Modeling the Relationship Between Triathletes Split Times and Race Performance

Abstract

This project analyzed real-world race data from collegiate triathletes to identify which race segments best predict overall sprint triathlon performance. A multiple linear regression model revealed that the bike split had the strongest influence on total time, with transitions also contributing meaningfully. Using these insights, an interactive tool was developed in Python to help athletes and coaches simulate race strategies and optimize pacing decisions.

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