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Design and Product Development

Grading Runners: A Pilot Study to Reverse Engineer Patterns to Understand the Sizing of a Population 

Authors
  • Susan L Sokolowski (University of Oregon)
  • Jacob Searcy (University of Oregon)
  • Anish Dulal
  • Chris Stone
  • KJ Fightmaster

Abstract

In the sports product industry, accurate apparel sizing is important for performance, yet traditional pattern grading methods rely on linear regression measurement assumptions that may not reflect real-world body diversity. This research investigates whether a dataset of 3D body scans of runners can depict improved approaches to grading and sizing athletic tights for male and female runners.

Keywords: Grading, Runners, Patternmaking, Machine Learning

How to Cite:

Sokolowski, S. L., Searcy, J., Dulal, A., Stone, C. & Fightmaster, K., (2025) “Grading Runners: A Pilot Study to Reverse Engineer Patterns to Understand the Sizing of a Population ”, International Textile and Apparel Association Annual Conference Proceedings 1(1). doi: https://doi.org/10.31274/itaa.21754

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Published on
2025-12-17

Peer Reviewed

License

Creative Commons Attribution 4.0