All-NBA Teams’ Selection Based on Unsupervised Learning

João Vítor Rocha da Silva,Paulo Canas Rodrigues

doi:10.3390/stats5010011

Abstract

All-NBA Teams’ selections have great implications for the players’ and teams’ futures. Since contract extensions are highly related to awards, which can be seen as indexes that measure a players’ production in a year, team selection is of mutual interest for athletes and franchises. In this paper, we are interested in studying the current selection format. In particular, this study aims to: (i) identify the factors that are taken into consideration by voters when choosing the three All-NBA Teams; and (ii) suggest a new selection format to evaluate players’ performances. Average game-related statistics of all active NBA players in regular seasons from 2013-14 to 2018-19, were analyzed using LASSO (Logistic) Regression and Principal Component Analysis (PCA). It was possible: (i) to determine an All-NBA player profile; (ii) to determine that this profile can cause a misrepresentation of players’ modern and versatile gameplay styles; and (iii) to suggest a new way to evaluate and select players, through PCA. As the results of this paper a model is presented that may help not only the NBA to better evaluate players, but any basketball league; it also may be a source to researchers that aim to investigate player performance, development, and their impact over many seasons.

Highlights

The evolution of a sport can occur in a variety of ways, e.g., that gameplay style may change over the years
The present study aims to identify the factors that are taken into consideration by voters when choosing the three All-National Basketball Association (NBA) Teams via Least Absolute Shrinkage and Selection Operator (LASSO) (Logistic) Regression, and suggests a new way to evaluate players’ performances and select teams via Principal Component Analysis (PCA) and its first principal component (PC1)
The following variables were studied: Player position (POS), Games played (G), Minutes per game (MP), Three-point shots converted per game(3P), Three-point shots attempted per game (3PA), Two-point shots converted per game (2P), Two-point shots attempted per game (2PA), Free-throws converted per game(FT), Free-throws attempted per game (FTA), Offensive rebounds per game (ORB), Defensive rebounds per game (DRB), Assists per game (AST), Blocks per game (BLK), Turnovers per game (TOV), Personal Fouls per game (PF) and Points per game (PTS)

Summary

Introduction

The evolution of a sport can occur in a variety of ways, e.g., that gameplay style may change over the years. As a result of these changes, it is possible to notice that concepts, styles, and strategies that were commonly used in the past may not correspond to what is seen in the contemporary sport [4]. This affects the roles and functions performed by players on court [5], their classifications, and ways to evaluate/compare them

Objectives

Methods

Results

Conclusion