Download

Download Full Text (338 KB)

Instructor/Advisor

Melissa St. Hilaire

Keywords

Data Science, NHL, Circadian Advantage

Abstract

This project utilizes Data Science methodologies within RStudio to investigate the "circadian advantage" in the NHL. The core focus is to determine how travel across time zones impacts team performance, specifically analyzing the effects of circadian rhythm disruptions on game outcomes. I am data mining and feature engineering for two datasets. The datasets are from the 2016-17 and 2017-18 NHL Seasons. Game-level data include the Date and Time of the game (in local time), the Home and Away team, the total number of goals for each team, the division of each team, the Time Zone of the game, how many time zones the Away team traveled to get to the game, the days since their last game, the distance (in miles) they traveled, and the winner of the game. This data will be used to analyze how the timing of the games and the direction of travel affect the win percentage. The goal is to provide insights into the real-world effects of circadian rhythms on athletic performance, with potential implications for optimizing team travel schedules and improving player performance.

Data Mining to Understand the Impact of the

Share

COinS