Scholarlink

<p>This study utilizes a restricted Naval Special Warfare (NSW) dataset to showcase how “big data” in the context of Navy Sea, Air, Land (SEAL) training can be used to predict performance success of various Basic Underwater Demolition/SEAL (BUD/S) training evolutions. Our study focuses on multiple human characteristics and compares their correlation to evolution pass rates in training using Ordinary Least Squares (OLS) for our prediction models. From our initial regression analysis of over 232,000 data points, our findings indicate higher pass rates for BUD/S candidates who are older, married, and officers as well as increased pass rates in individuals who were taller and those with lower body weights. This study is an example of how long-term efficiencies could be gained from greater automation of data using simple software that could provide long-term benefit if captured in a more persistent and accurate manner. We advocate for the implementation of a more automated data/software collection system that can capture each students training career in one cohesive data profile. Moving forward, NSW studies should continue to leverage the use of “big data” to optimize its performance across all domains of the force.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Finding the next Navy SEALs: using big data to predict training success

  • 期刊论文

  • 发表日期：

    2026年2月8日

查看全文
  • Sawyer   J. Rogers,
  • Ryan S. Sullivan,
  • Simon Véronneau,
  • William C. Weldin

摘要

This study utilizes a restricted Naval Special Warfare (NSW) dataset to showcase how “big data” in the context of Navy Sea, Air, Land (SEAL) training can be used to predict performance success of various Basic Underwater Demolition/SEAL (BUD/S) training evolutions. Our study focuses on multiple human characteristics and compares their correlation to evolution pass rates in training using Ordinary Least Squares (OLS) for our prediction models. From our initial regression analysis of over 232,000 data points, our findings indicate higher pass rates for BUD/S candidates who are older, married, and officers as well as increased pass rates in individuals who were taller and those with lower body weights. This study is an example of how long-term efficiencies could be gained from greater automation of data using simple software that could provide long-term benefit if captured in a more persistent and accurate manner. We advocate for the implementation of a more automated data/software collection system that can capture each students training career in one cohesive data profile. Moving forward, NSW studies should continue to leverage the use of “big data” to optimize its performance across all domains of the force.