Big Ideas in Sports Analytics and Statistical Tools for their Investigation

TL;DR

This paper reviews key concepts in sports analytics, including game state value, win probability, team strength, and betting data, highlighting their methodologies, applications, and opportunities for sharing insights across sports.

Contribution

It provides a comprehensive overview of core analytical ideas in sports analytics, emphasizing commonalities, differences, and integration of data and models across sports.

Findings

Identifies four central ideas in sports analytics.

Explores analytical approaches and methodologies.

Highlights opportunities for data sharing and collaboration.

Abstract

Sports analytics -- broadly defined as the pursuit of improvement in athletic performance through the analysis of data -- has expanded its footprint both in the professional sports industry and in academia over the past 30 years. In this paper, we connect four big ideas that are common across multiple sports: the expected value of a game state, win probability, measures of team strength, and the use of sports betting market data. For each, we explore both the shared similarities and individual idiosyncrasies of analytical approaches in each sport. While our focus is on the concepts underlying each type of analysis, any implementation necessarily involves statistical methodologies, computational tools, and data sources. Where appropriate, we outline how data, models, tools, and knowledge of the sport combine to generate actionable insights. We also describe opportunities to share analytical work, but omit an in-depth discussion of individual player evaluation as beyond our scope. This paper should serve as a useful overview for anyone becoming interested in the study of sports analytics.