Date of Graduation

5-2021

Document Type

Thesis

Degree Name

Bachelor of Science in Industrial Engineering

Degree Level

Undergraduate

Department

Industrial Engineering

Advisor/Mentor

Chimka, Justin

Committee Member/Reader

Nachtmann, Heather

Abstract

Regression analysis can be an effective way of examining performance in the marathon event. By splitting up the race into segments or in runner terminology “splits” the significance of each segment as it relates to the total finish time can be explored. Because the idea of splits is already ingrained into the minds of runners, it makes intuitive sense to use these as the metrics to define a race. Additionally, marathons generally make participant age and gender date publicly available which can then be used to find trends within specific demographics. This tailors trends to smaller groups of people, making the lessons from these trends more easily applied during the marathon. The most popular warning within the marathon community is that of the “fast start” which is translated to mean running faster than your average pace at the beginning of the race and consequently slowing down through the remainder of the race. Because of this, after segregating our data into four subsets, each runner’s pace in the first 10 kilometers (23.66%) of the race was plotted against their total finish time. In three out of the four subsets of the data the anecdote appeared to be clearly substantiated as runners who started slowest in relation to their mean race pace tended to have lower total finish times, and finish time generally increased as the percentage above mean race pace of runners increased.

Keywords

Regression, Marathon, Multivariate Statistical Analysis

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