NBA Draft Decision-Making Using Play-By-Play Data
Total Page：16
File Type：pdf, Size：1020Kb
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. 2019 Research Papers Competition Presented by: 1 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. 2019 Research Papers Competition Presented by: 2 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.