Using Data and Sports Equipment Technology to Predict Athletic Performance

Ryan Savitz (), Andrew Bjorkelo, Helen Cooney, Oliver DiDonato, Caley Gee, Christopher Greve, Bo Wagonner and Jared Ward
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Ryan Savitz: Neumann University
Andrew Bjorkelo: Neumann University
Helen Cooney: Neumann University
Oliver DiDonato: Neumann University
Caley Gee: Neumann University
Christopher Greve: Neumann University
Bo Wagonner: University of Colorado—Boulder
Jared Ward: Brigham Young University

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1319-1333 from Springer

Abstract: Abstract The aim of this book chapter is to present a review of how performance data, as well as knowledge of the type of sports equipment technology employed, can be used to predict athletic performance. This chapter focuses on sports previously studied by this chapter’s authors, namely, ice hockey, karate, field hockey, baseball, and track and field. This chapter examines how both past data and contemporaneously collected data can be used to predict future athletic performance. In addition to this, a brief look is taken at how the use of a specific type of sports equipment (carbon-plated running shoes) can be used to predict the improvement in one’s performance in marathon races. Taken as a whole, this chapter provides an overview of how statistical analysis can be used in a wide variety of sports (many of which lack previous study) to predict sports performance.

Keywords: Elite marathon running; Women’s collegiate field hockey; Women’s ice hockey; Major League Baseball; Shotokan karate; Predicting athletic performance (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16368-4_63

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DOI: 10.1007/978-3-032-16368-4_63

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