Modeling Player and Team Performance in Basketball

TL;DR

This paper reviews quantitative methods for analyzing basketball gameplay, focusing on team strategies and player evaluation tools, and discusses future directions emphasizing causal inference in sports analytics.

Contribution

It provides a comprehensive overview of current analytical techniques for basketball, highlighting new methods for player and team performance assessment and future research directions.

Findings

Methods for characterizing team strategy and performance

Metrics for evaluating player value and defensive ability

Discussion on the importance of causal inference in sports analytics

Abstract

In recent years, analytics has started to revolutionize the game of basketball: quantitative analyses of the game inform team strategy, management of player health and fitness, and how teams draft, sign, and trade players. In this review, we focus on methods for quantifying and characterizing basketball gameplay. At the team level, we discuss methods for characterizing team strategy and performance, while at the player level, we take a deep look into a myriad of tools for player evaluation. This includes metrics for overall player value, defensive ability, and shot modeling, and methods for understanding performance over multiple seasons via player production curves. We conclude with a discussion on the future of basketball analytics, and in particular highlight the need for causal inference in sports.