Electronic sports (e-sports) can be defined as digital games played competitively for an audience (Hodge et al., 2017). With a global consumer base of roughly 450 million people and projected 2019 revenues of US$1.1 billion, the e-sports industry continues to grow (Pannekeet, 2019). Behind this growth is a thriving ecosystem which includes e-sports game publishers, e-sports players and coaches, e-sports content producers, e-sports consumers, and more. As this ecosystem flourishes, so too flourishes an e-sports labor market, a portion of which comprises e-sports analytics jobs. Demand for e-sports analytics is expected to increase significantly in the coming years in the same way that demand for sports analytics increased after the 2004 publication of Michael Lewis's Moneyball (Davenport, 2014). Given current and anticipated demand for e-sports analytics, some universities might do well to prepare students for e-sports analytics careers. To this end, this article serves as a primer and resource for student research projects and lesson plans around e-sports performance analytics. This objective is served through four main sections. In the first section, the intelligence needs that fuel the growth of e-sports performance analytics are described. The second and third sections provide practical and detailed information about e-sports data collection. The fourth section draws from the authors' analysis of postings for e-sports analytics jobs to discuss two broad areas of e-sports analytics work: attribute creation for visualizations and dashboards; and machine learning modeling. The article concludes with a brief discussion of popular programming languages and software applications for e-sports analytics.