NBA Draft Decision-Making using Play-by-Play Data

Alex Beene

1. Leading with a focus on infinite goals

In this paper, we suggest a different approach to drafting in the National Basketball Association (NBA). There are two main objectives to this approach—leading an organization with a focus on infinite goals and building a model focused on simplicity to help decision-making using play-by-play (PBP) data. Leading an organization and selecting a person in the NBA Draft go hand-in-hand. Leadership is not discussed in the current NBA Draft modeling research, and this paper explores how to lead effectively in the draft selection process. Data Science is simply a field that helps us make more informed decisions, so leadership is the most important element to study in NBA drafting. We must understand that analytics are not a “one-size-fits-all” solution to drafting.

A model helps in understanding who is most likely to be an efficient NBA player on the court. However, selecting a person to be part of an organization is not the same as betting on sports or selecting stocks. The person selected must be a vital part of the organization’s culture and must work together with teammates and staff to achieve synergy. Furthermore, with an average of one first round pick per year, an NBA organization cannot minimize risk across a diverse portfolio, as in sports betting or the stock market. It is a necessity to understand the subjective information that will add to the knowledge-base of the decision—detailed basketball insights, background on a prospective draftee’s values and their fit within the organization. These insights must be considered with analytical results, and the front office must come together to make the best long-term decision.

A common definition of success in the basketball world was defined by John Wooden in 1934— “Peace of mind which is a direct result of self-satisfaction in knowing you did your best to become the best you are capable of becoming”. This definition stemmed from his father’s advice, which is our definition of infinite goals: “Don’t try to be better than somebody else, but never cease trying to be the best you can be”[1]. People within an organization are to focus on growth rather than an end-result whose goal is focused on winning and is finite in scope. Though finite results are a measuring stick for the culmination of great leadership and culture, if a leader builds on limiting finite goals, such as a playoff berth or divisional title, how will performance progress with the goal for the season in-hand and in the rear-view mirror? If focus is on our infinite goals, then we never hit plateaus caused from achieving finite goals. Such goals limit our ability to be the best version of ourselves. “Finite players play within boundaries; infinite players play with boundaries”[2].

The player drafted must thrive in a culture of growth, grit and helping their teammate when no one else is looking. A player’s beliefs must be taken into account, as it is necessary for a thriving organization to build with people who believe what their organization believes [3]. A culture that acts consistently by their clearly-defined infinite goals will have a much higher chance of facing challenges and growing through them. Beliefs, and the belief-system within an organization, will determine whether their people are empowered to grow and become better as people and teammates through adversity. Having people in an organization to empower one another is critical.

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A common process in deciding who to select in the NBA Draft is triangulation. The three parts to triangulation are eyes, ears and numbers. When focusing on numbers, one must understand that a central part of triangulation is to avoid cross-contamination. If an analyst converses with scouting personnel in parallel with building models to forecast NBA performance, the work the analyst does will be directed by this expert information at some level. Even if this is a subconscious process, after conversing with the scout in detail, the analyst now has a base rate that biases him or her towards the information they received from the scouting personnel. This is considered cross-contamination. Similarly, scouting personnel are to avoid discussing analytical results with analytical personnel during the season to avoid having a base rate that biases them towards certain players. Scouting and analytics professionals are to act in accordance with their gifts, abilities and expertise without contaminating the others’ detailed focus and progress. An unbiased approached, which is not influenced by outside voices, rigorously assesses organizational fit for each prospective draftee.

Assessing standards, such as discussed by Bill Walsh in The Score Takes Care of Itself, can be swayed by contaminated information. Team members who live by high standards— such as “Take pride in my effort as an entity separate from the result of that effort.” “Put the team’s welfare and priorities ahead of my own.” “Demonstrate respect for each person in the organization and the work he or she does.” “Honor the direct connection between details and improvement, and relentlessly seek the latter” [4]—are vital to a thriving organization. An organization’s culture will influence players to grow in these areas, and leaders in such cultures are both demanding and supportive. Conversely, working with players who immediately contribute positively to the culture are vital in creating unshakable core values that are reinforced by daily, consistent action. Evaluating such characteristics, values and beliefs is a subjective process that requires the upmost honesty, deliberate focus and unbiased assessment. Rigorous assessments of standards may be gathered in part through an unbiased scoring of each prospect on a Grit scale and a Growth scale [5].

2. Building a simple, explainable model from 30,000+ metrics

PBP statistics bring specificity and clarity to a projection. They give us detailed take-aways in conversation with scouts and front office personnel regarding why a player is projected to be an efficient NBA player. These metrics include statistics versus the top 100 teams in National Collegiate Athletic Association (NCAA) Division 1 basketball, statistics for times within games when the score is close, shots near the basket, tip-ins, lay-ups, shot types from long two, shot types from three, assisted shots by shot type, assists by shot type, recovered blocks, putbacks, dunks and turnovers by type. Statistics versus top 100 teams help us take into account players who consistently face difficult opponents. On-off statistics are also calculated from PBP information1. On- off statistics are box-score, team statistics while a player is on the floor and off the floor. Subtracting metrics while on the floor and metrics while off the floor gives us a view of the player’s impact on that statistic. This allow for clarity into how a player affects their team in each area of the box-score.

With the understanding that collaboration and communication are key, here is our approach to building a simple model using PBP information. Since players have the opportunity to sign a

1 NCAA PBP and on-off data are provided by Will Schreefer [6]. The data is then stored in a database, which is used for analysis.

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contract after year four, we will predict the average of seasons three thru five of each player’s NBA career.

2.1. Metric descriptions Box Plus-Minus (BPM) [7] is the metric we use to evaluate NBA performance. The creator of BPM, Daniel Myers, regressed efficiency-based statistics onto Real Plus-Minus (RPM), which measures a player’s effect on their team adjusting for factors such as who they play with or against. BPM accounts for the league’s three-point attempt rate and each team’s True Shooting%. Throughout this paper, we will refer to BPM as overall efficiency, Defensive BPM as defensive efficiency and Offensive BPM as offensive efficiency. Team metrics will be explicitly stated, and all other metrics in this paper are player metrics. BPM includes parts that add up to overall efficiency. One such part is REB&AST Efficiency, which includes Offensive Rebounding Percentage (ORB%), Defensive Rebounding Percentage (DRB%) and Assist Percentage (AST%). Efficiency-based statistics estimate box-score statistics, such as rebounds or assists, divided by how many opportunities they had.

Dean Oliver, the author of Basketball on Paper, created an Individual Offensive Rating and Individual Defensive Rating. His metrics estimate players’ impact on their teams on both sides of the ball. He notes the poor evaluation of defensive performance through the box-score, even when accounting for individual possessions [8]. Since BPM is statistically related to RPM, which takes into account defensive factors outside the box-score, we use BPM as our measure of performance. To keep Dean’s metric from being confused with individual Net Ratings calculated on nba.com, we will refer to his Individual Offensive Rating minus Individual Defensive Rating as Dean’s Net Rating.

PBP data allows for the calculation of statistics which do not include the parts of games in which one team has a large scoring margin over another team. Many refer to these metrics as “non- garbage” statistics. However, these parts of the game can be great opportunities for growth in a thriving organization. So, instead of “non-garbage”, we refer to these statistics as close-game statistics. Close-game statistics is a more appropriate phrase for an organization whose main directive is growth and effort, rather than the score. The words personnel use from the top to the bottom are extremely important for culture. Close-game statistics help us differentiate player statistics in close-game situations from statistics amassed while the game outcome was likely decided. This helps when projecting a player’s NBA efficiency.

Ken Pomeroy’s Adjusted Net Rating—each team’s rating adjusted for strength of schedule, which he calls AdjEM [9]—is used in the calculation of NCAA BPM2. We use Ken’s adjusted net rating alongside player metrics, which allows for us to calculate BPM since 2007. Our calculated NCAA BPM has a 48% correlation to NBA BPM in years three through five. Ken’s adjusted net rating is used to find the Top 100 Teams, which allows for statistics vs Top 100 teams. In avoiding contamination, we purposefully avoid the use of subjective, scouting information such as RSCI rank. Our goal is to provide results that are orthogonal to scouting information, rather than achieve a certain accuracy score without regard to the implications within the big picture.

2.2. Using PBP data to classify players by 3 positions Classifying players by three positions has become a common practice, allowing basketball experts to abandon preconceived ideas of where, and how, players play. More importantly, it has allowed

2 NBA BPM is scraped from basketball-reference.com; NCAA metrics are scraped from kenpom.com and BPM is calculated from this database

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decision-makers to more easily select the best players to bring into an organization, and it has provided more clarity into which players coaches play in games. Some researchers have classified three positions as Guards (PG and SG), Forwards (SF and PF) and Centers (C). More recently, Daniel Sailofsky split players into Ball-Handlers (PG), Wings (SG and SF) and Bigs (C) [10].

We propose a new strategy for classifying players by three positions. Many shooting guards are commonly the primary ball-handler for their team and defend more positions than a typical guard. Similarly, many power forwards are able to play and defend beyond the three-point line. In our definition of three positions, shooting guards and power forwards are split into Guards/Wings and Wings/Bigs, respectively, using PBP data as a proxy. In developing these proxies, the correlation of each metric to NBA BPM in years three through five are considered. This shows us good and bad tendencies of NBA Guards, Wings and Bigs when they played in the NCAA. PBP information allows for specific context, and metrics are chosen to describe player tendencies on both sides of the ball.

Shown in Equation (1), players whose SG Proxy is greater than the median of this metric for guards and wings are considered Wings in the NBA. Offensive Foul Turnovers (TOVs) are positively related to NBA efficiency for Wings, as this is an indicator that they attack the basket with high frequency. Conversely, Offensive Foul TOVs are negatively related to NBA efficiency for guards, as this indicates poor ball-control. Guards require extreme ball-control in order to be the primary ball- handler in the top basketball league in the world. Team DRBs while a player is on the floor gives us insight that a player’s team is effective on the boards when the player is on the floor. Lastly, good agility is valuable for a player to be a guard in the NBA. , SG Proxy < , , SG Proxy > 𝑁𝑁𝑁𝑁𝑁𝑁 𝑆𝑆𝑆𝑆 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑥𝑥� (1) 𝑆𝑆𝑆𝑆𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 ⟼ � 𝑤𝑤ℎ𝑒𝑒𝑒𝑒𝑒𝑒 SG Proxy = 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊Offensive𝑁𝑁𝑁𝑁𝑁𝑁 Foul TOVs During𝑥𝑥� a𝑆𝑆𝑆𝑆 Close𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 Game Team DRBs while on the Floor Agility

For NBA guards, SG Proxy has a -14%∗ correlation to NBA BPM in years∗ three through five and a +13% correlation for NBA wings.

Similarly, Equation (2) illustrates how NCAA power forwards are mapped to NBA wings and bigs. Players whose PF Proxy is greater than the mean of this metric are NBA wings. Transition 3PM give us a threshold that ensures wings play on the perimeter and score more frequently in transition. To further assure us that players who are defined as wings are effective in transition, Transition Putbacks on 2-pt Jump Shots (2PJ) give more information about transition tendencies and effectiveness. On-off steals, a team’s steals while the player is on the court, compared to when the player is on the bench, provide valuable insight that this player has a large part in their team’s ability to turn the opponent over. See Figure 3 for an illustration of these proxies.

, PF Proxy < , , PF Proxy > 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑁𝑁𝑁𝑁𝑁𝑁 𝑥𝑥̅𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 ⟼ � 𝑤𝑤ℎ𝑒𝑒𝑒𝑒𝑒𝑒 𝐵𝐵𝐵𝐵𝐵𝐵𝑁𝑁𝑁𝑁𝑁𝑁 3 𝑥𝑥̅𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 (2) PF Proxy = On Off Steals

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃 Transition Putbacks on∗ 2PJ− 𝑇𝑇𝑇𝑇𝑇𝑇 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 For NBA wings, PF Proxy has a +26% correlation∗ to NBA BPM and a -18% correlation for NBA bigs.

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2.3. Data Preparation After adding interactions within each positional group – Guards, Wings and Bigs—there are over 30,000 variables in the design matrix. The Top 75 variables are kept based on correlation to NBA BPM in years three through five. All PBP data before 2010 are imputed. Since the data is primarily missing before 2010, predicting the missing data with Multivariate Imputation is a great way to use the relationships within the data to fill in the missing values. We split the data before imputing, so the relationships with other variables in the data being imputed are related. We separately impute player measurement, BPM, box-score, 3-point, block, assist, rebound, turnover, foul and dunk statistics. For example, 3-point statistics such as 3PM, 3PA and 3P% are the values used to fill in Transition 3PM, Transition 3PA and other missing 3-point statistics.

One full game, 48 minutes, of -2 BPM is added to every player’s NBA Box Plus-Minus to reduce outliers. Examples of these outliers are the numbers for Deandre Liggins and Jarnell Stokes. Deandre only played 1 total minute for the Miami Heat, and Jarnell only played 7 minutes for the Denver Nuggets in years 3 through 5. Without adding this full game of -2 BPM, their data points have the largest residual. However, we do not want sample sizes of 1 minute and 7 minutes to carry weight in predicting a player’s efficiency.

The feature selection method used is Least Absolute Shrinkage and Selection Operator (LASSO). LASSO allows us to reduce a large dataset into a simple and explainable model, as it penalizes models with a large number of covariates. To see this, if we let N models be given by = + + + , then our LASSO parameter, is given by minimizing: 𝑦𝑦𝑖𝑖 𝛽𝛽0 𝛽𝛽1𝑥𝑥𝑖𝑖1 ⋯ 𝛽𝛽𝑝𝑝𝑥𝑥𝑖𝑖𝑖𝑖 1 ( 𝑝𝑝 ) + 𝑝𝑝 | |, 2 𝑁𝑁 (3) 2 where > 0 is� a tuning𝑦𝑦𝑖𝑖 − 𝛽𝛽 parameter0 − � 𝛽𝛽𝑗𝑗 to𝑥𝑥𝑖𝑖𝑖𝑖 scale the𝜆𝜆 � penalty𝛽𝛽𝑗𝑗 [11]. 𝑖𝑖=1 𝑗𝑗=1 𝑗𝑗=1 In doing so, this shrinks many𝜆𝜆 of the 226 coefficients to 0. The LASSO method for feature selection allows us to take this high-variable dataset and condense it to important variables that are explainable. We use Leave-One Out Cross-Validation to prevent overfitting.

2.4. Intelligence with transparent takeaways for decision-makers Understanding that data science is simply a process to impact decision-making in any industry, it is necessary to study NBA decision-makers. One must seek to understand each person at the table in the draft room. Respecting all members of the organization is central to the decision-making process. It is necessary to come to the table with an open mind, curious, eager to learn and relentlessly seeking to make the best decision. Results are to be explained in a clear manner.

Growing to understand the detailed insights brought by basketball experts is paramount. With these ideals in mind, our model process is focused on simplicity and explainability. In explaining and discussing results, we must deliver to each individual based on his or her background. It must be done in a manner that takes their expertise into account. Respect on both sides is paramount in order to make the best possible decision. For example, consider a decision-maker whose basketball experience and academic background are near the median for each category of current final decision-makers – Presidents or General Managers (Figure 1). They have 23 years of professional basketball experience. So, let’s say they have 17 years of Front Office Experience and 6 years of

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Coaching and Scouting experience. They have worked for two NBA organizations. They graduated with an undergraduate degree while competing on the basketball team. They did not compete in professional basketball as a player, as they went directly into coaching after the completion of their undergraduate degree.

In working with this decision-maker, the first step is to understand as much about the game of basketball as possible. 23 years of professional basketball experience allows this decision-maker to have the hours of deliberate practice required to have high-level recognition with respect to NBA basketball. They graduated with a degree while practicing, training and competing in a rigorous NCAA Division 1 program. They have had the grit and perseverance to gain 23 years of experience in the professional ranks and achieve a position of General Manager or President of an NBA organization.

As the vast majority of final decision-makers have a long history in basketball and far less formal training with respect to analytics, it is vital to communicate results in the language of basketball. If communicated properly, their basketball expertise allows for deep dives into the assumptions models make, the results of the model and the caution that is to be taken in explaining concepts in ways that will lead to synergy with scouting information. Results must be conveyed in a way that contributes to helpful discussion and action-items when working with this decision-maker. This will include complex explanation with respect to basketball, while being strategic in Figure 1. Box plot of the basketball experience of the 30 final decision- using words to explain statistical breakdowns. For example, makers in the NBA and bar chart of their academic background instead of using a standard colon for interactions, we use the as of December 2018. word “and” or use the multiplication operator. After all, teachers who have changed our lives met us where we were. Both leaders and analysts are to do the same.

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3. Models with results Table 1. Models for guards, wings and bigs3. Results are thought of as three separate models.

3NCAA Metrics are ordered based on their correlation to fitted values. Given a linear model, Pearson’s correlations to the model results tell us the importance of each metric. 2019 Research Papers Competition Presented by: 7

The fitted results are thought of as three separate models. The only variable that is shared between all three positions—Guards, Wings and Bigs—is NCAA BPM. Table 1 presents the model for each of the three positions. Below, Figure 2 shows the fitted results for players who were rookies between 2007 and 2013. The fitted results from the model have a 60% correlation to actual BPM in years three through five of a player’s NBA career.

Figure 2. Fitted versus actual NCAA Efficiency in years three thru five for players who were rookies between 2007 and 2013.

4. How do we use this information in the NBA Draft?

When coming to the draft table, both analytics and scouting personnel must have clear action-items and take-aways. We use our model results for players who competed in NCAA basketball in 2017- 18 to discuss how an organization selects a player after conveying this information. As our example, we will discuss Murray State’s Ja Morant. For the following discussion, we are including only players who returned to play NCAA basketball in 2018-19, unless otherwise stated.

4.1. Numbers Ja is the model’s top Guard who played at least 22 minutes per game (MPG) in the NCAA in 2017-18, as shown in Table 2. Ja’s REB&AST Efficiency * Dean’s Net Rating was the highest for guards in NCAA basketball in 2017-18. Ja is a highly efficient offensive rebounder, defensive rebounder and shot creator for his teammates. He does this in tandem with high overall efficiency, which is a reason he is projected to have the highest NBA Efficiency in years three thru five. Including players

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Table 2. Model results for 2018-19 NCAA players who played at least 22MPG in 2017-18. Ja Morant’s top and bottom metrics are enlarged. Metrics are displayed as the player’s percentile compared to players of the same position for the 2017-18 NCAA season.

Ja’s lowest percentile of any metric used in projections is FTM vs Top 100 Teams * Defensive Efficiency. This metric largely tells us that Ja did not play many Top 100 Teams, though he did get to the line and hit his FTs against those teams. He made 6.4 FTs per 100 possessions against his only two opponents in the Top 25, West Virginia and Auburn (compared to his season average of 6FTM per 2019 Research Papers Competition Presented by: 9

100 possessions). Another statistical concern is Ja’s Team ASTs while off the Floor * Defensive Efficiency (76th Percentile). Ja’s 11th worst Team ASTs while off the court tells us that Ja played a lot of his team’s minutes and had a much bigger role than other potential draftees. As his teammates only had 56 assists in 2017-18 while he was on the bench, Ja was expected to create shots for his teammates in higher volume than other draftees who have other shot creators on their teams. Assists while the player is on the bench coupled with defensive efficiency tells us that a player is effective defensively and has other teammates who share the responsibility of creating shots for others. This suggests the need to ensure guards from mid-major conferences, such as the Ohio Valley, who do not have other shot creators on their team are efficient on defense.

4.2. Eyes & Ears With respect to scouting intel, an unbiased and thorough score on the Grit Scale and Growth Scale are taken for Ja. Let us assume that Ja scored in the 97th Percentile in Grit and the 81st Percentile in Growth4. Scouting personnel like Ja’s ability to get inside defenses and create shots. His speed and ability to play above the rim counteract his 175-lb frame, especially with the NBA’s rule change in 2004 which restricts hand-checking. He is methodical on both ends of the floor. His ability to find an open player at any time in a possession and his awareness in help-defense are likely to translate well in the NBA. Ja seems to get along well with his teammates, and he seems to have a great relationship with Coach McMahon and Murray State’s staff. Scouting personnel can confirm his Dunk Assists and Transition Blocks. Scouts say transition blocks illustrate Ja’s athleticism. One scout saw two Dunk Assists against a 1-3-1 Trap. Though Ja will not see this type of defense in the NBA, his ability to make difficult passes on time and on target will be impactful immediately. His ball-handling and hesitation moves, both in transition and in half-court, keep defenses on their heels. One scout saw Ja’s lowest-scoring game of the season. There were only 2,000 people in attendance, and Ja’s teammate had the best game of his career. Ja continued to distribute the ball and praise his teammates for every made shot. Scouts’ concerns are Ja’s ability to make catch-and- shoot 3’s and his tendency to get out of position when guarding on the ball.

4.3. Making our selection We know that communication is not about speaking, but rather listening [3]. With this understanding, scouting and analytics personnel both value the others’ insights and critiques. Scouts confirm that even though Ja has not played a difficult NCAA schedule, he has done well when playing a higher level of competition. Scouts raise the question of Ja’s 3-point shooting, and analysts point out that NCAA FT%, not NCAA 3P%, is the best predictor of NBA 3PT%. Ja’s 81% FT% give scouts more confidence in his ability to develop his 3-point shot off the pass. Ja’s ability to create shots in tandem with rebounding, outstanding defensive efficiency, dunk assists and transition blocks make everyone a believer in his ability to impact the organization on the court. Ja’s impressive REB&AST efficiency outweigh his role in carrying the weight of creating shots within a mid-major conference. His overall play is efficient (93rd Percentile) when adjusting for schedule, team shooting efficiency and league three-point rate. Ja has achieved all of this at 18 years old. Most of all, scouting personnel believe Ja will be a great fit for the organization. They believe his grit will be impactful for his teammates and the organization. Ja respects his teammates, has a ferocious work ethic and has shown an appreciation for continual improvement. Ja’s lowest-scoring game helped us believe Ja puts his team’s welfare above his own and that he takes pride in his effort as an entity separate from results. These last assessments cannot be overlooked, as our culture’s focus on

4 These are hypothetical percentiles used for this conversation, not measured values.

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infinite goals is at the forefront of thought in the final decision. In the end, our organization selects Ja Morant.

5. Conclusion and Future Work

We focus on values that align with our organization’s infinite goals. We believe an organization thrives when infinite goals are the consistent focus. With infinite goals in mind, evaluating values and standards must be at the forefront in making a selection in the NBA Draft. After starting with over 30,000 variables, 13 or fewer variables per position satisfies the simplicity and explainability focus. The specific information provided by PBP data, such as team statistics when a player is on or off the court, give analytics personnel action-items that can be discussed with non-analytics personnel. Explaining the results in basketball language enhances further conversation and industry-specific insights.

A future work consideration is codifying scouting results, such as basketball details and tendencies. If the organization narrows it down to multiple young men, quantifying subjective assessments may be a great solution. The resulting decision weights can be assigned to eyes, ears and numbers to break ties. This is already being done in Major League Baseball.

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References

[1] J. Wooden and S. Jamison, My Personal Best. New York: McGraw-Hill, 2004, pp. 3-87. [2] J. Carse, Finite and Infinite Games. New York: Free Press, 1986, p. 10. [3] S. Sinek, Start with why. New York: Penguin Group, 2009, pp. 80-160. [4] B. Walsh, S. Jamison and C. Walsh, The Score Takes Care of Itself. New York: Portfolio, 2014, p. 16. [5] A. Duckworth, Grit. New York: Scribner, 2016, pp. 180-255. [6] W. Schreefer, “College Basketball ‘Draft Model Starter Kit’ Database”, 2018. [Online] Available: https://www.thestepien.com/2018/05/15/college-basketball-draft-model-starter-kit-database/. [Accessed: 10-Dec-2018] [7] D. Myers, "About Box Plus/Minus (BPM)", 2014. [Online]. Available: https://www.basketball- reference.com/about/bpm.html. [Accessed: 10-Dec-2018]. [8] D. Oliver, Basketball on paper. Washington, D.C.: Potomac Books, Inc., 2004, pp. 150-204. [9] “2018 Pomeroy College Basketball Ratings”, 2018. [Online] Available: https://kenpom.com. [Accessed: 10-Dec-2018] [10] D. Sailofsky, "Drafting Errors and Decision Making Bias in the NBA", in MIT Sloan Sports Analytics Conference, Boston, MA, 2018, p. 4. [11] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York: Springer, 2017, pp. 68-69. [12] G. Wong and C. Deubert, "National Basketball Association General Managers: An analysis of the responsibilities, qualifications and characteristics", Villanova Sports and Entertainment, vol. 18, no. 1, pp. 37-39, 2011.

Appendix

Figure 3. Scatterplot illustrating SG Proxy and PF Proxy. An NCAA SG whose SG Proxy is greater than the median of all guards and wings are NBA wings, as seen in Equation (1). A PF whose PF Proxy is greater than the mean of Equation (2) are NBA Wings. Data is imputed for rookies before 2010.

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