at the factor investing aspects broadly, in the two categories listed above, “common” and “individual”. Looking at them both separately, such macroeconomic aspects can be related to league-wide actions that change how the game itself is played and understood. They should be considered as so because such rule and legislation alterations are felt by each team (security) across the league (asset class) and will affect their returns. However, such individual factors are distinct to each team, and effect their own performance. These include such statistics as Points per Game, Offensive Rating, and

Effective Field Goal Percentage (all of which were mentioned above). A study, using 9 limited factors, with data solely from the 2018-2019 NBA season, found that Offensive and Defensive

Ratings were the best predictors of wins for that year (Vaidya). So, while this may be true for the past season, it purposely leaves behind data for past seasons when macroeconomic factors truly come into play.

Finding a correlation between such statistics and wins for an NBA team falls back to factor investing. The best way to test how a factor impacts a security’s return is by running a linear regression. In a financial setting, the security’s performance would be the dependent variable, while whatever variable one chooses to test would be the independent. By running this analysis, one should be able to see if and how certain variables are correlated. 7

It is also necessary to note the possible imperfections of using a regression in such an analysis. The first being that the outcome will be particularly sensitive and greatly influenced by outliers. Just a few statistics that stray immensely from the mean will result in showing a relationship that doesn’t exist or is greatly flawed. Another drawback is that data has to be independent and not influence other statistics. Using such data points as three-point shooting percentage and Effective Field Goal Percentage would establish misguided results, due to the fact that a team’s three-point percentage is used to calculate its Effective Percentage as well.

Finally, a linear regression obviously implies that the relationship between the dependent and independent variable(s) is strictly linear, and takes on linear assumptions. Meaning that, the graph of the regression is a straight line and the independent variable is always a result of the dependent multiplied by the slope, plus a constant. Keeping in mind possible detriments of running linear regression is important when assessing the results (Flom).

Of the three types of linear regressions to run, it would be advantageous to focus on both simple and multiple linear regressions, because the goal is to find how one and/or multiple statistics are related. Yet, if it is found that one variable does not correlate to success, then a nonlinear regression will be the end result. The equation for a simple linear regression is:

y= B0 + B1X1 + E (Yan)

where Y is the dependent variable, B0 is the y intercept, B1 represents the slope of the graphed line, X1 is the independent variable, and E is the random . As for a multiple linear regression, the formula obviously includes more variables, to come out as:

y= B0 + B1X1 + … + BpXp + E (Yan) 8

The goals of these formulas are to find what relationship is occurring between the variables and if one can predict the dependent variable, y, in the future.

Finally, defining Y is critical to this process. Coaches, players, fans, and analysts will all define success differently. For one it could be individual statistics, another could be highlight plays, or winning championships. One interesting study found that from 1970-2016, the team with the best record at the All-Star Break (halfway through the season) went on to appear in the

NBA finals 56.5% of the time and would win approximately 41.3% of the time (Ewing). In the end, only one team can win the championship, but that does not mean other teams were not successful. Many would agree losing the championship or making a deep playoff run is impressive in itself and is promising looking into the future. Therefore, in terms of this research,

I would define Y, or success in the NBA, as the number of wins a certain team has.

Considering the vast number of one could use to evaluate a team, it is crucial to include an array of statistics to use in the regression for the independent variables.

Also, when choosing them, one should look to utilize data points that cover the various features of the game, such as speed, offense, defense, shooting and fouls. Connecting this methodology with research showing the overall significance of stats like Pace, Offensive Rating, and Free

Throw Percentage, lead to the five independent variables used in the regression (Crunching).

They are both Defensive and Offensive Rating, Effective Shooting Percentage, Pace, Free Throw

Percentage, and Assist-to-Turnover Ratio. Prior

Using the Regression modeling in Excel leaves you with some valuable statistics. The first of these is Multiple R, which explains how solid the relationship between the variables is and is called the correlation coefficient. Next is 푅2, or the Coefficient of Determination, which allows one to see how much of the dependent variable is explained by a singular independent 9 variable. In other terms, it is also equal to the data is the fitted regression line (Regression).

Following this is Adjusted 푅2, which is critical to this research, as it modifies 푅2 for multiple independent variables. Significance F explains how trustworthy the outcomes are and should typically be beneath .05 to be rendered useful. There are also the coefficients for each independent variable, which depict how the mean will change with a single unit change in that variable, assuming all others are kept the same. The t-statistic aids in deciding if one can reject or accept the null hypothesis, as it attempts to locate a “significant difference” between the sets of data (Editor). Working with the t-statistic works with that variable’s p-value, which tests the probability of finding extreme outputs with the true accepted hypothesis.

Finally, after explaining the specific statistics and the test to be used, one only needs to consider the actual data being analyzed. For this research, figures from basketball-reference.com will be used, as the website offers legitimate data from every year of each separate franchise’s history. Other than running the regression with wins as a factor of Offensive and Defensive

Rating, Assist-to-Turnover Ratio, Effective Field Goal Percentage, Free Throw Percentage, and

Pace; the only other limit was imposed in the amount of data used. As the three-point line was introduced in the 1979-80 season, data from teams who existed beyond that point was intentionally left out of the test as it would lead to both erroneous shooting percentages and invalid findings.

After placing the essential data from all 30 active franchises, since the 1979 season, in an

Excel worksheet, it was significant to also create a Table of Summary Statistics. Building one is a way to summarize such a large portion of data and visualize it.

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Summary Statistics

Looking at the statistics summary can help one determine where a certain team in a precise year falls in comparison to any other. The analysis in this paper will be to evaluate the statistics even further, but having such a table allows for a baseline study of the team data.

Chapter 5: Results

After running the linear regression analysis in Excel, the following table listed the outputs:

Regression Results

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When looking at the following figures, there are a few notable points to take attention of, first being the Adjusted 푅2. With an Adjusted 푅2 of almost .85, the linear relationship between wins and the previously listed independent variables is decently strong. Also, a Significance F of basically 0, tells one that the product is very reliable. Moving to the individual variable results, the p-values are especially important. Remembering that a p-value is statistically significant when it is low (closer to 0.01), Offensive Rating and Defensive Rating immediately jump out of the group of variables. With a p-value of .009, Effective Field Goal Percentage has a trend towards significance, but misses it the mark. Due to the positive and close relationship between both overall ratings, and the close significance of the EFG%, one could infer that in order to build a successful team, a manager should focus on a strong team on both sides of the ball, but that can also efficiently shoot three-pointers, over regular two-point shots.

However, in the process of analyzing a linear regression it is vital to also study the correlation between the independent variables themselves. This is because if two variables contain any similar or like elements, it will completely distort the results. In this case, it was especially important to observe the correlation between all tested variables because some of them are closely related.

Correlation Table

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A correlation table, like the one depicted above, explains how correlated two given variables are. For the previously run linear regression, most of the variables have fairly regular or low correlations, causing no concern. Yet, what should catch an eye is the large correlation between the Offensive Rating and Effective Field Goal Percentage. Although the formulas share no identical factors in them, the relationship is too strong to base the analysis on it. One may infer that this interconnectedness derives from the idea that if a team’s Effective Field Goal

Percentage is high that, theoretically, they should score more points. Therefore, if they score more points their Offensive Rating should rise as well and lead to an increased number of wins.

So, although the two variables have no direct connections, a correlation of .80575112 may be too high to work with.

After deciding to remove Offensive Rating from the analysis, a new regression output table looks as such:

Revised Regression Table

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As one can see, the new table is completely changed. By taking out one variable that held too strong of a correlation, the results vary noticeably. As per using the criteria from the previous regression and judging an output table, it is imperative to study the P-values. The lower the number the more significant it is, specifically under 0.05. According to this new linear regression

Assist-to-Turnover Ratio, EFG%, Free Throw Percentage, and Defensive Rating are statistically significant.

By creating a scatter plot for total wins and each independent variable, it is possible for one to see the relationship between the two, thereby confirming the results of the second regression.

Scatter Plots for Each Independent Variable

Panel A: Free Throw Percentage and Wins FT%

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70 y = 78.804x - 19.565 R² = 0.03096 60

50

s n i 40 W FT% Linear (FT%) 30

20

10

0 0.6 0.65 0.7 0.75 0.8 0.85 Free Throw %

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Panel B: Assist-to-Turnover Ratio and Wins

Assist-TO Ratio y = 30.612x - 6.0548 80 R² = 0.20454

70

60

50

s

s n i 40 W Assist-TO Ratio Linear (Assist-TO Ratio)

30

20

10

0 0.75 0.95 1.15 1.35 1.55 1.75 1.95 2.15 2.35 Assist/Turnover Ratio

Panel C: Pace and Wins

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Panel D: Effective Field Goal Percentage and Wins

Panel E: Defensive Rating and Wins

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By just looking at the following scatter plots, one can see obvious relationships between certain statistics and total wins per team. With positive linear graphs, Assist-to-Turnover, Free

Throw Percentage, and Effective Field Goal Percentage have strong correlations to this research’s definition of success. By solely going off this analysis, a team could infer that in order to increase its number of wins, it should focus more on developing their offense with precise three-point shooters, as Effective Field Goal Percentage takes into account the added benefit of making a three.

Also, the graphed representation of the link between Defensive Rating and Wins is a negative correlation. This is because the formula for a team’s Defensive Rating is the number of points allowed per 100 possesions. Therefore, it would make sense that the rating and number of wins has a negative correlation, because as the Defensive Rating becomes higher (the team’s defense is worse) the number of wins decrease.

Finally, Pace does have a low p-value and the scatter plot exhibits evidence of a noticeable correlation, but considering that p-value is .07, it is not worth saying that they have a strong relationship.

After this, it will be intriguing to examine these relationships across teams who were dominant for a notable period of time. These dynasties include the recent historic run by the

Golden State Warriors, ’s , and the 1980’s Boston Celtics.

Chapter 6: Prominent Team Analyses

It is evident that teams do not typically follow a classic model to attempt to maximize their win total. Coaches and general managers are often hired because they believe they know or have figured out an unexplored path to success. In many cases, they have been mostly failures, as 17 the NBA has the highest turnover rate for coaches of the four major sports leagues (NFL, NBA,

MLB, and the NHL). A 20-year study has found that a new basketball coach is hired every 2.4 seasons (Kennedy). Evidently, team presidents in the NBA do not give their coaches much time to figure out a solution. Therefore, this type of analysis is especially prevalent for team front offices in their attempt for consistent achievement. Examples of teams with persistent accomplishment and notable accolades include; the recent Golden State Warriors (3 championships in 5 years), the Chicago Bulls (under and Michael Jordan) and

Boston Celtics (with ).

For each of the previously listed teams, a regression was run using the same dependent variable (total wins per season) against the independent variables; EFG%, Assist-to-Turnover

Ratio, Defensive Rating and Pace. Beginning with the Boston Celtics, from 1979-1994, a regression output table looks as so:

Boston Celtics Regression

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The variables to take notice of are Effective Field Goal Percentage and Defensive Team

Rating, respectively. Many would attribute this to the All-Star lineup Boston employed during this time. Dominant scorers and skilled shooters like Larry Bird and Kevin McHale attribute to the strong relationship with EFG%. As shown in the top 10 ranking of all time three-point shooters, Bird had a career average 37.6% success rate and was the top shooter of the

1980’s (Goss). In addition, a nine-time All-Defensive Team player in and an increasingly physical view on defense (rather than currently), made for a team that was ultimately hard to score on. This team put together a squad that knew how to shoot efficiently, with more emphasis on three-pointers, while playing rigorous defense on the other end of the court; leading them to 3 championships over the period.

Another appropriate team to study is the 1984-1998 Chicago Bulls, brought into the national spotlight and lead by arguably one of the best players of all time, Michael Jordan. With the same variables, the regression output is:

Chicago Bulls Regression

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Following the set rules of a strong 푅2 and low Significance F, the regression is substantial enough to analyze. According to the output, the Bulls magnified their wins by doing exceptionally well in Assist-to-Turnover and EFG%. Also, a case could be made for Defensive

Rating, as it still low. An idea behind this is that the Bulls were shaped around Michael Jordan and had few weak spots on the roster. Jordan’s sidekick was and they formed one of the most lethal duos in NBA history. The two players combined, led to offensive greatness with lockdown defense and were the core of a team that won 6 championships. It should be noted that these teams also had some amazing complimentary pieces like and Steve

Kerr (ranked on the same top shooters list that Larry Bird was).

One last renowned team to mention is the Golden State Warriors. The previously mentioned teams were dominant in the 1980’s and 1990’s, so it would be prudent to also conduct an inquiry into a modern team working with common rules and current players. No other team in the current NBA exemplifies continued success like the Warriors. In the past 5 years alone, they have made it to the NBA Finals every year and won three of them. Spanning from 2006 to this past season, Golden State’s output table is:

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Golden State Warriors Regression

For any NBA fans, this table actually may be a bit surprising. The Warriors are infamous for their backcourt of and , who some could argue are the best shooters in basketball history. However, after observing the output statistics, it is easy to see that

Defensive Rating has a noticeable relationship with a higher number of wins. After doing some external research, observations made while on the court back up the regression conclusions as well. Another Bleacher Report article describes that 73-win, 2016 Warriors were not only great at scoring the ball, but even better at defending. The author attributed this to the team having multiple players who were athletic enough to guard various positions on the court (Murphy). In doing so, it made it harder for teams to switch onto different defenders and exploit a mismatch when any Warrior could guard any opposing player. It should also be noted that for this particular team, Defensive Rating had the highest effect on standard deviation of the variables. 21

This does not mean that other inputs were not important, as is evident by the team’s historic shooting, but that it simply was a superior indicator to total wins.

Chapter 7: Conclusions

There will never be a perfect, unblemished strategy to enhance the total wins, and ultimately earn a championship. Every year there will be rule changes, shifts in playing styles, coaching tactics, and new stars dominating possessions. The team-based regressions illustrate that a multitude of strategies can be used to discover a path to success. However, there are certainly approaches and steps coaches and managers can take to improve their team and hopefully maximize their wins, according to this study’s results. It is also important to consider that this analysis was based off of five statistics many agree on to be vital indicators of success, but that there is an overabundance of data points that are tracked and could be used.

While some may see the outcomes as palpable and interpretations thereby vague, looking just a little closer at them could yield some valuable insights for NBA franchises. Per the league- wide examination, Assist-to-Turnover Ratio, EFG%, Free Throw Percentage, and Defensive

Rating are statistically significant, and therefore play a considerable role in winning more games.

So, while judging this at surface-level, one may deem that in order to prevail in the NBA, it is important to field a team productive in most facets of the game (obviously much easier said than done). Yet, due to salary cap requirements, players choosing their own teams, and franchises drafting certain players, it is virtually impossible to do so. So, based off those findings, what can a team do?

Deriving a solution from the regression outputs, a team should build a team around players who can both efficiently shoot the ball (more specifically from three-point range) and be 22 versatile enough on defense to cover a few positions. Where does this conclusion come from?

First, being the EFG%, it is obvious that a team needs to shoot better and score more than an opposing team, as it proves in the study. This ties into Free Throw Percentage and Assist-to-

Turnover (A/TO), as with higher statistics for both, teams will be scoring more methodically, rather than hurting the rest of the team. Finally, demonstrated by the team-by-team analysis,

Defensive Rating is a key to any championship-level team. After all the cliché of, “Offense wins games, but defense wins championships”, came about for a reason. As proven by the Warriors, the ability to play defense, and more importantly switch to numerous opponents, restricts scoring options for opposing teams. Putting together proficient defensive attributes with accurate shooting evidently creates the ideal player to recruit to a team. “Where is the indisputable, authentic, qualitative evidence?”, a typical sports fan may ask.

Hone in onto the three teams previously examined. First, the Boston Celtics of the 1980’s assembled a team of well-rounded basketball players. Not athletes who were solely high-volume scorers or only knew how to play defense. Their team was full of those who knew how to choose what shot to take, and play solid defense on the other end. The Chicago Bulls employed who some consider the best two-way player to ever pick up a basketball. Michael Jordan was named to 9 All-NBA Defensive Teams and was also the 1998 Defensive Player of the Year

(Mihajlovski). Centering a team around someone who could shoot the ball with accuracy (not always astonishing totals), and lockdown his opponents created the perfect infrastructure for the

Bulls. Finally, for all of the Warriors’ trips to NBA Finals, they have had the benefit of a core of

Stephen Curry, Klay Thompson, . Assembled, it is two of the best shooters in league history and a defensive juggernaut. None more importantly, possibly, than Klay

Thompson. That influence may have been on display when he actually was not on the court. 23

During last season’s NBA Finals when Thompson was out after hurting his hamstring and also tearing his ACL, the team felt his absence (Flannery). Not only did they miss his almost 42% career three-point shooting percentage (per basketball-reference.com), but his duties on defense.

Curry and Thompson are universally known as the “Splash Brothers” for their accomplishments behind the three-point arch. Yet, although Curry is widely accepted to be the better player, Klay bears the duty of guarding his opposition or the rival’s best player. Klay Thompson’s defensive prowess may present itself through even the team regression, as Defensive Rating was the single variable to be statistically significant and have a correlation with higher wins for the Warriors.

To conclude, if a recommendation was to be made to a team, it would be search for and recruit players with a high Effective Field Goal Percentages and that perform at a high-defensive level. To tie it back to the example of Klay Thompson and the Warriors, his team lost the

Toronto Raptors last year thanks in large part to Kawhi Leonard (who some believe to be the league’s best two-way player). Such players that shoot at solid percentages, while guarding a number of positions are consistently at, or near the , of the core of prosperous teams.

Evidence includes Leonard’s Clippers, Lebron James’ , Giannis

Antetokounmpo’s , and Klay Thompson’s Golden State Warriors. There is a reason why such prolific scorers like and have yet to be the central figure in a flourishing team, like those previously listed.

In summation, even with the implementation of the previous recommendations, it is not guaranteed to always yield the desired outcome, due to an ever-changing field of factors.

However, based on decades’ worth of data, this strategy appears to produce a peak number of wins. And in that case, someone should send this thesis to the .

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