Modelling basketball players' performance and interactions between teammates with a regime switching approach

TL;DR

This paper introduces a regime switching model to analyze basketball players' shooting performance variability and explores how lineup composition influences individual and team dynamics using network analysis.

Contribution

It develops a Markov Switching model for performance variability and links it to lineup effects through ARIMA and network analysis, offering new insights into player interactions.

Findings

Performance variability is effectively modeled with a two-regime Markov process.

Lineup composition significantly influences individual performance variability.

Network analysis reveals positive and negative interactions among teammates.

Abstract

Basketball players' performance measurement is of critical importance for a broad spectrum of decisions related to training and game strategy. Despite this recognized central role, the main part of the studies on this topic focus on performance level measurement, neglecting other important characteristics, such as variability. In this paper, shooting performance variability is modeled with a Markov Switching dynamic, assuming the existence of two alternating performance regimes. Then, the relationships between each player's variability and the lineup composition is modeled as an ARIMA process with covariates and described with network analysis tools, in order to extrapolate positive and negative interactions between teammates