Network Analysis of Orchestral Concert Programming

TL;DR

This paper applies statistical network models to analyze orchestral concert programming, revealing that composition type significantly influences which composers are programmed together, offering a quantitative approach to understanding programming decisions.

Contribution

It introduces a novel application of network analysis to orchestral programming, quantitatively identifying key factors influencing concert programming choices.

Findings

Composition type is the most influential factor in programming decisions.

Additive and multiplicative effects align with programming practices.

Network analysis offers a promising framework for future research.

Abstract

Orchestral concert programming is a challenging, yet critical task for expanding audience engagement and is usually driven by qualitative heuristics and common musical practices. Quantitative analysis of orchestral programming has been limited, but has become more possible as many orchestras archive their performance history online. The contribution of this work is to use statistical network models to quantitatively explore orchestral concert programming, focusing on which factors determine if two composers are programmed together in the same concert by the Boston Symphony Orchestra. We find that the type of composition is the most important covariate in determining which composers are performed together and the additive and multiplicative effects are logical from an orchestral programming perspective. These results suggest that a network analysis is a promising approach for the analysis of concert programming, with several directions for future extensions.