The Impact of Altitude Training on NCAA Division I Female Swimmers’ Performance

Abstract

In this study, we investigate the effects of altitude training on female swimmers prior to college and how that training ultimately affects perfor- mance in their college career. In particular, for female athletes training in college at altitudes above 5,000 feet, we test whether athletes that trained at altitudes above 5,000 feet prior to college had different time drops between collegiate and high school best times, compared to athletes whose pre-collegiate training was below 5,000 feet. We test the hypothesis that having trained in college at altitudes above 5,000 feet and training prior to college at altitudes above 5,000 feet compared to altitudes below 5,000 feet will result in different observed time drops between collegiate and high school best times. We considered swimmers from four NCAA Division I colleges located in Colorado at altitudes ranging from approximately 5,000 to 7,000 feet above sea level. We collected data on the best pre- collegiate and collegiate times of 167 sprint event (50 freestyle) swimmers, 164 middle-distance event (200 freestyle) swimmers, and 69 distance event (1650 freestyle) swimmers. The subjects were grouped by event specialty and whether or not they trained at altitude prior to their collegiate careers. Time improvements from best pre-collegiate times to best collegiate times between altitude groupings were computed for swimmers and event groups. We conclude that there is no significant drop in time between swimmers who swam at below 5,000 feet in altitude prior to their collegiate careers and those who swam at or above 5,000 feet in altitude.

Additional information

Notes on contributors

Katherine L. Manzione

Katherine L. Manzione attended Colorado State University and earned her bachelor’s degree in statistics with minors in mathematics and business administration. She competed in swimming at the National Collegiate Athletic Association Division I level at Oregon State University and Colorado State University. Manzione furthered her education by earning a master’s degree in applied and computational mathematics and statistics from the University of Notre Dame. During her time at Notre Dame, she was a graduate research assistant, conducting quantitative behavioral research in the learning analytics and measurement in behavioral sciences lab. Her research interests included medicine, psychology, and sports analytics. Manzione is currently a data scientist at Drumline, a Dallas-based, women-owned data and marketing consulting company.

Bailey K. Fosdick

Bailey K. Fosdick is an institute data scientist at GTI Energy and an adjoint associate professor in the department of biostatistics & bioinformatics at the Colorado School of Public Health. Fosdick’s research contributions include novel statistical methods for the analysis of multivariate data, specifically network, array and survey data, with applications in the social, biological, and biomedical sciences. Most recently her work involved collaborative projects on SARS-CoV-2, sports statistics, gender diversity in academia, and trauma care in international settings. She has served as an associate editor for the Journal of Computational and Graphical Statistics, Bayesian Analysis, and Biometrics.

Ryan Elmore

Ryan Elmore is an associate professor in the department of business information and analytics at Daniels College of Business. Prior to Daniels, he worked as a senior scientist in the computational sciences center at the National Renewable Energy Lab in Golden, Colorado. He has also held positions at The Australian National University, Colorado State University, and Slide, Inc. Elmore’s research interests include statistics in sports, nonparametric statistical methods, and energy efficient high-performance computing. He is currently an associate editor for the Journal of Quantitative Analysis in Sports. Elmore’s work has been featured in The Economist, The Wall Street Journal, The Telegraph, The Guardian, and The Fried Egg Podcast.

Connor Gibbs

Connor Gibbs is a senior associate with Alvarez & Marsal’s Disputes and Investigations in Miami. Gibbs is a statistician and software developer specializing in the design and application of advanced statistical methodologies, including machine learning, network analysis, and causal inference. Gibbs earned his PhD and MS in statistics from Colorado State University, earning accolades through multiple first-author and collaborative publications, notably in the Annals of Applied Statistics. Prior to that, he earned a BS degree in statistics and a BS degree in mathematics from the University of Georgia, graduating summa cum laude with highest honors. He is an actively contributing member of the American Statistical Association and Phi Beta Kappa.