Document Type
Article
Publication Date
5-10-2023
Publication Title
WIREs Computational Statistics
Volume
15
Issue
6
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 article, 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 idiosyncracies 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 article should serve as a useful overview for anyone becoming interested in the study of sports analytics.
First Page
e1612
Recommended Citation
Baumer, Benjamin S.; Matthews, Gregory J.; and Nguyen, Quang, "Big Ideas in Sports Analytics and Statistical Tools for their Investigation" (2023). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/72
Digital Object Identifier (DOI)
doi.org/10.1002/wics.1612
Rights
Licensed to Smith College and distributed CC-BY 4.0 under the Smith College Faculty Open Access Policy.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Data Science Commons, Other Computer Sciences Commons, Statistics and Probability Commons
Comments
Peer reviewed accepted manuscript.