A Vision for the Future of Academic Publishing in Sports Analytics

Founding Editorial for the Journal of Statistics and Data Science in Sports

Authors

  • Ryan Elmore
  • Benjamin Baumer
  • Brian Macdonald
  • Gregory Matthews
  • Michael Schuckers

Keywords:

open access, reproducibility, new journal, sports analytics, statistics, data science, editorial

Abstract

This article introduces the Journal of Statistics and Data Science in Sports (JSDSS), a Diamond Open Access, peer-reviewed journal. The journal is founded on three core principles. First, our commitment to open access is absolute. Second, reproducibility is critical and fundamental to the journal. Third, we believe sport is a rich and underutilized laboratory for statistical and data science innovation. The aim of the Journal of Statistics and Data Science in Sports is to provide an outlet for original, rigorous, practical, state-of-the-art, reproducible, and peer-reviewed analysis of sports data as well as the data science tools (software, applications, data, etc.) that support those analyses. We define sports broadly to include any nondeterministic competition or game. Finally, we welcome submissions that analyze any aspect of sports data in appropriate and innovative ways.

Downloads

Published

2026-08-24

Issue

Vol. 1 No. 1 (2026): Founding Documents

Section

Editorials

License

Copyright (c) 2026 Ryan Elmore, Ben Baumer, Brian Macdonald, Gregory Matthews, Michael Schuckers

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

All content contained within this journal may be used as set forth in the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license permits users to share and adapt material for non-commercial purposes, provided appropriate credit is given to the creator. This legal tool ensures creators maintain control over commercial exploitation while allowing free, non-commercial use of their work." 

 

You are free to: 

Share — copy and redistribute the material in any medium or format 

Adapt — remix, transform, and build upon the material for any purpose, even commercially.

 

Under the following terms: 

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

NonCommercial — You may not use the material for commercial purposes.