INTRODUCTION

The last 25 years have witnessed a rapid growth in interest in both sports economics and sports analytics. Sports economics has developed in two general directions: discipline-specific and industry-specific. By “discipline-specific”, I mean the use of sports, particularly professional team sports, as an empirical context to test economic theory. Sport is data-rich, and almost unique as an industry in its transparency and data availability. Economists have increasingly recognized the possibilities of using sports data to empirically investigate issues in the theory of the firm, consumer demand, labour markets and business strategy (Kahn 2000). Sports economics has also seen several industry-specific developments, particularly in connection with competitive balance and the uncertainty of outcome hypothesis.

Paralleling the rapid growth of interest in sports economics has been the explosive growth in sports analytics. Initially sports analytics focused on applying data analysis to inform player recruitment. This has now developed into a more general use of data analytics to support a variety of coaching decisions. Economists, with their combination of analytical, statistical and econometric skills, are increasingly involved as analysts not only in the business operations of elite sports teams but also in the sporting operations. This trend in the economists’ labour market is merely replicating a well-established pattern in other industries, despite the continued criticism of mainstream economics as unrealistic, especially in its assumption of rational economic agents.

It seems rather paradoxical that students of the most abstract theoretical social science should be in such high demand in one of the most competitive and anti-intellectual industries. Data analytics is best understood as data analysis for practical purpose that seeks to produce actionable insights, and economists can be effective as analysts only if they are able to appreciate the unique context of any decision. Data analysts must aim to create a meaningful synthesis of the empirical results of their data analysis and the context-specific insights provided by the expertise and experience of the decision-makers.